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Goli, A. ,
Babaee tirkolaee, E. ,
Golmohammadi, A. ,
Atan, Z. ,
Weber, G. ,
Ali, S.S. Central European Journal of Operations Research (16139178) 33(3)pp. 1025-1046
Supply chain network design is one of the most important issues in today’s competitive environment. Moreover, the ratio of transportation costs to the income of manufacturing companies has increased significantly. In this regard, strategic decisions, as well as tactical decisions making, are of concern for supply chain network design. In this research, a flexible, sustainable, multi-product, multi-period, and Internet-of-Things (IoT)-based supply chain network with an integrated forward/reverse logistics system is configured where the actors are suppliers, producers, distribution centers, first- and second-stage customers, repair/disassembly centers, recycling centers, and disposal centers. In order to create flexibility in this supply chain, it is possible to dispatch directly to customers from distribution centers or manufacturing plants. For direct shipping, the application IoT system is taken into account in the transportation system to make them able to manage direct and indirect delivery at the same time. The options and considerations are then incorporated into a Multi-Objective Mixed-Integer Linear Programming model to formulate the problem which is then converted into a single-objective model using Goal Programming (GP) method. Moreover, in order to deal with uncertainty in the demand parameter, robust optimization approach is applied. The obtained results from a numerical example reveal that the proposed model is able to optimally design the supply chain network whose robustness is highly dependent on the budgets of uncertainty whereas up to 213.528% increase in the GP objective function is observed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
Computers and Industrial Engineering (03608352) 207
Agricultural supply chains are crucial for ensuring food security and sustainability, especially under the growing pressure of global demand and environmental concerns. Efficient management of these supply chains, particularly in the face of uncertainties, is vital for optimizing resource use and minimizing waste. This research proposes a multi-objective optimization model for multi-period and multi-product agricultural supply chains with a focus on corn products under conditions of uncertainty. The proposed model includes two separate channels for the collection, production, and distribution of corn grain products (canned and corn oil) as well as the production and distribution of biofuel from corn residues. To deal with the uncertainty in the most important parameters, scenario-based robust optimization has been utilized. Next, a hybrid augmented epsilon constraint and Lagrangian Relaxation is proposed to find the Pareto solution. Finally, a real case study in Iran is analyzed to validate the model's applicability and effectiveness. The numerical results demonstrate that optimizing the first objective function reduced the total system cost to 6.96 billion dollars, while the optimization of the second objective function minimized the total energy consumption of the network to 6.86 terawatt hours. In addition, scenario analysis highlights the importance of energy consumption in enhancing efficiency and reducing supply chain costs. Furthermore, using a robust optimization approach stabilizes supply chain performance in the face of uncertainties, establishing a balance between economic and environmental goals. The results demonstrate a significant improvement in the supply chain's sustainability, showcasing novel insights into optimizing agricultural supply chains that have not been extensively explored in previous studies. © 2025 Elsevier Ltd
Journal of Dynamics and Games (21646074) 12(3)pp. 243-266
A stable and reliable electricity supply is indispensable for various sectors, including industries, households, schools, hospitals, and more. Precise electricity demand forecasting plays a pivotal role in enabling energy producers and distributors to make informed decisions regarding energy generation and distribution, ensuring the stability of the electricity grid. This research addressed the challenge of electricity consumption prediction by harnessing the power of artificial intelligence techniques, specifically hybrid neural networks, synergized with advanced meta-heuristic algorithms, including the shuffled frog leaping algorithm (SFLA), gray wolf optimizer (GWO), and genetic algorithm (GA). Over a decade-long period, we conducted a comprehensive study encompassing industrial, household, and agricultural sectors in Iran. The results of our investigation, evaluated using the coefficient of determination (R2), revealed that the hybrid neural networks coupled with the GWO algorithm exhibited superior predictive performance, particularly in the household and industrial sectors. This innovative approach not only provided more accurate electricity consumption predictions but also furnished valuable insights to empower decision-makers in effective energy management. In summary, our research pioneered the fusion of hybrid neural networks with meta-heuristic algorithms, presenting a novel methodology for electricity consumption prediction. This approach not only enhanced forecast accuracy but also contributed to the advancement of intelligent energy resource management systems, which were vital for the sustainable development of energy sectors worldwide. © (2025), (American Institute of Mathematical Sciences). All rights reserved.
Zhang, G. ,
Chen, C. ,
Shokouhifar, M. ,
Goli, A. Journal Of Engineering Research (23071877) 13(2)pp. 751-762
The management of agritourism supply chains plays a pivotal role in promoting sustainable rural development and cultivating economic growth within agricultural communities. The study devises a comprehensive methodology aimed at designing and optimizing agritourism supply chain networks. To achieve this purpose, a two-objective mixed-integer linear programming mathematical model is introduced, tailored to accommodate diverse water consumption priorities across different gardens within the chain. Recognizing the NP-hardness of this intricate problem, a metaheuristic-driven solution approach based on MIN-MAX goal programming and dragonfly algorithm is presented to solve the designed model, aiming to simultaneously maximize the total profit and minimize overall water consumption within the supply chain. To assess the effectiveness of the proposed methodology, we adopted a real-world case study of an agritourism supply chain in China. The obtained results indicate the adoption of the proposed method can efficiently lead to the development and mechanization of agriculture in villages. The numerical analysis highlights that recruitment and production costs significantly influence the total profit of the agritourism supply chain. Additionally, the research underscores the pivotal role of sustainable rural development in creating job opportunities within the agritourism supply chain, thereby stimulating economic growth and fostering more profitable enterprises. © 2024 The Authors
Rahmani, S. ,
Aghalar, H. ,
Jebreili, S. ,
Goli, A. pp. 68-115
The onset of the Fourth Industrial Revolution, also known as Industry 4.0 or 4IR, has marked the beginning of an epoch where data has become as valuable as oil. In this era of digitization, a plethora of data is being produced from a variety of sources including the Internet of Things (IoT), business processes, healthcare systems, and more. This data, irrespective of whether it is structured, semistructured, or unstructured, carries enormous potential for influencing strategic decision-making across a wide range of application areas [85]. Data science has risen as a comprehensive field that utilizes statistical methods, data analysis, and related techniques to comprehend and scrutinize phenomena through data. Sophisticated analytics techniques, inclusive of machine learning models, are utilized to derive actionable intelligence or profound understanding from data. This procedure of converting raw data into significant insights is recognized as DataDriven Decision Making (DDDM) [14, 85]. © 2025 CRC Press.
Barati, H. ,
Rafiee, S. ,
Zanjirani, H. ,
Bandi, B. ,
Haji-hashemi, A. ,
Shafiepour, S. ,
Karami, N. ,
Goli, A. Engineering Reports (25778196) 7(7)
This research provides a comprehensive evaluation of seven emergent meta-heuristic algorithms, including flying fox optimization (FFO), Giza pyramids construction (GPC), Harris Hawks optimizer (HHO), red deer algorithm (RDA), whale optimization algorithm (WOA), mayfly optimization algorithm (MOA), and stochastic paint optimizer (SPO) applied to the vehicle routing problem (VRP). The algorithms were implemented in MATLAB and assessed based on solution quality, execution time, and convergence rate across small, medium, and large-scale problems. The evaluation revealed significant performance variations among these algorithms. WOA consistently achieved top ranks in small and medium-scale problems, demonstrating its robustness and efficiency. In contrast, GPC excelled in large-scale problems, outperforming other algorithms in handling complex and extensive datasets. SPO, however, consistently ranked lowest across all scales, indicating its limited effectiveness for VRP under the tested conditions. The study employed the Shannon Entropy method for weighting the evaluation criteria and a multi-criteria decision-making method for the final ranking of the algorithms, providing a structured and comprehensive assessment approach. The findings suggest that WOA is the most effective algorithm, offering reliable and high-quality solutions with efficient execution times and convergence rates, while SPO requires significant enhancements. These insights are valuable for practitioners and managers in logistics and supply chain management, guiding the selection of appropriate algorithms based on problem scale. The research also opens avenues for future work, including the refinement of lower-performing algorithms, comprehensive testing with broader datasets, advanced parameter optimization, and exploration of algorithm applicability in other domains, such as scheduling and resource allocation. This study not only benchmarks the performance of emerging meta-heuristic algorithms on VRP but also lays a foundation for future advancements in optimization techniques. © 2025 The Author(s). Engineering Reports published by John Wiley & Sons Ltd.
Ghasemi, P. ,
Goli, A. ,
Goodarzian, F. ,
Ehmke, J.F. Engineering Applications of Artificial Intelligence (09521976) 139
The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain. © 2024 The Authors
Shahsavani, I. ,
Goli, A. ,
Hajiaghaei-keshteli, M. Annals of Operations Research (02545330)
In the past decade, the implementation of the circular economy has emerged as a compelling alternative to the linear model, proving effective in addressing critical global challenges such as climate change, population growth, and the depletion of non-renewable resources. Integrating the circular economy into supply chain networks results in the formation of a circular supply chain. Regrettably, many industries, including agriculture, have not fully embraced this valuable approach within their supply chain networks. This study addresses this gap by presenting an integrated circular supply chain network designed specifically for citrus fruits. The proposed model is grounded in the fundamental principles of the circular economy, leveraging both closed and open loops simultaneously. To address the complexities, a set of exact and metaheuristic methods is employed. In contrast to previous works, a comprehensive comparison of circular, closed-loop, and linear supply network structures is undertaken. Moreover, a sensitivity analysis is performed on key parameters. The model achieves significant milestones: firstly, a zero-waste outcome by eliminating waste in plastic crate usage and fruit residuals; secondly, an impressive 42% reduction in water consumption compared to traditional linear models. Environmental sustainability is further demonstrated by a 3% reduction in carbon emissions within the circular model. Importantly, these achievements do not compromise economic efficiency, as the circular supply chain showcases a nearly 1% decrease in total costs compared to its linear counterparts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Ala, A. ,
Goli, A. ,
Mirjalili, S. ,
Simic, V. Applied Soft Computing (15684946) 150
Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and ε-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the ε-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators. © 2023 Elsevier B.V.
Babaee tirkolaee, E. ,
Goli, A. ,
Gütmen, S. ,
Weber, G. ,
Szwedzka, K. Annals of Operations Research (02545330) 332(1-3)pp. 1215-1215
This erratum published as several formatting issues were noticed with Tables and equations. Original article has been corrected. © Springer Science+Business Media, LLC, part of Springer Nature 2022.
Hosseini, A. ,
Rahmani, S. ,
Khedri, M.A. ,
Goli, A. pp. 140-169
The agricultural supply chain (ASC) is constantly evolving, but with increasing demand and a significant role in human life, challenges to maintaining efficiency, productivity, and sustainability are becoming increasingly apparent. Agriculture 4.0, a term coined by Industry 4.0, aims to develop the ASC’s capabilities to respond to diverse demands and achieve optimal sustainability and productivity. This involves utilizing intelligent technologies like the Internet of Things (IoT), cyber-physical systems (CPSs), blockchain, big data processing, and additive manufacturing. However, implementing these technologies in the ASC can be challenging due to economic, environmental, and social challenges. To overcome these challenges, it is crucial to implement smart and powerful technologies like the IoT, CPSs, blockchain, big data processing, and additive manufacturing. © 2025 selection and editorial matter, Bharti, Shikha Singh, Anand Pandey and Amit Sachan; individual chapters, the contributors.
