This work is concerned with customer-oriented catalog segmentation that each catalog consists of specific number of products. In this problem, requirements of a specific ratio of customers should be satisfied. According to the definition, when a customer is satisfied that at least t required products exist in his/her catalog. The objective of this problem is to minimize the number of catalogs, regarding to minimum number of customers constraint that was comply. In this paper, we present a mixed-integer programming model for this clustering problem. This problem is NP-Hard in large scales and the optimum solution is almost impossible to reach. Hence, a solution procedure is developed based on genetic algorithm. Then, the results of computational experiments are reported, in which the GA solution is compared with exact solution of mixed-integer programming model. ©2009 IEEE.
This work is concerned with the fuzzy clustering problem of different products in k variant catalogs, each of size r products that maximize customer satisfaction level in customer relationship management (CRM). The satisfaction degree of each customer is defined as a function of his/her needed product number that exists in catalog and also his/her priority. To determine the priority level of each customer, firstly customers are divided to three clusters with high, medium and low importance based on his/her needed products list. Then, all customers have been ranked based on their membership level in each of the above three clusters. In this paper in order to cluster customers, fuzzy c-means algorithm is applied. The proposed problem is firstly modeled as a bi-objective mathematical programming model. The objective functions of the model are to maximize the number of covered customers and overall satisfaction level results of delivering service. Then, this model is changed to a single integer linear programming model by applying fuzzy theory concepts. Finally, the efficiency of the proposed solution procedure is verified by using a numerical example. ©2009 IEEE.
Journal Of Intelligent Manufacturing (15728145)20(4)pp. 347-357
This paper presents a novel approach to the facility layout design problem based on multi-agent society where agents' interactions form the facility layout design. Each agent corresponds to a facility with inherent characteristics, emotions, and a certain amount of money, forming its utility function. An agent's money is adjusted during the learning period by a manager agent while each agent tries to tune the parameters of its utility function in such a way that its total layout cost can be minimized in competition with others. The agents' interactions are formed based on market mechanism. In each step, an unoccupied location is presented to all applicant agents, for which each agent proposes a price proportionate to its utility function. The agent proposing a higher price is selected as the winner and assigned to that location by an appropriate space-filling curve. The proposed method utilizes the fuzzy theory to establish each agent's utility function. In addition, it provides a simulation environment using an evolutionary algorithm to form different interactions among the agents and makes it possible for them to experience various strategies. The experimental results show that the proposed approach achieves a lower total layout cost compared with state of the art methods. © 2008 Springer Science+Business Media, LLC.
International Journal of Advanced Manufacturing Technology (02683768)45(7-8)pp. 759-771
This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0-1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions. © 2009 Springer-Verlag London Limited.
Zadeh, A.H.,
Maleki, H.,
Kianfar, K.,
Fathi, M.,
Zaeri, M.S. Advances in Intelligent and Soft Computing (18675670)73pp. 161-169
This work is concerned with the fuzzy clustering problem of different products in j variant catalogs, each of size i products that maximize customer satisfaction level in customer relationship management. The satisfaction degree of each customer is defined as a function of his/her needed product number that exists in catalog and also his/her priority. To determine the priority level of each customer, firstly customers are divided to three clusters with high, medium and low importance based on his/her needed products list. Then, all customers have been ranked based on their membership level in each of the above three clusters. In this paper in order to cluster customers, fuzzy c-means algorithm is applied. The proposed problem is firstly modeled as a bi-objective mathematical programming model. The objective functions of the model are to maximize the number of covered customers and overall satisfaction level results of delivering service. Then, this model is changed to a single integer linear programming model by applying fuzzy theory concepts. Finally, the efficiency of the proposed solution procedure is verified by using a numerical example. © Springer-Verlag Berlin Heidelberg 2010.
International Journal of Advanced Manufacturing Technology (02683768)47(1-4)pp. 269-281
Reverse logistics is becoming more important in overall industry area because of the environmental and business factors. Planning and implementing a suitable reverse logistics network could bring more profit, customer satisfaction, and a nice social picture for companies. But, most of logistics networks are not equipped to handle the return products in reverse channels. This paper proposes a mixed integer linear programming model to minimize the transportation and fixed opening costs in a multistage reverse logistics network. Since such network design problems belong to the class of NP-hard problems, we apply a simulated annealing (SA) algorithm with special neighborhood search mechanisms to find the near optimal solution. We also compare the associated numerical results through exact solutions in a set of problems to present the high-quality performance of the applied SA algorithm. © 2009 Springer-Verlag London Limited.
Computers and Operations Research (03050548)39(12)pp. 2978-2990
This paper considers the problem of scheduling a single machine, in which the objective function is to minimize the weighted quadratic earliness and tardiness penalties and no machine idle time is allowed. We develop a branch and bound algorithm involving the implementation of lower and upper bounding procedures as well as some dominance rules. The lower bound is designed based on a lagrangian relaxation method and the upper bound includes two phases, one for constructing initial schedules and the other for improving them. Computational experiments on a set of randomly generated instances show that one of the proposed heuristics, used as an upper bound, has an average gap less than 1.3% for instances optimally solved. The results indicate that both the lower and upper bounds are very tight and the branch-and-bound algorithm is the first algorithm that is able to optimally solve problems with up to 30 jobs in a reasonable amount of time. © 2012 Elsevier Ltd. All rights reserved.
