Railway express freight train service sites planning: a two-stage entropy-TOPSIS approach

ABSTRACT

In order to select the railway express freight train service sites under the condition of modern logistics in China, the railway express site planning indexes are established, including the third-party logistics market demand environment of Second-level Logistics Area (SLA), the railway development strength of SLA, the traffic connection in SLA, the national policies and strategies coverage in SLA and the railway connection situation among the First-level Logistics Areas (FLA). Then a proposed two-stage Entropy-TOPSIS approach is applied based on the collected historical data of the index system. According to the planning results, Beijing, Harbin, Xi'an, Wuhan, Nanjing, Guangzhou, Chongqing, Taiyuan are selected as the 8 First-class Railway Express Logistics Bases (FRELBs), and other 28 cities are selected as the Second-class Railway Express Logistics Centers (SRELCs) in China.

KEYWORDS:

• Railway transportation
• express freight train service sites planning
• two-stage approach
• entropy-TOPSIS
• complex network

The authors would like to thank the anonymous referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was jointly supported by the Traffic and Transportation Engineering Experiment and Comprehensive Innovation Center, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan. Subsidized by Science and Technology Research and Development Project of China Railway Corporation [grant number 2012X012-F]; National Natural Science Foundation of China [grant number 71173177], State Railway Administration Science and Technology Project [grant number KF2014-041]; Southwest Jiaotong University 2015 Graduate Innovative Experimental Practice Program [grant number YC201507103]; Southwest Jiaotong University 2018 Postgraduate Academic Literacy Improvement Plan [grant number 2018KXK04].