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A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search |
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| Authors: | Ghasem Moslehi Mehdi Mahnam |
| Institution: | 1. Department of Business Administration, Fu Jen Catholic University, 510, Zhongzheng Road, Xinzhuang District, New Taipei City, Taiwan, ROC;2. Department of Industrial Management, National Taiwan University of Science and Technology, 106, no. 43, Keelung Road, Taipei, City, Taiwan, ROC;1. Stratégies d’Optimisation et Informatique intelligentE, Higher Institute of Management of Tunis, Bardo, Tunis, Tunisia;2. Higher Business School of Tunis, University of Manouba, Tunisia;1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;2. Computer School, Liaocheng University, Liaocheng 252000, PR China;3. Singapore Institute of Manufacturing Technology, Nanyang Drive 638075, Singapore |
| Abstract: | The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches. |
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