

اين مجله تا شماره 7 با نام فصلنامه مهندسي صنايع (Journal of Industrial Engineering) منتشر شده است. 

Journal of Optimization in Industrial Engineering
ISSN 22519904
دوفصلنامه داراي رتبه علمي  پژوهشي (فني مهندسي) به زبان انگليسي
سال نهم، شماره 20، 2016




 Monitoring process variability: a hybrid Taguchi loss and multiobjective genetic algorithm approach Heng , Soon Gan , Abdul Sattar Safaei* Pages 18 Abstract Full Text [PDF 1170KB]   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 biobjective model of the economicstatistical design of the S control chart to minimize the mean hourly loss cost while minimizing outofcontrol average run length and maintaining reasonable incontrol 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 Paretooptimal 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.
Keywords: Economic, Statistical design, Taguchi loss function, NSGA, II Algorithm, process variability, immeasurable costs
  
 A MultiObjective Particle Swarm Optimization for MixedModel Assembly Line Balancing with Different Skilled Workers Parviz Fattahi * , Parvaneh Samouei Pages 918 Abstract Full Text [PDF 547KB]   This paper presents a multiobjective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixedmodel 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 multiobjective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixedmodel 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
Keywords: Mixed, model assembly line balancing, multi, objective optimization, different skilled workers, particle swarm optimization, simulated annealing
  
 Fuzzy Programming for Parallel Machines Scheduling: Minimizing Weighted Tardiness/Earliness and Flowtime through Genetic Algorithm Mohammad Asghari * , Samaneh Nezhadali Pages 1930 Abstract Full Text [PDF 1369KB]   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 justintime (JIT) scheduling and in this area، motivated by JIT manufacturing، this study investigates a mathematical model for appraising a multiobjective 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 stateoftheart commercial solver and the simulated annealing method that is followed by illustrating some instances for indicating validity and efficiency of the method.
Keywords: Mathematical optimization, Fuzzy multi, objective model, Parallel machines scheduling, Weighted tardiness,earliness, Genetic Algorithm
  
 Performance Improvement through a Marshaling Yard Storage Area in a Container Port Using Optimization via Simulation Technique (Case Study at Shahid Rajaee Container Port) Mohammad Reza Ghanbari * , Parham Azimi Pages 3140 Abstract Full Text [PDF 548KB]   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.
Keywords: Optimization via Simulation, Conyainer Port, Marshalling Yard
  
 Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm Adib Hosseini , Mohammad Mahdi Paydar , Iraj Mahdavi * , Javid Jouzdani Pages 4151 Abstract Full Text [PDF 685KB]   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 biobjective 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 multichoice goal programming (MCGP). Since CM models are NPHard، a genetic algorithm (GA) is utilized to solve the model for largesized 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 biobjective 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 multichoice goal programming (MCGP). Since CM models are NPHard، a genetic algorithm (GA) is utilized to solve the model for largesized problem instances and the results obtained from both methods are compared. Finally، a conclusion is made and some visions for future works are offered.
Keywords: Virtual Cellular Manufacturing, Mathematical Programming, Multi, Choice Goal Programming, Genetic Algorithm
  
 Fuzzy MultiObjective Linear Programming for Project Management Decision under Uncertain Environment with AHP Based Weighted Average Method Md. Sanowar Hossain* , Shahed Mahmud , Md. Mosharraf Hossain Pages 5360 Abstract Full Text [PDF 386KB]   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 software
Keywords: Project management, Multi, objective linear programming, Minimum operator, Analytical Hierarchy Process
  
 A hybrid intuitionistic fuzzy multicriteria group decision making approach for supplier selection Ahmad Makui * , Mohammad Reza Gholamian , Erfan Mohammadi Pages 6173 Abstract Full Text [PDF 1336KB]   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 multicriteria group decision making approach is proposed. Intervalvalued 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 multicriteria decisionmaking problems due to containing vague perception of decision makers’ opinions. Therefore، in future، intuitionistic fuzzy set can be used for dealing with uncertainty in multicriteria decisionmaking problems such as project selection، manufacturing systems، pattern recognition، medical diagnosis and many other areas of management decision problems.
Keywords: Multi, Criteria Group Decision Making, Supplier Selection, Interval, Valued Intuitionistic Fuzzy Set, TOPSIS Method, GRA Method
  
 A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units Ali Yaghoubi * , Maghsoud Amiri , Azamdokht Safi Samghabadi Pages 7590 Abstract Full Text [PDF 1493KB]   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 (DRFDEA) 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€DRFDEA 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 DRFDEA model to its equivalent multiâ€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 multiobjective 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€Ž
Keywords: Stochastic Data envelopment analysis, Dynamic programming, random fuzzy variable, Monte Carlo simulation, Genetic algorithm.â€Ž,
  
 A Compromise Decisionmaking Model for Multiobjective Largescale Programming Problems with a Block Angular Structure under Uncertainty behnam vahdani * , meghdad Salimi , behrouz Afshar najafi Pages 91102 Abstract Full Text [PDF 1317KB]   This paper proposes a compromise model، based on the technique for order preference through similarity ideal solution (TOPSIS) methodology، to solve the multiobjective largescale 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 largedimensional objective space into a twodimensional space. A multiobjective identical crisp linear programming is derived from the fuzzy linear model for solving the problem. Then، a singleobjective largescale linear programming problem is solved to find the optimal solution. Finally، to illustrate the proposed method، an illustrative example is provided.
Keywords: TOPSIS, MCDM, MODM, Multi, Objective Large, Scale Linear Programming (MOLSLP), Block angular structure
  
 Efficiency evaluation of wheat farming: a network data envelopment analysis approach Reza Kazemi Matin* , Roza Azizi Pages 103109 Abstract Full Text [PDF 1206KB]   Traditional data envelopment analysis (DEA) models deal with measurement of relative efficiency of decision making units (DMUs) in which multipleinputs consumed to produce multipleoutputs. 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 multipleinputs consumed to produce multipleoutputs. 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.
Keywords: Data Envelopment Analysis, Network DEA, Efficiency, Wheat production
  
تاريخ انتشار: 14/7/95 تلفن: 33665275، 33682976 (028)
تاريخ درج در سايت: 14/7/95
شمار بازديدکنندگان اين شماره: 363




