Journal of Modelling in Management (17465672)18(2)pp. 285-317
Purpose: This paper aims to minimize the total carbon emissions and costs and also maximize the total social benefits. Design/methodology/approach: The present study develops a mathematical model for a closed-loop supply chain network of perishable products so that considers the vital aspects of sustainability across the life cycle of the supply chain network. To evaluate carbon emissions, two different regulating policies are studied. Findings: According to the obtained results, increasing the lifetime of the perishable products improves the incorporated objective function (IOF) in both the carbon cap-and-trade model and the model with a strict cap on carbon emission while the solving time increases in both models. Moreover, the computational efficiency of the carbon cap-and-trade model is higher than that of the model with a strict cap, but its value of the IOF is worse. Results indicate that efficient policies for carbon management will support planners to achieve sustainability in a cost-effectively manner. Originality/value: This research proposes a mathematical model for the sustainable closed-loop supply chain of perishable products that applies the significant aspects of sustainability across the life cycle of the supply chain network. Regional economic value, regional development, unemployment rate and the number of job opportunities created in the regions are considered as the social dimension. © 2021, Emerald Publishing Limited.
International Journal on Interactive Design and Manufacturing (19552513)17(6)pp. 3305-3319
This research extends the constrained vehicle routing problem concept to solve flexible flow shop scheduling problems. Mixed-integer linear programming and constraint programming formulations are developed for a flow shop problem with no-wait, time lags and release time restrictions to minimize the makespan in both permutation and non-permutation schedules. The comparative analysis of various models reveals that constraint programming models have superior computational performance than mixed-integer linear programming models. However, the mixed-integer linear programming models are also timely-efficient. Moreover, the efficiency of developed models is also represented in comparison with several benchmark datasets. Based on the findings, while the objective function values of the mixed-integer linear programming and constraint programming models in non-permutation schedules exhibit lower values than their respective equivalents in permutation schedules, both models demonstrate longer runtime in non-permutation schedules. Results represent that the proposed constraint programming and mixed-integer linear programming models are among the top three models of the benchmark datasets in terms of the number of decision variables and computational performance. One of the limitations of the research is that there is no comprehensive dataset in the literature considering all the restrictions in permutation and non-permutation schedules. © 2023, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.
Computers and Industrial Engineering (03608352)172
Electronic vehicles (EVs) are receiving increasing attention to addressing global warming challenges since fossil fuel is replaced with fuel cell technology. Hence, new challenges arise as demands have increased for using EVs. One of these challenges is the long waiting time of charging EVs spent in queues, especially during peak hours. So, in this study, we aim to propose an efficient method for the electric vehicle charging scheduling problem (EVCSP), which an actual charging station inspires. The most important constraint in this problem is balancing power consumption between charging lines, leading to a limited number of devices that can be charged simultaneously. Also, in this problem, EVs may have interrelationships with each other during the scheduling procedure. So, the estimation of distribution algorithm (EDA) as a competent method in handling the possible relations among decision variables is applied in our proposed hybrid EDA-based solving method. Our proposed method comprises two EDAs, a Markov network-based EDA and a Mallows model-based EDA. It achieves an appropriate schedule and charging line assignment simultaneously while minimizing the total tardiness considering problem constraints. We compared our method with a constraint programming (CP) model and the state-of-art meta-heuristic methods in terms of the objective function value by simulation on a benchmark dataset. Results from the experimental study show significant improvement in solving the introduced EVCSPs. © 2022
International Journal of Services and Operations Management (17442370)42(3)pp. 339-352
Packaging is one of the most substantial and determinative processes of goods consumption and supply, which can be a competitive advantage in comparison with rivals. Foodstuff packaging ensures that export products are sent to the considered destination without any problem. Moreover, proper packaging of goods and customer satisfaction would boost the sale rate in target markets. This study aimed at designing a model for factors affecting the packaging of agricultural products for export. In order to present a new and comprehensive research in the export and packaging field, qualitative method and qualitative content analysis approach were used. In qualitative content analysis method, experts in the field of packaging and export, businessmen, and top managers of companies that work in fruits and vegetables export were interviewed to identify factors affecting export agricultural goods packaging. Variables were identified by using theme analysis method through MAXQDA Software. The obtained results indicated that factors affecting the packaging include seven categories: environmental factors, physical characteristics, shipping terms, rules and regulations, export destination features, product information, and product type. © 2022 Inderscience Enterprises Ltd.
