Affiliations: [a] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
| [b] National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China
| [c]
National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, China
Correspondence:
[*]
Corresponding author. Junxiang Xu, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, China. E-mail: [email protected].
Abstract: Taking into account the uncertainties of the factors of in-transit transportation cost, hub transshipment cost, hub construction cost, in-transit transportation time, hub transshipment time, and demand, this study uses triangular fuzzy numbers, expected value criteria, and distribution of credibility measure to minimise the total transportation cost of the hub-and-spoke road-rail combined transport (RRCT) network and the maximum transportation limit time between the origin and destination of the network. Firstly, a non-linear programming mathematical model is constructed for the regional hub-and-spoke RRCT network based on uncertain cost-time-demand. Then, an improved genetic algorithm is designed to obtain an optimized scheme. The algorithm uses genetic algorithm to search the global space, and uses two local search methods, i.e. shift and exchange, to search the local space. Finally, the RRCT network along the Yaan-Linzhi section of the Sichuan-Tibet Railway is used as the research object to verify the applicability and effectiveness of the regional hub-and-spoke RRCT network model and the algorithm proposed in the study.
