Elucidating multiprocessors flow shop scheduling with dependent setup times using a twin particle swarm optimization |
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| Affiliation: | 1. School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Department of Electrical Electronic and Control Engineering, Hankyong National University, Sukjong-dong, Ansung-si, Gyunggi-do 456-749, Republic of Korea;1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong, China;2. School of Electronics and Computer Science, University of Southampton Malaysia Campus, Nusajaya, Johor, Malaysia;3. Department of Electrical and Computer Engineering, Curtin University, WA, Australia;1. Department of Applied Sciences, Haldia Institute of Technology, Haldia, Purba Medinipur 721657, India;2. Department of Mathematics, Jhargram Raj, College, Jhargram 721507, West Bengal, India;3. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore 721 102, West Bengal, India;1. Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg;2. Laboratoire d’Informatique Fondamentale de Lille, University of Lille 1, France;1. Hamedan Science and Technology Park, Hamedan, Iran;2. Department of Industrial Engineering, Mazandaran University of Science & Technology, Babol, Iran;3. Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran |
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| Abstract: | Particle swarm optimization (PSO) is a novel metaheuristic, which has been applied in a wide variety of production scheduling problems. Two basic characteristics of this algorithm are its efficiency and effectiveness in providing high-quality solutions. In order to improve the traditional PSO, this study proposes the incorporation of a local search heuristic into the basic PSO algorithm. The new, hybrid, metaheuristic is called “twin particle swarm optimization (TPSO)”. The proposed metaheuristic scheme is applied to a flow shop with multiprocessors scheduling problem, which can be considered a real world case regarding the production line. This study, as far as the multiprocessors flow shop production system is concerned, utilizes sequence dependent setup times as constraints. Finally, simulated data confirm the effectiveness and robustness of the proposed algorithm. The data test results indicate that TPSO has potential to replace PSO and become a significant heuristic algorithm for similar problems. |
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| Keywords: | Twin particle swarm optimization Multiprocessors flow shop scheduling Setup time |
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