基于改进蚁群算法降低电商物流多仓库车辆路径调度成本问题
Reducing the Routing Cost of Multi-Warehouse Vehicles in E-Commerce Logistics Based on Improved Ant Colony Algorithm

本文针对带有时间窗约束的多仓库开放型车辆路径问题，提出了一种梯形模糊数方法对时间窗进行模糊化处理。在用户满意度函数和时间惩罚成本函数的基础上，建立了更加贴近实际场景的优化模型。为降低电商物流成本，本文假设系统允许存在一个或多个虚拟配送中心，并设计了一种改进的蚁群优化算法以求解系统的最小成本问题。通过在不同规模的实验数据集上进行测试，结果表明该算法在总成本方面具有显著优势：相较于基本蚁群算法降低了10%，相较于随机生成方法降低了29%。因此，本文所提出的数学模型具有合理性和有效性，特别适用于多中心、多需求点和开放型系统背景下的电商物流成本优化场景。
In this paper, a trapezoidal fuzzy number method is proposed to blur the time window for multi-warehouse open vehicle routing problem with time window constraint. On the basis of user satisfaction function and time penalty cost function, an optimization model which is closer to the actual scenario is established. In order to reduce the cost of e-commerce logistics, this paper assumes that the system allows one or more virtual distribution centers, and designs an improved ant colony optimization algorithm to solve the minimum cost problem of the system. When tested on experimental datasets of different sizes, the results show that the algorithm has a significant advantage in terms of total cost: 10% lower than the basic ant colony algorithm and 29% lower than the random generation method. Therefore, the mathematical model proposed in this paper is reasonable and effective, especially applicable to the scenario of e-commerce logistics cost optimization under the background of multi-center, multi-demand point and open system.