A novel approach for remanufacturing process planning considering uncertain and fuzzy information

Remanufacturing, as one of the optimal disposals of end-of-life products, can bring tremendous economic and ecological benefits. Remanufacturing process planning is facing an immense challenge due to uncertainties and fuzziness of recoverable products in damage conditions and remanufacturing quality requirements. Although researchers have studied the influence of uncertainties on remanufacturing process planning, very few of them comprehensively studied the interactions among damage conditions and quality requirements that involve uncertain, fuzzy information. Hence, this challenge in the context of uncertain, fuzzy information is undertaken in this paper, and a method for remanufacturing process planning is presented to maximize remanufacturing efficiency and minimize cost. In particular, the characteristics of uncertainties and fuzziness involved in the remanufacturing processes are explicitly analyzed. An optimization model is then developed to minimize remanufacturing time and cost. The solution is provided through an improved Takagi-Sugeno fuzzy neural network (T-S FNN) method. The effectiveness of the proposed approach is exemplified and elucidated by a case study. Results show that the training speed and accuracy of the improved T-S FNN method are 23.5% and 82.5% higher on average than those of the original method, respectively.