杜丹丰,王晓倩,张凤梅.带时间窗约束的逆向物流路径优化研究[J].包装工程,2025,(1):214-221.
DU Danfeng,WANG Xiaoqian,ZHANG Fengmei.Reverse Logistics Path Optimization with Time Window Constraints[J].Packaging Engineering,2025,(1):214-221. |
| 带时间窗约束的逆向物流路径优化研究 |
| Reverse Logistics Path Optimization with Time Window Constraints |
| 投稿时间：2024-08-19 |
| DOI：10.19554/j.cnki.1001-3563.2025.01.024 |
| 中文关键词: 逆向物流 路径优化 时间窗 平衡优化器算法 |
| 英文关键词: reverse logistics path optimization time window Equilibrium Optimizer (EO) |
| 基金项目:黑龙江自然科学基金联合引导项目(LH2020E006) |
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| 中文摘要: |
| 目的 研究退货逆向物流的路径优化，以降低企业成本，促进退货逆向物流的发展。方法 聚焦于退货逆向物流的路径优化，充分考虑了运输成本和时间窗违规的惩罚因素，构建了一个旨在最小化回收成本的车辆路径优化模型。为了克服平衡优化器(Equilibrium Optimizer，EO)算法容易陷入局部最优的限制，将其与变量邻域下降法结合起来加以改进，并将改进后的EO算法与模拟退火(Simulated Annealing，SA)算法进行比较分析，同时也将原来的EO算法与变量邻域下降法进行比较分析。结果 优化后的EO算法相比于SA算法配送时间减少8.82%，总成本减少4.63%;相比于优化前的EO算法配送时间减少1.40%，总成本减少3.55%。结论 改进后的EO算法在求解车辆路径优化模型上有更好的适应性和收敛性，可以有效减少成本，缩短路径和时间。 |
| 英文摘要: |
| The work aims to study the path optimization of reverse logistics to reduce the enterprise costs and promote the development of reverse logistics for returns. With the focus on the path optimization of reverse logistics, a vehicle routing optimization model aiming at minimizing recovery costs was constructed by fully considering factors such as transportation costs and penalties for violating time windows. To overcome the limitation of the Equilibrium Optimizer (EO) algorithm being prone to local optima, the EO algorithm was combined with the variable neighborhood descent for improvement. The improved EO algorithm was then compared and analyzed against the Simulated Annealing algorithm and the original EO algorithm. At the same time, the original EO algorithm was also compared and analyzed against the variable neighborhood descent. The optimized EO algorithm reduced delivery time by 8.82% compared to the SA algorithm, and decreased the total cost by 4.63%. Compared to the EO algorithm before optimization, the delivery time decreased by 1.40%, and the total cost decreased by 3.55%. The improved EO algorithm has better adaptability and convergence in solving the vehicle path optimization model, which can effectively reduce costs and shorten path and time. |
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