Advances in Operations Research (16879155)2012
Operating room scheduling is an important operational problem in most hospitals. In this paper, a novel mixed integer programming (MIP) model is presented for minimizing Cmax and operating room idle times in hospitals. Using this model, we can determine the allocation of resources including operating rooms, surgeons, and assistant surgeons to surgeries, moreover the sequence of surgeries within operating rooms and the start time of them. The main features of the model will include the chronologic curriculum plan for training residents and the real-life constraints to be observed in teaching hospitals. The proposed model is evaluated against some real-life problems, by comparing the schedule obtained from the model and the one currently developed by the hospital staff. Numerical results indicate the efficiency of the proposed model compared to the real-life hospital scheduling, and the gap evaluations for the instances show that the results are generally satisfactory. Copyright © 2012 Somayeh Ghazalbash et al.
Kianfar, K.,
Fatemi ghomi s.m.t., ,
Oroojlooy jadid a., Engineering Applications of Artificial Intelligence (09521976)25(3)pp. 494-506
A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the problem is developed for the purpose of experimentation. The most commonly used dispatching rules from the literature and two new methods presented in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization, setup time level and number of stages. The results indicate that methods proposed in this study are much better than the traditional dispatching rules. © 2011 Published by Elsevier Ltd. All rights reserved.
Discrete Applied Mathematics (0166218X)161(13-14)pp. 2205-2206
In this note, through a counter-example we show that the results in a recent paper (Kacem, 2010) [1] are incorrect. Since the proposed dynamic programming in Kacem (2010) [1] fails to optimally solve the problem in some instances, the designed FPTAS with O(n2/ε) complexity is wrong. Then, we provide a modified FPTAS of O(n3/ε) time complexity for the considered problem. © 2013 Elsevier B.V. All rights reserved.
European Journal of Industrial Engineering (17515254)7(1)pp. 100-118
In this paper, the dynamic single-machine scheduling problem with a sequence-dependent setup time and with minimising total weighted tardiness of jobs as the objective is investigated. Due to the dynamic nature of the problem, a function-based approach is developed that can capture dynamic characteristics associated with the environment. In order to find a function which maps the environment's states to an action at each decision point, a combination of simulated annealing and a multi-layer feed-forward neural network is employed in an algorithm named SANN. The efficiency of the proposed function-based approach is compared with the most commonly used dispatching rules and with an agent-based approach, which employs the Q-learning algorithm to develop a decision-making policy. Numerical results reveal that the proposed approach outperforms dispatching rules and the Q-learning algorithm. The mean value of the results is about 93% better than the mean of the best results obtained with dispatching rules. Copyright © 2013 Inderscience Enterprises Ltd.
Journal Of Applied Mathematics (16870042)2014
This paper addresses a new performance measure for scheduling problems, entitled "biased tardiness penalty." We study the approximability of minimum biased tardiness on a single machine, provided that all the due dates are equal. Two heuristic algorithms are developed for this problem, and it is shown that one of them has a worst-case ratio bound of 2. Then, we propose a dynamic programming algorithm and use it to design an FPTAS. The FPTAS is generated by cleaning up some states in the dynamic programming algorithm, and it requires O n 3 / ε time. © 2014 G. Moslehi and K. Kianfar.
Journal of Supercomputing (15730484)71(3)pp. 1143-1162
Scheduling dynamically arriving parallel jobs on a grid system is one of the most challenging problems in supercomputer centers. Response time guarantee is one aspect of providing quality of service (QoS) in grids. Jobs are differently charged depending on the response time demanded by the user and the system must provide completion time guarantees. To tackle these challenges, we propose a new type of utility function for defining QoS in user-centric systems. The proposed utility function is a general form of functions in the literature. This function provides customers and system managers with more options to design SLA contracts. Also, its two due dates can make customers more confident and produce more profit for system providers. This paper develops a novel simulated annealing algorithm combined with geometric sampling (GSSA) for scheduling parallel jobs on a grid system. The proposed algorithm is compared with two other methods from the literature using three metrics of total utility, system utilization and the percentage of accepted jobs. The results show that the proposed GSSA algorithm is able to improve the metrics via better use of resources and also through proper acceptance or rejection decisions made on newly arriving jobs. © 2014, Springer Science+Business Media New York.
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.
