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A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Author

Listed:
  • Wenxiang Xu

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Shunsheng Guo

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

Suggested Citation

  • Wenxiang Xu & Shunsheng Guo, 2019. "A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1329-:d:210641
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    References listed on IDEAS

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    1. Hao-Chin Chang & Tung-Kuan Liu, 2017. "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1973-1986, December.
    2. Lacomme, Philippe & Larabi, Mohand & Tchernev, Nikolay, 2013. "Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Economics, Elsevier, vol. 143(1), pages 24-34.
    3. Danijela Pjevcevic & Milos Nikolic & Natasa Vidic & Katarina Vukadinovic, 2017. "Data envelopment analysis of AGV fleet sizing at a port container terminal," International Journal of Production Research, Taylor & Francis Journals, vol. 55(14), pages 4021-4034, July.
    4. Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
    5. Olatunde T. Baruwa & Miquel A. Piera, 2016. "A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4773-4792, August.
    6. Maryam Mousavi & Hwa Jen Yap & Siti Nurmaya Musa & Farzad Tahriri & Siti Zawiah Md Dawal, 2017. "Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
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