Research Of Facility Location On Manufacturing Logistics Networks In Uncertain Condition

Remanufacturing is the transformation of used product into products, which meet or exceed the performance and quality of new products. Remanufacturing is accomplished through necessary cleaning, disassembly, examination and repair as well as replacement of components and parts. In the face of limited natural resources and the disposal capacity of waste, remanufacturing can realize efficiently the synthetically goal of optimal utilization of natural resources, environmental protection and sustainable development of economics. So, remanufacturing becomes one of the most efficient approaches that realize the sustainable development. However, the efficient implement of remanufacturing needs appropriate logistics network structures. Whether the design of remanufacturing logistics network is reasonable or not decides fundamentality the efficiency of logistics management for remanufacturing. Remanufacturing logistics networks is more complicated than traditional production/distribution networks owing to its inherent characteristics. Therefore, it is essential to do an in-depth study about the design problem of remanufacturing logistics networks.In view of this, this paper designs and optimals facility location of remanufacturing logistics networks on uncertain condition. First, chapter one and two put forward the concept of remanufacturing and analyze the features of remanufacturing network. Chapter three introduces the location models and the uncertain programming. The former three parts provide a theoretical basis of the construction and optimization of remanufacturing logistics networks. Second, the chapter four designs a facility location model with uncertain condition for enterprise with single-product, multi-hierarchy and closed-loop network, considers the quantity, utilization rate, remanufacturing rate and the demand of product as fuzzy variable, and uses the uncertain programming to transform the model into a fuzzy chance-constrained programming model. Chapter five designs a hybrid intelligent algorithm composed by Simulated Annealing algorithms and Genetic algorithms based on fuzzy simulation. Chapter six gives a case study, demonstrates the validity of the model and algorithm, and compares the results of the traditional optimization and intelligent optimization methods.