## 2 Literature Review  
Researchers and practitioners have recognized uncertainty as one of the most challenging elements in making decisions regarding remanufacturing [21]. The term \"uncertain\" has been used to refer to situations in which something is not known or something is not \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Research Questions** & **Research Objectives** \\ \hline a. & How does one overcome the sorting & & \\  & problem when the incoming cores feature & a. & To develop a multi-criteria sorting model \\  & uncertain quality? & with uncertain quality. \\ b. & What criteria should be considered in the & b. & To identify and propose the criteria for \\  & quality evaluation of incoming cores? & & the quality evaluation of incoming cores. \\ c. & How does one evaluate the relative & c. & To determine the weights of the \\  & importance among the quality criteria & & considered criteria. \\  & with incomplete data? & d. & To establish the effective method for \\ d. & How does one assign the set of incoming & & cores assignment associated with the \\  & cores into pre-determined quality & & sorting methods \\  & classes? & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relevant research questions for research objectives. complete or not certain. Such situations may include physical measurements or unknown variables [22]. Table 2 lists several techniques that have been developed to deal with distinct kinds of uncertainties. The use of probability statistics is one of the most common methods for investigating uncertainty conditions. Unfortunately, such methods require large sample data quantities based on historical law for determining probability density function. Fuzzy systems are the most popular methods for investigating subjective uncertainty in terms of vagueness or ambiguity in linguistic statements. Fuzzy systems concern the study of cognitive uncertainty issues, where the research objects have the characteristics of clear and unclear information. Different from fuzzy sets, a rough set approximates a set of numbers using two definable upper and lower sets. In terms of data granularity (referring to the quality of information), a rough set is described and a finer granularity provides a more accurate approximation [15]. Meanwhile, the grey system approach has many attractive features, notably, to support for any data distribution requirement with a small data size. Further, compared to other methods applied to handle uncertainty, grey systems do not require extensive data and are more pragmatic and efficient in complex systems and require fewer mathematical calculations [23]. The following presents a brief description of the literature analysis regarding uncertainty in remanufacturing systems. It can be seen in Figure 1 that the front end in remanufacturing systems is the acquisition of worn-out products (usually referred to as cores). Product acquisition acts as a gatekeeper in remanufacturing for obtaining used products or parts from end users/customers for further reprocessing. This task affects all remanufacturing operations but is complicated by the high uncertainty about the quality, timing, and quantity of returns. Used product returns in remanufacturing may show extremely uncontrolled product condition variability, varying from minor to major damage and sometimes requiring repair in advance. In addition, the conditions of incoming cores are unknown to the remanufacturer in advance, and because of variations in usage patterns, they differ from one another. In order to identify quality levels of products and to justify the economic feasibility of remanufacturing operations, the recovery, inspection, and sorting of cores is worth implementing. In summary, quality uncertainty needs to be considered in the decision-making process of core acquisition. Several factors are known to influence quality uncertainty during core acquisition in remanufacturing.