Jafarian-namin, S. ,
Shishebori, D. ,
Goli, A. Journal Of Applied Research On Industrial Engineering (26766167) 11(1)pp. 76-92
The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the advances in modelling capabilities. ARIMA is a popular model for describing the underlying stochastic structure of available data. Artificial Neural Networks (ANNs) can also be appropriate alternatives. In the literature, forecasting the temperature of Tehran using both techniques has not been presented so far. Therefore, this article focuses on modelling air temperatures in the Tehran metropolis and then forecasting for twelve months by comparing ANN with ARIMA. Particle Swarm Optimization (PSO) can help deal with complex problems. However, its potential for improving the performance of forecasting methods has been neglected in the literature. Thus, improving the accuracy of ANN using PSO is investigated as well. After evaluations, applying the seasonal ARIMA model is recommended. Moreover, the improved ANN by PSO outperforms the pure ANN in predicting air temperature. © 2024, Research Expansion Alliance (REA). All rights reserved.
Environmental Science and Pollution Research (09441344) 31(24)pp. 34787-34816
The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Ali, S.S. ,
Weber, G. ,
Babaee tirkolaee, E. ,
Goli, A. Sustainability (Switzerland) (20711050) 16(4)
Goli, A. ,
Bozanic, D. ,
Zhou, F. ,
Ali, I. Frontiers in Energy Research (2296598X) 12
International Journal of Production Economics (09255273) 278
This research contributes significantly to the domain of Industry 4.0 by offering a nuanced approach to the multi-objective optimization of the resource-constrained project scheduling problem (RCPSP) under uncertainty. Focused on the context of smart product platforming, this study introduces a novel methodology that not only considers traditional factors like time and cost but also incorporates quality and risk aspects, crucial for personalized product fulfillment. In this regard, a comprehensive four-objective mathematical model is proposed to minimize project completion time, total project costs, and project risks while simultaneously enhancing overall project quality. Real-world uncertainty is acknowledged through the incorporation of uncertain parameters for the time, risk, and quality associated with each project activity. To address this uncertainty, a robust optimization method is applied based on Bertsimas and Sim's approach. Moreover, to optimize the proposed model, the Hybrid Red Deer and Genetic Algorithm (HRDGA) is proposed, which is leveraging a machine learning approach for clustering solutions. The numerical results demonstrate that increasing the project budget by 30% leads to an upward trend in total project costs and a reduction in the minimum acceptable quality by 10%–30% results in a decreasing trend in the total project cost. This research emphasizes the adoption of Industry 4.0 enabling technology within the project scheduling platform, particularly highlighting its significance for personalized product fulfillment. © 2024
Zhang, P. ,
Guizani, M. ,
Abualigah, L. ,
Goli, A. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (02184885) 32(7)
Zhang, P. ,
Liu, L. ,
Wu, Z. ,
Zakarya, M. ,
Abualigah, L. ,
Goli, A. ,
Kibalya, G. Electronics Letters (1350911X) 60(14)
Engineering Applications of Artificial Intelligence (09521976) 136
Operating rooms (ORs) are key to a hospital's profitability, and increasing their benefits helps decrease surgical costs, minimize patient waiting times, and boost the number of patients admitted for surgery. This study presents a mixed-integer linear programming (MILP) model to address the issue of treating inpatients in operating rooms waiting for admission to the referral department. Moreover, it proposes a practical algorithm that minimizes overtime costs in operating rooms and increases patient satisfaction. Our model uses an integrated machine learning - tabu search (ML-TS) method. The proposed model uses differential ML and heuristic rules to achieve enhanced performance and scalability in solving complex optimization problems. A fairness policy is implemented, combined with an optimization method, to guide patient prioritization. This approach considers factors like urgency, waiting times, or specific patient needs and helps determine the order in which patients are scheduled for surgery. The outcomes imply that patient adaptability has led to savings ranging from 50% to 65% per surgery. Additionally, the Machine Learning-Tabu Search (ML-TS) algorithm has increased performance by about 35%. These algorithms also let the OR manager define the size of the uncertainty set and regulate overtime costs while accommodating surgeons' preferences as managerial implications insight. It adeptly navigates the solution space, identifies feasible solutions, and accelerates convergence. © 2024 Elsevier Ltd
Sustainability (Switzerland) (20711050) 16(16)
This research paper investigates the role of blockchain technology in sustainable business model innovation by proposing a comprehensive framework. The study addresses the following research questions: How can blockchain technology enhance business model components? What are the specific innovations enabled by blockchain technology? To answer these questions, a hybrid approach was employed. Initially, a thorough review of existing literature identified key components of business model innovation. Subsequently, a survey was conducted among technology experts to evaluate the applications of blockchain technology in these components. The analysis revealed five categories of innovation: macro-activities of the value network, micro-activities within the organization, governance, financial aspects, as well as sustainable innovation. Next, a novel conceptual model was developed to assess the impact of digital technologies on business model performance, and then it was evaluated using Structural Equation Modeling (SEM). Key findings indicate that blockchain technology significantly enhances data transparency, security, and efficiency capabilities, leading to improved innovation and increased sales volume. Practical implications include recommendations for organizations to prioritize investments in platform technologies, insight analysis, and sensor-based data collection to achieve sustainable business model innovation. The study underscores the importance of a holistic approach to integrating blockchain technology across all business model components to maximize its potential. © 2024 by the authors.
Zarreh, M. ,
Khandan, M. ,
Goli, A. ,
Aazami, A. ,
Kummer, S. Sustainability (Switzerland) (20711050) 16(15)
In an era where sustainability and efficient resource utilization are paramount, the closed-loop supply chain (CLSC) emerges as a critical approach, particularly in the context of perishable goods. The perishability of products adds a layer of complexity to supply chain management, necessitating innovative strategies for maximizing product life and minimizing waste. This comprehensive review article delves into the integration of perishable products within the framework of CLSC. The study thoroughly examines existing research to identify gaps and outline future research directions. It emphasizes the unique challenges and complexities of managing perishable goods, a crucial but often overlooked component in sustainable supply chain practices. The review highlights the balance between efficiency and sustainability, underscoring the importance of reverse logistics and circular economy principles in enhancing supply chain resilience. By synthesizing various methodologies and findings, the article presents a holistic view of the current state of perishable product management in CLSCs, offering valuable insights for academia and industry practitioners. The study not only contributes to the theoretical understanding of CLSCs, but also proposes practical approaches for their optimization, aligning with broader sustainability goals. © 2024 by the authors.
The phrase “Agriculture 4.0” refers to the incorporation of a variety of technology solutions into agricultural practices with the goal of increasing production levels across the board. If farmers make use of the real-time data that are made available to them by technologies developed during the Fourth Industrial Revolution, they are able to improve their performance by identifying risks that are associated with the supply chain and making educated decisions about how to mitigate those risks (4.0). This will result in an increase in the whole supply chain’s degree of efficacy, sustainability, flexibility, agility, and resilience as a direct consequence of population growth and the impacts of climate change. Additionally, this will have an influence on the level of population growth. Following an assessment, the technologies that were discovered to be the most pertinent were discovered to include big data analytics, the Internet of things, blockchain technology, and cyber-physical systems (CPSs). The implementation of Industry 4.0, and more specifically, CPSs, in the agri-food supply chain is the subject of this research. This research examines both the challenges that may arise as a result of utilizing these technologies as well as the potential benefits that may result from their use. © 2024 Elsevier Inc. All rights reserved.
Shokouhifar, M. ,
Naderi, R. ,
Goli, A. ,
Gultom, P. ,
Shafiei nikabadi, M. ,
Weber, G. Computers and Industrial Engineering (03608352) 191
In this research, an exergetic mathematical model is proposed for closed-loop food supply chain network design considering economic, environmental, and social aspects. The studded closed-loop food supply chain consists of six echelons, four echelons for forwarding the products (suppliers, factory, distribution centers, and customers), and two echelons for returning the products (collecting centers and disposal centers). The exergetic model is based on the extended exergy accounting method, which calculates the consumed exergy in all six echelons by considering the equivalents of labor and capital exergy. This model aims to minimize both the economic and exergetic costs associated with the food supply chain. To solve the proposed mathematical model, an ensemble global–local search metaheuristic algorithm utilizing a combined whale optimization algorithm (WOA) and simulated annealing (SA), called CWOASA, is proposed. In the CWOASA algorithm, first, WOA is used to search the entire search space. Next, SA is applied to further improve the best solution obtained by WOA. A real dataset of a dairy supply chain is used to evaluate the efficiency of the proposed metaheuristic-driven exergy analysis model and claim its benefits over the existing methods. Simulation results revealed that the proposed method leads to a 6.74% reduction in the total exergy consumption of the supply chain. The proposed methodology offers valuable insights, shedding light on the prospective gains in terms of mitigated environmental impact for each incremental economic expenditure, thus laying the foundation for promising avenues of future research. © 2024 Elsevier Ltd
Golmohammadi, A. ,
Abedsoltan, H. ,
Goli, A. ,
Ali, I. Computers and Industrial Engineering (03608352) 187
This research addresses optimizing production, inventory, location, and routing in a multi-level supply chain. In the studied supply chain, products are first sent to several distribution centers (DC) after production. Next, these products are delivered to several companies. Companies are divided into two independent groups considering sharing logistics resources. In this regard, vehicle routing optimization is applied for each group. For this purpose, a mixed-integer linear programming model has been developed. This model simultaneously minimizes total supply chain costs and total negative environmental impacts. A multi-objective dragonfly algorithm (MODA) has been developed to solve this model. Then, the implementation results are compared with the Non-Dominated Sorting Genetic Algorithm and Epsilon Constraint Method (EPC). Comparing the solution methods based on numerous test problems shows that the proposed MODA algorithm can be examined differently. The MODA algorithm has been able to have a significant advantage in providing solutions close to ideal points. Moreover, this algorithm has provided the most effective Pareto solutions in different test problems. However, the NSGA-II algorithm performs better regarding the spread of non-dominated solutions. © 2023 Elsevier Ltd
Scientific Reports (20452322) 14(1)
In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners. © The Author(s) 2024.
Goli, A. ,
Golmohammadi, A. ,
Verdegay, J. Operations Management Research (19369743) 17(2)pp. 801-801
The Publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher, in consultation with the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Author Alireza Goli has stated that none of the authors agree with this retraction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Golmohammadi, A. ,
Goli, A. ,
Jahanbakhsh-javid, N. ,
Farughi, H. Engineering Applications of Artificial Intelligence (09521976) 134
Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems. © 2024 Elsevier Ltd
Xu, Z. ,
Jain, D.K. ,
Shamsolmoali, P. ,
Goli, A. ,
Neelakandan, S. ,
Jain, A. Neural Computing And Applications (09410643) 36(5)pp. 2215-2229
Crowd counting (CC) and density estimation are crucial for ensuring public safety and security in surveillance videos with large audiences. As computer vision-based scene interpretation advances, automatic analysis of crowd situations is becoming increasingly prevalent. However, existing crowd analysis algorithms may not accurately interpret the video footage. To address this challenge, we propose a new approach called SMOHDL-CCA. This approach combines a Slime Mold Optimization algorithm with a Hybrid Deep Learning Enabled CC Approach. Our system uses the SMO algorithm with an optimized neural network search network (NASNet) model as the front-end to take advantage of transfer learning and flexible characteristics. The back-end model employs Dilated Convolutional Neural Networks, and the hyperparameter tuning process is done using the Chicken Swarm Optimization algorithm. Given a crowded video input frame, our SMOHDL-CCA model estimates the density map of the image. Each pixel value indicates the crowd density at the corresponding location in the picture. The final crowd count is obtained by summing all the values in the density map. We evaluated our proposed approach using three standard datasets. Furthermore, the state-of-the-art combining the proposed SMOHDL-CCA model achieves comparable performance such as improved precision is 96.97%, recall is 96.94%, and F1 score is 96.61%, reduced mean squared error of 61.15 values for the NWPU-crowd, UCF_QNRF, and World Expo datasets. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Goli, A. ,
Shahsavani, I. ,
Fazli, F. ,
Golmohammadi, A. ,
Tavakkoli-moghaddam, R. International Journal Of Supply And Operations Management (23831359) 10(4)pp. 545-563
The circular economy is one of the most important issues in the optimal use of resources all around the world. The combination of circular economy and supply chain creates a new concept called circular supply chain, which seeks to increase the efficiency of the supply chain by making the best use of resources. In this research, the main purpose is to apply a hybrid Multi-Criteria Decision-Making (MCDM) method to evaluate the effective factors in implementing the circular supply chain. First, the effective factors in the field of the circular supply chain are identified, and in the next step, the weight of the factors is obtained by implementing the Analytic Hierarchy Process (AHP) method. Next, the intensity of the effect of each factor is calculated. Moreover, the correlation between the factors affecting the circular supply chain and the effectiveness of the factors is analyzed using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. Finally, using the Simple Additive Weighting (SAW) method, the most important factors in the implementation of the circular supply chain are identified. The core results of this research show that the quality of final products is the most important factor in implementing a circular supply chain. Moreover, applying the circular economy approach leads to the zero-waste goal, which can increase the efficiency of supply chains. © 2023 All rights reserved.
