Abstract
The railway freight center stations location and wagon flow organization in railway transport are interconnected, and each of them is complicated in a large-scale rail network. In this paper, a two-stage method is proposed to optimize railway freight center stations location and wagon flow organization together. The location model is present with the objective to minimize the operation cost and fixed construction cost. Then, the second model of wagon flow organization is proposed to decide the optimal train service between different freight center stations. The location of the stations is the output of the first model. A heuristic algorithm that combined tabu search (TS) with adaptive clonal selection algorithm (ACSA) is proposed to solve those two models. The numerical results show the proposed solution method is effective.
Highlights
In the past decade, China railway has invested many railway freight center stations, which are equipped comprehensive transportation facilities and logistics facilities for the purpose of centralized transportation
This constraint is related to the maximum covering location problem (MCLP), whereas the investment of service point may change the transport demand
N14 and N12 are consolidated in train service 1 → 2
Summary
China railway has invested many railway freight center stations, which are equipped comprehensive transportation facilities and logistics facilities for the purpose of centralized transportation. Most of the stations with small transport demand were closed. In order to attract the transport demand, railway must improve the wagon flow organization to provide service with high level. The center station location and wagon flow organization are interacted and interconnected. In order to organize products with high level of service, the amount of goods must meet the train size limitation. The efficiency of wagon flow organization can decide the operation efficiency of stations. The limited storage capacity of station will be occupied, if there is no suitable train to serve the demand. A numerical example is provided to illustrate the application of the models and algorithm
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