模糊时间窗多目标冷链物流路径规划

Research on the logistics path planning of fuzzy time window multi-objective cold chain

针对近几年冷链物流行业高额的配送成本和能源消耗等问题,以冷链物流配送路径为研究对象,建立基于碳排放量、配送总成本和客户满意度的多目标配送路径优化模型。采用贴近实际的模糊时间窗配送方式和自适应灾变遗传算法,对冷链物流运输车辆路径规划和在实际配送中复杂路径问题下的多目标路径优化进行研究。算例分析表明:1)在冷链物流路径配送中,目标函数考虑碳排放时的碳排放量相比不考虑碳排放降低了56%;2)该模型能够在考虑碳排放量和客户满意度的基础上有效地降低配送成本,使多个目标进行有机统一,全局优化;3)该算法对于多目标冷链物流路径优化问题在寻优效率和计算时间上均优于标准遗传算法。

Focusing on problems of high distribution costs and energy consumption of cold chain logistics industry,an optimization model for multi-objective distribution paths was established based on carbon emission,total distribution cost,and custom satisfaction in this study.By using distribution methods addressing fuzzy time window and adaptive catastrophe genetic algorithm,the path planning of the cold chain logistics vehicles and the multi-objective path optimization with complicated path choices during realistic distributions were investigated.The results showed that:1)Comparing to ignoring carbon emission,the inclusion of carbon emission in the model reduced by 56%in the objective function;2)Including carbon emissions and customer satisfaction,the model could obviously reduce the distribution costs and reach the global optimization while balancing multi-objectives effectively;3)The proposed algorithm outperformed the traditional genetic algorithm for solving multi-objective cold chain logistics path optimization problem in terms of optimization search and calculation time.