Babaee tirkolaee, E. ,
Goli, A. ,
Gütmen, S. ,
Weber, G. ,
Szwedzka, K. Annals of Operations Research (02545330) 324(1-2)pp. 189-214
Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.
Babaee tirkolaee, E. ,
Goli, A. ,
Mardani, A. Annals of Operations Research (02545330) 324(1-2)pp. 795-823
The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Annals of Operations Research (02545330) 328(1)pp. 493-530
Organ transplantation is a crucial task in the healthcare supply chain, which organizes the supply and demand for various vital organs. In this regard, dealing with uncertainty is one of the main challengings in designing an organ transplant supply chain. To address this gap, in the present research, a mathematical formulation and solution method is proposed to optimize the organ transplants supply chain under shipment time uncertainty. A possibilistic programming model and simulation-based solution method are developed for organ transplant center location, allocation, and distribution. The proposed mathematical model optimizes the overall cost by considering the fuzzy uncertainty of organ demands and transportation time. Moreover, a novel simulation-based optimization is applied using the credibility theory to deal with the uncertainty in the optimization of this mathematical model. In addition, the proposed model and solution method are evaluated by implementing different test problems. The numerical results demonstrate that the optimal credibility level is between 0.2 and 0.6 in all tested cases. Moreover, the patient’s satisfaction rate is higher than the viability rate in the designed organ supply chain. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Environment, Development and Sustainability (1387585X)
The circular economy and circular supply chain (CSC) can be considered as environmentally friendly alternatives for the linear economy and linear supply chains. The objective of CSC is to apply the economic, social, and environmental dimensions to reduce the total consumed energy. Despite its growing importance and popularity, a limited number of studies have investigated the circular supply chain network design. This paper assesses the difference between CSC and the available known supply chains. A systematic review is conducted with a focus on energy consumption by applying optimization models. The results show that in approximately 9% of the mathematical models, three sustainability dimensions are included, and 63% of the proposed models apply deterministic models. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Babaee tirkolaee, E. ,
Torkayesh, A.E. ,
Tavana, M. ,
Goli, A. ,
Simic, V. ,
Ding, W. Engineering Applications of Artificial Intelligence (09521976) 126
Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the ε-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines. © 2023 Elsevier Ltd
Computers and Operations Research (03050548) 155
Product portfolio design is one of the important and effective factors in the financial and physical flows of various supply chains, especially dairy products. Accordingly, the financial and physical flows of the portfolio should be taken into consideration during the supply chain design. In this study, a closed-loop supply chain (CLSC) network is designed for dairy products aiming at maximizing the net cash flow from assets and maximizing the amounts paid to shareholders simultaneously. To find the optimal policy, an accelerated Benders decomposition (ABD) algorithm is implemented to tackle the complexity of the model. Moreover, three multi-objective optimization solution approaches of the weighted sum method (WSM), augmented ε-constraint (AEC), and fuzzy multi-objective programming (FMOP) are implemented to tackle the bi-objectiveness of the model. Next, a real case study in Iran is investigated to reveal the applicability of the developed methodology. Numerical results reveal that the ABD method reduces CPU time by about 10.8%. Moreover, the results of the case study demonstrate that by integrating financial and physical flows, an improvement of 4.8478% in the net cash flow from assets and a 2.3% improvement in the amounts paid to shareholders compared to the current situation. © 2023 Elsevier Ltd
International Journal of Environmental Research and Public Health (16617827) 20(5)
Blood platelets are a typical instance of perishable age-differentiated products with a shelf life of five days (on average), which may lead to significant wastage of some collected samples. At the same time, a shortage of platelets may also be observed because of emergency demands and the limited number of donors, especially during disasters such as wars and the COVID-19 pandemic. Therefore, developing an efficient blood platelet supply chain management model is highly necessary to reduce shortage and wastage. In this research, an integrated resilient–sustainable supply chain network of perishable age-differentiated platelets considering vertical and horizontal transshipment is designed. In order to achieve sustainability, economic cost, social cost (shortage), and environmental cost (wastage) are taken into account. A reactive resilient strategy utilizing lateral transshipment between hospitals is adopted to make the blood platelet supply chain powerful against shortage and disruption risks. The presented model is solved using a metaheuristic based on a local search-empowered grey wolf optimizer. The obtained results demonstrate the efficiency of the proposed vertical–horizontal transshipment model in reducing total economic cost, shortage, and wastage by 3.61%, 30.1%, and 18.8%, respectively. © 2023 by the authors.
Goli, A. ,
Ala, A. ,
Hajiaghaei-keshteli, M. Expert Systems with Applications (09574174) 213
This study investigates the optimization of non-permutation flow-shop scheduling problems and lot-sizing simultaneously. Contrary to previous works, we first study the energy awareness of non-permutation flow-shop scheduling and lot-sizing using modified novel meta-heuristic algorithms. In this regard, first, a mixed-integer linear mathematical model is proposed. This model aimed to determine the size of each sub-category and determine each machine's speed within each sub-category to minimize makespan and total consumed energy simultaneously. In order to optimize this model, Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Keshtel Algorithm (MOKA), and Multi-objective Keshtel and Social Engineering Optimizer (MOKSEA) are proposed. First, the validation of the mathematical model is evaluated by implementing it in a real case of the food industry using GAMS software. Next, the Taguchi design of the experiment is applied to adjust the meta-heuristic algorithms' parameters. Then the efficiency of these meta-heuristic algorithms is evaluated by comparing with Epsilon-constraint (EPC), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) using several test problems. The results demonstrated that the MOALO, MOKA, and MOKSEO algorithms could find optimal solutions that can be viewed as a set of Pareto solutions, which means the used algorithm has the necessary validity. Moreover, the proposed hybrid algorithm can provide Pareto solutions in a shorter time than EPC and higher quality than NSGA-II and MOPSO. Finally, the model's key parameters were the subject of sensitivity analysis; the results showed a linear relationship between the processing time and the first and second objective functions. © 2022 Elsevier Ltd
Sustainability (Switzerland) (20711050) 15(16)
In today’s dynamic and competitive free market, businesses strive to gain a distinct competitive advantage, enabling them to seize opportunities and overcome potential threats. Achieving and sustaining superior performance has become a fundamental objective for companies. Accordingly, the main objective and contribution of this research is to delve into the profound impact of circular economy practices, which are known to foster sustainability and resource efficiency, on financial performance—an essential metric for evaluating a company’s success. Through the development of a proposed mathematical model, we simulate and quantify the influence of circular economy practices on financial outcomes, capturing the intricate relationship between the two. Employing state-of-the-art optimization methods and statistical analysis, our analysis reveals that the implementation of circular economy principles significantly impacts financial performance, contributing to 15.7% of its variance. Interestingly, production diversity, while critical for corporate governance, does not exert a statistically significant influence on financial performance. Notably, although production diversity remains a pivotal aspect of effective corporate governance, our analysis indicates that it does not wield a statistically significant impact on financial performance. Moreover, the combined synergy of circular economy practices and financial performance unveils a noteworthy 24.8% variance in overall company performance, underscoring the intricate interdependence of these pivotal elements. By harnessing state-of-the-art modeling techniques and meticulous analysis, this research yields profound insights into the intricate interplay between circular economy practices and financial performance. This illumination empowers businesses to discern potential pathways for harnessing competitive advantages and nurturing sustainable growth in the dynamic tapestry of today’s business landscape. © 2023 by the authors.
Goli, A. ,
Babaee tirkolaee, E. ,
Weber, G.
Computers and Industrial Engineering (03608352) 179
Blockchain technology is one of the latest financial concepts influencing international trades, which contributes to speeding the supply chains by managing faster and more accurate financial transactions. Moreover, the product portfolio is highly effective on the financial flow of the supply chain. Accordingly, in this research, a comprehensive framework is proposed to design a blockchain-enabled closed-loop supply chain (BCSC), considering the role of the product portfolio. First, a mathematical model for robust product portfolio design is proposed. Next, a closed-loop supply chain network is designed where the financial flow via blockchain is of concern. To optimize this supply chain, the change in equity, as well as satisfaction from the blockchain network, is maximized. An exact solution method using GAMS software is applied to optimize the proposed mathematical model. The results obtained by implementation and analysis of this proposed framework indicate that by designing a product portfolio based on a robust model, BCSC would achieve the ideal value for its financial indexes, the change in equity in specific. Comparison of this newly proposed framework with the integrated portfolio-supply chain model reveals a 0.33% average error and a 1.5% reduction in lost revenue. It is concluded that this proposed framework assists supply chain managers in reducing their inaccurate decisions in both the physical and financial flow considerably. © 2023 Elsevier Ltd
Weber, G. ,
Goli, A. ,
Babaee tirkolaee, E. Sustainability (Switzerland) (20711050) 15(17)
Babaee tirkolaee, E. ,
Goli, A. ,
Malmir, B. Logistics (23056290) 7(3)
Environmental Science and Pollution Research (09441344) 30(36)pp. 86268-86299
The excessive consumption of fossil fuels has sparked debates and caused environmental damage, leading the global community to search for a suitable alternative. To achieve sustainable development goals and prevent harmful climate scenarios, the world needs to increase its use of renewable energy. Biodiesel, a clean and eco-friendly fuel with a high flash point and more lubrication than petroleum-based fuels, and without the emission of harmful environmental gases, has emerged as one of the fossil fuel alternatives. To promote the mass-level production of biodiesel, a sustainable supply chain (SC) that does not depend on laboratory production is necessary. For this purpose, this research proposes a multi-objective mixed-integer non-linear mathematical programming (MINLP) model to design a sustainable canola oil-based biodiesel supply chain network (CO-BSCND) under supply and demand uncertainty. This mathematical model aims to minimize the total cost (TC) and total carbon emission while maximizing the total number of job opportunities simultaneously. A scenario-based robust optimization (SBRO) approach is applied to deal with uncertainty. The proposed model is implemented in a real case study in Iran, and numerical experiments and sensitivity analysis are conducted to demonstrate its applicability. The results of this research demonstrate that designing a sustainable supply chain network for the production and distribution of biodiesel fuel is achievable. Moreover, this mathematical modeling makes mass-scale production of biodiesel fuel a possibility. In addition, the SBRO method adopted in this research enables managers and researchers to explore the design conditions of the supply chain network by controlling the uncertainties that affect it. This approach allows the chain’s performance to be as close as possible to the actual conditions. As a result, the SBRO method enhances the efficiency of the supply chain network and boosts productivity toward achieving desired goals. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Babaee tirkolaee, E. ,
Goli, A. ,
Golpîra, H. ,
Santibañez gonzalez, E.D.R. Sustainability (Switzerland) (20711050) 15(16)
Aghighi, A. ,
Goli, A. ,
Malmir, B. ,
Babaee tirkolaee, E. Journal of Ambient Intelligence and Humanized Computing (18685145) 14(6)pp. 6497-6516
The transport of perishable products is in need of specific control and safety operations, either due to their short shelf life or their particular storage circumstances. This study investigates an extended Location-routing-inventory problem (LRIP) for perishable products, in which a two-phase hybrid mathematical model is developed. In the first phase, the location-routing problem (LRP) is formulated with stochastic demands and travel time, and then in the second phase, a queue system is employed to model the inventory control problem based on the established locations and routes. Moreover, the effects of reneging and balking behaviors are studied in the second phase, and hereby, holding, shortage, product expiration, customer waiting times, and customer loss costs are calculated. To tackle the complexity of the problem, an improved genetic algorithm (IGA) is designed and is compared with the classic genetic algorithm (GA) and GAMS software. Finally, two small and large-sized illustrative examples and then different problem instances are taken into account to test the applicability of the suggested methodology. The obtained results demonstrate that the developed methodology of the research has an appropriate performance to deal with the high complexity of the problem. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Goli, A. ,
Zare, H.K. ,
Tavakkoli-moghaddam, R. ,
Sadegheih a., A. Scientia Iranica (23453605) 29(3E)pp. 1638-1645
This research is aimed to address the optimization of a product portfolio problem under uncertainty using the principles of nancial portfolios theory. Since the success of a product portfolio is dependent on strategic decision making as well as on future changes of return, the return is best considered when it is deemed an uncertain parameter. The specific innovation of this research is the use of a robust optimization approach and providing an exact solution algorithm based on the model of Bertsimas and Sim. Given the uncertainty of the returns, the product portfolio model was developed based on the robust counterpart formulation of Bertsimas and Sim. An exact solution algorithm was also formulated to reduce the solution time. The results obtained by applying the model to a real case study of the dairy industry in Iran showed that increasing the confidence level would decrease total returns of the portfolio and increase its total risk. A comparison between the proposed algorithm and similar methods showed that, on average, it would make 3% improvement in the solution time. © 2022 Sharif University of Technology. All rights reserved.