Expert Systems (14680394)39(2)
Production scheduling and reliability of machinery are prominent issues in flexible manufacturing systems that are led to decreasing of production costs and increasing of system efficiency. In this paper, multiobjective optimization of stochastic failure-prone job shop scheduling problem is sought wherein that job processing time seems to be controllable. It endeavours to determine the best sequence of jobs, optimal production rate, and optimum preventive maintenance period for simultaneous optimization of three criteria of sum of earliness and tardiness, system reliability, and energy consumption. First, a new mixed integer programming model is proposed to formulate the problem. Then, by combining of simulation and NSGA-II algorithm, a new algorithm is put forward for solving the problem. A set of Pareto optimal solutions is achieved through this algorithm. The stochastic failure-prone job shop with controllable processing times has not been investigated in the earlier research, and for the first time, a new hedging point policy is presented. The computational results reveal that the proposed metaheuristic algorithm converges into optimal or near-optimal solution. To end, results and managerial insights for the problem are presented. © 2019 John Wiley & Sons, Ltd
European Journal of Industrial Engineering (17515254)15(5)pp. 643-674
This paper schedules capacitated parallel machines of a real production system by considering different quantities of production and processing times required to complete customer orders. A new mixed linear programming model is developed according to the concept of constrained vehicle routing problems to have a complete schedule for machines by determining the sequence of both jobs and idle times for each machine. The optimisation model minimises the total cost of the production system, including tardiness, earliness and sequence-dependent setup costs. A constraint programming (CP) model and a meta-heuristic hybrid algorithm are also developed to compare their results with the mixed linear programming model. The numerical findings show that the total cost estimated by the mixed integer programming model is 10%–13% better (lower) than the ones estimated by the CP model and the meta-heuristic algorithm when small instances of the scheduling problem are solved. By increasing the size of the scheduling problem, the meta-heuristic algorithm shows the best computational performance estimating 11% better (lower) total cost compared with the CP model. [Received: 14 April 2020; Accepted: 26 October 2020] Copyright © 2021 Inderscience Enterprises Ltd.
Soft Computing (14327643)24(6)pp. 4483-4503
Clustering, a famous technique in data analysis and data mining, attempts to find valuable patterns in datasets. In this technique, a set of alternatives is partitioned into logical groups which are called clusters. The partitioning is based on some predefined attributes to find clusters in which their alternatives are similar to each other comparing to other clusters. In conventional methods, the similarity is usually defined by a distance-based measurement, whereas in this study, we have proposed a new multi-attribute preference disaggregation method called DISclustering in which a new measurement named global utility is introduced for cluster similarity. In DISclustering, the global utility of each alternative is calculated through a feed-forward neural network in which its parameters are determined using SA algorithm. Each alternative is assigned to a cluster based on comparing the obtained global utility with cluster boundaries, called utility thresholds; aim to minimize the intra-cluster distances (ICD). For this purpose, all utility thresholds are estimated using PSO algorithm. The performance of the proposed method is compared with 18 clustering algorithms on 14 real datasets based on F-measure and object function values (ICD values using intra-cluster or Gower distances). The experimental results and hypothesis statistical test indicate that DISclustering algorithm significantly improved clustering results on F-measure criteria in which outperforms in almost 13 compared algorithms out of 18. Note that, DISclustering calculates cluster centroid in a different way comparing to other algorithms. Hence, its ICD values are less eligible to perform a fair comparison. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
International Journal of Industrial Engineering : Theory Applications and Practice (1943670X)27(3)pp. 321-344
This research proposes a mathematical model in which the procurement and production lot-sizing are integrated with scheduling. In this model, the procurement lot-sizing comprises supplier selection and multiple transportation modes. This profit-maximizing model is developed with demand choice flexibility using two arrangements: with and without demand packages. The results represent that the objective function value of the model with demand packages is less than the model without packages while the computational time of the model with packages is greater than the other one. The impact of demand’s correlation on results is investigated through solving the problem with two correlation levels. Results show that the number of setups, total costs, and the number of incomplete packages are influenced by the demand correlation. Besides, the objective function value of the problem with positive demand correlation is higher. Results also certify the impact of discount schemes on productions, purchases, costs, and revenues. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING
Journal of Industrial and Management Optimization (15475816)16(3)pp. 1235-1259
Integrated process planning and scheduling (IPPS) problems are one of the most important flexible planning functions for a job shop manufac-turing. In a manufacturing order to produce n jobs (parts) on m machines in a flexible manufacturing environment, an IPPS system intends to generate the process plans for all n parts and the overall job-shop schedule concurrently, with the objective of optimizing a manufacturing objective such as make-span. The optimization of the process planning and scheduling will be applied through an integrated approach based on Fuzzy Inference System (FIS), to provide for flexibilities of the given components and consider the qualitative parameters. The FIS, Constraint Programming (CP) and Simulated Annealing (SA) algo-rithms are applied in this design. The objectives of the proposed model consist of maximization of processes utility, minimization of make-span and total pro-duction costs including costs of flexible tools, machines, process and TADs. The proposed approach indicates that The CP and SA algorithms are able to resolve the IPPS problem with multiple objective functions. The experiments and related results indicate that the CP method outperforms the SA algorithm. © 2020, American Institute of Mathematical Sciences.
