Research And Application On Reverse Logistics Recycling Qualitity Of Abandoned Medical Equipment

The reverse logistics of the abandoned medical equipments which can cause somesocial impact for their high added value and pollution can be beneficial to the resourcesaving and environment protection. During this process, these enterprises that arehanding these abandoned medical equipments will meet the problem for the uncertaintyof the recovery time and quantity of these equipments, this uncertainty will affects thedesign of the recovery net and the inventory management of these equipments, and willfurther more affect the efficiency of the equipments’ recovery and handing. Thus, howto predict the quantity of reflux of the abandoned medical equipments is the keyproblem during the reverse logistics of the abandoned medical equipments.This paper studied on how to accurately predict the quantity of reflux of theabandoned medical equipments in the next stage and to apply the prediction results toinventory management when several factors influencing the quantity of reflux can notbe quantified. The main work can be summarized as the following:Firstly, it analyzes the uncertainty problem during the abandoned medicalequipments’ recovery process. Based on the analysis above, it puts forward theinfluence of these uncertainties.Secondly, it proposes to use the optimized grey model to set the forecasting modelbased on the situation that some of these factors which influence the recovery can not bequantified. The particle swarm optimization is proposed to solve the model parametersand forecast the recovery amount, then the result is compared with the traditionalGM(1,1); at last, it puts forward to use the result to select the suitable inventory strategy.Thirdly, the forecast method is applied to the prediction prototype system for therecovery amounts of abandoned medical equipments. And it conducts the needs analysisand design for the system.Finally, the prediction prototype system for the recovery amounts of abandonedmedical equipments has been implemented, including the structure of predictionprototype system, business components and part of the key programs and finally part ofkey running interface was also presented.