Goli, A. ,
Golmohammadi, A. ,
Edalatpanah s.a., pp. 43-56
Nowadays, the speed of implementation of technological advances is increasing. Moreover, the economic challenges posed by technological and social developments have led industrial companies to increase their agility and responsiveness in order to be able to manage the entire value chain. On the other hand, one of the most interesting and at the same time one of the most important challenges facing supply chains is Industry 4.0, which is based on digital technology and varies greatly in scale and complexity. It is more than what humanity has experienced through previous industrial revolutions, and decisions need to be made faster and more accurately. Accordingly, in this paper, a comprehensive framework for accelerating supply chain decisions with respect to Industry 4.0 is provided. In this regard, first, Industry 4.0 is described in detail. Next, a framework for demand forecasting in the 4.0 industry-based supply chain is provided using artificial intelligence tools. In this context, the simultaneous use of time series methods and machine learning methods is emphasized. The analysis of the proposed framework demonstrates the suitable benefits of using it in different supply chains. © 2023 Scrivener Publishing LLC. All rights reserved.
Babaee tirkolaee, E. ,
Goli, A. ,
Mirjalili, S. Environmental Science and Pollution Research (09441344) 29(53)pp. 79667-79668
Babaee tirkolaee, E. ,
Goli, A. ,
Ghasemi, P. ,
Goodarzian, F. Journal of Cleaner Production (09596526) 333
This study develops a novel mathematical model to design a sustainable mask Closed-Loop Supply Chain Network (CLSCN) during the COVID-19 outbreak for the first time. A multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to address the locational, supply, production, distribution, collection, quarantine, recycling, reuse, and disposal decisions within a multi-period multi-echelon multi-product supply chain. Additionally, sustainable development is studied in terms of minimizing the total cost, total pollution and total human risk at the same time. Since the CLSCN design is an NP-hard problem, Multi-Objective Grey Wolf Optimization (MOGWO) algorithm and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented to solve the proposed model and to find Pareto optimal solutions. Since Meta-heuristic algorithms are sensitive to their input parameters, the Taguchi design method is applied to tune and control the parameters. Then, a comparison is performed using four assessment metrics including Max-Spread, Spread of Non-Dominance Solution (SNS), Number of Pareto Solutions (NPS), and Mean Ideal Distance (MID). Additionally, a statistical test is employed to evaluate the quality of the obtained Pareto frontier by the presented algorithms. The obtained results reveal that the MOGWO algorithm is more reliable to tackle the problem such that it is about 25% superior to NSGA-II in terms of the dispersion of Pareto solutions and about 2% superior in terms of the solution quality. To validate the proposed mathematical model and testing its applicability, a real case study in Tehran/Iran is investigated as well as a set of sensitivity analyses on important parameters. Finally, the practical implications are discussed and useful managerial insights are given. © 2021 Elsevier Ltd
Environment, Development and Sustainability (1387585X) 24(9)pp. 10540-10569
In this research, a new method to determine the supply chain performance based on its sustainable strategies is proposed. This method consists of a balanced scorecard, path analysis, and hybrid Shapley value and Multimoora method. The main contribution of this research is to design an intelligent performance evaluation system for different supply chains. In this intelligent performance evaluation method, first, a set of strategies are determined through the balanced scorecard, next, by applying the path analysis method, the best strategic paths are specified, and then the Shapely value of the listed paths is calculated. Among these, five with the highest Shapley value are selected through the hybrid Dematel-based analytical network process and Multimoora method. This method is implemented in the petrochemical supply chain in Iran, and the results are analyzed. This application revealed that the best policy in organizational–operational management optimization is subject to applying this up-to-date technological apparatus at its best. In this approach, the production and delivery time cycle would be reduced. This intelligent system reduces production costs as well. The findings here can be applied in any industry of concern as to improve operations. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Hamedirostami, A. ,
Goli, A. ,
Gholipour-kanani, Y. Journal of Industrial and Management Optimization (15475816) 18(5)pp. 3103-3131
This study concerns the optimization of green supply chain network design under demand uncertainty. The issue of demand uncertainty has been addressed using a scenario-based analysis approach. The main contribution of this research is to investigate the optimization of cross-dock based supply chain under uncertainty using scenario-based formulation and metaheuristic algorithms. The problem has been formulated as a two-objective mathematical model with the objectives of minimizing the costs and minimizing the environmental impact of the supply chain. Two metaheuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization (MOIWO), have been developed to optimize this mathematical model. This paper focuses on the use of new metaheuristic algorithms such as MOIWO in green supply chain network design, which has received less attention in previous studies. The performance of the two solution methods has been evaluated in terms of three indices, which measure the quality, spacing, and diversification of solutions. Evaluations indicate that the developed MOIWO algorithm produces more Pareto solutions and solutions of higher quality than NSGA-II. A performance test carried out with 31 problem instances of different sizes shows that the two methods perform similarly in terms of the spread of solutions on the Pareto front, but MOIWO provides higher quality solutions than NSGA-II. © 2022. Journal of Industrial and Management Optimization. All Rights Reserved.
Gütmen, S. ,
Weber, G. ,
Goli, A. ,
Babaee tirkolaee, E. pp. 193-201
Aggregate production planning (APP) within a supply chain is known as one of the main activities in general planning of large and leading companies throughout the world. In the present study, a survey is conducted on the importance of three main factors: (1) human paradigm, (2) reliability, and (3) uncertainty in the APP. To do so, these three factors are investigated and reviewed and the most significant challenges are discussed accordingly. Moreover, the most relevant studies performed recently are reviewed to find the major gaps in the literature. Finally, the survey is concluded through discussing the main challenges, limitations, and recommendations for future research. © 2022 Elsevier Inc. All rights reserved.
Zheng, B. ,
Wang, H. ,
Golmohammadi, A. ,
Goli, A. Sustainable Energy Technologies and Assessments (22131388) 52
This study examines how to build customer loyalty in the online retailing industry of China. It focuses on whoever does online shopping for physical goods in the Business to Consumer (B2C) internet store. The study also discusses the drive mechanism of logistics service quality, energy efficiency, and customer loyalty to the internet store, which is very important for reducing energy consumption and sustainable development under the circular economy. Exploration and empirical tests are performed to identify the relationships among the five dimensions of the logistics service quality and energy efficiency of B2C Internet stores, which enriches the theoretical basis of logistics service quality and energy efficiency in the Internet environment. Considering those five dimensions as antecedent variables, the study has developed and tested a consumer-driven model of consumer loyalty to the B2C Internet retailing system, including five dimensions of logistics service quality and energy efficiency and the two core concepts of relationship quality namely satisfaction and trust. The original model is checked against rival models, and a fitter model is achieved. The findings of this research provide the field with a new insight. © 2022
Journal of Industrial and Management Optimization (15475816) 18(6)pp. 3807-3830
In this research, a parallel machine sequence-dependent group scheduling problem with the goal of minimizing total weighted earliness and tardiness is investigated. First, a mathematical model is developed for the research problem which can be used for solving small-sized instances. Since the problem is shown to be NP-hard, this research focuses on proposing metaheuristic algorithms for finding near-optimal solutions. In this regard, the main contribution of this research is to apply the Biogeography-based Optimization (BBO) algorithm as a novel meta-heuristic and Variable Neighborhood Search (VNS) algorithm as a best-known one. In order to evaluate the mathematical model and solution methods, several computational experiments are conducted. The computational experiments demonstrate the efficiency of the proposed meta-heuristic algorithms in terms of speed and solution quality. The maximum gap of BBO algorithm is 1.04% and for VNS algorithm, it is 1.35%. © 2022, Journal of Industrial and Management Optimization. All rights reserved.
International Journal Of Supply And Operations Management (23831359) 9(4)pp. 483-495
Development of supply chains is one of the practical concepts in the field of production and sales in competitive conditions. Accordingly, it is necessary to properly study the competitive conditions in which supply chain networks can be designed. In this regard, the present research contributes to the field by incorporating the market share and customer satisfaction to the competitive conditions of supply chains. For this purpose, a nonlinear mathematical model is presented in order to find locations and perform distributions in a closed-loop supply chain under competitive conditions. This model has two objectives including profit maximization and market share maximization. To solve the model, LP-metric and goal programming are implemented, and then the results of these two methods are discussed. Comparisons are also made in terms of the value of the objective functions as well as the solution time. Finally, the simple weighted sum method is used to select the superior method. The results show that the LP-metric method is worth performing to solve the mathematical model of the research. © 2022 Authors. All rights reserved.
Goli, A. ,
Babaee tirkolaee, E. ,
Weber, G. Foundations of Computing and Decision Sciences (23003405) 47(2)pp. 107-110
This special issue of the Foundations of Computing and Decision Sciences, titled "Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics", is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field. © 2022 Alireza Goli et al., published by Sciendo.
