QIAUJournal of Optimization in Industrial Engineering2251-990492020160925Monitoring process variability: a hybrid Taguchi loss and multiobjective genetic algorithm approach1824510.22094/joie.2016.245ENHeng-SoonGanUniversity of MelbourneAbdul SattarSafaeiBabol University of TechnologyJournal Article20151121The 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.http://www.qjie.ir/article_245_59e50e4617342a3271d2ba4d7428b99c.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers91824610.22094/joie.2016.246ENParvizFattahiBu-Ali Sina UniversityParvanehSamoueiBu-Ali Sina UniversityJournal Article20140902This 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 algorithmhttp://www.qjie.ir/article_246_f4a11b067ffa0ea046885a8261301508.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160901Neurological Analysis of Consumer Behavior in Shopping Malls Using EEG192967497010.22094/joie.2016.240ENMahmoudSamadiDepartment of Management Science,Abhar branch, Islamic Azad University, Abhar, IranMohammad MahdiMozaffariFaculty of Social Science, Imam Khomeini International University, Qazvin, IranJafarShaghaghiFaculty of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranJournal Article20200823Today, shopping centers go beyond purchasing based behavior. As intangible variables, effective social factors also affect consumer behavior. These variables cannot always lead to purchasing but create a potential customer who is exactly exposed to promotional incentives. It should be noted that identifying these intangible factors can only be extracted through non-self-reporting methods. Thus, the use of neuroscience techniques can be a necessity. This study was an exploratory research that applied electroencephalographic tools to record brain signals through EPOC+ 14-Electrode Wireless EEG device. The signals were cleared using EEGLAB Software through ASR Method. The population consisted of two groups of three people (control and experimental group). The seven elements of the shopping centers proposed by Arpita Jare were used to provide stimulus. Thermal map analysis for F3 and F7 electrodes and correlation between the seven elements and brain waves showed that control group had a significant behavior in comparison to experimental group due to their interest in shopping centers (P-Value <.05). Results showed that consumer behavior in shopping centers is not affected by tangible factors such as product brand but social and cultural interactions and intangible elements are also effective that are hidden from marketers and designers’ view. http://www.qjie.ir/article_674970_e05f4348e8e0c98ffb6c0439e3d4b5b6.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925Performance Improvement through a Marshaling Yard Storage Area in a Container Port Using Optimization via Simulation Technique (Case Study at Shahid Rajaee Container Port)314024810.22094/joie.2016.248ENMohammad RezaGhanbariIslamic Azad University (Qazvin Branch)ParhamAzimiIslamic Azad University (Qazvin Branch)Journal Article20130306Container 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.http://www.qjie.ir/article_248_8d708235dae6d57176762a50c6b61996.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm415124910.22094/joie.2016.249ENAdibHosseiniDepartment of Industrial Engineering, Mazandaran University of Science and Technology, Babol, IranMohammad MahdiPaydarDepartment of Industrial Engineering, Babol Noshirvani University of TechnologyIrajMahdaviDepartment of Industrial Engineering, Mazandaran University of Science and Technology, Babol, IranJavidJouzdaniPhD, Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, IranJournal Article20140415Cellular 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.<br />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.http://www.qjie.ir/article_249_0159345102bf0e6671885dc97088c14b.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925Fuzzy Multi-Objective Linear Programming for Project Management Decision under Uncertain Environment with AHP Based Weighted Average Method536025010.22094/joie.2016.250ENMd. SanowarHossainDepartment of Industrial &amp; Production Engineering, Rajshahi University of Engineering &amp; Technology.ShahedMahmudAssistant Professor, Department of Industrial and Production Engineering, Rajshahi University of Engineering and Technology, BangladeshMd. MosharrafHossainProfessor, Department of Industrial and Production Engineering, Rajshahi University of Engineering and Technology, BangladeshJournal Article20151129Smooth 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 softwarehttp://www.qjie.ir/article_250_11a34db7387fa23c2ffeea83d2ecefa3.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925A hybrid intuitionistic fuzzy multi-criteria group decision making approach for supplier selection617325110.22094/joie.2016.251ENAhmadMakuiIran University of Science and TechnologyMohammad RezaGholamianIran University of Science and TechnologyErfanMohammadiIran University of Science and TechnologyJournal Article20141219Due 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.http://www.qjie.ir/article_251_09c395054823216ee137010f8bda723a.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units759025210.22094/joie.2016.252ENAliYaghoubiDepartment of Industrial Engineering, Faculty of Engineering, Payam-e-Noor University, Tehran, IranMaghsoudAmiriDepartment of Industrial Management, Allameh Tabatabaei University, Tehran, IranAzamdokhtSafi SamghabadiDepartment of Industrial Engineering, Faculty of Engineering, Payam-e-Noor University, Tehran, IranJournal Article20141002Data 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€Žhttp://www.qjie.ir/article_252_f499261f1b44c7e8bb765919ba8765a3.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925A Compromise Decision-making Model for Multi-objective Large-scale Programming Problems with a Block Angular Structure under Uncertainty9110225310.22094/joie.2016.253ENBehnamVahdaniAssistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranMeghdadSalimiMSc, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranBehrouzAfshar NajafiAssistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranJournal Article20110902This 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.http://www.qjie.ir/article_253_bcfd7bc3787fcf8c7ea7e25122d1e1f8.pdfQIAUJournal of Optimization in Industrial Engineering2251-990492020160925Efficiency evaluation of wheat farming: a network data envelopment analysis approach10310925410.22094/joie.2016.254ENRezaKazemi MatinIslamic Azad University, Karaj BranchRozaAziziAzad University, Karaj BranchJournal Article20130902Traditional 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.<br />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.http://www.qjie.ir/article_254_24b29e7bf87a13691581d2c69fcdc8bb.pdf