Evolutionary multi-objective blocking lot-streaming flow shop scheduling with interval processing time |
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| Affiliation: | 1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. School of Electrical Engineering and Information Engineering, LanZhou University of Technology, Lanzhou 730050, China;3. Department of Computing, University of Surrey, Guildford, Surrey GU2 7XH, UK;4. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China;1. Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor, Malaysia;2. School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia;1. Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran;2. School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia;1. Computer Science Department, Federal University of Paraná (UFPR), PO 19081, 81531-970 Curitiba, Brazil;2. Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), Paseo Manuel de Lardizabal 1, 20080 San Sebastián, Guipúzcoa, Spain;1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;2. Collaborative Innovation Center of High Performance Computing, Sun Yat-sen University, Guangzhou 510006, China;1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. School of Electrical Engineering and Information Engineering, LanZhou University of Technology, Lanzhou 730050, China |
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| Abstract: | A blocking lot-streaming flow shop scheduling problem with interval processing time has a wide range of applications in various industrial systems, however, not yet been well studied. In this paper, the problem is formulated as a multi-objective optimization problem, where each interval objective is converted into a real-valued one using a dynamically weighted sum of its midpoint and radius. A novel evolutionary multi-objective optimization algorithm is then proposed to solve the re-formulated multi-objective optimization problem, in which non-dominated solutions and differences among parents are taken advantage of when designing the crossover operator, and an ideal-point assisted local search strategy for multi-objective optimization is employed to improve the exploitation capability of the algorithm. To empirically evaluate the performance of the proposed algorithm, a series of comparative experiments are conducted on 24 scheduling instances. The experimental results show that the proposed algorithm outperforms the compared algorithms in convergence, and is more capable of tackling uncertainties. |
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| Keywords: | Blocking lot-streaming flow shop scheduling Interval parameter Evolutionary multi-objective optimization Local search |
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