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Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

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  • Maryam Mousavi
  • Hwa Jen Yap
  • Siti Nurmaya Musa
  • Farzad Tahriri
  • Siti Zawiah Md Dawal

Abstract

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0169817
    DOI: 10.1371/journal.pone.0169817
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    References listed on IDEAS

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    Cited by:

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    2. Jianxun Li & Wenjie Cheng & Kin Keung Lai & Bhagwat Ram, 2022. "Multi-AGV Flexible Manufacturing Cell Scheduling Considering Charging," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    3. Adrian Kampa & Iwona Paprocka, 2021. "Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times," Energies, MDPI, vol. 14(21), pages 1-25, November.
    4. Zhuoling Jiang & Xiaodong Zhang & Pei Wang, 2023. "Grid-Map-Based Path Planning and Task Assignment for Multi-Type AGVs in a Distribution Warehouse," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
    5. Moussa Abderrahim & Abdelghani Bekrar & Damien Trentesaux & Nassima Aissani & Karim Bouamrane, 2020. "Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints," Energies, MDPI, vol. 13(18), pages 1-19, September.
    6. 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.
    7. Yangkun Xia & Zhuo Fu & Lijun Pan & Fenghua Duan, 2018. "Tabu search algorithm for the distance-constrained vehicle routing problem with split deliveries by order," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.

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