QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
02
Scheduling of a flexible flow shop with multiprocessor task by a hybrid approach based on genetic and imperialist competitive algorithms
1
11
EN
Javad
Rezaeian
Department of industrial engineering, Mazandaran University of Science and Technology, Babol, Iran
j.rezaeian@ustmb.ac.ir
Hany
Seidgar
Department of industrial engineering, Mazandaran University of Science and Technology, Babol, Iran
hany_seidgar@yahoo.com
Morteza
Kiani
Department of industrial engineering, Mazandaran University of Science and Technology, Babol, Iran
morteza.kiyany@gmail.com
This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA and ICA are proposed to solve the generated problems. The performances of algorithms are evaluated by computational time and Relative Percentage Deviation (RPD) factors. The results indicate that ICA solves the problems faster than other algorithms and the hybrid algorithm produced best solution based on RPD.
Hybrid flow shop scheduling,Multi processor tasks,sequence dependent setup time,Preemption
http://www.qjie.ir/article_129.html
http://www.qjie.ir/article_129_40dd7b5e8fb719178570651eef549774.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
02
A cost-oriented model for multi-manned assembly line balancing problem
13
25
EN
Abolfazl
Kazemi
Department of industrial engineering, Islamic Azad University, Qazvin Branch, Qazvin Iran
abkaazemi@gmail.com
Abdolhossein
Sedighi
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
ab_kaazemi@yahoo.com
In many real world assembly line systems which the work-piece is of large size more than one worker work on the same work-piece in each station. This type of assembly line is called multi-manned assembly line (MAL). In the classical multi-manned assembly line balancing problem (MALBP) the objective is to minimize the manpower needed to manufacture one product unit. Apart from the manpower, other cost drivers like wage rates or machinery are neglected in this classical view of the problem. <br />However due to the high competition in the current production environment, reducing the production costs and increasing utilization of available resources are very important issues for manufacturing managers. In this paper a cost-oriented approach is used to model the MALBP with the aim of minimizing total cost per production unit. A mathematical model is developed to solve the problem. Since the proposed model is NP-hard, several heuristic algorithms and a genetic algorithm (GA) are presented to efficiently solve the problem. Parameters and operators of the GA are selected using the design of experiments (DOE) method. Several examples are solved to illustrate the proposed model and the algorithms.
Multi-manned assembly line,Cost-oriented approach,Heuristic,Genetic Algorithm,Design of experiments
http://www.qjie.ir/article_137.html
http://www.qjie.ir/article_137_66232200736f343a352a980af9cb8521.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
02
A New Algorithm for the Discrete Shortest Path Problem in a Network Based on Ideal Fuzzy Sets
27
37
EN
Sadollah
Ebrahimnejad
Assistant Professor, Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Seyed Meysam
Mousavi
Ph.D. Student, Young Researches Club, South Tehran Branch, Islamic Azad University, Tehran, Iran
Behnam
Vahdani
Assistant Professor, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
A shortest path problem is a practical issue in networks for real-world situations. This paper addresses the fuzzy shortest path (FSP) problem to obtain the best fuzzy path among fuzzy paths sets. For this purpose, a new efficient algorithm is introduced based on a new definition of ideal fuzzy sets (IFSs) in order to determine the fuzzy shortest path. Moreover, this algorithm is developed for a fuzzy network problem including three criteria, namely time, cost and quality risk. Several numerical examples are provided and experimental results are then compared against the fuzzy minimum algorithm with reference to the multi-labeling algorithm based on the similarity degree in order to demonstrate the suitability of the proposed algorithm. The computational results and statistical analyses indicate that the proposed algorithm performs well compared to the fuzzy minimum algorithm.
