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Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

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  • Yubang Liu
  • Shouwen Ji
  • Zengrong Su
  • Dong Guo

Abstract

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.

Suggested Citation

  • Yubang Liu & Shouwen Ji & Zengrong Su & Dong Guo, 2019. "Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    • Handle: RePEc:plo:pone00:0226161
    DOI: 10.1371/journal.pone.0226161
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    References listed on IDEAS

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    1. Yan, Rundong & Dunnett, S.J. & Jackson, L.M., 2018. "Novel methodology for optimising the design, operation and maintenance of a multi-AGV system," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 130-139.
    2. Karimi, Behzad & Niaki, S.T.A. & Haleh, Hassan & Naderi, Bahman, 2018. "Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 92-104.
    3. Zengliang Han & Dongqing Wang & Feng Liu & Zhiyong Zhao, 2017. "Multi-AGV path planning with double-path constraints by using an improved genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
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    Cited by:

    1. Lining Xing & Yuanyuan Liu & Haiyan Li & Chin-Chia Wu & Win-Chin Lin & Xin Chen, 2020. "A Novel Tabu Search Algorithm for Multi-AGV Routing Problem," Mathematics, MDPI, vol. 8(2), pages 1-16, February.

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