Zakery, A.,
Shariatpanahi, Peyman,
Zolfagharzadeh M.M.,
Pourezzat, Ali Asghar Futures (00163287)
Simulation is likely to become a prominent method of theory development. Futures studies have used simulation in different ways such as evaluating scenarios. Nonetheless, the central attributes of computer simulation such as reductionism-based abstraction, determinism and elimination of stakeholders are the main barriers of successful implementation of simulation in FS. In this paper, we would paint the plausible evolutionary panorama of futures of simulation in futures studies after looking at the role of simulation in FS so far. The possible mechanisms and partnerships required to be applied to grapple the above-mentioned difficulties will be enumerated and investigated. These, in three categories, comprise firstly, human-machine interactions such as quasi-game simulations, and scenario visualization, secondly, large-network simulations including crowd sourcing, and thirdly, simulation platforms for replication of emergence. Ergo, crafting a classification of simulation in futures studies and the possible developments will be the main contribution of this paper. A novel double diamond classification will be presented as well which reflects the past and plausible futures of simulation in futures studies. © 2015 Elsevier Ltd
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.
Hosseini-motlagh, S.,
Nematollahi, M.,
Johari, M.,
Sarker, B.R. International Journal of Production Economics (09255273)204pp. 108-122
In recent years, competition among enterprises has been significantly increased. Trade credit and promotional effort are two important tools that have been extensively used for increasing competitive advantage. In today's business environment, retailers compete each other on new factors such as the length of credit period offered to end customers. In this paper, the performance of a supply chain (SC) consisting of a monopolistic manufacturer and two competing retailers has been analyzed under a promotional-effort credit-period dependent demand. The promotional efforts made by the manufacturer and the trade credits offered by competing retailers stimulate the market demand. The investigated SC is modeled under the decentralized, centralized and coordinated decision-making structures. In the decentralized model, three game structures are proposed to reflect the retailers' behaviors according to their market dominance: (1) retailers' Cournot behavior, (2) retailers' Collusion behavior, and (3) retailers' Stackelberg behavior. In the centralized model, the optimal decisions on promotional efforts and credit periods are determined to maximize the profits of the entire channel. However, the results indicate that the centralized solution will not necessarily be acceptable to all members as it does not consider the individual profit of each SC member. To remedy shortcomings of the centralized model and coordinate the channel, a novel collaborative model is proposed in order to not only increase the whole SC profits, but also guarantee participation of all SC members. Finally, a numerical example along with a comprehensive sensitivity analysis is carried out to compare the performance of the proposed models. © 2018 Elsevier B.V.
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].
Johari, M.,
Hosseini-motlagh, S.,
Nematollahi, M.,
Goh, M.,
Ignatius, J. Transportation Research Part E: Logistics and Transportation Review (13665545)114pp. 270-291
A bi-level credit period coordination scheme is proposed for a supplier-retailer supply chain with a periodic review replenishment policy and price-credit dependent demand. The credit period offered by the retailer to its customers is a promotional effort to induce customer demand and gain market share. We model the problem under decentralized, centralized, and coordinated structures. Through the incentive scheme, the supplier seeks to increase the retailer's credit period by offering the retailer a credit period. Our results suggest that coordinating the inventory, pricing, and credit financing can improve the overall chain and individual member profitability. © 2018 Elsevier Ltd
Uncertain Supply Chain Management (discontinued) (22916822)6(1)pp. 25-48
In this paper, the issue of cooperative (co-op) promotion efforts is addressed in a two-stage supply chain (SC). The investigated SC includes one monopolistic manufacturer and two duopolistic retailers facing different market demands. The customers’ demand is affected by both advertising efforts of the manufacturer and two retailers. Moreover, the retailers compete with each other on local advertising investments within the market. In order to boost the retailers’ advertising level, it is assumed that the manufacturer pays a ratio of the retailers' advertising expenditures. We propose four non-cooperative game scenarios and one cooperative game. Non-cooperative models are established through both Stackelberg and Nash game between two echelons. Moreover, both Cournot and Collusion behaviors are assumed to be followed by two retailers. We develop a promotion cost sharing contract to achieve the channel coordination. Under cooperation model, all SC members seek to reach the highest profit for the entire SC by considering the bargaining power of the SC participants. In each game scenario the optimal solution and unique equilibrium are determined. In addition, a comparison on the advertising level of all SC members along with the value of participation rate are provided. In addition, the feasibility of the cooperative game is discussed and resulted. © 2018 Growing Science Ltd. All rights reserved. and 2018 by the authors.
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).
Journal of Manufacturing Technology Management (1741038X)29(8)pp. 1296-1315
Purpose: The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators. Design/methodology/approach: First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined. Findings: The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively. Research limitations/implications: While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve. Originality/value: While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies. © 2018, Emerald Publishing Limited.
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.
Computational and Applied Mathematics (18070302)37(2)pp. 867-895
This paper addresses minimizing Tardy/Lost penalties with common due dates on a single machine. According to this penalty criterion, if tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. The problem is formulated as an integer programming model, and a heuristic algorithm is constructed. Then, using the proposed dominance rules and lower bounds, we develop two dynamic programming algorithms as well as a branch and bound. Experimental results show that the heuristic algorithm has an average optimality gap less than 2 % in all problem sizes. Instances up to 250 jobs with low variety of process times are optimally solved and for high process time varieties, the algorithms solved all instances up to 75 jobs. © 2016, SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional.
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.
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.
