Recycling Model Of Quality Uncertainty And Sensitivity Analysis In A Remanufacturing Environment

Along with social progress, the concept of sustainable development, circular economy and green manufacturing have been deeply rooted among the people. Scholars from domestic and foreign have put forward some related theories such as remanufacturing, reverse logistics and closed-loop supply chain. Recycling and Remanufacturing of discarded product is a typical module in a closed-loop supply chain system. Recovery strategy is also the key to be profitable in the recycle and remanufacturing business. The usecycle of the product and the uncertainty in itself characteristic shown obvious and serious quality differentiation, Research of quality of recycled product also have been a hot area of the related studies, This article is mainly studying the enterprise’s best recovery strategy from the perspective of quality uncertainty.First of all, This paper have sortted out the basic problem of related field in remanufacturing and supply chain, and points out the shortages of current research. Then, on the basis of it, indicates the study possibility and significance that the quality uncertainty of the recycled product is taken into account in recycling logisticsAnd then, Putting forward four kinds profit model of recovery strategy for recycled and remanufacturing enterprise, namely Pure Recycling, The Old Replacement, Determined Proportion of The Combinated Recycling, Uncertain Proportion of The Combinated Recycling. Then, according to uncertainty of quality level of recycle product, let quality level density function to be presented as exponential distribution or normal distribution respectively.Finally, We have arranged the related parameter data, combined with the case application, carried out model algorithm. Analysis and comparison of minimum acceptable quality level and maximum profits under the condition of four different recovery strategies in different quality level distribution. At last, detailed analysis have been put into effect to prove the influence of several important parameters on the model.