Reverse Logistic Network Design And Application Under Risk

In the 21 century, the global economy enters a growth period based on industrialization and shapes an industrial cluster relying on cars, real estate and manufacturing. Steel is the most important materials supporting manufacturing, at the same time scrap steel is also very important materials. But the resource of scrap steel is limited, then how to solve the problem of returns is urgent. But in the course of recovery, there is a lot of risk and uncertainty may lead to the disruption of transportation or supply. Our study does research on network design considering the uncertainty based on this problem.Logistics network design is a strategic issue of prime importance for the economic viability of product recovery activities. The difficulty of reverse logistic remanufacturing network system is uncertainty. There are three main parts in this study as following.First, a reverse logistic network includes customer locations, recovery and dismantling center and factory. The returned products ship from the collection site to the recovery and dismantling center first. Then the products are tested in the recovery and dismantling center, some abandoned products are disposed of and others ship to the factories. To build a mathematical model for such a complex problem, we need to consider various cost factors. The costs are transporting costs, operating costs, processing fees and testing fees. We establish and calculate the total cost of the minimum value for these models, then get the optimal transport path and capacity allocation.Second, path risk is a main content which we have to do in-depth study in a reverse logistic network. That's because it connects all the nodes in entire network. Therefore, we build a two-objective model to minimize the cost which contains transporting risk based on the third chapter. Similarly, we solve this model and get the optimal transport path and capacity allocation under the transporting risk.At last, in this work we propose a reverse network optimization of multi-objective function model which consider not only uncertainty of return and requiring but also transporting risk and reverse logistic supply risk which dues to uncertain return, in order to determine the location of various facilities and the number in the network. The objective is minimization of the overall costs, transporting risk costs and delay time punishment costs. The model is proofed feasibility and effectiveness through a set of experiment data reflecting practical business situation and sensitivity. An algorithm based on LINGO8.0 is developed and the model is tested on sensitivity analysis. It verifies the influence of optimal location strategy and cost under the risk varying, and provides theoretical support for designing an enterprise reverse logistics network and it also has some certain significance for the enterprises.