Applied Mathematical Modelling (0307904X)84pp. 1-18
This study addresses the multi-level lot-sizing and scheduling problem with complex setups and considers supplier selection with quantity discounts and multiple modes of transportation. The present research proposes a mixed-integer linear programming (MILP) model in which the purchase lot-sizing from multiple suppliers, production lot-sizing with multiple machines and scheduling of various products of different families are accomplished at the same time. However, these decisions are not integrated in traditional environments and are taken separately. In this study, two different types of lot-sizing models called aggregated and disaggregated are developed for the problem to evaluate and compare the computational efficiency of them under deterministic and stochastic demands and provide some managerial insights. To deal with the stochastic demands, Chance-Constrained Programming (CCP) approach is applied. Based on the results of this study, the average profit of the separated (purchase from production) lot-sizing model under demand choice flexibility and stochastic demand is 24% and 22% less than the integrated model, respectively. Moreover, the results also confirm the effect of discount structure on the amount of purchases, productions, revenues and costs. © 2020 Elsevier Inc.
International Journal of Environmental Science and Technology (17351472)16(7)pp. 3389-3402
In this paper, multi-objective optimization of energy-aware multi-product failure-prone manufacturing system is explored. The purpose is to determine the best sequence of jobs, optimal production rate and optimum preventive maintenance time for simultaneous optimization of three criterions of total weighted quadratic earliness and tardiness, system reliability and energy-consumption cost. Considering the uncertainties of the problem such as stochastically machine breakdown and maintenance, stochastic processing times as well as NP-hard nature of the problem, it is not possible to propose an analytical solution to this problem. Therefore, two novel algorithms by combining (1) simulation and NSGA-II/PSO and (2) simulation and NSGA-II/GA are proposed for solving this problem. A set of Pareto optimal solutions was obtained via this algorithm. Results show that the both methods converge to a same optimal solution, but the rate of convergence with NSGA-II/PSO is faster than NSGA-II/GA. The algorithms are evaluated by solving small-, medium- and large-scale problems. To the best of our knowledge, multi-product failure-prone manufacturing systems by considering sequence of jobs have not been explored in any paper and for the first time a new hedging point policy is presented for the mentioned problem. © 2018, Islamic Azad University (IAU).
Measurement: Journal of the International Measurement Confederation (02632241)115pp. 27-38
We introduce a novel method called subdividing labeling genetic algorithm (SLGA) to solve optimization problems involving n – dimensional continuous nonlinear functions. SLGA is based on the mutation and crossover operators of genetic algorithms, which are applied on a subdivided search space where an integer label is defined on a polytope built on the n – dimensional space. The SLGA method approaches a global optimal solution by reducing the feasible search region in each iteration. One of its main advantages is that it does not require computing the derivatives of the objective function to guarantee convergence. We apply the SLGA method to solve optimization problems involving complex combinatorial and large-scale systems and illustrate numerically how it outperforms several other competing algorithms such as Differential Evolution even when considering problems with a large number of elements. © 2017 Elsevier Ltd
Technological Forecasting and Social Change (00401625)125pp. 188-205
Technology foresight (TF) studies the appropriate extrapolation methodologies for predicting the most likely technology development scenarios in the future. Although there is a vast literature dealing with the classification and development of technology foresight methods (TFMs), the problem of selecting those that best reflect the characteristics of an organization is challenging and remains mostly overlooked. We propose a TFM evaluation procedure that allows decision makers and managers to successfully address this problem. The proposed procedure identifies the most relevant TFMs and organizational criteria and uses them in a multiple correspondence analysis (MCA) model to select the most suitable method(s) for implementation. The proposed MCA model combines the doubling data technique with a row principal scoring procedure to allow for the reduction of dimensionality and, consequently, the graphical analysis of the patterns of relationships among TFMs and evaluation criteria. We present a case study in a knowledge-based organization to demonstrate the applicability and efficacy of the proposed evaluation procedure. The results show that the proposed model can be properly adapted to allow for a wide range of applications involving business organizations and government agencies. © 2017 Elsevier Inc.
