基于遗传算法的低碳导向的物流中心配送优化

Optimization of Low-carbon Oriented Logistics Center Distribution Based on Genetic Algorithm

“双碳”背景下,交通运输行业作为碳排放的主要贡献者之一,亟需有效的降碳改革以助力国家实现“双碳”目标。针对当前主流的物流中心式物流模式,以单位货运周转量碳排放最小、货运成本最低及配送时间最短为目标,建立低碳导向的物流优化多目标模型,并针对该模型和场景的特点改进NSGA-Ⅱ多目标遗传算法。利用抽象后的某快递公司数据样例进行实验,验证上述多目标优化模型及改进NSGA-Ⅱ算法的有效性和先进性。实验结果表明:从优化调度和路径规划两个角度切入,针对配送全流程进行优化搜索求解,能够有效实现预设控本降碳的目标,为物流企业配送决策提供理论依据。研究结果同时也表明:降碳和成本控制作为物流中的制约因素,不同的目标偏好会对决策产生重大影响。

The transportation industry,as one of the main contributors to carbon emissions,urgently needs effective carbon reduction reforms to help the country achieve thecarbon peaking and neutrality goals.Aiming at the current mainstream logistics center logistics model,a low-carbon oriented logistics optimization multi-objective model is established with the goals of minimizing carbon emissions per unit freight turnover,minimizing freight costs,and minimizing delivery time.The NSGA-Ⅱ multi-objective genetic algorithm is improved based on the characteristics of this model and the characteristics of the scenario.An abstract data sample of an express company is used to test the effectiveness and progressiveness of the multi-objective optimization model and the improved NSGA-Ⅱ algorithm.Experimental results show that from the perspectives of optimization scheduling and path planning,optimizing the entire distribution process through search and solution can effectively achieve the preset goal of cost control and carbon reduction,and provide theoretical basis for logistics enterprise distribution decision-making.The research results also indicate that carbon reduction and cost control are constraints in logistics,and different target preferences can have a significant impact on decision-making.