物流配送同时取送货低碳车辆调度模型及其QEA研究

发布时间：2018-03-22 00:31

本文选题：物流配送　切入点：同时取送货　出处：《浙江工业大学》2015年硕士论文　论文类型：学位论文

【摘要】：随着现代物流产业的高速发展与人们对温室效应问题的高度关注,考虑碳排放量和客户的多需求已经成为车辆路径问题研究的热点,通过合理设计车辆配送路线,在节约企业成本的同时降低二氧化碳的排放对于增加企业竞争力和抑制全球变暖有着重要的现实意义。本文针对同时取送货低碳车辆调度中几类典型问题,在分析其理论与实践背景的基础上,建立了多车型、基于多配送中心的多目标以及动态网络多目标同时取送货低碳VRP模型,并研究了量子进化算法对上述模型的求解,本文具体的研究工作如下:1.首先综述了课题的研究背景和意义,通过调研国内外同时取送货VRP和低碳VRP来研究同时取送货低碳VRP,针对目前研究中还存在的很多问题,提出了课题的主要研究内容。2.根据车型种类,车辆总数,客户的取送货需求,建立了以碳排放量最小为目标的多车型同时取送货低碳VRP的模型。模型中使用了考虑车型、距离和车辆载重量的碳排放计算方法。模型采用量子进化算法进行求解,最后通过算例测试,进行了相应的比较,实验结果表明了模型的正确性和算法的有效性。3.针对多配送中心问题,提出了将多个配送中心抽象成一个配送中心体系的策略,建立了多配送中心同时取送货低碳VRP的多目标优化模型,模型以碳排放量最小和总路径最短为目标进行优化。针对该模型提出了多目标QEA求解Pareto解。最后通过算例测试,实验结果表明了模型的正确性和本章提出的多目标QEA求解多目标问题的有效性。4.结合客户的取送货以及时间窗要求,根据旅行速度依赖函数,建立了以总旅行时间最小和总碳排放量最低为目标的动态网络同时取送货多目标优化模型。针对该模型提出了多目标协同QEA进行求解。最后通过算例的参数分析与算法比较,实验结果表明了模型的正确性和本文所提出多目标协同QEA对所求问题的有效性和求解的高效性。5.结合上述理论研究的基础上搭建了同时取送货低碳车辆调度仿真平台,该平台集成了上述同时取送货低碳调度模型以及所提的量子进化算法,验证了所提算法的有效性。
[Abstract]:With the rapid development of modern logistics industry and people's high attention to Greenhouse Effect, considering the carbon emissions and customers' multi-needs has become a hot spot in the research of vehicle routing problem, through the rational design of vehicle distribution routes. Reducing carbon dioxide emissions while saving the cost of enterprises has important practical significance to increase the competitiveness of enterprises and curb global warming. This paper aims at several typical problems in the simultaneous delivery of low-carbon vehicle scheduling. Based on the analysis of its theoretical and practical background, a multi-vehicle model, a multi-objective model based on multi-distribution center and a multi-objective and multi-objective dynamic network model are established, and the solution of these models by quantum evolutionary algorithm (QEA) is studied. The specific research work of this paper is as follows: 1. Firstly, the research background and significance of the subject are summarized. Through the investigation and research, both domestic and foreign VRP and low carbon VRP are used to study the simultaneous delivery of low carbon VRP. There are still many problems in the current research. 2. According to the type of vehicle, the total number of vehicles and the customer's demand for receiving and delivering goods, a model of multi-vehicle model with minimum carbon emission and low carbon VRP is established. In the model, the model is used to consider the type of vehicle. The model is solved by quantum evolutionary algorithm (QEA). The experimental results show the correctness of the model and the validity of the algorithm. 3. Aiming at the problem of multiple distribution centers, a strategy of abstracting multiple distribution centers into a distribution center system is proposed. A multi-objective optimization model for multi-distribution centers is established, in which the minimum carbon emission and the shortest total path are taken as the objectives of multi-objective optimization model. A multi-objective QEA solution for the model is proposed. Finally, an example is given to test the solution. The experimental results show the correctness of the model and the effectiveness of the multi-objective QEA proposed in this chapter. 4. According to the travel speed dependent function, combined with customer delivery and time window requirements, A dynamic network with minimum total travel time and minimum total carbon emission as the target is established, and the multi-objective optimization model of delivery is proposed. Finally, a multi-objective cooperative QEA is proposed to solve the model. Finally, the parameter analysis and algorithm comparison are carried out through an example. The experimental results show the correctness of the model and the effectiveness of the multi-objective cooperative QEA proposed in this paper to solve the problem. 5. Based on the above theoretical research, a simulation platform for simultaneous delivery and low-carbon vehicle scheduling is built. The platform integrates the proposed low carbon scheduling model and the proposed quantum evolutionary algorithm to verify the effectiveness of the proposed algorithm.
【学位授予单位】：浙江工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U492.22

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