Collaborative intermodal freight transport network design and vehicle arrangement with applications in the oil and gas drilling equipment industry

• Published in: Transportmetrica (Abingdon, Oxfordshire, UK) Oxfordshire, UK), 2020-01, Vol.16 (3), p.1574-1603
• Main Authors: Liu, Dan, Yan, Pengyu, Deng, Zhenghong, Wang, Yinhai, Kaisar, Evangelos I.
• Format: Article
• Language: English
• Subjects: batch strategy; Collaboration; Collaborative intermodal transport network; Computing time; Decision making; Drilling machines (tools); Freight forwarding; Freight transportation; Genetic algorithms; hybrid genetic algorithm and particle swarm optimization algorithm; Intermodal; Network design; network flow planning; Operating costs; Particle swarm optimization; tactical planning; Transportation networks; Transportation services; vehicle arrangement
• ISSN: 2324-9935; 2324-9943
• DOI: 10.1080/23249935.2020.1758235

Decentralized freight decision making has been proven to be one of the barriers to achieve the optimal cost-saving freight transportation network. This study presents a collaborative intermodal freight network for the transportations of oil and gas drilling equipment, where a freight forwarder serves as a centralized decision-maker to coordinate transportation activities. We formulate the problem as a minimum intermodal transport cost model with a nonlinear objective function. Also, novel path-based decision variables instead of arc-based decision variables are used to formulate the selections of transportation services. A hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) in combination with a batch strategy is designed. The experimental results show that the proposed hybrid GA-PSO method has a better performance compared with existing algorithms in terms of the solution quality, and computational time. Furthermore, the proposed approach is applied to real-world instances of O&G drilling equipment in the 'China Railway Express' network.