Optimization Of Closed-loop Supply Chain Network For Automobile Remanufacturing

With the development of world economy and the increasing amount of car ownership, it results in resource consumption and serious environmental pollution. The idea of Circular Economy is rising. Remanufacturing is the only way which must be passed the development of circular economy and building a conservation oriented society. As we all know, the automobile remanufacturing is an important part of the remanufacturing industry. At present, China’s automobile remanufacturing industry is the initial stage of development, the government has issued a series of regulations and policies to regulate and guide the development of industry. Recovery and remanufacturing of automobile products are an important component while developping of circular economy, and are an important foundation to come true resource saving and environment friendly society. So the research has important theoretical value and practical significance.The paper mainly optimizes and designs the closed-loop supply chain network of automotive remanufacturing and builds a mathematical model. The goal of the model is the total cost of network operators to minimize and the environment effect of minimum, taking into account the different needs of the market of new products and remanufactured products. The automotive product life cycle is taken into account to ensure the amount of the recovery of waste products from the market. Then the paper build a closed-loop supply network mathematical model of automobile remanufacturing of multi-objectives, multi-period, multistage, multi-product features. Finally, a group of small scale example validates the model’s correctness.Multi-objective optimization problem does not have a unique global optimal solution, but a non dominated solution set. It is called the Pareto optimal solution. According to the mathematical model in the paper, the corresponding fast nondominated sorting genetic algorithm(NSGA-II) is designed. According to the characteristics of the solution variables, the algorithm generate a group of better chromosomes added to the initial chromosome population, combined with fast nondominated sorting method and crowding distance operator and the selection, crossover and mutation of genetic algorithm to speed up the convergence of the population. At the end of the paper, the accuracy of the mathematical model and the effectiveness of the designed algorithm are verified by the other data.