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.

中文翻译：

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协作式多式联运货运网络设计和车辆安排在石油和天然气钻井设备行业中的应用

分散的货运决策已被证明是实现最优成本节约货运网络的障碍之一。本研究提出了一个用于石油和天然气钻井设备运输的协作式多式联运货运网络，其中货运代理作为协调运输活动的集中决策者。我们将问题表述为具有非线性目标函数的最小多式联运成本模型。此外，使用新的基于路径的决策变量而不是基于弧的决策变量来制定运输服务的选择。结合批量策略设计了一种混合遗传算法和粒子群优化算法（GA-PSO）。实验结果表明，与现有算法相比，所提出的混合GA-PSO方法在求解质量和计算时间方面具有更好的性能。此外，所提出的方法应用于“中欧班列”网络中油气钻井设备的实际实例。