Model And Optimization Of Reverse Logistics Networks Based On Genetic Algorithm And Differential Evolution Algorithm

Recently, the concept of protecting environment and saving resource prevail. Government, producer and customers pay a close attention to reverse logistics, and it has become a hot research field. There is a concern, in people's mind, that the reverse logistics is not only the effective way to save resource, decrease waste, but also it can reduce the cost and improve the satisfaction degree of customers. Therefore, reverse logistics were built by many companies. The network of reverse logistics is the key of the whole reverse logistics. Many companies want to set up economic and high effective reverse logistics network, so how to built better reverse logistics network has practical value on research.Firstly, the thesis introduces the backgrounds, the significance, the innovative achievements and the motivations of choosing this topic. Although, the research on reverse logistics is justat beginning phase, but the design of network reverse logistics has huge applied fields. Also, this thesis, from macroscopic concept and microcosmic concept, presents the goal and main content of the research.Secondly, the thesis reviews the literatures of location theories and differential evolution algorithm, and summarizes the fundamental models of location theories. On the base of these, it analyzes the characteristics and insufficiencies of theoretic research of locating. Further more, the thesis introduce the heuristic search methods, such as greedy algorithm, genetic algorithm and differential evolution algorithm.Thirdly, based on the queuing theory, the writer studies the design of reverse logistics. The factories are regarded as service station, and returned goods are regarded as customers. There are many treatments to deal with the returned goods. Above all, this thesis, according to GI/G/1 model, selects production ability from many choices for a factory and write self-adaptive genetic algorithm to solve the model.Fourthly, this thesis extends the model based on GI/G/1 to GI/G/m model. Along with the complexity increasing, the running speed of genetic algorithm plays an important role. Parallel genetic algorithm (PGA) improves the speed of solving problem and, owing to the huge population and insulated sub-populations, enhances the diversity of population. So PGA can effectively prevent convergence of immature individual. The writer designs PGA to solve complicated optimizing model. Fifthly, the thesis studies the research on multi-echelon reverse logistics. Customers, collection points and factories form the multi-echelon reverse logistics network, and we select the site and quantity of the collection points and factories, after every customer's satisfactory is met. Every collection point has the maximum capacity of collecting returned products. The model is nonlinear integer programming model and the goal of the model is to achieve the maximum revenue.Sixthly, the thesis extends the model to integrate and optimize the reverse logistics and positive supply chain, at the same time. The factories can be the same one to producs goods and deal with the returned product. But the departments and collections are different. The departments, collection points and factories can be extended to satisfy customers' need. The differential evolution algorithm was designed to solve the model. To the optimization problem of nonconve, multi-modal and non-linear function, the algorithm is much more robust and quicker in convergence than other evolution algorithms.Finally, the thesis turns the model into a more complicated one. It is multi-period, multi-echelon, has capacity limitation and integrates the reverse logistics and positive logistics. In order to solve the model, a fuzzy self-adaptive differential evolution algorithm was developed. At present, fuzzy control develops towards adaptive control and self-learning control. The adaptive controllers modify the parameters and rules, so the best performance of the control system can be obtained, hi the classic differential evolution algorithm, the F and CR are fixed, the thesis, combining the fuzzy control and differential algorithm, develops a fuzzy self-adaptive differential evolution algorithm that modifies the mutation factor and crossover rate by using the difference of twe generations.