Modeling And Optimization On Reverse Logistics Network In Automobile Industry Based On Third-party Logistics

With the improvement of people's life and the increasing desire of their consumption, the automobile industry is forging ahead at a tremendous speed, which is accompanied by the problem of rejected-car callback and scrap recycling. On one hand, the environmental pollution is getting serious and relevant laws are becoming consummate, which requires benefit as well as environmental protection implemented by closed-loop supply chain of rejected-car callback. On the other hand, the car product needs the conception of environmental protection to be the support of its own on sales promotion and brand reputation. Nevertheless, the profit of reverse logistics network in automobile industry is not high. Thus, third-party logistics can do the automobile industry a favor to enhance its sales promotion and brand reputation while opportunities of payoff for itself do exist.The whole thesis is structured as follows. First of all, related literature is reviewed and the theories of reverse logistics and third-party logistics are summarized. Then, the feasibility of the third-party logistics market-entering in reverse logistics network in automobile industry is illustrated by the study on the payoff mechanism of the field. Based on a traditional structure of reverse logistics network in automobile industry, the reverse logistics network with a closed-loop supply chain environment in automobile industry is established. Following that, the mixed integral linear programming model for reverse logistics network based on third-party logistics in automobile industry is set up, which is computed with heuristic algorithm afterwards. In the optimization, several suggestions become apparent via the study on stability of the callback source points and the route adjustment of small flux. To be concrete, the harvest includes the risk management strategy for the product number fluctuation in the callback source, the economical area for cost fluctuation, and routes merging for the sake of invisible cost reduction, which provides decision-making support for real operations.