Shortest path problem,Single criterion networks,Multiple criteria networks,Fuzzy sets,Ideal fuzzy sets
http://www.qjie.ir/article_144.html
http://www.qjie.ir/article_144_d85c20492b1a214701dc86923bd91ce6.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
01
A New Fuzzy Method for Assessing Six Sigma Measures
39
47
EN
Seyed Habib A
Rahmati
Instructor, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
Abolfazl
Kazemi
Assistant Professor, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
Mohammad
Saidi Mehrabad
Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, , Iran
Alireza
Alinezhad
Faculty of industrial and mechanical engineering, Qazvin branch, Islamic Azad Univeristy, Qazvin, Iran
alinezhad_ir@yahoo.com
Six-Sigma has some measures which measure performance characteristics related to a process. In most of the traditional methods, exact estimation is used to assess these measures and to utilize them in practice. In this paper, to estimate some of these measures, including Defects per Million Opportunities (DPMO), Defects per Opportunity (DPO), Defects per unit (DPU) and Yield, a new algorithm based on Buckley's estimation approach is introduced. The algorithm uses a family of confidence intervals to estimate the mentioned measures. The final results of introduced algorithm for different measures are triangular shaped fuzzy numbers. Finally, since DPMO, as one of the most useful measures in Six-Sigma, should be consistent with costumer need, this paper introduces a new fuzzy method to check this consistency. The method compares estimated DPMO with fuzzy customer need. Numerical examples are given to show the performance of the method. All rights reserved
Six Sigma,Fuzzy set,Fuzzy estimation,DPU,DPO,Yield,DPMO
http://www.qjie.ir/article_145.html
http://www.qjie.ir/article_145_6d0d4db4b9211e3dc5a3e0e959836446.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
02
Optimization of Plastic Injection Molding Process by Combination of Artificial Neural Network and Genetic Algorithm
49
54
EN
MOHAMMAD SALEH
MEIABADI
ARTICLE
saleh.meiabadi@yahoo.com
abbas
Vafaei
THESIS ADVISOR
abbas_v@yahoo.com
Fatemeh
Sharifi
computer engineering department, chamran university, ahvaz, iran
Injection molding is one of the most important and common plastic formation methods. Combination of modeling tools and optimization algorithms can be used in order to determine optimum process conditions for the injection molding of a special part. Because of the complication of the injection molding process and multiplicity of parameters and their interactive effects on one another, analytical modeling of the process is either impossible or difficult. Therefore Artificial Neural Network (ANN) is used for modeling the process. Process conditions data is needed for modeling the process by the neural network. After modeling step, the model is combined with the Genetic Algorithm (GA). Based on the injection molding goals that have been turned into fitness function, the optimized conditions are obtained.
Optimization,Solution space,Control variable,Neural Network,Genetic Algorithm
http://www.qjie.ir/article_138.html
http://www.qjie.ir/article_138_1c5b28f9870f3292302cdaeffbcbe3e0.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
02
A Multi-level Capacitated Lot-sizing Problem with Safety Stock Deficit and Production Manners: A Revised Simulated Annealing
55
64
EN
Esameil
Mehdizadeh
Assistant Professor, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
emqiau@yahoo.com
Mohammad Reza
Mohammadizadeh
M.Sc.Student , Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
[1] Corresponding author e-mail: mehdi.foumani@monash.edu <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br />[1] Corresponding author e-mail: mehdi.foumani@monash.edu <br /> <br /> <br /> <br /> <br /> <br /> <br />Lot-sizing problems (LSPs) belong to the class of production planning problems in which the availability quantities of the production plan are always considered as a decision variable. This paper aims at developing a new mathematical model for the multi-level capacitated LSP with setup times, safety stock deficit, shortage, and different production manners. Since the proposed linear mixed integer programming model is NP-hard, a new version of simulated annealing algorithm (SA) is developed to solve the model named revised SA algorithm (RSA). Since the performance of the meta-heuristics severely depends on their parameters, Taguchi approach is applied to tune the parameters of both SA and RSA. In order to justify the proposed mathematical model, we utilize an exact approach to compare the results. To demonstrate the efficiency of the proposed RSA, first, some test problems are generated; then, the results are statistically and graphically compared with the traditional SA algorithm.
Lot-sizing problem,simulated annealing,Shortage,Safety stock deficit,Production manners
http://www.qjie.ir/article_146.html
http://www.qjie.ir/article_146_5f57175f126121b819ad0d276124cb45.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
6
13
2013
09
02
The project portfolio selection and scheduling problem: mathematical model and algorithms
65
72
EN
Bahman
Naderi
Young Researches Club, Qazvin Branch, Islamic Azad University
bahman.naderi@aut.ac.ir
This paper investigates the problem of selecting and scheduling a set of projects among available projects. Each project consists of several tasks and to perform each one some resource is required. The objective is to maximize total benefit. The paper constructs a mathematical formulation in form of mixed integer linear programming model. Three effective metaheuristics in form of the imperialist competitive algorithm, simulated annealing and genetic algorithm are developed to solve such a hard problem. The proposed algorithms employ advanced operators. The performance of the proposed algorithms is numerically evaluated. The results show the high performance of the imperialist competitive algorithm outperforms the other algorithms.
Project portfolio selection and scheduling,Imperialist Competitive Algorithm,simulated annealing,Genetic Algorithm,Mixed Integer programming
http://www.qjie.ir/article_140.html
http://www.qjie.ir/article_140_73ac9fddc0f128714d5f83a11124b2e2.pdf