An iterated local search for the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times |
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| Affiliation: | 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;2. Department of Statistics, Feng Chia University, Taichung, Taiwan;3. Faculty of Science, Kunming University of Science and Technology, Kunming 650093, China;1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116,China;2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100090,China;3. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China;1. School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China;2. Department of Computer Science and Information Engineering, College of Electrical and Computer Engineering, National Formosa University, Yunlin, Taiwan;3. Department of Industrial Engineering and Engineering Management, College of Engineering, National Tsing Hua University, Taiwan;4. Centre for Quantum Computation and Intelligent Systems, Advanced Analytics Institute, University of Technology, Sydney, Ultimo, Australia;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Department of Computer Engineering, Hashtgerd Branch, Islamic Azad University, Alborz, Iran;1. Firat University, Technology Faculty, Electrical and Electronics Engineering Dept., Elazig, Turkey;2. University of Illinois at Springfield, Department of Computer Science, Springfield, Illinois, USA;3. Abant Izzet Baysal University, Engineering Faculty, Electrical and Electronics Engineering Dept., Bolu, Turkey |
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| Abstract: | Due to its simplicity yet powerful search ability, iterated local search (ILS) has been widely used to tackle a variety of single-objective combinatorial optimization problems. However, applying ILS to solve multi-objective combinatorial optimization problems is scanty. In this paper we design a multi-objective ILS (MOILS) to solve the multi-objective permutation flowshop scheduling problem with sequence-dependent setup times to minimize the makespan and total weighted tardiness of all jobs. In the MOILS, we design a Pareto-based variable depth search in the multi-objective local search phase. The search depth is dynamically adjusted during the search process of the MOILS to strike a balance between exploration and exploitation. We incorporate an external archive into the MOILS to store the non-dominated solutions and provide initial search points for the MOILS to escape from local optima traps. We compare the MOILS with several multi-objective evolutionary algorithms (MOEAs) shown to be effective for treating the multi-objective permutation flowshop scheduling problem in the literature. The computational results show that the proposed MOILS outperforms the MOEAs. |
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| Keywords: | Iterated local search Multi-objective optimization Permutation flowshop scheduling with sequence-dependent setup times |
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