Abstract
Classification yards are crucial nodes of railway freight transportation network, which plays a vital role in car flow reclassification and new train formation. Generally, a modern yard covers an expanse of several square kilometers and costs billions of Chinese Yuan (CNY), i.e., hundreds of millions of dollars. The determination of location and size of classification yards in multiple periods is not only related to yard establishment or improvement cost, but also involved with train connection service (TCS) plan. This paper proposes a bi-level programming model for the multi-period and multi-classification-yard location (MML) problem. The upper-level is intended to find an optimal combinatorial investment strategy for candidate nodes throughout the planning horizon, and the lower-level aims to obtain a railcar reclassification plan with minimum operation cost on the basis of the strategy given by the upper-level. The model is constrained by budget, classification capacity, the number of available tracks, etc. A numerical study is then performed to evaluate the validity and effectiveness of the model.
Highlights
Classification yards are generally referred to as the nerve centers of a railway system, where a great many inbound trains are reclassified, and outbound trains are dispatched
We formulate the multi-period and multi-classification-yard location (MML) problem as a multi-period location–allocation problem with railway characteristics, and established a bi-level programming model constrained by budget, classification capacity, and number of available tracks
The upper-level is intended to find an optimal combinatorial investment strategy for all candidate nodes throughout the planning horizon, and the lower-level aims to obtain a least costly railcar reclassification plan on the basis of the strategy given by the upper-level
Summary
Classification yards are generally referred to as the nerve centers of a railway system, where a great many inbound trains are reclassified, and outbound trains are dispatched. Building or improving a yard constitutes a high portion of capital investment of railroads, and the spatial configuration of yards significantly affects the routing of traffic flows over the whole network. Marshalling stations ( called classification yards) can be divided into different types, based on the number and configuration of yards. As the improvement of classification capacity and efficiency provide a solid support for handling more railcars, the change of yards’ spatial configuration might invalidate the current train formation plan, and affect the workload of each yard. Given the highly-nonlinear interrelation among yards, the freight train formation plan needs to be taken into account in investment analysis, which should be carried out from the perspective of railway network, rather than focusing on a certain yard. Quantitatively analyzing the MML problem, from the perspective of capital investment and train formation cost, has already become a theoretically and practically urgent problem
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