Study On Vehicle Disposition And Routing Optimization Of Reverse Logistics

Recently, with the increasing emphasis on resources and environmental issues, the reverse logistics is gradually be the focus of attention, because it can realize the resource recycling of useless goods and can control environmental pollution efficiently. Transportation is an important component of the reverse logistics activities, and how to configure the number of vehicle and choose the routing effeciently, is the key to reduce transport costs of reverse logistics. As demand for reverse logistics and the dispersion of the time, location, size, weight and other characteristics of uncertainty, making reverse logistics vehicles decision and optimize the configutation are more complex than logistics.So when discusse the theories, models and methods of vehicles disposition and path optimization on the reverse logistics, we shoulde combine the characteristics of reverse logistics.In the view of this, this paper model and optimize vehicle disposition and routing optimization of reverse logistics on certain and uncertain conditions. Paper is divided into six chapters. The first chapter introduced the thesis topics' background, meaning, and study at home and abroad. The second chapter introduced the concept of reverse logistics and its classification, the characteristics of demand for reverse logistics and analysis the type and description network. Chapter 3 is mainly related to introductions of the basic theories about the reverse logistics and vehicle routing problem. In chaper 4, mainly model and optimize the demands certain condition, minimum the cost, and with allowing loading, capacity and allowed distance constraints. The model is solved by the improve Genetic Algorithms (GA) which is chose by the comparison of the different algorithms, and analyze diversification on parameters. Taking the reality of the needs are often uncertain into account, chapter 5 give an uncertain model. The model based on customer goods volume and quality demand for recycling of uncertainty, analyzed and solved by the Max-Min Ant Colony Algorithms (MMACA). Both models give a case study. In chaper 6, summary the research of the paper, analysis and forecasts the future need of this study and development direction of the future.