A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers

Fei Peng, Xian Fan, Puxin Wang and Mingan Sheng ()
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Fei Peng: School of Automotive Engineering, Beijing Polytechnic, Beijing 100176, China
Xian Fan: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Puxin Wang: School of Automotive Engineering, Beijing Polytechnic, Beijing 100176, China
Mingan Sheng: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China

Sustainability, 2022, vol. 14, issue 21, 1-19

Abstract: The driver scheduling problem at Chinese electric multiple-unit train depots becomes more and more difficult in practice and is studied in very little research. This paper focuses on defining, modeling, and solving the depot driver scheduling problem which can determine driver size and driver schedule simultaneously. To solve this problem, we first construct a time-space network based on which we formulate the problem as a minimum-cost multi-commodity network flow problem. We then develop a Lagrangian relaxation heuristic to solve this network flow problem, where the upper bound heuristic is a two-phase method consisting of a greedy heuristic and a local search method. We conduct a computational study to test the effectiveness of our Lagrangian relaxation heuristic. The computational results also report the significance of the ratio of driver size to task size in the depot.

Keywords: driver scheduling; electric multiple-unit (EMU) train depot; optimization; time-space network; Lagrangian relaxation; local search (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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