Grey-Markov Model Prediction Of Reverse Logistics Product Recovery

Along with social progress, technological development, more and more people pay more attention on the ecological imbalance, the energy crisis, the environmental issues. Problems faced by a variety of solutions have been proposed, such as in the 1992 "United Nations Framework Convention on Climate Change," in the 2000 "Biological Protocol", etc., from the national strategic , China make sustainable development, the harmonious development of man and society strategy.The same is true in the logistics sector, people considering the forward logistics, reverse logistics also concern for more and more. Appears from the reverse logistics to the present, in practice there is a problem has been wound - reverse logistics uncertainty. Because of the uncertainty of reverse logistics, reverse logistics capacity for the forecast becomes difficult, and in practice, the forecast for the amount of reverse logistics is the basis of reverse logistics to carry on. Based on this, on the basis of previous research, analyze the characteristics of reverse logistics, reverse logistics identify the impact of uncertain factors, and the improved method is given the key factors to make the amount of reverse logistics as smooth as possible, thereby improving accuracy of the forecasts. On this basis, combining the advantage of Grey forecasting & Markov forecasting , using of Gray - Markov prediction of reverse logistics product recovery.The progress of this thesis is as follows:(1) Analysis of Factors Affecting Reverse Logistics: All along, the reverse logistics for its tremendous uncertainty impact on the forecast volume of reverse logistics, thus affecting the implementation of the reverse logistics. In this paper the impact of reverse logistics as the three factors, internal organizational factors, external environmental factors and the process of cooperation factors. Qualitative Delphi and quantitative Analytic Hierarchy Process gives the weight of the factors to affect the ranking, and weights for the two biggest factors in the process of cooperation in information sharing factors and internal organizational factors that raised the quality of the corresponding solutions.(2) After uncertainty in reverse logistics solution to stabilize the reverse logistics, the combination of gray forecasting model has a short time, insufficient data, volatility small, and characteristics for long-term trend prediction, consider a Markov Model in the prediction can better address the characteristics of random fluctuations, the formation of gray - Markov forecasting model to predict the relative accuracy of reverse logistics.(3)As'A''company in Hunan Television reverse logistics for example, through comparing the relative and absolute error rate in the gray forecasting model, Markov Model, Grey - Markov forecasting model, obtained gray - Markov forecasting model does have more advantages.