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

Chunxiao Zhao, Junhua Chen, Xingchen Zhang and Zanyang Cui
Additional contact information
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

Sustainability, 2022, vol. 14, issue 1, 1-18

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.

Keywords: high-speed railway; crew scheduling; multi-crew depots; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/1/491/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/1/491/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:1:p:491-:d:716865

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().