Abstract
The distribution-based logistics support is an important way to improve the efficiency of spare parts supply, and the scientific scheduling of vehicle is crucial to achieve this target. The mathematical model of the vehicle scheduling problem in wartime spare parts distribution was formulated, and an improved ant colony optimization algorithm was utilized to solve it. In our algorithm, the transition rule was improved, and the local search heuristics were integrated into the algorithm. The VRPTW benchmark instances were revised and solved under different parameter settings, and the experimental results showed that our improved transition rule can significantly enhance the algorithm's performance.
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