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.

Remanufacturing is a key pillar of a circular economy and helps in recovering used products by extending their life cycle via remanufacturing them into new products. A vital aspect in a remanufacturing system is the quality assessment of incoming worn-out products (cores) prior to remanufacturing to ensure that non-conforming cores are discarded at an early stage in order to avoid unnecessary processing. Therefore, quality sorting plays an important role in core acquisition for remanufacturing systems when attempting to mitigate uncertain incoming core quality as an immediate solution. The main problem is that it is difficult to acquire the important information required to decide on the sorting of incoming cores, such as the core quality. The data are also commonly limited, not always available, or inaccurate. Grey systems are powerful methods in decision making when handling uncertainty with small data. In this paper, we consider the usefulness of grey systems for handling uncertain quality information for sorting incoming cores in a remanufacturing system. For this reason, we propose a multi-criteria quality sorting model based on an analytical hierarchy process (AHP)-entropy model that is coupled with grey clustering using possibility functions. The quality criteria for sorting the incoming cores are considered according to the technological, physical, and usage conditions. To demonstrate the practical contribution of this research, a case study of the quality sorting problem with a heavy-duty equipment remanufacturer is presented. The proposed model consistently classifies the quality of used hydraulic cylinders into two grey classes.

Remanufacturing is a major area of interest within the field of sustainable manufacturing. Since remanufacturing has widely recognized as a strategy to recover of used products. This recovery means that salvaged values in the used products can be generated again in multi-lifecycle of product through many operations in remanufacturing system. Previous research has established that there are many challenges in remanufacturing practices. The crucial challenge is a shortcoming of control respecting uncertainty in quality, quantity, and return time of a used product. The existence of quality uncertainty incurs risks to incoming core, to remanufacturing planning, and to remanufactured product itself. This uncertainty, if unmanaged correctly, could lead to high cost of quality and remanufacturing operations. Therefore, the main problem is how to analyze the causes of quality uncertainty in remanufacturing activities. In this work, the factors of quality uncertainty were investigated using a fish bone diagram as a tool for cause and effect analysis. This tool is particularly useful in investigating factors and root causes of the quality uncertainty problem in remanufacturing system. In the first step we show that the factors exist in core acquisition and remanufacturing activity. The next step, we propose a quality control framework for reducing quality uncertainty. The framework characteristic in this paper is generic in nature, so that it can be potential for general applicability in remanufacturing industries.