Journal Of Applied Research On Industrial Engineering (26766167) 9(2)pp. 165-179
Today, most supply chains are moving towards green business with a greater focus on environmental protection as a competitive advantage. Among them, the design of a three-stage green supply chain with optimal allocation, a multiple supply chain that includes supplier (first stage), manufacturer (second stage) and distributor (third stage), based on maximum efficiency and considering the internal processes and products between these three levels, can be of special importance; because, it will increase the economic and environmental performance of the supply chain. One of the methods used to evaluate efficiency in Green Supply Chain Management (GSCM) is Data Envelopment Analysis (DEA). Therefore, performance evaluation is vital for companies to improve the effectiveness and efficiency of the supply chain. In this study, using the three-stage approach of DEA, the data collected in 2020 from 9 Selected home appliance companies have been analyzed. The results show that company 1 has the best efficiency and the greenest supply chain and company 7 has the worst value of efficiency, which makes it necessary to pay more attention to low performance companies. In order to show the capability of the proposed model, the developed model was compared with its equivalent base model, and companies 1 and 2 were identified as inefficient in the proposed model, but identified as efficient in the base model. Given that the efficiency score in the proposed model is always lower than the base model, so the accuracy of the developed model can be concluded. © 2022, Research Expansion Alliance (REA). All rights reserved.
Mathematical Problems In Engineering (1024123X) 2022
Home healthcare, a novel type of health treatment provided at residents' homes for specific populations (elderly, disabled), is typically less expensive, more convenient, and efficient. The purpose of this article is to offer a paradigm for scheduling medical professionals from various medical institutes. With the need for home health care likely to expand significantly, future work is critical to lowering costs and ensuring service quality. The high expense of hiring nurses and doctors in this home health system (HHS) necessitates optimization approaches. To handle choice difficulties, we present a new algorithm that generates numerous scenarios, including current nurse schedules, the new demand under discussion, and future requests generated by the system. By analyzing several real-life factors and a new algorithm that produces numerous situations involving nurses' schedules to solve three problems, the model offered is integer number programming. The results indicate which center each nurse is dispatched from, and each nurse returns to the original routing point. Finally, average daily visits increase by roughly 20%, while daily travel times of each visit drop by 50%. © 2022 Ali Ala et al.
Tang, A. ,
Alsultany, F.H. ,
Borisov, V. ,
Mohebihafshejani, A. ,
Goli, A. ,
Mostafaeipour, A. ,
Riahi, R. Sustainable Energy Technologies and Assessments (22131388) 52
The process of heating water consumes a lot of energy. In South Africa (SA) up to 40% of household energy consumption is utilized for water heating. In this regard, the use of renewable energies, especially solar, can help reduce the energy crisis in this country. In this study, applying the solar water heaters (SWHs) in home-scale has been explored for the first time, by using climatic information for 21 cities in South Africa. The techno-environmental assessment was performed by TSOL PRO 5.5 on two types of the evacuated tube (ET) and flat plate (FP water heaters). Furthermore, these cities are ranked using GAMS 24.1 and two types of DEA methods. The results indicate the efficiency of evacuated tube SWHs is better than that of flat plate SWHs at all cities and if we use the FP water heater, the average solar fraction is 95.93%, which prevents about 23.5 tons of CO2 emissions annually. These values for ET water heaters are 99.16% and 24.4 tons per year, respectively. For the FP collector, the findings indicate Beaufort West, Mmabatho and Welkom cities are preferable cities in both models, DEA-CCR and DEA-Additive models. © 2022 Elsevier Ltd
Goli, A. ,
Golmohammadi, A. ,
Verdegay, J. Operations Management Research (19369743) 15(3-4)pp. 891-912
Since the last decade, transportation and distribution systems have experienced significant growth and development. Designing distribution systems utilizing electric vehicles is one of the main issues in this field. Accordingly, this research provides a novel solution method for a two-echelon distribution system using electric vehicles. At the first level, the required products are sent from a central depot to satellite stations. At the second level, these products are distributed among different customers. In routing electric vehicles, battery capacity and visiting charging stations are taken into consideration. In this regard, a mathematical model is developed for the electric vehicle routing at both levels. To solve the model, a newly developed meta-heuristic algorithm is proposed as Improved Moth-Flame Optimization (IMFO) Algorithm. The evaluation of IMFO indicates that, in small and medium-scale test problems, this algorithm has errors of about 1.2%. It also performs better than the classic moth-flame algorithm as well as the genetic algorithm on large-scale test problems. Moreover, the sensitivity analysis of the demand and time window parameters shows that a rise in demand leads to a sharp increase in the cost of the distribution system, but the opening of the time window can help to reduce costs. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Goli, A. ,
Mahmoudi, N. ,
Khazaei, H. ,
Ardakanian, O. International Conference on Cloud Computing and Services Science, CLOSER - Proceedings (21845042) 2021pp. 190-198
Microservice architecture is the mainstream pattern for developing large-scale cloud applications as it allows for scaling application components on demand and independently. By designing and utilizing autoscalers for microservice applications, it is possible to improve their availability and reduce the cost when the traffic load is low. In this paper, we propose a novel predictive autoscaling approach for microservice applications which leverages machine learning models to predict the number of required replicas for each microservice and the effect of scaling a microservice on other microservices under a given workload. Our experimental results show that the proposed approach in this work offers better performance in terms of response time and throughput than HPA, the state-of-the-art autoscaler in the industry, and it takes fewer actions to maintain a desirable performance and quality of service level for the target application. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Babaee tirkolaee, E. ,
Goli, A. ,
Weber, G. Communications in Computer and Information Science (18650937) 1458pp. 3-23
This study proposes a robust two-echelon periodic multi-commodity Location Routing Problem (LRP) by the use of RFID which is one of the most useful utilities in the field of Internet of Things (IoT). Moreover, uncertain demands are considered as the main part to design multi-level petroleum logistics networks. The different levels of this chain contain plants, warehouse facilities, and customers, respectively. The locational and routing decisions are made on two echelons. To do so, a novel mixed-integer linear programming (MILP) model is presented to determine the best locations for the plants and warehouses and also to find the optimal routes between plant level and warehouse facilities level, for the vehicles and between warehouse facilities level and customers’ level in order to satisfy all the uncertain demands. To validate the proposed model, the CPLEX solver/GAMS software is employed to solve several problem instances. These problems are analyzed with different uncertain conditions based on the applied robust optimization technique. Finally, a case study is evaluated in Farasakou Assaluyeh Company to demonstrate the applicability of our methodology and find the optimal policy. © 2021, Springer Nature Switzerland AG.
Goli, A. ,
Zare, H.K. ,
Tavakkoli-moghaddam, R. ,
Sadegheih a., A. ,
Sasanian, M. ,
Malekalipour kordestanizadeh, R. Network: Computation in Neural Systems (0954898X) 32(1)pp. 1-35
This research specifically addresses the prediction of dairy product demand (DPD). Since dairy products have a short consumption period, it is important to have accurate information about their future demand. The main contribution of this research is to provide an integrated framework based on statistical tests, time-series neural networks, and improved MLP, ANFIS, and SVR with novel meta-heuristic algorithms in order to obtain the best prediction of DPD in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using the Pearson correlation coefficient, and statistically significant variables are determined. Since the regression relation is not able to predict this demand properly, the artificial intelligence tools including MLP, ANFIS, and SVR are implemented and improved with the help of novel meta-heuristic algorithms such as grey wolf optimization (GWO), invasive weed optimization (IWO), cultural algorithm (CA), and particle swarm optimization (PSO). The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The high accurate results confirm that the proposed hybrid methods have the ability to improve the prediction of the demand for various products. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Goli, A. ,
Babaee tirkolaee, E. ,
Weber, G. Foundations of Computing and Decision Sciences (23003405) 46(1)pp. 27-42
This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN). © 2021 Sciendo. All rights reserved.
Goli, A. ,
Babaee tirkolaee, E. ,
Aydin, N.S. Ieee Transactions On Fuzzy Systems (10636706) 29(12)pp. 3686-3695
In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems including the scheduling of parts within cells in a cellular manufacturing system (CMS) where several automated guided vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs' collision as well as parts pickup/delivery. This article investigates the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and intercellular movements of parts. Due to the NP-hardness of the problem, a hybrid genetic algorithm (GA/heuristic) and a whale optimization algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and the other two well-known algorithms, i.e., particle swarm optimization and ant colony optimization, in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem. © 1993-2012 IEEE.
Mostafaeipour, A. ,
Goli, A. ,
Rezaei, M. ,
Qolipour, M. ,
Arabnia, H. ,
Goudarzi, H. ,
Behnam, E. Wind Engineering (0309524X) 45(2)pp. 245-256
This study seeks to provide a new method by proposing three hybrid algorithms. The proposed algorithms include genetic neural network hybrid algorithm, simulated annealing neural network hybrid algorithm, and shuffled frog-leaping neural network hybrid algorithm. The efficiency and reliability of the presented hybrid algorithms in prediction of wind speed behavior were evaluated using meteorological data of the city of Abadeh for a 10-year period from 2005 to 2015. The forecasting horizon is monthly for this study. The study parameters consisted of TMAX, TMIN, VP, RHMIN, RHMAX, WIND SPEED, PRECIPITATION, and SUNSHINE HOURS. These eight parameters are used as the inputs, and one parameter (ET) is used as the output. Research findings show that the shuffled frog-leaping neural network hybrid algorithm providing a root mean square error value of 0.0761 and reliability of 0.91 is more suitable than other hybrid algorithms for prediction of wind speed behavior in the study area. © The Author(s) 2019.
