针对农场入口依赖的车辆路径问题，提出有序遗传算法提升求解效率。
Ordered Genetic Algorithm for Entrance Dependent Vehicle Routing Problem in Farms
- 设计基于入口规模的有序遗传算法，优化农场车辆路径规划。
- 实验表明该算法优于随机策略和无序遗传算法，路径成本更低。
- 新引入算子经消融实验验证有效，适合农业物流场景优化。
车辆路径问题（VRP）在众多生产场景中具有重要意义。我们注意到，在某些实际场景中，城市规模及其入口特征会显著影响优化过程。为此，本文构建了入口依赖的车辆路径问题（EDVRP）以描述此类情形，并为农场场景提供了数学建模。提出一种有序遗传算法（OGA）来求解该问题。通过大量随机生成案例的实验，验证了OGA的有效性：其表现优于随机策略基线和无序遗传算法。此外，论文中提出的新型算子经消融实验验证，证实其能有效提升算法性能。
原文摘要 · Abstract (English)
Vehicle Routing Problems (VRP) are widely studied issues that play important roles in many production scenarios. We have noticed that in some practical scenarios of VRP, the size of cities and their entrances can significantly influence the optimization process. To address this, we have constructed the Entrance Dependent VRP (EDVRP) to describe such problems. We provide a mathematical formulation for the EDVRP in farms and propose an Ordered Genetic Algorithm (OGA) to solve it. The effectiveness of OGA is demonstrated through our experiments, which involve a multitude of randomly generated cases. The results indicate that OGA offers certain advantages compared to a random strategy baseline and a genetic algorithm without ordering. Furthermore, the novel operators introduced in this paper have been validated through ablation experiments, proving their effectiveness in enhancing the performance of the algorithm.
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