An integrated optimization decision method for remanufacturing process based on conditional evidence theory under uncertainty

Abstract

The uncertainty of worn parts is a challenge for the remanufacturing process. Therefore, an integrated optimization decision method for remanufacturing process based on conditional evidence theory under uncertainty is proposed. On the basis of production history data of remanufacturing enterprises, prior information of the remanufacturing process is generated, and the prior evidence is constructed. Then, depending on the relationship between the parameters and the processing technology, the information of detection and evaluation of parts' characteristic parameters is transformed into evidence. The prior evidence and the evidence are fused corresponding to the parameter value. Then, the influence law among production data, characteristics of worn parts and technological process in remanufacturing is revealed. The fusion result has a one-to-one correspondence with the processing technology of worn parts. According to the decision rules, the optimal processing technology of worn parts can be obtained by judging the fusion results. The statistical data of remanufacturing worn crankshafts shows that the quality improved by 2.5%, the production cost reduced by 5.6%, and the time saved by 7.3%. This study provides theoretical and methodological support for the optimization of remanufacturing production.