Local neighborhood genetic algorithm for stochastic disassembly line balancing problem

被引：0
|
作者：
Zhang Z. [1 ,2 ]
Li L. [1 ,2 ]
Cai N. [1 ,2 ]
Jia L. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu
[2] Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Disassembly line balancing problem; Genetic algorithms; Local neighborhood; Stochastic task times;
D O I：
10.13196/j.cims.2019.03.008
中图分类号：
学科分类号：
摘要：
Aiming at the randomness characteristics of disassembly task time due to a lot of uncertain factors in the actual disassembly line, a stochastic model of disassembly line balancing problem was constructed which was based on the disassembly model for part precedence relation diagram definition and takes the number of workstations, the balance index and the stability index as the optimization objective under the premise of meeting the cycle time constraint, and an local neighborhood genetic algorithm based on Pareto was proposed. A decoding method for random operation time was designed. The global search of the population was realized by two kinds of crossover operations, and a local search strategy combining depth neighborhood and breadth neighborhood was constructed to expand the scope of local search and improve local search ability. The proposed algorithm was applied to solve two large-scale disassembly cases, and the result indicated the effectiveness of the proposed algorithm and the validity of the improvement strategy. By taking a television disassembly case including 27 tasks as an example, the practical application of the proposed model and algorithm was identified by analyzing the application process and result. © 2019, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：607 / 618
页数：11
相关论文
共 22 条
  • [1] Green manufacturing engineering implementation guide(2016-2020)
  • [2] Gungor A., Gupta S.M., Disassembly line in product recovery, International Journal of Production Research, 40, 11, pp. 2569-2589, (2002)
  • [3] Gungor A., Surenda M., Gupta S.M., A solution approach to the disassembly line balancing problem in the presence of task failures, International Journal of Production Research, 39, 7, pp. 1427-1467, (2001)
  • [4] McGovern S.M., Gupta S.M., Ant colony optimization for disassembly sequencing with multiple objectives, The International Journal of Advanced Manufacturing Technology, 30, 5, pp. 481-496, (2006)
  • [5] McGovern S.M., Gupta S.M., A balancing method and genetic algorithm for disassembly line balancing, European Journal of Operational Research, 179, 3, pp. 692-708, (2007)
  • [6] Altekin F.T., Kandiller L., Ozdemirel N.E., Profit-oriented disassembly-line balancing, International Journal of Production Research, 46, 10, pp. 2675-2693, (2008)
  • [7] Ding L., Tan J., Feng Y., Et al., Multiobjective optimization for disassembly line balancing based on Pareto ant colony algorithm, Computer Integrated Manufacturing Systems, 15, 7, pp. 1406-1413, (2009)
  • [8] Kalayci C.B., Polat O., Gupta S.M., A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem, Annals of Operations Research, 242, 2, pp. 321-354, (2016)
  • [9] Riggs R.J., Jin X., Hu S.J., Two-stage sequence generation for partial disassembly of products with sequence dependent task times, Procedia CIRP, 29, pp. 698-703, (2015)
  • [10] Mete S., Cil Z.A., Agpak K., Et al., A solution approach based on beam search algorithm for disassembly line balancing problem, Journal of Manufacturing Systems, 41, pp. 188-200, (2016)
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