Pahlevan, S.M. ,
Hosseini, S.M.S. ,
Goli, A. Environmental Science and Pollution Research (09441344)
This study provides a three-objective mixed-integer linear mathematical model to design a sustainable closed-loop supply chain network in the aluminum industry. In this regard, the proposed model optimizes economic, social, and environmental objectives simultaneously. The main contribution of this research is to provide a framework for the sustainable aluminum supply chain in Iran by applying the life cycle assessment (LCA) to estimate the environmental impacts and using two novel meta-heuristic algorithms to optimize the proposed mathematical model. In this regard, the multi-objective gray wolf optimizer (MOGWO), the multi-objective red deer algorithm (MORDA), and augmented epsilon constraint (AEC) are used to achieve Pareto optimal solutions. Comparisons between solutions methods show that the MOGWO algorithm and MORDA have a very high advantage over the AEC method in terms of the scatter of Pareto solutions. Moreover, the statistical tests indicate that the MORDA has an advantage over MOGWO in terms of Pareto boundary diversification as well as the quality of solutions. On the other hand, results of the implementation in the aluminum industry show that increasing the coefficient of recycled materials’ use in the production of secondary aluminum has a significant impact on the Pareto boundary and leads to reducing production costs and in particular the reduction of carbon dioxide emissions. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
Siampour, L. ,
Vahdatpour, S. ,
Jahangiri, M. ,
Mostafaeipour, A. ,
Goli, A. ,
Shamsabadi, A.A. ,
Atabani, A. Sustainable Energy Technologies and Assessments (22131388) 43
Roughly 8 percent of the final energy used for heating and cooling of buildings and industries around the world is provided by renewable energies. Nevertheless, a major part of the energy consumed for heating is based on fossil fuels. Turkey's energy policies are aimed at providing secure, sustainable, and cost-effective energy, enhancing domestic energy production, and increasing energy efficiency in order to reduce consumption. To this end, renewable energies play an important role in Turkey's energy policies and, given the high energy potentials in Turkey, if these potentials are sufficiently realized, they can make a significant contribution to meeting future's energy demands. Therefore, in the present work for the first time, the use of solar water heaters (SWHs) in household scale has been investigated, by using climate data of radiation intensity and temperature for 45 stations in Turkey. Technical and environmental analysis was carried out by commercial software TSOL PRO 5.5 on two types of flat plate (FP) water heaters and evacuated tubes (ET), also, these stations are ranked using GAMS 24.1 software and DEA-BCC and DEA-Additive methods. The results show that the efficiency of ET water heaters is better than that of FP water heaters at all stations. For the FP SWH, the annual generation of heating, the annual heating generation for domestic hot water, and annual CO2 emission mitigation were 132,605 kWh, 120,365 kWh, and 68.4 t, whereas the same parameters were 228,814 kWh, 128,578 kWh, and 93.4 t for the ET SWH. For the FP collector, the results show that Akhisar, Bodrum, Finike, Hakkari and Iskenderun stations are superior stations and Sinop is the inappropriate station in both models, DEA-BCC and DEA-Additive models. On the other hand, for ET collector, the results show that Akhisar, Bodrum, Finike, Hakkari, Dalaman and Iskenderun stations are superior stations in both DEA-BCC and DEA-Additive methods and in DEA-BCC method, Zonguldak station and in DEA-Additive method Sinop station is the inappropriate station. © 2020 Elsevier Ltd
International Journal of Intelligent Transportation Systems Research (13488503) 18(1)pp. 140-152
Optimizing the distribution and allocation of resources among individuals is one of the most important measures to be taken at the time of crisis. Time, as a vital factor, has a significant impact on the increase in the number of people rescued by relief activities. This paper presents an allocation and routing model for relief vehicles in the areas affected by a disaster. It uses a covering tour approach to reduce response time. Moreover, because determining the exact amount of demand for essential goods in the event of a disaster is very difficult and even impossible in some cases, the demand parameter is considered as a fuzzy parameter in this model. Accordingly, an optimization method is designed based on credibility theory, and a harmony search algorithm with random simulation is developed. Finally, the efficiency of the harmony search algorithm is analyzed by comparing the CPLEX solver and GRASP algorithm. The results show that the proposed algorithm performs well over a short operating time. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Gholami, S. ,
Goli, A. ,
Bezemer, C. ,
Khazaei, H. pp. 150-160
With the increasing popularity and complexity of containerized software systems, satisfying the performance requirements of these systems becomes more challenging as well. While a common remedy to this problem is to increase the allocated amount of resources by scaling up or out, this remedy is not necessarily cost-effective and, therefore, often problematic for smaller companies. In this paper, we study an alternative, more cost-effective approach for satisfying the performance requirements of containerized software systems. In particular, we investigate how we can satisfy such requirements by applying software multi-versioning to the system's resource-heavy containers. We present DockerMV, an open-source extension of the Docker framework, to support the multi-versioning of containerized software systems. We demonstrate the efficacy of multi-versioning for satisfying the performance requirements of containerized software systems through experiments on the TeaStore, a microservice reference test application, and Znn, a containerized news portal application. Our DockerMV extension can be used by software developers to introduce multi-versioning in their own containerized software systems, thereby better allowing them to meet the performance requirements of their systems. © 2020 ACM.
International Journal of Manufacturing Technology and Management (13682148) 34(2)pp. 174-177
In order to coordinate the supply chain, reordering strategy of needed goods must be synchronised and sequence of production and replenishment cycle time must be optimised in terms of cost. Therefore, this paper studies the economic lot and delivery scheduling problem for multi-stage supply chain. The common cycle time and integer multiplier policies were adopted to accomplish the desired synchronisation. In this regard, a new mathematical model has been presented where a manufacturer with open-shop system purchases raw materials from suppliers and sends them to packaging companies after converting them into the final product and then they are sold. Since this is a non-deterministic polynomial-time hard (NP-hard) problem, simulated annealing algorithms have been developed for it. For this algorithm, two different scenarios have been proposed for solving the study problem and at the end the numerical results have been applied on problems with different dimensions by the algorithm. Copyright © 2020 Inderscience Enterprises Ltd.
Mostafaeipour, A. ,
Qolipour, M. ,
Rezaei, M. ,
Jahangiri, M. ,
Goli, A. ,
Sedaghat, A. Journal of Engineering, Design and Technology (17260531) 19(3)pp. 698-720
Purpose: Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives. Design/methodology/approach: In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software. Findings: Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified. Originality/value: As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant. © 2020, Emerald Publishing Limited.
Desalination and Water Treatment (19443994) 203pp. 388-402
Curdled milk, as a novel nano biosorbent, was utilized for ultrasound-assisted removal of eosin blue and aniline blue from aqueous solutions. Curdled milk was characterized by Fourier-transform infrared spectroscopy, Brunauer–Emmett–Teller isotherm, and scanning electron microscopy-energy-dispersive X-ray spectroscopy techniques. The main factors of pH, sonication time, amount of biosorbent, temperature, and initial dye concentrations were investigated. Maximum adsorption capacities of 147.1 and 131.6 mg g–1 were acquired for eosin blue and aniline blue respectively at optimum conditions including pH of 3–4, biosorbent amount of 10 mg, the temperature of 25°C, time of 5 min, and initial dye concentration of 10–30 mg L–1. Hybrid artificial neural net-work-genetic algorithm and shuffled frog leaping algorithm (SFLA) were employed for prediction and optimization of the process respectively. The results revealed that SFLA had the high capabil-ity for the optimization of the process. The biosorption data ideally fitted to the Langmuir model. Adsorption of aniline blue and eosin blue on to the curdled milk follows the pseudo-second-order kinetic model. Thermodynamic studies presented the negative values of ΔH° in which indicated the exothermic nature of the adsorption process and negative values of ΔG° showed the favorability and spontaneously occurrence of the adsorption process. © 2020 Desalination Publications. All rights reserved.
Goli, A. ,
Moeini, E. ,
Shafiee, A.M. ,
Zamani, M. ,
Touti, E. International Journal on Artificial Intelligence Tools (02182130) 29(5)
As the dairy products have a short consumption period, the accurate prediction of their demand is very important for the dairy industry. Accordingly, this research specifically addresses the prediction of dairy product demand (DPD). The main contribution of this research is to provide an integrated framework based on statistical tests, time-series prediction and artificial intelligence with the runner-root algorithm (RRA) as a novel meta-heuristic algorithm to obtain the best prediction of DPD in Iran. First, a series of economic and social indicators that seemed to be effective in the demand for dairy products are identified and the ineffective indices are eliminated. Next, the artificial intelligence tools including MLP, ANFIS, and LSTM are implemented and improved with the help of RRA. The designed hybrid methods are implemented by using data from 2013 to 2017 of the Iran diary industry. This novel algorithm is compared to gray wolf optimization, invasive weed optimization, and particle swarm optimization. The results show that the proposed MLP-RRA has the most ability to improve by using meta-heuristic algorithms. The coefficient of determination is 98.19%. Moreover, in each artificial intelligence tools, RRA causes better results than the other tested algorithms. The highly accurate results confirm that the proposed hybrid methods based on the RRA algorithm are able to improve the prediction of demand for various products. © 2020 World Scientific Publishing Company.
Sangaiah, A.K. ,
Goli, A. ,
Babaee tirkolaee, E. ,
Ranjbar-bourani, M. ,
Pandey, H.M. ,
Zhang, W. IEEE Access (21693536) 8pp. 82215-82226
The integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms. © 2013 IEEE.
Babaee tirkolaee, E. ,
Goli, A. ,
Weber, G. Ieee Transactions On Fuzzy Systems (10636706) 28(11)pp. 2772-2783
Flow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear programming (MILP) model is proposed for FSS with an outsourcing option and just-in-time delivery in order to simultaneously minimize the total cost of the production system and total energy consumption. Each job is considered to be either scheduled in-house or to be outsourced to one of the possible subcontractors. To efficiently solve the problem, a hybrid technique is proposed based on an interactive fuzzy solution technique and a self-adaptive artificial fish swarm algorithm (SAAFSA). The proposed model is treated as a single objective MILP using a multiobjective fuzzy mathematical programming technique based on the ϵ-constraint, and SAAFSA is then applied to provide Pareto optimal solutions. The obtained results demonstrate the usefulness of the suggested methodology and high efficiency of the algorithm in comparison with CPLEX solver in different problem instances. Finally, a sensitivity analysis is implemented on the main parameters to study the behavior of the objectives according to the real-world conditions. © 1993-2012 IEEE.
Goli, A. ,
Babaee tirkolaee, E. ,
Sangaiah, A.K. Advances in Parallel Computing (1879808X) 35pp. 264-280
Edge and fog computing mainly deal with Internet of Things (IoT). Practically, problems related to remote sensors or devices are typically where edge computing and fog computing incorporate. Thermal comfort of urban open spaces is one of the most important topics in the field of edge and fog computing. It is necessary to fulfill the demands for more pleasant thermal comfort in urban planning and design new urban open spaces, as well as reviewing and improving the existing ones. The thermal comfort of urban open spaces is variable since it depends on climatic parameters and other influences, which are inconstant throughout the year, as well as during the day. Therefore, the prediction of thermal comfort is significant in order to enable planning of the usage time of urban open spaces. This research aims to develop an Improved Cuckoo Search (ICS) algorithm for forecasting physiological equivalent temperature (PET) values one hour ahead. Usually, the parameters of Cuckoo Optimization Algorithms (COAs) are kept constant, which may lead to efficiency reduction. To cope with this issue, a proper strategy for tuning the parameters is presented. Moreover, the generation of laid eggs is done by implementing the cross-over operator of a Genetic Algorithm (GA). Then, it is employed to train feed forward neural networks for PET prediction. Finally, the performance of the proposed algorithm is compared to the state-of-the-art; i.e., traditional COA and GA. Our simulation results demonstrate the effectiveness of the proposed algorithm for about a 93% compliance rate. © 2020 The authors and IOS Press. All rights reserved.
Goli, A. ,
Hajihassani, O. ,
Khazaei, H. ,
Ardakanian, O. ,
Rashidi, M. ,
Dauphinee, T. pp. 20-25
Serverless computing is steadily becoming the implementation paradigm of choice for a variety of applications, from data analytics to web applications, as it addresses the main problems with serverfull and monolithic architecture. In particular, it abstracts away resource provisioning and infrastructure management, enabling developers to focus on the logic of the program instead of worrying about resource management which will be handled by cloud providers. In this paper, we consider a document processing system used in FinTech as a case study and describe the migration journey from a monolithic architecture to a serverless architecture. Our evaluation results show that the serverless implementation significantly improves performance while resulting in only a marginal increase in cost. © 2020 ACM.
Babaee tirkolaee, E. ,
Goli, A. ,
Faridnia, A. ,
Soltani, M. ,
Weber, G. Journal of Cleaner Production (09596526) 276
Cross-docking practice plays an important role in improving the efficiency of distribution networks, especially, for optimizing supply chain operations. Moreover, transportation route planning, controlling the Greenhouse Gas (GHG) emissions and customer satisfaction constitute the major parts of the supply chain that need to be taken into account integratedly within a common framework. For this purpose, this paper tries to introduce the reliable Pollution-Routing Problem with Cross-dock Selection (PRP-CDS) where the products are processed and transported through at least one cross-dock. To formulate the problem, a Bi-Objective Mixed-Integer Linear Programming (BOMILP) model is developed, where the first objective is to minimize total cost including pollution and routing costs and the second is to maximize supply reliability. Accordingly, sustainable development of the supply chain is addressed. Due to the high complexity of the problem, two well-known meta-heuristic algorithms including Multi-Objective Simulated-annealing Algorithm (MOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are designed to provide efficient Pareto solutions. Furthermore, the ε-constraint method is applied to the model to test its applicability in small-sized problems. The efficiency of the suggested solution techniques is evaluated using different measures and a statistical test. To validate the performance of the proposed methodology, a real case study problem is conducted using the sensitivity analysis of demand parameter. Based on the main findings of the study, it is concluded that the solution techniques can yield high-quality solutions and NSGA-II is considered as the most efficient solution tool, the optimal route planning of the case study problem in delivery and pick-up phases is attained using the best-found Pareto solution and the highest change in the objective function occurs for the total cost value by applying a 20% increase in the demand parameter. © 2020 Elsevier Ltd
Goli, A. ,
Zare, H.K. ,
Tavakkoli-moghaddam, R. ,
Sadegheih a., A. Computational Intelligence (08247935) 36(1)pp. 4-34
This paper addresses the multiobjective, multiproducts and multiperiod closed-loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large-sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε-constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices. © 2019 Wiley Periodicals, Inc.
