Journal of Optimization in Industrial EngineeringJournal of Optimization in Industrial Engineering
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Feed provided by Journal of Optimization in Industrial Engineering. Click to visit.An Ant Colony approach to forward-reverse logistics network design under demand certainty
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Forward-reverse logistics network has remained a subject of intensive research over the past few years. It is of significant importance to be issued in a supply chain because it affects responsiveness of supply chains. In real world, problems are needed to be formulated. These problems usually involve objectives such as cost, quality, and customers' responsiveness and so on. To this reason, we have studied a single-objective model for an integrated forward/reverse logistics network design. This model includes seven echelons; four echelons in the forward direction and three in the reverse direction. We present an effective algorithm based on ant colony optimization for this NP-hard problems to maximize the benefit. The proposed metaheuristic algorithm is a new approach in the field of closed-loop supply chain network design. Furthermore, the developed model is a three-objective one which regards incomes, costs, and the emissions of CO2. A new approach is utilized in order to integrate three various-dimension objective functions. The performance of the proposed algorithm has been compared utilizing the optimum solutions of the LINGO software. Besides, various instances with small, medium, and large sizes are generated and solved so as to make the evaluation of the algorithm reliable. The obtaining results clearly demonstrate superiority performance of the proposed algorithm.Fri, 08 Jul 2016 19:30:00 +0100Monitoring process variability: a hybrid Taguchi loss and multiobjective genetic algorithm approach
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The common consideration on economic model is that there is knowledge about the risk of occurrence of an assignable cause and the various cost parameters that does not always adequately describe what happens in practice. Hence, there is a need for more realistic assumptions to be incorporated. In order to reduce cost penalties for not knowing the true values of some parameters, this paper aims to develop a bi-objective model of the economic-statistical design of the S control chart to minimize the mean hourly loss cost while minimizing out-of-control average run length and maintaining reasonable in-control average run length considering Taguchi loss function. The purpose of Taguchi loss function is to reflect the economic loss associated with variation in, and deviations from, the process target or the target value of a product characteristic. In contrast to the existing modeling approaches, the proposed model and given Pareto-optimal solution sets enables the chart designer to obtain solutions that is effective even for control chart design problems in uncertain environments. A comparison study with a traditional economic design model reveals that the proposed chart presents a better approach for quality system costs and the power of control chart in detecting the assignable cause.Sat, 24 Sep 2016 20:30:00 +0100A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with ...
http://www.qjie.ir/article_246_34.html
This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm. This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithmSat, 24 Sep 2016 20:30:00 +0100A bi-objective airport gate scheduling with controllable processing times using Harmony Search ...
http://www.qjie.ir/article_234_0.html
Optimizing gate assignment at airports is an old, but also a broad problem. The main purpose of this problem is to find an assignment for the flights arriving and departing while satisfying a set of constraints. In all studies, airport gate processing time was considered fixed. In this research, however, we investigate the more realistic situation that airport gate processing time is controllable; it leads to increase the total performance of gate scheduling. It is assumed that the possible compression/expansion processing time of a flight can be continuously controlled, i.e. it can be any number in a given interval. Depending on the situation, different objectives become important. The aim of this study is to simultaneously (1) minimize total cost of tardiness, earliness, delay as well as compression and expansion costs of job processing time and (2) minimize the passengers overcrowding on gate. In this study, we first propose a mixed-integer programming model for the considered problem. Due to complexity of problem, two multi-objective meta-heuristic algorithms, i.e. multi-objective harmony search algorithm (MOHSA) and non-dominated sorting genetic algorithm II (NSGA-II) are applied. For calibrating the parameter of the algorithms Taguchi method is used and the optimal levels of the algorithmâ€™s performance are selected. The algorithms are tested with real life data from Mehrabad International Airport for medium size problems. Finally, the experimental results show that NSGA-II has better convergence near the true Pareto-optimal front as compared to MOHSA; however, MOHSA finds a better spread in the entire Pareto-optimal region.Fri, 08 Jul 2016 19:30:00 +0100Fuzzy Programming for Parallel Machines Scheduling:
Minimizing Weighted Tardiness/Earliness ...
