Integrated Planning for Depot Location and Line Planning Problems in the Intercity Railway Network with Passenger Demand Uncertainty

Zanyang Cui, Zhimei Wang (), Junhua Chen, Xingchen Zhang and Chunxiao Zhao
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Zanyang Cui: School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China
Zhimei Wang: School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China
Junhua Chen: School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China
Xingchen Zhang: School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing 100044, China
Chunxiao Zhao: School of Mathematics, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872, China

Sustainability, 2023, vol. 15, issue 20, 1-22

Abstract: In this study, we present a mathematical model and solution approach for addressing the robust integrated intercity railway depot location and line planning problem (RIDLLPP), which encompasses the initial two stages of the railway planning process. Our primary objective is to identify depot locations that exhibit robustness across a range of likely future scenarios, and this is achieved by incorporating line planning decisions. The model focuses on five critical strategic determinations, namely the depot location, depot storage capacity, line operation, passenger assignment, and fleet allocation. To tackle this complex problem, we propose an iterative solution framework that combines the Differential Evolution (DE) algorithm with improved rounding heuristics (DE-IRH). To evaluate the effectiveness of our framework, we conduct a comparative analysis with the Gurobi solver using multiple medium-sized artificial instances. The results demonstrate that our proposed framework achieves an optimality gap of 4.87% while requiring less computational time. Furthermore, we validate the robustness of the model’s location choices across various input scenarios, thereby providing valuable insights for transportation planning agencies and railway companies that can inform their decision-making processes.

Keywords: railway planning; depot location; line planning; fleet allocation; differential evolution; rounding heuristics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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