Supply Chain Management(SCM) recently acknowledged as a critical issue in many industries. Human Resource Management as the basis of an organization and supply chain management plays a critical role in the performance of organizations. The cooperation between supply chain management and human resource management should lead to better accountability to customers, increase product quality and reduce costs in chain management. In this regard, this study investigates the important factors of HRM in supply chain management and examines their relationship, as well as a new conceptual model, which is designed to investigate the impact of its various factors. Efficiency and effectiveness are the most important part of this conceptual model. The results of the implementation of this conceptual model show among the dimensions of supply chain management, the efficiency dimension with the correlation coefficient of 0.278 has a significant impact on HR planning and the correlation coefficient of 0.238 comes in second place of effectiveness. Moreover, in the third-place is creating a value with a correlation coefficient of 0.234. © 2020 ACM.
Sangaiah, A.K. ,
Babaee tirkolaee, E. ,
Goli, A. ,
Dehnavi-arani, S. Soft Computing (14327643) 24(11)pp. 7885-7905
A constant development of gas utilization in domestic households, industry, and power plants has slowly transformed gaseous petrol into a noteworthy wellspring of energy. Supply and transportation planning of liquefied natural gas (LNG) need a great attention from the management of the supply chain to provide a significant development of gas trading. Therefore, this paper addresses a robust mixed-integer linear programming model for LNG sales planning over a given time horizon aiming to minimize the costs of the vendor. Since the parameter of the manufacturer supply has an uncertain nature in the real world, and this parameter is regarded to be interval-based uncertain. To validate the model, various illustrative examples are solved using CPLEX solver of GAMS software under different uncertainty levels. Furthermore, a novel metaheuristic algorithm, namely cuckoo optimization algorithm (COA), is designed to solve the problem efficiently. The obtained comparison results demonstrate that the proposed COA can generate high-quality solutions. Furthermore, the comparison results of the deterministic and robust models are evaluated, and sensitivity analyses are performed on the main parameters to provide the concluding remarks and managerial insights of the research. Finally, a comparison evaluation is done between the total vendor profit and the robustness cost to find the optimal robustness level. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
Goli, A. ,
Babaee tirkolaee, E. ,
Malmir, B. ,
Bian, G. ,
Sangaiah, A.K. Computing (14365057) 101(6)pp. 499-529
This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, robust optimization approach is applied to the proposed mixed integer linear programming model. A goal programming method is then implemented to cope with the multi-objectiveness and validate the suggested robust model. Since APP problems are classified as NP-hard, two solution methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve the problem. Moreover, Taguchi design method is implemented to increase the efficiency of the algorithms by adjusting the algorithms’ parameters optimally. Finally, several numerical test problems are generated in different sizes to evaluate the performance of the algorithms. The results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.
Babaee tirkolaee, E. ,
Goli, A. ,
Pahlevan, M. ,
Malekalipour kordestanizadeh, R. Waste Management and Research (0734242X) 37(11)pp. 1089-1101
Urban waste collection is one of the principal processes in municipalities with large expenses and laborious operations. Among the important issues raised in this regard, the lack of awareness of the exact amount of generated waste makes difficulties in the processes of collection, transportation and disposal. To this end, investigating the waste collection issue under uncertainty can play a key role in the decision-making process of managers. This paper addresses a novel robust bi-objective multi-trip periodic capacitated arc routing problem under demand uncertainty to treat the urban waste collection problem. The objectives are to minimize the total cost (i.e. traversing and vehicles’ usage costs) and minimize the longest tour distance of vehicles (makespan). To validate the proposed bi-objective robust model, the ε-constraint method is implemented using the CPLEX solver of GAMS software. Furthermore, a multi-objective invasive weed optimization algorithm is then developed to solve the problem in real-world sizes. The parameters of the multi-objective invasive weed optimization are tuned optimally using the Taguchi design method to enhance its performance. The computational results conducted on different test problems demonstrate that the proposed algorithm can generate high-quality solutions considering three indexes of mean of ideal distance, number of solutions and central processing unit time. It is proved that the ε-constraint method and multi-objective invasive weed optimization can efficiently solve the small- and large-sized problems, respectively. Finally, a sensitivity analysis is performed on one of the main parameters of the problem to study the behavior of the objective functions and provide the optimal policy. © The Author(s) 2019.
Goli, A. ,
Babaee tirkolaee, E. ,
Soltani, M. Production and Manufacturing Research (21693277) 7(1)pp. 294-315
Scheduling is known as a great part of production planning in manufacturing systems. Flow Shop Scheduling (FSS) problem deals with the determination of the optimal sequence of jobs processing on machines in a fixed order. This paper addresses a novel robust FSS problem with outsourcing option where jobs can be either scheduled for inside or outsourced to one of the available subcontractors. Capacity limitation for inside resource, just-in-time delivery policy and uncertain processing time are the key assumptions of the proposed model. The objective is to minimize the total-weighted time required to complete all jobs and the total cost of outsourcing. So, a Robust Mixed-Integer Linear Programming (RMILP) model is proposed to accommodate the problem with the real-world conditions. Finally, the obtained results show the effects of the robustness in optimizing the model under uncertainty condition. Moreover, the comparison analysis demonstrated the superiority of our proposed model against the previous Non-Linear Programming (NLP) model in the literature. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Babaee tirkolaee, E. ,
Goli, A. ,
Bakhshi, M. ,
Sangaiah, A.K. pp. 189-205
The process of distributing perishable goods considering quality is an important competitive and complicated task. In this paper, a novel mixed-integer linear programming (MILP) model is proposed to consider a location routing problem (LRP) for multiple perishable products with multi-trip vehicles having multiple trips, intermediate depots, and soft time windows. To cope with the solution complexity of the problem, an efficient biography based optimization algorithm (BBO) is designed. The algorithm is enhanced by tuning its parameters using Taguchi design method. In the first phase of the computational results, the proposed mathematical model is validated using CPLEX solver of GAMS in small-sized problems as an exact method. At the second phase, the performance of the proposed algorithm is evaluated compared to CPLEX, and it is demonstrated that it has an appropriate performance to generate high-quality and near-optimum solutions with the average gap of 0.839% for 3600 seconds of runtime limitation. Finally, a sensitivity analysis is performed on the demand parameter to study the behavior of the objective function in the real world condition. © 2019 Elsevier Inc. All rights reserved.
Goli, A. ,
Zare, H.K. ,
Tavakkoli-moghaddam, R. ,
Sadegheih a., A. International Journal of Interactive Multimedia and Artificial Intelligence (19891660) 5(6)pp. 15-22
This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved. © 2019, Universidad Internacional de la Rioja. All rights reserved.
Computers and Industrial Engineering (03608352) 130pp. 370-380
This research developed an integrated model for relief operations in critical situations. The model is aimed at minimizing the late arrival of relief vehicles that cross points en route to disaster locations. It locates temporary relief centers in affected areas and accordingly allocates and routes the first-aid commodities required by the centers. Vehicle routing in the model is underlain by the covering tour approach given that such method considerably increases the operational speed of a disaster logistics system, especially in terms of dispatching vehicles that carry essential commodities. Under the approach, vehicles pass through a small number of points where relief centers are established as temporary facilities. The model solves the issue of late arrival through hybrid benders decomposition and variable neighborhood search. The numerical results of application in Iran demonstrated the efficiency, quality, and speed of the proposed model. © 2019 Elsevier Ltd
Goli, A. ,
Zare, H.K. ,
Tavakkoli-moghaddam, R. ,
Sadegheih a., A. Numerical Algebra, Control and Optimization (21553289) 9(2)pp. 187-209
Product portfolio optimization (PPO) is a strategic decision for many organizations. There are several technical methods for facilitating this decision. According to the reviewed studies, the implementation of the robust optimization approach and the invasive weed optimization (IWO) algorithm is the research gap in this field. The contribution of this paper is the development of the PPO problem with the help of the robust optimization approach and the multi-objective IWO algorithm. Considering the profit margin uncertainty in real-world investment decisions, the robust optimization approach is used to address this issue. To illustrate the real-world applicability of the model, it is implemented for dairy products of Pegah Golpayegan Company in Iran. The numerical results obtained from the IWO algorithm demonstrate the effectiveness of the proposed algorithm in tracing out the efficiency frontier of the product portfolio. The average risk of efficient frontier solutions in the deterministic model is about 0.4 and for the robust counterpart formulation is at least 0.5 per product. The efficient frontier solutions obtained from robust counterpart formulation demonstrate a more realistic risk level than the deterministic model. The comparisons between CPLEX, IWO and genetic algorithm (GA) shows that the performance of the IWO algorithm is much better than the older algorithms and can be considered as an alternative to algorithms, such as GA in product portfolio optimization problems. © 2019, American Institute of Mathematical Sciences. All rights reserved.
Jafarian-namin, S. ,
Goli, A. ,
Qolipour, M. ,
Mostafaeipour, A. ,
Golmohammadi, A. International Journal of Energy Sector Management (17506220) 13(4)pp. 1038-1062
Purpose: The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria. Design/methodology/approach: The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months. Findings: The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480. Originality/value: Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO. © 2019, Emerald Publishing Limited.
Goli, A. ,
Zare, H.K. ,
Tavakkoli-moghaddam, R. ,
Sadegheih a., A. Computers and Industrial Engineering (03608352) 137
The optimization of the product portfolio problem under return uncertainty is addressed here. The contribution of this study is based on the application of a hybrid improved artificial intelligence and robust optimization and presenting a new method for calculating the risk of a product portfolio. By applying an improved neural network with runner root algorithm (RRA), the future demand of each product is predicted and the risk index of each product is calculated based on its predicted future demand. A two-objective (minimizing risk and maximizing return) mathematical model is proposed where, the effect of investments, reliability and allowable lost sales on the designed product portfolio are of concern. Due to the return uncertainty, two robust counterpart models based on the Bertsimas and Sim and Ben-Tal and Nemirovski approaches are developed. Then, an exact solution method is proposed to reduce the solving time of robust model. The results of the implementation in the dairy industry of Iran indicate that an increase in the confidence level, increase the investment risk and decrease the total return. The obtained results by the statistical tests indicate that the two newly proposed robust models are of similar performance in the finding the maximum return solutions, while, here the least risky solutions, the Bertsimas model outperforms its counterparts. Moreover, the results of the proposed exact solution method indicate that this method reduces the execution time by an average of 3%, indicative of proposed method effectiveness. © 2019 Elsevier Ltd
Mostafaeipour, A. ,
Qolipour, M. ,
Goudarzi, H. ,
Jahangiri, M. ,
Golmohammadi, A. ,
Rezaei, M. ,
Goli, A. ,
Sadeghikhorami, L. ,
Sedeh, A.S. ,
Khalifeh soltani, S.R. Journal Of Renewable Energy And Environment (24237469) 6(3)pp. 7-15
Fuel cells are potential candidates for storing energy in many applications; however, their implementation is limited due to poor efficiency and high initial and operating costs. The purpose of this research is to find the most influential fuel cell parameters by applying the adaptive neuro-fuzzy inference system (ANFIS). The ANFIS method is implemented to select highly influential parameters for proton exchange membrane (PEM) element of fuel cells. Seven effective input parameters are considered including four parameters of semi-empirical coefficients, parametric coefficient, equivalent contact resistance, and adjustable parameter. Parameters with higher influence are then identified. An optimal combination of the influential parameters is presented and discussed. The ANFIS models used for predicting the most influential parameters in the performance of fuel cells were performed by the well-known statistical indicators of the root-mean-squared error (RMSE) and coefficient of determination (R2). Conventional error statistical indicators, RMSE, r, and R2, were calculated. Values of R2 were calculated as of 1.000, 0.9769, and 0.9652 for three different scenarios, respectively. R2 values showed that the ANFIS could be properly used for yield prediction in this study. © 2019 Materials and Energy Research Center. All Rights Reserved.
