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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Flow shop,Idle time,Preemption,JIT scheduling
- چکیده:
- چکیده انگلیسی: Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs. In this paper, just-in-time (JIT) flow shop scheduling problem with preemption and machine idle time assumptions is considered in which objective function is minimizing the sum of weighted earliness and tardiness. A new non-linear mathematical model is formulated for this problem and due to high complexity of the problem meta-heuristic approaches have been applied to solve the problem for finding optimal solution. The parameters of algorithms are set by Taguchi method. Each parameter is tested in three levels. By implementation of many problems with different sizes these levels are determined .The proposed model is solved by three meta-heuristic algorithms: genetic algorithm (GA), imperialist competitive algorithm (ICA) and hybrid of GA and ICA. To evaluate the performance of the proposed algorithms many test problems have been designed. The Computational results indicate the superiority of the performance of hybrid approach than GA and ICA in finding thebest solution in reasonable computational time.
- انتشار مقاله: 18-04-1394
- نویسندگان: Javad Rezaeian,Sadegh Hosseini-Kia,Iraj Mahdavi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Batch processing,Incompatible Job Family,Release Date,Split Job Size,Family Setup Time
- چکیده:
- چکیده انگلیسی: This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by -constraint method.Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be comparedwith many test problemsby -constraint method based on performance measures. The results show that the proposed BOGAis found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases.
- انتشار مقاله: 21-04-1396
- نویسندگان: Javad Rezaeian,Yaser Zarook
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Multi-objective,perishable product,Production/distribution and inventory planning,Mixed integer non-linear programming
- چکیده:
- چکیده انگلیسی: This paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. This problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life time into a lot sizing to be shipped with multi-vehicle to multi-distribution-center to minimize multi-objective such as setup costs between products, holding costs, shortage costs, spoilage costs, transportation costs and production costs. There are many investigations which have been devoted on production/distribution planning area with different assumption. However, this research aims to extend this planning by integrating an inventory system with it in which for each distribution center, net inventory, shortage, FIFO system and spoilage of items are calculated. A mixed integer non-linear programming model (MINLP) is developed for the considered problem. Furthermore, a genetic algorithm (GA) and a simulated annealing (SA) algorithm are proposed to solve the model for real size applications. Also, Taguchi method is applied to optimize parameters of the algorithms. Computational characteristics of the proposed algorithms are examined and tested using t-tests at the 95% confidence level to identify the most effective meta-heuristic algorithm in term of relative percentage deviation (RPD). Finally, Computational results show that the GA outperforms SA although the computation time of SA is smaller than the GA.
- انتشار مقاله: 31-01-1393
- نویسندگان: Javad Rezaeian,Keyvan Shokoufi,Sepide Haghayegh,Iraj Mahdavi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Tabu search algorithm,Multiple Attribute Decision Making,Permutation Method,Countries Ranking,Combinatorial Problem
- چکیده:
- چکیده انگلیسی: The recent years have witnessed an increasing attention to the methods of multiple attribute decision making in solving the problems of the real world due to their shorter time of calculation and easy application. One of these methods is the ‘permutation method’ which has a strong logic in connection with ranking issues, but when the number of alternatives increases, solving problems through this method becomes NP-hard. So, meta-heuristic algorithm based on Tabu search is used to find optimum or near optimum solutions at a reasonable computational time for large size problems. This research is an attempt to apply the ‘permutation method’ to rank some countries of the West Asia and the North Africa based on the development criteria. Knowing the situation of each country as compared with other countries, particularly the respective neighbouring countries, is one of the most important standards for the assessment of performance and planning for the future activities.
- انتشار مقاله: 28-03-1392
- نویسندگان: Javad Rezaeian,Keyvan Shokoufi,Shahab Poursafary
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Hybrid flow shop scheduling,Multi processor tasks,sequence dependent setup time,Preemption
- چکیده:
- چکیده انگلیسی: 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.
- انتشار مقاله: 22-06-1391
- نویسندگان: Javad Rezaeian,Hany Seidgar,Morteza Kiani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Aging Effect,Preventive maintenance,Simulated Annealing,Two-Stage Assembly Flow Shop Problem
- چکیده:
- چکیده انگلیسی: This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.
- انتشار مقاله: 18-10-1395
- نویسندگان: Adeleh Rezghi,Javad Rezaeian
- مشاهده