基于多蚁群算法的低碳车辆路径研究

Research on Low-Carbon Vehicle Routing Using a Multi-Ant Colony Algorithm

摘要: 本文针对软时间窗约束下的低碳车辆路径优化问题，构建了以最小化车辆使用数量和总成本为目标的数学模型，包括燃油成本、碳排放成本及时间窗违约成本。本文采用多蚁群系统(MACS)求解这一NP‑hard问题，通过多蚁群协同搜索并引入Cross exchange局部搜索算子，增强了算法的全局探索与局部开发能力。基于实际案例的数值实验表明，该算法能有效减少车辆数、缩短行驶距离并降低总成本，验证了其在求解低碳、准时配送路径问题中的可行性与有效性，为物流企业践行低碳运营提供了决策参考。

Abstract: This paper addresses the vehicle routing optimization problem with soft time window constraints and low-carbon objectives, and develops a mathematical model aiming to minimize the number of vehicles used and the total cost—including fuel cost, carbon emission cost, and time window violation cost. A multi-ant colony system (MACS) is adopted to solve this NP-hard problem. The approach employs multiple ant colonies to conduct cooperative search and introduces a Cross exchange local search operator, which enhances the algorithm’s global exploration and local exploitation capabilities. Numerical experiments based on a real-world case demonstrate that the proposed algorithm can effectively reduce the number of vehicles, shorten travel distance, and lower the total cost, verifying its feasibility and effectiveness in solving low-carbon and on-time delivery routing problems. The study provides a decision-making reference for logistics enterprises to implement low-carbon operations.