对比两种算法在物流路径规划中的表现，助你选对工具。
Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics
- 用蚁群算法和谷歌工具箱分别求解开放式车辆路径问题
- 谷歌工具箱速度更快、结果更稳定，蚁群算法参数更灵活
- 适合需要实时调度的大型物流系统决策参考
在现代物流管理系统中，路径规划需高效。开放式容量车辆路径问题（OCVRP）旨在为一组车辆规划最优配送路线，且车辆无需返回起点。本研究对比了两种求解方法：受自然启发的元启发式算法蚁群优化（ACO）与行业标准优化工具包Google OR-Tools。两者均使用Python实现，并基于自定义数据集进行测试。评估指标包括路径效率、计算时间和可扩展性。结果显示，ACO在路由参数调整上更具灵活性；而OR-Tools运行速度快、结果更一致，所需输入更少。该比较有助于在可扩展的实时物流系统中选择合适的路径规划策略。
原文摘要 · Abstract (English)
In modern logistics management systems, route planning requires high efficiency. The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers, without requiring the vehicles to return to the depot after deliveries. The present study is comparative in nature and speaks of two algorithms for OCVRP solution: Ant Colony Optimization (ACO), a nature-inspired metaheuristic; and Google OR-Tools, an industry-standard toolkit for optimization. Both implementations were developed in Python and using a custom dataset. Performance appraisal was based on routing efficiency, computation time, and scalability. The results show that ACO allows flexibility in routing parameters while OR-Tools runs much faster with more consistency and requires less input. This could help choose among routing strategies for scalable real-time logistics systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。