Abstract
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.
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