A dynamic model for real-time track assignment at railway yards. (June 2020)

• Record Type: Journal Article
• Authors: Schasfoort, B.B.W.; Gkiotsalitis, K.; Eikenbroek, O.A.L.; van Berkum, E.C.
• Is Part Of: Journal of rail transport planning & management. Volume 14(2020)
• Publication Date: 2020-06
• Subjects: Track assignment -- Real-time control -- Rail operations -- Minimizing delays -- Rescheduling; Railroads -- Periodicals; Railroads -- Planning -- Periodicals; Railroads -- Management -- Periodicals; Railroads; Railroads -- Management; Railroads -- Planning; Periodicals; 385.068
• DOI: 10.1016/j.jrtpm.2020.100198
• Languages: English
• ISSNs: 2210-9706

Abstract

Abstract: In this paper, we study the real-time train assignment problem (RT-TAP) that arises from the unreliable arrival times of freight trains and the last-minute parking requests at railway yards. In the RT-TAP, the reassignment of trains to the yard is triggered every time a train arrives at the railway yard and needs to be assigned (event-based optimization). After introducing a problem formulation for the RT-TAP problem, we prove that RT-TAP is NP-Hard. In particular, the RT-TAP is modeled as a mixed integer program that strives to minimize the total weighted delay of trains. Because of its computational complexity and the time-critical nature of this problem, we introduce two real-time solution methods: (a) a problem-specific genetic algorithm (GA), (b) and a first-scheduled first-served (FSFS) heuristic. In small instances, we show that the GA returns a globally optimal solution which is identical to the solution of exact optimization methods. In larger problem instances, the heuristic approaches of FSFS and GA are tested at the Waalhaven Zuid railway yard in the Netherlands using two months of operational data. In the experimental results, the GA solutions reduce the average delays by more than 4 min compared to the solutions of the FSFS heuristic. Highlights: Model the real-time train assignment problem as a multi-commodity flow problem. Comparison of Genetic algorithm against first-scheduled first-served (FSFS) heuristic. Genetic algorithm provides the best solution considering both delay and calculation time. Application of our model to 1 day of operations in the Waalhaven Zuid yard. Reduced delays by 4 min and 42 s (on average).