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Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case

Author

Listed:
  • Chunxiao Zhao

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Junhua Chen

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xingchen Zhang

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Zanyang Cui

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

This paper presents a novel mathematical formulation in crew scheduling, considering real challenges most railway companies face such as roundtrip policy for crew members joining from different crew depots and stricter working time standards under a sustainable development strategy. In China, the crew scheduling is manually compiled by railway companies respectively, and the plan quality varies from person to person. An improved genetic algorithm is proposed to solve this large-scale combinatorial optimization problem. It repairs the infeasible gene fragments to optimize the search scope of the solution space and enhance the efficiency of GA. To investigate the algorithm’s efficiency, a real case study was employed. Results show that the proposed model and algorithm lead to considerable improvement compared to the original planning: (i) Compared with the classical metaheuristic algorithms (GA, PSO, TS), the improved genetic algorithm can reduce the objective value by 4.47%; and (ii) the optimized crew scheduling plan reduces three crew units and increases the average utilization of crew unit working time by 6.20% compared with the original plan.

Suggested Citation

  • Chunxiao Zhao & Junhua Chen & Xingchen Zhang & Zanyang Cui, 2022. "Solution of Multi-Crew Depots Railway Crew Scheduling Problems: The Chinese High-Speed Railway Case," Sustainability, MDPI, vol. 14(1), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:1:p:491-:d:716865
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    Cited by:

    1. Lihong Pan & Miyuan Shan & Linfeng Li, 2023. "Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms," Sustainability, MDPI, vol. 15(13), pages 1-21, July.

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