基于遗传算法的物流车辆配送路径优化模型

Logistics Vehicle Routing Optimization Model Based on Genetic Algorithm

摘要: 物流车辆路径优化是提高配送效率和缩减运输开支的核心问题。针对传统方法在处理复杂约束时易陷入局部最优的不足，本研究提出一种基于遗传算法的单目标与多目标物流车辆路径优化模型。单目标模型以最小化参与配送车辆的最大运输距离为目标，多目标模型进一步引入碳排放约束，综合考虑运输成本与环保需求，通过加权求和法将双目标转化为单目标求解。采用整数编码与锦标赛选择策略设计遗传算法，结合贪心初始化与动态调整机制提升解的可行性。实验结果表明，单目标模型将最大运输距离优化至25.79 km，多目标模型在权重系数为0.6时实现总运输距离100.42 km与碳排放86.65 kg的均衡解。本研究为绿色物流提供了兼顾效率与环保的路径优化框架。

Abstract: Vehicle routing optimization is a core issue in improving logistics efficiency and reducing transportation costs. To address the limitations of traditional methods in handling complex constraints, this study proposes a single-objective and multi-objective vehicle routing optimization model based on genetic algorithms. The single-objective model minimizes the maximum transportation distance of delivery vehicles, while the multi-objective model introduces carbon emission constraints and balances transportation costs with environmental requirements through weighted summation. An integer encoding scheme and tournament selection strategy are designed, combined with greedy initialization and dynamic adjustment mechanisms to enhance solution feasibility. Experimental results show that the single-objective model reduces the maximum transportation distance to 25.79 km, and the multi-objective model achieves a balanced solution of 100.42 km total distance and 86.65 kg carbon emissions when the weight coefficient is 0.6. This study provides a framework for green logistics that integrates efficiency and environmental protection.