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Appropriate scheduling and sequencing of tasks on machines is one of the basic and significant problems that a shop or a factory manager encounters with it, this is why in recent decades extensive researches have been done on scheduling issues. A type of scheduling problems is just-in-time (JIT) scheduling and in this area, motivated by JIT manufacturing, this study investigates a mathematical model for appraising a multi-objective programing that minimize total weighted tardiness, earliness and total flowtime with fuzzy parameters on parallel machines, simultaneously with respect to the impact of machine deterioration. Besides, in this paper is attempted to present a defuzzification approach and a heuristic method based genetic algorithm (GA) to solve the proposed model. Finally, several dominance properties of optimal solutions are demonstrated in comparison with the results of a state-of-the-art commercial solver and the simulated annealing method that is followed by illustrating some instances for indicating validity and efficiency of the method.Sat, 24 Sep 2016 20:30:00 +0100Performance Improvement through a Marshaling Yard Storage Area in a Container Port Using ...
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Container ports have been faced under increasing development during last 10 years. In such systems, the container transportation system has the most important effect on the total system. Therefore, there is a continuous need for the optimal use of equipment and facilities in the ports. Regarding the several complicated structure and activities in container ports, this paper evaluates and compares two different storage strategies for storing containers in the yard. To do so and covering all actual stochastic events occur in the system, a simulation model of the real system was developed using loading and unloading norms as important criteria to evaluate the performance of Shahid Rajaee container port. By replicating the simulation model and considering the two strategies, it has been shown that using a marshaling yard policy has a significant effect on the performance level of the port which leads to cost reductions.Sat, 24 Sep 2016 20:30:00 +0100Cell forming and cell balancing of virtual cellular manufacturing systems with alternative ...
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Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.Sat, 24 Sep 2016 20:30:00 +0100Fuzzy Multi-Objective Linear Programming for Project Management Decision under Uncertain ...
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Smooth implementation and controlling conflicting goals of a project with the usage of all related resources through organization is inherently a complex task to management. At the same time deterministic models are never efficient in practical project management (PM) decision problems because the related parameters are frequently fuzzy in nature. The project execution time is a major concern of the involved stakeholders (client, contractors and consultants). For optimization of total project cost through time control, here crashing cost is considered as a critical factor in project management. The proposed approach aims to formulate a multi objective linear programming model to simultaneously minimize total project cost, completion time and crashing cost with reference to direct, indirect cost in the framework of the satisfaction level of decision maker with fuzzy goal and fuzzy cost coefficients.. To make such problems realistic, triangular fuzzy numbers and the concept of minimum accepted level method are employed to formulate the problem. The proposed model leads decision makers to choose the desired compromise solution under different risk levels and the project optimization problems have been solved under multiple uncertainty conditions. The Analytical Hierarchy Process is used to rank multiple objectives to make the problem realistic for the respective case. Here minimum operator and AHP based weighted average operator method is used to solved the model and the solution is obtained by using LINGO softwareSat, 24 Sep 2016 20:30:00 +0100A benders' decomposition method to solve stochastic distribution network design problem with ...
