PLoS ONE (Jan 2018)

A study of the car-to-train assignment problem for rail express cargos in the scheduled and unscheduled train services network.

  • Boliang Lin,
  • Jingsong Duan,
  • Jiaxi Wang,
  • Min Sun,
  • Wengao Peng,
  • Chang Liu,
  • Jie Xiao,
  • Siqi Liu,
  • Jianping Wu

DOI
https://doi.org/10.1371/journal.pone.0204598
Journal volume & issue
Vol. 13, no. 10
p. e0204598

Abstract

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A freight train service network generally involves two categories of trains: unscheduled trains, whose operating frequencies fluctuate with the freight demand, and scheduled trains, which are operated based on regular timetables similar to passenger trains. The timetables for scheduled trains are released to the public once determined, and they are not influenced by the freight demand. Typically, the total capacity of scheduled trains can satisfy the predicted average demand of express cargos. However, in practice, the demand always changes. Therefore, a method to assign the shipments to scheduled and unscheduled train services has become an important issue faced in railway transportation. This paper focuses on the coordinated optimization of rail express cargo assignment in a hybrid train services network. On the premise of fully utilizing the capacity of scheduled train services, we propose a car-to-train assignment model to reasonably assign rail express cargos to scheduled and unscheduled trains. The objective aims to maximize the profit of transporting the rail express cargos. The constraints include the capacity restriction on the service arcs, flow balance constraints, transportation due date constraints and logical relationship constraints among the decision variables. Furthermore, we discuss a linearization technique to convert the nonlinear transportation due date constraint into a linear constraint, making it possible to solve by a standard optimization solver. Finally, an illustrative case study based on the Beijing-Guangzhou Railway Line is carried out to demonstrate the effectiveness and efficiency of the proposed solution approach.