Research On Reuse Reverse Logistics Centers' Location Model And Algorithm

Reverse logistics is an emerging field of research in recent years. The late 20th century, with the increasing importance of environmental protection and significant value of waste for reusing, reverse logistics systems are built by many enterprises, to create new economic benefits. The problem of reverse logistics facility location is the foundation and the key to reverse logistics system and economic, efficient reverse logistics network is the goal of many companies. So, how to determine the most reasonable location of the program has practical value in the reverse logistics field.Under this situation, the logistics center location problem of direct reuse reverse logistics network has researched based on the results of related research of home and abroad. The paper starts form the concept of reverse logistics, and has a system meaning based on integration of both at home and abroad. Then the structure of reverse logistics system is studied, the business processes is introduced, and the type of reverse logistics network has been divided from different angles. All of these have provided basis and premise for network location. After that, the paper builds the model of direct reuse reverse logistics center location followed by the uncertainty random variables of the demand of retailers and the recovery of collection points. This model assumes that the enterprises are expanding on the traditional network, increasing functions, and at the terms of cost, only the economic cost which can be measured is considering, such as transportation cost, fixed capital investment, operating cost, the cost of time, social factors are without considering. The model considers a single product and the closed-loop reverse logistics network, the current distribution centers can be expanded to assume reverse logistics business, so the retail point is also responsible for the recovery of used packaging beyond selling new products. For the random variables, they will be solved by using stochastic simulation, genetic algorithm and linear programming. And through specific examples, the sensitivity analysis of parameters and the confrontation to traditional algorithm is given, the effectiveness of model is verified the model. At last, the paper makes a summary and outlook of research.