基于软时间窗的多车舱生鲜品配送路径优化  

Optimization of Multi-compartment Fresh Product Distribution Path Based on Soft Time Window

作　　者：温廷新[1] 李可昕 胡迎春 WEN Ting-xin;LI Ke-xin;HU Ying-chun(School of Business Administration,Liaoning Technical University,Huludao Liaoning 123000,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123000

出　　处：《公路交通科技》2023年第9期232-238,256,共8页Journal of Highway and Transportation Research and Development

基　　金：国家自然科学基金项目(71371091);辽宁省社会科学规划基金项目(L14BTJ004)。

摘　　要：随着信息技术和经济的发展,人们的生活品质也在不断提升,人们对生鲜产品的需求不断增加,对冷链运输的需要也随之提高。生鲜产品不易储存的特点促使冷链物流迅速发展。冷链物流能耗高、碳排放高,还会对生态环境造成一定的破坏,这与国家倡导“绿色物流”、“低碳物流”的理念相悖。在双碳背景下,为了解决生鲜品在冷链配送过程中的高成本和高碳排放问题,从低碳视角和处于不同温层生鲜品的货损两个角度进行分析,在满足车辆载重及生鲜品的新鲜程度的约束条件下,综合考虑配送车辆产生的碳排放量及货损问题,以带软时间窗的车辆路径优化模型为基础,构建了一个配送成本及能耗最低的多车舱多温共配路径优化模型。为了进一步求解该模型,设计了一种知识型蚁群算法。首先,将知识模型融入到蚁群算法中。其次,采用动态概率进行选择。最后,将知识型精英战略下的禁忌搜索算子进行融合。利用该算法对上述模型求解并进行实证分析。结果表明:采用多车舱多温共配运输方式可极大程度地节约运输成本、降低碳排放、提高顾客满意度;采用知识型蚁群算法进行求解有效提高了算法的求解性能。With the development of information technology and economy,people’s quality of life is improving,the demand for fresh products is increasing,the demand for cold chain transportation is also increasing.The storage difficulty of fresh products promotes the rapid development of cold chain logistics.Cold chain logistics has high energy consumption,high carbon emissions,and will cause some damage to the ecological environment,which is contrary to the state’s advocacy of“Green logistics”and“Low-carbon logistics”concepts.Under the double-carbon background,in order to solve the problems of high cost and high carbon emission in the cold chain distribution of fresh produce,from the perspective of low-carbon and the loss of fresh produce in different thermosphere,under the condition of satisfying the constraint of vehicle load and freshness of fresh food,the carbon emission and cargo damage of distribution vehicle are considered comprehensively,and the vehicle routing optimization model with soft time window is based on,a multicompartment multi-temperature co-location path optimization model with the lowest distribution cost and energy consumption is proposed.In order to further solve the model,a knowledge-based ant colony algorithm is designed.First,the knowledge model is integrated into the ant colony algorithm.Second,dynamic probability is used for selection.Finally,the taboo search operators are integrated with the knowledge-based elite strategy.The algorithm is used to solve the model and carry out empirical analysis.The result shows that(1)the multi-cabin and multi-temperature co-distribution transportation mode can greatly reduce transportation costs,reduce carbon emissions and improve customer satisfaction;(2)knowledge-based ant colony algorithm is used to improve the performance of the algorithm.

关 键 词：物流工程 多温共配 知识型蚁群算法 生鲜品 车辆路径优化 

分 类 号：F252[经济管理—国民经济]