考虑低碳的生鲜多配送中心路径优化  

Fresh product multi-distribution center route optimization considering low-carbon

作　　者：张涵 曹冒君[1] ZHANG Han;CAO Maojun(School of Business Administration,Liaoning Technical University,Huludao,Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学工商管理学院,辽宁葫芦岛125105

出　　处：《公路交通科技》2025年第4期179-188,共10页Journal of Highway and Transportation Research and Development

基　　金：辽宁省社会科学规划基金项目(L20BJY032)。

摘　　要：【目标】研究低碳在生鲜多配送中心路径优化中所发挥的作用,在降低企业总成本的同时帮助企业实现低碳发展。【方法】首先,基于转型低碳物流的经济发展理念,将碳排放融合在生鲜冷链物流路径优化过程中。其次,在企业拥有多个配送中心、客户拥有多个时间窗的基础上,考虑车辆的固定成本与低碳成本、生鲜产品的货损成本与制冷成本等多个目标成本。然后,将碳排放成本用车辆在运输过程中所产生的油耗成本及车辆在整个配送过程中因碳排放对环境造成污染所需要支付的环境成本二者进行求和来表示,在保证企业总成本最小、车辆的运输距离最短的双目标前提下建立模型。最后,设置车辆载重量、客户需求时间、各个配送中心与服务地区之间的距离等多个制约因素。【结果】根据模型自身特点引用自适应免疫算法,融合粒子群局部搜索算法和自学习算法求解,并与传统遗传算法进行对比分析验证,最终证明免疫优化算法相比较遗传算法而言收敛度更高、群体多样性保持的更好、能最大程度地避免陷入局部最优。【结论】在配送过程中考虑碳排放能够更好地为生鲜产品冷链物流运输带来合理规划,自适应免疫算法也比遗传算法更能降低生鲜运输总成本以及缩短运输距离。[Objective]To study the role of low-carbon in fresh product multi-distribution center route optimization,reduce the total cost for enterprises,and help enterprises achieve low-carbon development.[Method]First,carbon emissions were integrated into the fresh product cold chain logistics route optimization process,based on the economic development concept of low-carbon logistics.Second,on the basis of enterprises having multi-distribution centers and customers having multiple time windows,the multiple target costs(i.e.,the fixed cost and low-carbon cost of vehicles,the fresh product damage cost and refrigeration cost)were considered.Then,the carbon emission cost was expressed by the sum of fuel consumption cost during vehicle transport and environmental pollution cost caused by carbon emissions in the whole distribution process.The model was established under premises of ensuring the minimum total cost for enterprise and the shortest transportation distance for vehicles.Finally,multiple constraints were set,i.e.,vehicle capacity,customer demand time,and distances between various distribution centers and service area.[Result]Adaptive immune algorithm was adopted based on the characteristics of model itself,integrating particle swarm local search algorithm and self-learning algorithm to solve.Compared with the traditional genetic algorithm,it was finally proved that the immune optimization algorithm had higher convergence degree,better population diversity,and could avoid falling into local optimal to the greatest extent.[Conclusion]Considering carbon emissions in distribution process can plan the fresh product cold chain logistics transport more reasonably.The adaptive immune algorithm can also reduce the total cost,and shorten the transport distance of fresh food transport more than the genetic algorithm.

关 键 词：物流工程 多配送中心 自适应免疫算法 生鲜产品 碳排放 

分 类 号：U116[交通运输工程]