Research on Route Optimization of Single-Supply-Point Perishable Goods Multimodal Transport Considering Transportation Vibration Loss

Abstract

As the market of perishable goods in China continues to expand, reducing quality loss during transportation has become an urgent issue for the industry. Multimodal transport, as a key approach to optimizing the transportation structure and lowering logistics costs, has been increasingly adopted in practice. However, multimodal transport involves multiple transfers, and continuous vibration from transportation equipment throughout the transport process, together with impacts during transfer operations, can easily increase the loss of perishable goods, making it highly significant in practice to consider vibration loss during transportation in route planning. Since different transportation equipment generates different levels of vibration acceleration, this study considers the vibration losses caused by road, rail, and air transport. A bi-objective route optimization model for fresh produce multimodal transport is established, aiming to minimize total cost while maximizing product quality satisfaction. A hybrid algorithm combining an improved Strength Pareto Evolutionary Algorithm and a multi-objective adaptive large neighborhood search algorithm is designed to solve the model. The effectiveness of the model and algorithm is verified through case analysis, followed by sensitivity analysis on different time-sensitive factors of vibration damage and vibration acceleration. The results show that, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, the proposed algorithm can obtain solutions with lower total costs or higher product quality satisfaction. The minimum total cost of the multimodal transport scheme obtained by the proposed algorithm is reduced by 0.27% and 2.4%, respectively, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, while the maximum satisfaction is increased by 2.1% and 0.35%, respectively.