A new multi-criteria method based on DEMATEL, ANP and grey clustering for quality sorting of incoming cores in remanufacturing systems under epistemic uncertainty: a case study of heavy-duty equipment

The main activities in a remanufacturing system includes core acquisitions, remanufacturing operations, and remarketing. Core acquisitions are challenging for remanufacturers because remanufacturing in closed-loop supply chains is characterized by uncertainty in quality, timing and volume. The uncertainty is generally defined as incomplete or incomplete information. Sorting of quality incoming products in the remanufacturing system is a complex problem. On account of all quality conditions can be related either directly or indirectly, making it difficult to define certain conditions individually. Considering that the acquisition process involves many conflicting assessment criteria and subjective-qualitative considerations, an integrated method is needed to prioritize criteria selected. Therefore, this study makes a major contribution to research on multi-criteria sorting problem by developing a new hybrid method. This study focuses on development of a multi-criteria quality sorting model in the remanufacturing system based on a hybrid approach, namely Decision Making Trial Evaluation and Laboratory (DEMATEL) Analytical Network Process (ANP) and Grey Clustering. The DEMATEL approach is proposed to evaluate and to develop the dependency of criteria, and ANP method is utilized for weighting the criteria as well as Grey Clustering is proposed for modelling of uncertainty issues. The model was successful as it was able to identify the most important criteria which proposed. This work has found that maintenance history is the most important attribute in assessing the quality of incoming cores. This finding has important implication for selecting the best maintenance strategy of heavy-duty equipment during the use phase.