Design and Freight Corridor-Fleet Size Choice in Collaborative Intermodal Transportation Network Considering Economies of Scale

Dan Liu, Zhenghong Deng, Qipeng Sun, Yong Wang and Yinhai Wang
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Dan Liu: School of Economics and Management, Chang’an University, Xi’an 710064, Shaanxi, China
Zhenghong Deng: School of Automation, Northwestern Polytechnical University, Xi’an 710064, Shaanxi, China
Qipeng Sun: School of Economics and Management; Economics and management research center for integrated transportation, Chang’an University, Xi’an 710064, Shaanxi, China
Yong Wang: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Yinhai Wang: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA

Sustainability, 2019, vol. 11, issue 4, 1-19

Abstract: Decentralized freight decision has been proved to be one of the inhibitors to achieve a sustainable transport network. One important method also a key challenge is to determine how to coordinate and consolidate the transportation flow to get the best logistics performance. This study presents an intermodal transportation network considering freight consolidation through freight forwarders’ cooperation. We formulate the problem as a minimum intermodal transport cost model, which is a nonlinear, nonconvex and discontinuous function that involves volume economies of scale, distance economies of scale and vehicle size economies of scale. A hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in combination with a batch strategy are used to solve the problem. Five different transport demand scenarios are tested on a real case on “China Railway Express” (Crexpress). The choices of reasonably corridor and fleet size combination are provided.

Keywords: collaborative intermodal transportation network; tactical planning; batch strategy; genetic algorithm and particle swarm optimization algorithm; economies of scale (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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