Research On Capacitated Location Selection Based On Heuristic Search Algorithm

In recent years, with the gradually increased public awareness of environmentalprotection, the provisions of relevant government policies and the appearance of theconsiderable economic benefits, reverse logistics has aroused more and more attention，it has also become a focus of academic research at home and abroad.By the early21stcentury, research on the reverse logistics rose to a strategic level, its academic studyalso began to refine. Reverse logistics involved in the various aspects of the industry,the ultimate goal is to reduce the use of resources, the forward and the reverselogistics can be more efficiency. Reverse logistics through the remanufacturingprocess saved a huge number of resources and raw materials, it reducedmanufacturing costs of the enterprise, the enterprise can create considerableeconomic benefits accordingly, and also bring social and environmental benefitscommon improve. Reverse logistics has highly uncertainty and complexity whichmakes it difficult to control the balance between supply and demand, so the operationof reverse logistics is very dependent on the logistics network, the most importantthing in the research process of the reverse logistics is to optimize the reverselogistics network design, location problem is its top priority. This paper majorresearch on the scrap vehicle disposal station construction and route planning problem,a site selection model is developed, and uses the improved genetic simulatedannealing hybrid algorithm and Bi-level programming to solve the problem. Thispaper through a simple example to test the differences of two algorithms, and then useant colony algorithm to search an optimal path from scrap cars produce to scrapvehicle disposal station.The route planning problem is solved. Computationalexperience, based on comparative results obtained from MTALAB, shows theefficiency of these algorithms.