{'fraction_non_alphanumeric': 0.018760195758564437, 'fraction_numerical': 0.0, 'mean_word_length': 5.668478260869565, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Collaborative intermodal freight transport network design and vehicle arrangement with applications in the oil and gas drilling equipment industry

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.