Journal of Shanghai Jiao Tong University

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2020 , Vol. 54 >Issue 12: 1278 - 1290

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2019.146

Modeling and Optimization of Robust Scheduling Template Considering Equipment Failure

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  • School of Mechanical Engineering, Tongji University, Shanghai 201804, China

Received date: 2019-05-27

  Online published: 2020-12-31

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Abstract

In order to solve the scheduling problem of aircraft moving assembly line in uncertainty environment, this paper proposes a perceptron-dependent fruit fly optimization algorithm(PDFOA) to generate the assembly scheduling template with strong robustness. The proposed PDFOA, taking the fruit fly optimization algorithm as the basis, designs narrow-field osphresis-search operation and narrow-field vision-search operation which is helpful for searching and choosing neighborhood solutions. Simultaneously, the PDFOA sets up a memory library to enhance the global search ability. Finally, simulation experiments are conducted using different testing samples on different job scales. The proposed PDFOA is compared with tabu search, the genetic algorithm and the immune particle swarm optimization algorithm. The results demonstrate the effectiveness of the PDFOA.

Key words： failure and repair of equipments; fruit fly optimization algorithm; proactive scheduling; sampling simulation; robust baseline scheduling

Cite this article

FANG Jia, LU Zhiqiang . Modeling and Optimization of Robust Scheduling Template Considering Equipment Failure[J]. Journal of Shanghai Jiaotong University, 2020 , 54(12) : 1278 -1290 . DOI: 10.16183/j.cnki.jsjtu.2019.146

References

[1] KUMAR N, VIDYARTHI D P. A model for resource-constrained project scheduling using adaptive PSO[J]. Soft Computing, 2016, 20(4): 1565-1580.
[2] ELSAYED S, SARKER R, RAY T, et al. Consolidated optimization algorithm for resource-constrained project scheduling problems[J]. Information Sciences, 2017, 418/419: 346-362.
[3] LIU J, QIAO F, MA Y M, et al. Novel slack-based robust scheduling rule for a semiconductor manufacturing system with uncertain processing time[J]. Frontiers of Engineering Management, 2018, 5(4): 507-514.
[4] ELLOUMI S, FORTEMPS P, LOUKIL T. Multi-objective algorithms to multi-mode resource-constrained projects under mode change disruption[J]. Computers & Industrial Engineering, 2017, 106: 161-173.
[5] PAPROCKA I, SKOLUD B. A hybrid multi-objective immune algorithm for predictive and reactive scheduling[J]. Journal of Scheduling, 2017, 20(2): 165-182.
[6] VAN DE VONDER S, DEMEULEMEESTER E, HERROELEN W. Proactive heuristic procedures for robust project scheduling: An experimental analysis[J]. European Journal of Operational Research, 2008, 189(3): 723-733.
[7] VAN DE VONDER S, DEMEULEMEESTER E, HERROELEN W. A classification of predictive-reactive project scheduling procedures[J]. Journal of Scheduling, 2007, 10(3): 195-207.
[8] VARAKANTHAM P, FU N, LAU H C. A proactive sampling approach to project scheduling under uncertainty[C]∥30th AAAI Conference on Artificial Intelligence. Phoenix, Arizona, USA: AAAI, 2016: 3195-3201.
[9] 张国辉，吴立辉，聂黎，等. 考虑机器故障的柔性作业车间鲁棒调度方法[J]. 系统仿真学报，2016, 28(4): 867-873.
[9] ZHANG Guohui, WU Lihui, NIE Li, et al. Robust flexible job shop scheduling method with machine breakdowns[J]. Journal of System Simulation, 2016, 28(4): 867-873.
[10] WANG D J, LIU F, WANG Y Z, et al. A knowledge-based evolutionary proactive scheduling approach in the presence of machine breakdown and deterioration effect[J]. Knowledge-Based Systems, 2015, 90: 70-80.
[11] 赵婵媛，陆志强，崔维伟. 考虑随机故障的流水线调度问题前摄优化方法[J]. 浙江大学学报(工学版), 2016, 50(4): 641-649.
[11] ZHAO Chanyuan, LU Zhiqiang, CUI Weiwei. Proactive scheduling optimization on flow shops with random machine breakdowns[J]. Journal of Zhejiang University (Engineering Science), 2016, 50(4): 641-649.
[12] 陆志强，张思源，崔维伟. 集成预防性维护和流水线调度的鲁棒性优化研究[J]. 自动化学报，2015, 41(5): 906-913.
[12] LU Zhiqiang, ZHANG Siyuan, CUI Weiwei. Integrating production scheduling and maintenance policy for robustness in flow shop problems[J]. Acta Automatica Sinica, 2015, 41(5): 906-913.
[13] PAN W T. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012, 26(26): 69-74.
[14] LAMBRECHTS O, DEMEULEMEESTER E, HERROELEN W. A tabu search procedure for developing robust predictive project schedules[J]. International Journal of Production Economics, 2008, 111(2): 493-508.
[15] RANJBAR M, KIANFAR F, SHADROKH S. Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm[J]. Applied Mathematics & Computation, 2008, 196(2): 879-888.
[16] LAMBRECHTS O, DEMEULEMEESTER E, HERROELEN W. Time slack-based techniques for robust project scheduling subject to resource uncertainty[J]. Annals of Operations Research, 2011, 186(1): 443-464.
[17] 孙虎，周晶燕. 装配作业车间调度的免疫粒子群算法实现[J]. 武汉理工大学学报(信息与管理工程版), 2019, 41(3): 282-286.
[17] SUN Hu, ZHOU Jingyan. Implementation of immune particle swarm optimization algorithm for assembly job shop scheduling[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2019, 41(3): 282-286.
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