Multi-objective evolutionary algorithm to solve interval flexible job shop scheduling problem

被引：0
|
作者：
Wang C. [1 ]
Wang Y. [1 ]
Ji Z.-C. [1 ]
机构：
[1] School of Internet of Things, Jiangnan University, Wuxi
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 05期
关键词：
Flexible job shop; Greedy insertion method; Interval processing time; Multi-objective scheduling; Possibility degree;
D O I：
10.13195/j.kzyjc.2017.1492
中图分类号：
学科分类号：
摘要：
For the uncertain multi-objective flexible job shop scheduling problem, the processing time is presented by interval number. A multi-objective interval flexible job shop scheduling problem model is established, and an effective multi-objective evolutionary algorithm (MOEA) is proposed to minimize interval makespan and interval total workload. Firstly, a population is initialized by adopting the hybrid strategy, and a greedy insertion method is designed for chromosome decoding. Then, an interval dominance relationship based on the possibility degree is employed to evaluate two individuals. In addition, a crowding measure hybridized with interval normalization is further used to reflect the distribution of optimal solutions. Finally, the experimental results demonstrate the effectiveness of the proposed algorithm. © 2019, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：908 / 916
页数：8
相关论文
共 29 条
  • [1] Lei D.M., Novel teaching-learing-based optimization algorithm for low carbon scheduling of flexible job shop, Control and Decision, 32, 9, pp. 1621-1627, (2017)
  • [2] Zuo X., Mo H., Wu J., A robust scheduling method based on a multi-objective immune algorithm, Information Sciences, 179, 19, pp. 3359-3369, (2009)
  • [3] Homg S.C., Lin S.S., Yang F.Y., Evolutionary algorithm for stochastic job shop scheduling with random processing time, Expert Systems with Applications, 39, 3, pp. 3603-3610, (2012)
  • [4] Shen X.N., Han Y., Fu J.Z., Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems, Soft Computing, 21, 21, pp. 6531-6554, (2017)
  • [5] Zhu C.J., Qiu W., Zhu M.Z., Multi-objective flexible job shops robust scheduling problem under stochastic processing times, China Mechanical Engineering, 27, 12, pp. 1667-1671, (2017)
  • [6] Wang B., Li Q.Y., Yang X.F., Three-point satisfaction-degree model for fuzzy job-shop scheduling problem, Control and Decision, 27, 7, pp. 1082-1086, (2012)
  • [7] Liu A.J., Yang Y., Xing Q.S., Et al., Multi-population genetic algorithm in multiobjective fuzzy and flexible job shop scheduling, Computer Integrated Manufacturing Systems, 17, 9, pp. 1954-1961, (2011)
  • [8] Wang X., Gao L., Zhang C., Et al., A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem, Int J of Computer Applications in Technology, 45, 45, pp. 115-125, (2012)
  • [9] Wang X., Li W., Zhang Y., An improved multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem, Int J of Computer Applications in Technology, 47, 2-3, pp. 280-288, (2013)
  • [10] Zheng Y.L., Li Y.X., Lei D.M., Multi-objective swarm-based neighborhood search for fuzzy flexible job shop scheduling, Int J of Advanced Manufacturing Technology, 60, 9, pp. 1063-1069, (2012)
123