基于模糊时间窗的多目标冷链配送优化

随着生鲜冷链行业竞争逐渐白热化，成本高、时效性强、新鲜度难以保持等问题已成为制约冷链物流配送的瓶颈。为提高生鲜配送效率，考虑客户满意度，以货损成本、惩罚成本等综合配送成本最低为目标函数，构建了一个多目标配送路径优化模型。设计带精英策略的非支配排序遗传算法（Elitist Non-dominated Sorting Genetic Algorithm，NSGA-II）求解该问题，利用Solomon标准数据集进行仿真模拟实验。实验结果对比分析表明，考虑满意度时冷链物流配送所需车辆更少，总路径长度更短，设计的算法可以在较短的时间内获取到帕累托最优解集，能够有效地解决模糊时间窗下的配送路径优化问题。

Multi-objective Cold Chain Distribution Optimization Based on Fuzzy Time Window

With the increasingly fierce competition in the fresh cold chain industry, the problems such as high cost, strong timeliness and difficult to maintain freshness have become the bottleneck restricting the distribution of cold chain logistics. In order to improve the efficiency of fresh distribution, a multi-objective distribution path optimization model is established in this paper, which considers the customer satisfaction and takes the cargo damage cost, penalty cost and other comprehensive distribution costs as the lowest objective function. The Elitist Non-dominated Sorting Genetic Algorithm （NSGA-II） is designed to solve this problems, and the simulation experiment is carried out by using the Solomon standard data set. The comparative analysis of the experimental results shows that considering the satisfaction, the cold chain logistics distribution needs fewer vehicles and the total path length is shorter, and the designed algorithm can obtain the Pareto optimal solution set in a short time. It can effectively solve the distribution route optimization problem under the fuzzy time window.