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A two-stage approach to the depot shunting driver assignment problem with workload balance considerations
Jiaxi Wang1, Manfred Gronalt2, Yan Sun3
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing, People's Republic of China.
Plos One
|July 14, 2017
Summary
This study introduces a two-stage optimization method for electric multiple unit (EMU) shunting driver assignment. The approach determines optimal driver size and balances workload while minimizing walking distance for efficient railway operations.
Area of Science:
- Operations Research
- Transportation Science
- Railway Logistics
Background:
- Railway transportation is crucial for sustainable and energy-efficient logistics.
- Crew sizing and scheduling are significant challenges in railway operations.
- Optimizing shunting driver assignments in depots is essential for efficiency.
Purpose of the Study:
- To develop a novel two-stage optimization approach for the electric multiple units (EMU) depot shunting driver assignment problem.
- To determine optimal shunting driver workforce size.
- To minimize total walking distance and maximize workload balance for drivers.
Main Methods:
- A two-stage optimization framework was proposed.
- The first stage determines the optimal number of shunting drivers.
- The second stage uses a bi-objective optimization model with the normalized normal constraint method and a modified Pareto filter algorithm.
Main Results:
- The approach successfully determined optimal driver sizes and generated Pareto solutions for workload balance and walking distance.
- Regression analysis yielded a driver size predictor.
- Sensitivity analysis provided valuable insights for decision-makers.
Conclusions:
- The proposed two-stage optimization approach effectively addresses the EMU depot shunting driver assignment problem.
- The method provides a practical tool for optimizing workforce size and improving operational efficiency.
- The findings offer actionable insights for railway management and planning.
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