Clustering and differential evolution algorithm for solving multi-objectives IPPS problem

被引：0
|
作者：
Du X. [1 ,2 ]
Pan Z. [2 ,3 ]
机构：
[1] Hubei Provincial Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang
[2] College of Mechanical & Power Engineering, China Three Gorges University, Yichang
[3] Yichang Changjiang Machine Technology Co., Ltd., Yichang
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 07期
关键词：
Clustering and differential evolution algorithm; Multi-objective optimization; Pareto non-dominated solution; Process planning; Scheduling;
D O I：
10.13196/j.cims.2019.07.013
中图分类号：
学科分类号：
摘要：
Aiming at the multi-objective integrated process planning and scheduling problem with makespan, tardiness and equipment load optimization objectives, a multi-objective non-chain process planning integration model was built, and a hybrid clustering with differential evolution algorithm was proposed. Combined with the clustering algorithm, differential evolution algorithm and genetic algorithm operations, the scheme optimization and scheduling process information were optimized effectively, the diversity in the feasible solution space was kept, and the rapid updating of Pareto non-dominated solutions was realized. Through the Pareto of a solution set of search area, it could be more close to the Pareto optimal front. The feasibility and superiority of the algorithm were verified by some examples. © 2019, Editorial Department of CIMS. All right reserved.
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页码：1729 / 1738
页数：9
相关论文
共 18 条
  • [1] Lyu S., Qiao L., Current status and developing trend of process planning and job shop scheduling, Computer Integrated Manufacturing Systems, 20, 2, pp. 290-300, (2014)
  • [2] Chryssolouris G., Chan S., Cobb W., Decision making on the factory floor: an integrated approach to process planning and scheduling, Robotics and Computer-Integrated Manufacturing, 1, 3, pp. 315-319, (1984)
  • [3] Ba L., Li Y., Yang M., Et al., Integrated process planning and scheduling problem with consideration of just-in-time scheduling, Computer Integrated Manufacturing System, 22, 10, pp. 2323-2332, (2016)
  • [4] Ba L., Li Y., Yang M., Et al., Investigating integrated process planning and scheduling problem with flexible batch splitting considered, Mechanical Science and Technology for Aerospace Engineering, 36, 3, pp. 426-435, (2017)
  • [5] Huang X., Zhang X., Ai Y., ACO integrated approach for solving flexible job-shop scheduling with multiple process plans, Computer Integrated Manufacturing System, 24, 3, pp. 558-569, (2018)
  • [6] Zhang S., Wong T., Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning, Journal of Intelligent Manufacturing, 29, 3, pp. 585-601, (2018)
  • [7] Petrovic M., Vukovic N., Mitic M., Et al., Integration of process planning and scheduling using chaotic particle swarm optimization algorithm, Expert Systems with Applications, 64, 34, pp. 569-588, (2016)
  • [8] Elahe S., Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints, Journal of Production & Manufacturing Research, 6, 1, pp. 61-89, (2018)
  • [9] Baykasoglu A., Ozbakir L., Sonmerz A., Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems, Journal of Intelligent Manufacturing, 15, 6, pp. 777-785, (2004)
  • [10] Baykasoglu A., Ozbakir L., A grammatical optimization approach for integrated process planning and scheduling, Journal of Intelligent Manufacturing, 20, 2, pp. 211-221, (2009)
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