Babaee tirkolaee, E. ,
Goli, A. ,
Weber, G. Lecture Notes in Mechanical Engineering (21954356) pp. 81-96
This paper investigates a novel fuzzy multi-objective multi-period Aggregate Production Planning (APP) problem under seasonal demand. As two of the main real-world assumptions, the options of workforce overtime and outsourcing are studied in the proposed Mixed-Integer Linear Programming (MILP) model. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, triangular fuzzy numbers are considered for demand parameters. Then the proposed model is validated by solving an illustrative example using a Weighted Goal Programming (WGP) method and CPLEX solver. Finally, it is demonstrated that uncertain conditions and considering real-world assumptions can yield different results in developing a practical aggregate production plan. Moreover, a sensitivity analysis is then performed to provide qualitative managerial insights and decision aids. © Springer Nature Switzerland AG 2019.
Babaee tirkolaee, E. ,
Goli, A. ,
Hematian, M. ,
Sangaiah, A.K. ,
Han, T. Computing (14365057) 101(6)pp. 547-570
This study addresses the multi-objective multi-mode resource-constrained project scheduling problem with payment planning where the activities can be done through one of the possible modes and the objectives are to maximize the net present value and minimize the completion time concurrently. Moreover, renewable resources including manpower, machinery, and equipment as well as non-renewable ones such as consumable resources and budget are considered to make the model closer to the real-world. To this end, a non-linear programming model is proposed to formulate the problem based on the suggested assumptions. To validate the model, several random instances are designed and solved by GAMS-BARON solver applying the ε-constraint method. For the high NP-hardness of the problem, we develop two metaheuristics of non-dominated sorting genetic algorithm II and multi-objective simulated annealing algorithm to solve the problem. Finally, the performances of the proposed solution techniques are evaluated using some well-known efficient criteria. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.
International Journal of Artificial Intelligence (09740635) 16(1)pp. 88-112
Transportation represents one of the major human activities all over the world; besides, it is an important part of economy, the improvement of which results in a considerable reduction in costs. Routing is one of the most well-known problems in the field of transportation optimization, which is of high complicacy due to being categorized as an NP-hard problem. In this research, in order to approximate this problem to real conditions, the customer satisfaction is considered in the model along with cost reduction. The main innovation of this study is to consider the competitive conditions as well as customer satisfaction in vehicle routing; besides, another innovation is to present a developed meta-heuristic algorithm based on cuckoo optimization algorithm (COA) in order to solve the problem in a short time and with a high quality. COA is a subset of the evolved computations, which is directly related with the artificial intelligence (AI); in fact, this algorithm is a subset of AI. In the proposed algorithm, instead of k-means clustering, the simulated annealing algorithm (SAA) is used to accelerate the cuckoo clustering. The results show that the proposed algorithm can accurately solve the problem with large dimensions in a reasonable time and with minimum errors. In this regard, a case study on dairy products distribution is conducted and solved using the proposed algorithm, and accordingly the efficacy and effectiveness of the developed algorithm and model are proved by sensitivity analysis of the main parameters. © 2018 [International Journal of Artificial Intelligence].
Production Engineering (09446524) 12(5)pp. 621-631
Coordinating a supply chain necessitates a synchronization strategy for reordering products and a cost-effective production and replenishment cycle time. The aim of this paper is to present an optimization framework for producing and distribution in the supply chains with a cooperating strategy. The main contribution of this paper is to integrate closed loop supply chain with open-shop manufacturing and economic lot and delivery scheduling problem (ELDSP). This integration is applied with the aim of better coordination between the members of the supply chain. This study examines the ELDSP for a multi-stage closed loop supply chain, where each product is returned to a manufacturing center at a constant rate of demand. The supply chain is also characterized by a sub-open-shop system for remanufacturing returned items. Common cycle time and multiplier policies is adopted to accomplish the desired synchronization. For this purpose, we developed a mathematical model in which a manufacturer with an open-shop system purchases raw materials from suppliers, converts them into final products, and sends them to package companies. Given that the ELDSPR is an NP-hard problem, a simulated annealing (SA) algorithm and a biography-based optimization (BBO) algorithm is developed. Two operational scenarios are formulated for the simulated annealing algorithm, after which both the algorithms are used to solve problems of different scales. The numerical results show that the biography-based optimization algorithm excellently performs in finding the best solution to the ELDSPR. © 2018, German Academic Society for Production Engineering (WGP).
Naderi, P. ,
Shirani, M. ,
Semnani, A. ,
Goli, A. Ecotoxicology and Environmental Safety (01476513) 163pp. 372-381
The novel green bioadsorbent, Centaurea stem, was utilized for crystal violet removal from aqueous solutions. SEM and FT-IR were used for characterization of Centaurea stem. The effects of the pH, time, temperature, bioadsorbent amount, and initial dye concentration were investigated. Response surface methodology was used to depict the experimental design and the optimized data of pH 12.57, time 19.661, temperature 38.94 °C, amount of bioadsorbent 12.218 mg, and initial dye concentration 36.62 mg L−1 were achieved. Moreover, artificial neural network (ANN) and simulated annealing (SA) were applied for prediction and optimization of the process respectively. The SA acquired optimum conditions of 10.114, 7.892 min, 25.127 °C, 64.405 mg L−1, 14.54 mg for pH, time, temperature, initial dye concentration, and bioadsorbent amount, respectively which were more close to the experimental results and indicated higher ability of SA-ANN in prediction and optimization of the process. The adsorption isotherms confirm the experimental data were appropriately fitted to the Langmuir model with high adsorption capacity of 476.190 mg g−1. The thermodynamic parameters were evaluated. The positive ΔH° and ΔS° values described endothermic nature of adsorption. The adsorption of crystal violet followed the pseudo-second order kinetic model. © 2018 Elsevier Inc.
Shirani, M. ,
Akbari, A. ,
Hassani, M. ,
Goli, A. ,
Habibollahi, S. ,
Akbarian, P. International Journal of Environmental Analytical Chemistry (10290397) 98(3)pp. 271-285
Facile and potent homogeneous liquid–liquid microextraction via flotation assistance method (HLLME-FA) combined with gas chromatography-mass spectrometry was proposed for determination of trace amounts of myclobutanil in fruit and vegetable samples. The paramount parameters, such as extraction and homogeneous solvent types and volumes, ionic strength and extraction time were studied. Under optimum conditions, the detection limit of 0.005 ng g−1, the linear range of 0.05–100 ng g−1, and the precision of 3.8% were acquired. A three-layer artificial neural network (ANN) model was used with 10 neurons and tan-sigmoid function at hidden layer and a linear transfer function at output layer were developed to predict the process. The results indicated that the proposed ANN model could perfectly predict the process with the mean square error of 0.89%. Then genetic algorithm was utilised to optimise the parameters. The proposed procedure showed satisfactory results for analysis of cucumber, tomato, grape, and strawberry. © 2018 Informa UK Limited, trading as Taylor & Francis Group.
Mostafaeipour, A. ,
Goli, A. ,
Qolipour, M. Journal of Supercomputing (15730484) 74(10)pp. 5461-5484
During the past few decades, many researchers have studied the issue of air travel demand in different countries. On the other hand, the development of airports requires considerable space in the vicinity of cities which needs planning and huge investment. However, development of air travel through different airports will be affected by various factors such as population growth and economic development. The purpose of this study is to predict air travel demand in Iran. Data were provided by the Civil Aviation Organization of Islamic Republic of Iran from 2011 to 2015. Collected information includes airports of the country and destination cities all across the country. For this purpose, the artificial neural network (ANN) is used to predict the air travel demand by considering income elasticity and population size in each zone. Evolutionary meta-heuristic algorithms have been implemented in order to improve the performance of ANN. Bat and Firefly algorithms are new meta-heuristic algorithms which have been examined in this study. The results show that the use of these algorithms increases adaptation rate of neural network (NN) prediction with real data. The coefficient of determination increases from 0.2 up to about 0.9 while using the meta-heuristics NN. This represents the high rate of efficiency using this new method. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Babaee tirkolaee, E. ,
Goli, A. ,
Bakhsi, M. ,
Mahdavi, I. Numerical Algebra, Control and Optimization (21553289) 7(4)pp. 417-433
Distribution of products within the supply chain with the highest quality is one of the most important competitive activities in industries with perishable products. Companies should pay much attention to the distribution during the design of their optimal supply chain. In this paper, a robust multi- trip vehicle routing problem with intermediate depots and time windows is formulated to deals with the uncertainty nature of demand parameter. A mixed integer linear programming model is presented to minimize total traveled distance, vehicles usage costs, earliness and tardiness penalty costs of services, and determine optimal routes for vehicles so that all customers’ demands are covered. A number of random instances in different sizes (small, medium, and large) are generated and solved by CPLEX solver of GAMS to evaluate the robustness of the model and prove the model validation. Finally, a sensitivity analysis is applied to study the impact of the maximum available time for vehicles on the objective function value. © 2017, American Institute of Mathematical Sciences. All rights reserved.
International Journal of Computational Intelligence Systems (18756891) 10(1)pp. 894-913
In this paper, an uncertain integrated model for simultaneously locating temporary health centers in the affected areas, allocating affected areas to these centers, and routing to transport their required good is considered. Health centers can be settled in one of the affected areas or in a place out of them; therefore, the proposed model offers the best relief operation policy when it is possible to supply the goods of affected areas (which are customers of goods) directly or under coverage. Due to that the problem is NP-Hard, to solve the problem in large-scale, a meta-heuristic algorithm based on harmony search algorithm is presented and its performance has been compared with basic harmony search algorithm and neighborhood search algorithm in small and large scale test problems. The results show that the proposed harmony search algorithm has a suitable efficiency. © 2017, the Authors.
Decision Science Letters (19295804) 4(4)pp. 572-579
Distribution and optimum allocation of emergency resources are the most important tasks, which need to be accomplished during crisis.When a natural disaster such as earthquake, flood, etc. takes place, it is necessary to deliver rescue efforts as quickly as possible. Therefore, it is important to find optimum location and distribution of emergency relief resources. When a natural disaster occurs, it is not possible to reach some damaged areas. In this paper, location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling is investigated. In this study, there is no need to visit all the places and some demand points receive their needs from the nearest possible location. The proposed study is implemented for some randomly generated numbers in different sizes. The preliminary results indicate that the proposed method was capable of reaching desirable solutions in reasonable amount of time. © 2015 Growing Science Ltd. All Right reserved.
