Optimization On Remanufacturing Reverse Logistics Network In Automobile Industry Based On Genetic Algorithm

As increasingly the natural resource shortage, ecological destruction andenvironmental degradation, more environment protection laws require to extentmanufacturer production responsibility. Manufacturers recover the expired and discardedproducts from consumers for remanufacturing and recycling. With the large scaledevelopment of the automobile industry and continuously shortening of the product lifecycle, automobile industry has become one of the largest resources and environmentpollution. The recycle of waste car has paid high attention by government, enterprises andthe public.This thesis discusses the optimization on remanufacturing reverse logistics networkin automobile industry. Through summarizing and reviewing the existing researches aboutautomobile reverse logistics network at home and abroad, firstly, this paper analyses thefunction, structure and content programming to confirm the structure and operationmechanism of automobile remanufacturing reverse logistics network.This paper chooses the hybrid linear integer programming for constructing theoptimization model of independent automobile remanufacturing reverse logistics network.On the basis of which structure the integration model. In order to make the results moreaccurate and stable, it is using the hybrid genetic algorithm. Finally, the model is verifiedby an example analysis, which aims to identify the numbers and locations of variousfacilities, allocate rational logistics flows to minimize the total cost of investment andoperation. The effectiveness and feasibility of the model is verified through a quantitativeangle. This conclusion can provide a reference for the actual decision of automobilemanufacturers.