The freight volume of long-haul trucking reaches 10 billion tons in the U.S. and approximately 40 billion tons in China per year, which has made important contributions to economic growth while leading to enormous fuel consumption and critical driver dissatisfaction. With the development of the Internet of Things and Sharing Economy, a new long-haul trucking strategy (a novel swap-body mode) we design, in which drivers cooperate in completing all transportation tasks by exchanging goods, has great potential for practical application to alleviate these two issues. In this paper, we developed a concise mixed integer programming model with dedicated efficient cuts to formulate the new mode and its two restricted versions and designed a heuristic minimum empty-load algorithm and an ant colony optimization algorithm to efficiently provide exchange solutions. Our experimental results show that the novel swap-body mode can reduce the total operating costs and the average working hours of drivers by an average of 34.06% and 29.29%, respectively, compared to the traditional mode. Its excellent performance remains under severe restrictions and various conditions, indicating its robustness, and remains when demands have different distribution characteristics claiming its applicability.
A Novel Swap-Body Mode for the Long-Haul Trucking Problem
IEEE Transactions on Intelligent Transportation Systems ; 25 , 11 ; 19030-19045
2024-11-01
2871392 byte
Article (Journal)
Electronic Resource
English
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