http://www.qjie.ir/article_238_0.html
In many practical distribution networks, managers face significant uncertainties in demand, local price of building facilities, transportation cost, and macro and microeconomic parameters. This paper addresses design of distribution networks in a supply chain system which optimizes the performance of distribution networks subject to required service level. This service level, which is consideredforeach arbitrary request arriving at a distribution center (facility), has a (pre-specified) small probability of being lost. In this mathematical model, customer’s demand is stochastic that followsuniform distribution. In this model, inter-depot transportation (transportation between distribution centers (DCs)), capacities of facilities and coverage radius restrictions are considered.For this restriction, each DC cannot service all customers. The aim of this model is to select and optimize location of plants and DCs. Also, the best flow of products between DCs and from plants to DCs and from DCs to customers will be determined. The paper presents a mixed integer programming model and proposed an exact solution procedure in regard to Benders’ decomposition method.Wed, 14 Sep 2016 19:30:00 +0100A hybrid intuitionistic fuzzy multi-criteria group decision making approach for supplier selection
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Due to the increasing competition of globalization, selection of the most appropriate supplier is one of the key factors for asupply chain management’s success. Due to conflicting evaluations and insufficient information about the criteria, Intuitionisticfuzzy sets (IFSs) considered as animpressive tool and utilized to specify the relative importance of the criteria. The aim of this paper is to develop a new approach for solving the decision making processes. Thusan intuitionistic fuzzy multi-criteria group decision making approach is proposed. Interval-valued intuitionistic fuzzy ordered weighted aggregation (IIFOWA) is utilized to aggregate individual opinions of decision makers into a group opinion. A linear programming model is used to obtain the weights of the criteria.Then acombined approach based onGRAand TOPSIS method is introduced and applied to the ranking and selection of the alternatives. Finally a numerical example for supplier selection is given to illustrate the feasibility and effectiveness of the proposed method. A combined method based on GRA and TOPSIS associated with intuitionistic fuzzy set has enormous chance of success for multi-criteria decision-making problems due to containing vague perception of decision makers’ opinions. Therefore, in future, intuitionistic fuzzy set can be used for dealing with uncertainty in multi-criteria decision-making problems such as project selection, manufacturing systems, pattern recognition, medical diagnosis and many other areas of management decision problems.Sat, 24 Sep 2016 20:30:00 +0100Using NSGA II and MOSA for solving multi-depots time-dependent vehicle routing problem with ...
http://www.qjie.ir/article_239_0.html
Time-dependent Vehicle Routing Problem is one of the most applicable but least-studied variants of routing and scheduling problems. In this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. To deal with the traffic congestions, we also considered that the vehicles are not forced to come back to the depots, from which they were departed. In order to solve our bi-objective formulation, we presented two well-known Meta-heuristic algorithms, namely NSGA II and MOSA and compared their performance based on a set of randomly generated test problems. The results show that our MILP model is valid and both NSGA II and MOSA works properly. While NSGA II finds closer solutions to the true Pareto front, MOSA finds evenly distributed solutions which allows the algorithm to search the space more diversely.Wed, 14 Sep 2016 19:30:00 +0100A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units
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Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which â€Žconsume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the â€Žefficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with â€Žcommon weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and â€Žexpected values of the objective functions. In the initial proposedâ€ â€DRF-DEA model, the inputs and outputs are assumed to be â€Žcharacterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this â€Žassumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-â€Žobjective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective â€Žfunctions are the expected values of functions of normal random variables. In order to improve in computational time, we then â€Žconvert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives â€Žprogramming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic â€ŽAlgorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results â€Žshow that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of â€Žruntime and solution quality. â€ŽSat, 24 Sep 2016 20:30:00 +0100Vendor Managed Inventory of a Supply Chain under Stochastic Demands
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AbstractIn this research, an integrated inventory problem is formulated for a single-vendor multiple-retailer supply chain that works according to the vendor managed inventory policy. The model is derived based on the economic order quantity in which shortages with penalty costs at the retailers` level is permitted. As predicting customer demand is the most important problem in inventory systems and there are difficulties to estimate it, a probabilistic demand is considered to model the problem. In addition, all retailers are assumed to share a unique number of replenishments where their demands during lead-time follow a uniform distribution. Moreover, there is a vendor budget constraint dedicated to each retailer. The aim is to determine the optimal order quantity of the retailers, the optimal order points, and the optimal number of replenishments so that the total inventory cost of the system is minimized. The proposed model is an integer nonlinear programming problem (NILP); hence, a meta-heuristic namely genetic algorithm (GA) is employed to solve it. As there is no benchmark available in the literature to validate the results obtained, another meta-heuristic called firefly algorithm (FA) is used for validation and verification. To achieve better solutions, the parameters of both meta-heuristics are calibrated using the Taguchi method. Several numerical examples are solved at the end to demonstrate the applicability of the proposed methodology and to compare the performance of the solution approaches.Wed, 14 Sep 2016 19:30:00 +0100A Compromise Decision-making Model for Multi-objective Large-scale Programming Problems with a ...
