Multi-cycle And Multi-obj Ective Remanufacturing Logistics Network Planning

The progress of science and the enhancement of environment protection consciousness prompt a growing number of countries and companies turning to the recycling of waste products. Remanufacturing is widely used in modern manufacturing as an advanced recycling approach. The successful implementation of remanufacturing is inseparable from the support of logistics facilities. Reasonable facilities distribution not only can help companies save costs, but also convenient for consumers, improve customer satisfaction.At present, there are three main kinds of remanufacturing logistics network design model which are expansion design model, independent design model and integration design model. The current research mainly focuses on the construction of independent remanufacturing logistics network with single cycle and single objective, and models are static. All those ignore the construction of integrated network and the fact that reasonable logistics operation plan is based on the multiple times, involving different interest subjects and can dynamic change.To solve these problems, the independent and integrated networks which are widely applied in real life are studied. Considering the uncertainty of the number of waste products and the demand of remanufactured goods, this paper gives independent and integrated network facilities location models which based on multiple cycles and various participants. Main work and results are as follows:(1)To solve the uncertainty of waste products, Unbiased Grey Markov forecasting model was proposed. His paper analyzed the advantages and disadvantages of GM (1,1) model and Markov model. And then, it gave the principle and calculation steps of Unbiased Grey Markov model which was the combination of Grey model and Markov model. An actual example was given to verify the validity and accuracy of these models. The results show that Unbiased Grey Markov forecasting model can overcome the defects of Grey and Markov models, and its accuracy was better than other two models. (2)Gave the physical structure of independent network and established an dynamic location model which targets are to minimize the remanufacturing costs and the side effects on residents. In this model, the number of waste products and the need of remanufactured goods are uncertainty which can be solved by the forecasting model in chapter three and the uncertain programming presented in chapter two. To verify the validity of the model, a numerical example was given. What’s more, through studying the example, we could realize the similarities and differences between static model and dynamic model as well as the inventory’s impact on the location decisions.(3)An integrated network structure was given which was the extension of independent network. And on this basis, a multi-time and multi-objective facilities site model was built with the same targets. A numerical example was given to high light the differences between independent network and integrated network and the impact of uncertainty factors on site strategies.