Esmaelian, M.,
Santos-arteaga, F.J.,
Tavana, M.,
Vali, M.,
Esmaelian, M.,
Santos-arteaga, F.J.,
Tavana, M.,
Vali, M. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025pp. 773-780
In most global optimization problems, finding a global optimum point in the whole multi-dimensional search space implies a high computational burden. We present a new approach called subdividing labeling genetic algorithm (SLGA) for continuous nonlinear optimization problems. SLGA applies mutation and crossover operators on a subdivided search space where an integer label is defined on a polytope built on a n-dimensional space. After calculating the fitness of each point composing the polytope, SLGA implements a mutation operator to generate offspring and computes an integer label for the population of the polytope. Then, after completely labeling the polytope, a crossover operator is implemented so as to approach the optimum point by reducing the search space. In this regard, new population is generated by subdividing the search space and further implementing the mutation operator. SLGA has been used to optimize the De Jong functions, as well as nonlinear constrained and unconstrained problems with discrete, continuous and mixed variables. It has also been compared with other well-known algorithms. Experimental results show that the SLGA method has good performance and reduces the number of generations within the solution space, which enhances its convergence capability. © 2017 IEEE.
Applied Soft Computing (15684946)49pp. 56-70
In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used as the utility function rather than using a piecewise linear function as an approximation of the utility function of each attribute. We called this method as PUTADIS. The objective is calculating the coefficients of the polynomial and the threshold limit of classes and weight of attributes such that it minimizes the number of misclassification error. Estimation of unknown parameters of the problem is calculated by using a hybrid algorithm which is a combination of particle swarm optimization algorithm (PSO) and Genetic Algorithm (GA). The results obtained by implementing the model on different datasets and comparing its performance with other previous methods show the high efficiency of the proposed method. © 2016 Elsevier B.V.
International Journal of Productivity and Quality Management (17466474)18(1)pp. 116-134
The aim of this research is to propose a framework for classifying 14 quality management tools based on quality principles. For this purpose, multi-criteria decision-making, importance performance analysis and a survey on 13 process owners of Marjan Tile Company have been conducted. Based on the findings, three packages have been proposed for quality management tools as: 1) relation diagram and process decisions planning chart; 2) check sheets, Pareto diagram, cause and effect diagram, histograms and tree diagram; 3) control chart, procedure diagram, matrix analysis of data, matrix diagrams, scatter diagram, affinity diagram and diagram of concentration of defects.
International Journal of Geographical Information Science (13658816)29(7)pp. 1187-1213
Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran. © 2015 Taylor & Francis.
International Journal of Information and Decision Sciences (17567017)7(2)pp. 140-165
The linear programming technique for multi-dimensional analysis of preferences (LINMAP) is one of the noted multi-attributes decision making (MADM) techniques and has been implemented in crisp and fuzzy environments. Robust optimisation attempts to obtain a solution which is feasible in all circumstances arising due to the uncertainty of parameters. The purpose of this study is to extend the LINMAP method for addressing robustness in MADM problems. In this methodology, robust optimisation concepts are used to describe robustness in decision information and decision making processes. Each alternative is evaluated based on its weighted distance to a robust positive ideal solution (RPIS). The RPIS and the robust weights of attributes are estimated using a new robust linear programming technique. Finally, Monte Carlo simulation is applied to test the robustness of the solution. A numerical example is provided to illustrate the effectiveness of the methodology. Copyright © 2015 Inderscience Enterprises Ltd.