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This paper proposes a compromise model, based on the technique for order preference through similarity ideal solution (TOPSIS) methodology, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. This compromise programming method is based on the assumption that the optimal alternative is closer to fuzzy positive ideal solution (FPIS) and at the same time, farther from fuzzy negative ideal solution (FNIS).An aggregating function that is developed from LP- metric is based on the particular measure of ‘‘closeness” to the ‘‘ideal” solution.An efficient distance measurement is utilized to calculate positive and negative ideal solutions. The solution process is as follows: first, the decomposition algorithm is used to divide the large-dimensional objective space into a two-dimensional space. A multi-objective identical crisp linear programming is derived from the fuzzy linear model for solving the problem. Then, a single-objective large-scale linear programming problem is solved to find the optimal solution. Finally, to illustrate the proposed method, an illustrative example is provided.Sat, 24 Sep 2016 20:30:00 +0100Multimodal transportation p-hub location routing problem with simultaneous pick-ups and deliveries
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Centralizing and using proper transportation facilities cut down cost and traffic. Hub facilities concentrate flows to cause economic advantage of scale and multimodal transportation helps use the advantage of another transporter. Distinctive feature of this paper was in proposing a new mathematical formulation for a three stage p-hub location routing problem with simultaneous pick-ups and deliveries on time. A few studies have been devoted to this problem; however, many people are still suffering from the problems of commuting in crowded cities. The proposed formulation controlled the tumult of each node by indirect fixed cost. Node-to-node traveling cost was followed by a vehicle routing problem between nodes of each hub. A couple of datasets were solved for small and medium scales by GAMS software. But, for large scale instances, a meta-heuristic algorithm was proposed. To validate the model, datasets were used and the results demonstrated the performance suitability of the proposed algorithm.Wed, 14 Sep 2016 19:30:00 +0100Efficiency evaluation of wheat farming: a network data envelopment analysis approach
http://www.qjie.ir/article_254_34.html
Traditional data envelopment analysis (DEA) models deal with measurement of relative efficiency of decision making units (DMUs) in which multiple-inputs consumed to produce multiple-outputs. One of the drawbacks of these models is neglecting internal processes of each system, which may have intermediate products and/or independent inputs and/or outputs. In this paper some methods which are usable for network systems are briefly reviewed. A new unified model is also introduced which can be easily applied for performance measurement of all type of network production process. As an application of network DEA models, performance evaluation of wheat production in Iran provinces is considered and the results are compared.Traditional data envelopment analysis (DEA) models deal with measurement of relative efficiency of decision making units (DMUs) in which multiple-inputs consumed to produce multiple-outputs. One of the drawbacks of these models is neglecting internal processes of each system, which may have intermediate products and/or independent inputs and/or outputs. In this paper some methods which are usable for network systems are briefly reviewed. A new unified model is also introduced which can be easily applied for performance measurement of all type of network production process. As an application of network DEA models, performance evaluation of wheat production in Iran provinces is considered and the results are compared.Sat, 24 Sep 2016 20:30:00 +0100Controlling the Bullwhip Effect in a Supply Chain Network with an Inventory Replenishment ...
http://www.qjie.ir/article_243_0.html
This paper develops a mathematical model using differential equations and considers a bullwhip effect in a supply chain network with multiple retailers and distributors. To ensure the stability of the entire system and reduce the bullwhip effect, a robust control method and an inventory replenishment policy are proposed. This shows that the choice of the output matrix may reduce the bullwhip effect. It has also observed in the inventory replenishment mechanism may be a negative impact on the robustness of the bullwhip effect. However, the inventory replenishment behavior may lead to the bullwhip effect on the presented model. This means that the complex supply relationships may have a significant role in controlling or reducing the bullwhip effect of fluctuations.Wed, 14 Sep 2016 19:30:00 +0100The effects of grasp conditions on maximal acceptable combined forces (pushing and pinch ...
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The objective of this study was to determine the effects of grasp conditions (types of grasp, grasp width, glove and types of coupling) on maximal pushing force (MPF) and required pinch force (RPF) during snap fit assembly. The results indicated that the type of grasp, the type of coupling and wearing gloves have significant (p<0.05) effects on both MPF and RPF. Regarding the pair wise comparison, there was no significant difference in the effect between the lateral pinch and chuck pinch. MPF and RPF were also not affected significantly by the width of the grasp. Furthermore, there was an interactional effect between the type of coupling and the wearing or rather not wearing a glove. This, of course, only affected the MPF.Wed, 14 Sep 2016 19:30:00 +0100