## 2 Literature Review
### Authors' Contribution Table  
To highlight the novelty of our study compared to the existing literature, we present an authors' contribution table (Table 1). This table summarizes the key aspects of our research and compares them with relevant studies in the field. Compared to [3], which presented a supplier selection framework using fuzzy DEMATEL, ANP, and DEA, our proposed method differs by integrating Shapley values to address cooperative alternatives, an aspect not considered in their work. While [4] introduced a green supplier selection model using ANP and modified GRA, our method goes beyond their approach by incorporating cooperative game theory concepts to capture the interdependence between alternatives. Similarly, Ref. [10] proposed the IR'DANP-MAIRCA model, combining interval rough numbers with DEMATEL, the ANP, and MAIRCA to handle uncertainties in MCDM. However, our study extends their work by considering the cooperative nature of alternatives and their implications for resource allocation decisions.  
Next, Ref. [16] focused on evaluating and prioritizing suppliers and customers in the supply chain using sustainability metrics. While their approach integrates economic, environmental, and social factors using the AHP and fuzzy inference, our study differs by addressing the cooperative nature of alternatives in resource allocation problems using Shapley values. Then, Ref. [19] determined the most suitable very light business jet using multi-criteria decision-making with the CRITIC and TOPSIS methods. In contrast, our study integrates the AHP/ANP with Shapley values to handle the interdependence between alternatives and incorporate cooperative game theory concepts, which are not addressed in their work. Finally, Ref. [20] developed an algorithm for selecting the optimal hybrid energy power plant using fuzzy multi-criteria decision-making. While their research focused on evaluating single and hybrid energy resources, our study distinguishes itself by considering the cooperative nature of alternatives and integrating Shapley values with the AHP/ANP for resource allocation decisions.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Study** & **MCDM Method** & \begin{tabular}{l} **Integration with** \\ **Other Techniques** \\ \end{tabular} & \
\begin{tabular}{l} **Interdependence** \\ **Criteria** \\ \end{tabular} &
\begin{tabular}{l} **Interdependence** \\ **Alternatives** \\ \end{tabular} & **Cooperative Game** \\ \hline
[3] & Fuzzy DEMATEL, ANP, & Fuzzy SET theory & Yes & No & No \\
[4] & ANP, Modified GRA & No & Yes & No & No \\
[10] & IR’DANP-MAIRCA & Interval rough & Yes & No & No \\
[16] & AHP & No & No & No &
\begin{tabular}{l} Shapley value (criteria \\ weights) \\ \end{tabular} \\
[19] & CRITIC-TOPSIS & No & Yes & No & No \\
[20] & Fuzzy TOPSIS & Fuzzy set theory & Yes & No & No \\ Ours & AHP/ANP & Shapley value & Yes & Yes &
\begin{tabular}{l} Shapley value \\ (cooperative alternatives) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Authors’ contribution table.  
As shown in Table 1, our study distinguishes itself from the existing literature by integrating the AHP/ANP with Shapley values to address the cooperative nature of alternatives in resource allocation problems. While previous studies have focused on the interdependence between criteria and the integration of MCDM methods with other techniques, our research fills the gap by considering the interdependence between alternatives and incorporating cooperative game theory concepts. In summary, this literature review highlights the research gaps in addressing the cooperative nature of alternatives in MCDM and the limited integration of cooperative game theory concepts into MCDM frameworks. Our study aimed to fill these gaps by proposing a novel method that combines the AHP/ANP with Shapley values to make more informed and equitable resource allocation decisions.