QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
Monitoring process variability: a hybrid Taguchi loss and multiobjective genetic algorithm approach
1
8
EN
Heng-Soon
Gan
University of Melbourne
hsg@unimelb.edu.au
Abdul Sattar
Safaei
Babol University of Technology
s.safaei@nit.ac.ir
10.22094/joie.2016.245
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.
Economic-Statistical design,Taguchi loss function,NSGA-II algorithm,Process Variability,immeasurable costs
http://www.qjie.ir/article_245.html
http://www.qjie.ir/article_245_59e50e4617342a3271d2ba4d7428b99c.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers
9
18
EN
Parviz
Fattahi
Bu-Ali Sina University
p.fattahi@alzahra.ac.ir
Parvaneh
Samouei
Bu-Ali Sina University
samouei_parvaneh@yahoo.com
10.22094/joie.2016.246
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 algorithm
Mixed-model assembly line balancing,multi-objective optimization,different skilled workers,particle swarm optimization,simulated annealing
http://www.qjie.ir/article_246.html
http://www.qjie.ir/article_246_f4a11b067ffa0ea046885a8261301508.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
Fuzzy Programming for Parallel Machines Scheduling:
Minimizing Weighted Tardiness/Earliness and Flowtime through Genetic Algorithm
19
30
EN
Mohammad
Asghari
Department of industrial engineering, Ferdowsi University of Mashhad, Azadi Sq., Mashhad, Iran
hooman.asghari@outlook.com
Samaneh
Nezhadali
Department of management, Iran Chamber of Commerce, Industries and Mines, Mashhad, Iran
nezhadalii@yahoo.com
10.22094/joie.2016.247
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.
Mathematical optimization,Fuzzy multi-objective model,Parallel machines scheduling,Weighted tardiness/earliness,Genetic Algorithm
http://www.qjie.ir/article_247.html
http://www.qjie.ir/article_247_f1610a55d0136f8ca528700e3c93a8b4.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
Performance Improvement through a Marshaling Yard Storage Area in a Container Port Using Optimization via Simulation Technique (Case Study at Shahid Rajaee Container Port)
31
40
EN
Mohammad Reza
Ghanbari
Islamic Azad University (Qazvin Branch)
ghanbari_mrg@yahoo.com
Parham
Azimi
Islamic Azad University (Qazvin Branch)
p.azimi@yahoo.com
10.22094/joie.2016.248
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.
Optimization via Simulation,Conyainer Port,Marshalling Yard
http://www.qjie.ir/article_248.html
http://www.qjie.ir/article_248_8d708235dae6d57176762a50c6b61996.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm
41
51
EN
Adib
Hosseini
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
hosseini.adib@gmail.com
Mohammad Mahdi
Paydar
Department of Industrial Engineering, Babol Noshirvani University of Technology
paydar@nit.ac.ir
Iraj
Mahdavi
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
irajarash@rediffmail.com
Javid
Jouzdani
PhD, Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
javidjouzdani@iust.ac.ir
10.22094/joie.2016.249
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.
Virtual Cellular Manufacturing,Mathematical Programming,Multi-Choice Goal Programming,Genetic Algorithm
http://www.qjie.ir/article_249.html
http://www.qjie.ir/article_249_0159345102bf0e6671885dc97088c14b.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
Fuzzy Multi-Objective Linear Programming for Project Management Decision under Uncertain Environment with AHP Based Weighted Average Method
53
60
EN
Md. Sanowar
Hossain
Department of Industrial &amp; Production Engineering, Rajshahi University of Engineering &amp; Technology.
h.shohag.rana@gmail.com
Shahed
Mahmud
Assistant Professor, Department of Industrial and Production Engineering, Rajshahi University of Engineering and Technology, Bangladesh
shahed.mahmud@ruet.ac.bd
Md. Mosharraf
Hossain
Professor, Department of Industrial and Production Engineering, Rajshahi University of Engineering and Technology, Bangladesh
mosharraf80@yahoo.com
10.22094/joie.2016.250
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
Project Management,Multi-objective linear programming,Minimum operator,Analytical Hierarchy Process
http://www.qjie.ir/article_250.html
http://www.qjie.ir/article_250_11a34db7387fa23c2ffeea83d2ecefa3.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
A hybrid intuitionistic fuzzy multi-criteria group decision making approach for supplier selection
61
73
EN
Ahmad
Makui
Iran University of Science and Technology
amakui@iust.ac.ir
Mohammad
Reza
Gholamian
Iran University of Science and Technology
gholamian@iust.ac.ir
Erfan
Mohammadi
Iran University of Science and Technology
erfanmohammadi@ind.iust.ac.ir
10.22094/joie.2016.251
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.
Multi-Criteria Group Decision Making,supplier selection,Interval-Valued Intuitionistic Fuzzy Set,TOPSIS Method,GRA Method
http://www.qjie.ir/article_251.html
http://www.qjie.ir/article_251_09c395054823216ee137010f8bda723a.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units
75
90
EN
Ali
Yaghoubi
Department of Industrial Engineering, Faculty of Engineering, Payam-e-Noor University, Tehran, Iran
phd_yaghoubi@yahoo.com
Maghsoud
Amiri
Department of Industrial Management, Allameh Tabatabaei University, Tehran, Iran
mg_amiri@yahoo.com
Azamdokht
Safi Samghabadi
Department of Industrial Engineering, Faculty of Engineering, Payam-e-Noor University, Tehran, Iran
safi.az@gmail.com
10.22094/joie.2016.252
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€Ž
Stochastic Data envelopment analysis,Dynamic programming,random fuzzy variable,Monte Carlo simulation,Genetic Algorithm
http://www.qjie.ir/article_252.html
http://www.qjie.ir/article_252_f499261f1b44c7e8bb765919ba8765a3.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
A Compromise Decision-making Model for Multi-objective Large-scale Programming Problems with a Block Angular Structure under Uncertainty
91
102
EN
behnam
vahdani
Assistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
meghdad
Salimi
MSc, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
behrouz
Afshar najafi
Assistant Professor, Faculty of Industrial & Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
10.22094/joie.2016.253
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.
TOPSIS,MCDM,MODM,Multi-Objective Large-Scale Linear Programming (MOLSLP),Block angular structure
http://www.qjie.ir/article_253.html
http://www.qjie.ir/article_253_bcfd7bc3787fcf8c7ea7e25122d1e1f8.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
9
20
2016
09
25
Efficiency evaluation of wheat farming: a network data envelopment analysis approach
103
109
EN
Reza
Kazemi Matin
Islamic Azad University, Karaj Branch
rkmatin@gmail.com
Roza
Azizi
Azad University, Karaj Branch
aziziroza@gmail.com
10.22094/joie.2016.254
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.
Data envelopment analysis,Network DEA,Efficiency,Wheat production
http://www.qjie.ir/article_254.html
http://www.qjie.ir/article_254_24b29e7bf87a13691581d2c69fcdc8bb.pdf