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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Modern Processes in Manufacturing and Production
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Scheduling,Simulated Annealing,Mathematical formulation,Virtual cellular manufacturing systems
- چکیده:
- چکیده انگلیسی: In this paper, we present a simulated annealing (SA) and a genetic algorithm (GA) based on heuristics for scheduling problem of jobs in virtual cellular manufacturing systems. A virtual manufacturing cell (VMC) is a group of resources that is dedicated to the manufacturing of a part family. Although this grouping is not reflected in the physical structure of the manufacturing system, but machines are spread on the shop floor physically. In this paper, there are multiple jobs with different manufacturing processing routes. First, we develop the mathematical model for the problem, and then we present the suggested algorithms. The scheduling objective is weighed tardiness and total travelling distance minimization. The problem is divided into two branches: small scale and large scale. For small scale, the results of GA and SA are compared to GAMS. For large scale problems, due to the time limitation of 3600 seconds, the results of GA and SA are compared to each other. Computational results show that both SA ad GA algorithms perform properly but SA is likely to turn out well in finding better solutions in shorter times especially in large scale problems.
- انتشار مقاله: 18-05-1394
- نویسندگان: Saeed Taouji Hassanpour,Reza Bashirzadeh,Abolfazl Adressi,Behnam Bahmankhah
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Modern Processes in Manufacturing and Production
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,particle swarm optimization algorithm,sequence dependent setup time,Unrelated parallel machines scheduling
- چکیده:
- چکیده انگلیسی: Nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. Unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. In some of the applications of unrelated parallel machines scheduling, when machines have different technological levels and are not necessarily able to process each one of the existing jobs in the group of jobs and in many of the industrial environments, a sequence dependent setup time takes place during exchanging jobs on the machines. In this research, the unrelated parallel machines scheduling problem has been studied considering the limitations of sequence dependent setup time of processing of jobs and limited accessibility to machines and jobs with the purpose of minimizing the total weighting lateness and earliness times. An integer scheduling model is proposed for this problem. Also, a meta-heuristically combined method consisting of Genetic algorithm and Particle swarm optimization (PSO) algorithm for its solutions is proposed. The obtained results of the proposed algorithm show that the proposed algorithm is very efficient especially in problems with large dimensions.
- انتشار مقاله: 01-03-1394
- نویسندگان: Mohammadreza Naghibi,Abolfazl Adressi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Modern Processes in Manufacturing and Production
- نوع مقاله: Journal Article
- کلمات کلیدی: Metaheuristic algorithms,Sequence Dependent Setup Times,Group Scheduling,No-wait Flow Shop
- چکیده:
- چکیده انگلیسی: Different manufacturing enterprises use regularly scheduling algorithms in order to help meeting demands over time and reducing operational costs. Nowadays, for a better useofresources and manufacturingin accordance withcustomer needs and given the level ofcompetitionbetweencompanies, employing asuitablescheduling programhasa double importance. Conventional productionmethods are constantly substituted with new ones for improving the efficiency and effectiveness of the entire production system. In this paper, two Meta-heuristic algorithms, Genetic and simulated annealing, have been used in order to solve the group scheduling problem of jobs in a single stage No-wait flow shop environment in which setup times are sequence dependent,. The purpose of solving the proposed problem is to minimize the maximum time needed to complete the jobs (Makespan). The results show that Genetic algorithm is efficient in problems with small and large dimensions, with respect to time parameter of problem solving.
- انتشار مقاله: 31-02-1394
- نویسندگان: Abolfazl Adressi,Reza Bashirzadeh,Vahid Azizi,Saeed Tasouji Hassanpour
- مشاهده