## 4 AHP/TOPSIS-Based Multi-Criteria Decision-Making
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Hybrid MCDM Approach** & **Application(s)** & **Strength(s)** & **Weakness(es)** \\ \hline \multirow{3}{*}{[28]} & AHP and Complex & \multirow{3}{*}{Sustainable hydrogen (COPRAS)} & (1) Controls uncertainties & Relied on data given \\  & Proportional Assessment & & production options & in all phases. (2) Improves & by only four experts \\  & & & group decision-making. & of the domain. \\ \hline \multirow{3}{*}{[13]} & Augmented Reality (AR) & \multirow{3}{*}{Building retrofitting} & (1) Visual information & \multirow{3}{*}{The technique cannot be applied to non-graphical problems where 3D models are not required.} \\  & and the Simos-Roy- & & & during the decision phase. & applied to non-graphical problems where 3D models \\  & & & (2) On average, takes & \\  & & & 15 min to evaluate & & are not required. \\ \hline \multirow{3}{*}{[29]} & \multirow{3}{*}{AHP, TOPSIS, and Simple Additive Weighting (SAW)} & \multirow{3}{*}{Renewable energy resources prioritization} & (1) The best alternative is the one that is selected best by at least two methods. & \multirow{3}{*}{Results highly reliant on the input of occupational safety specialists.} \\  & & & resources prioritization & (2) Convances a lot of time \\  & & & since ranking is generated & \\  & & & using three methods. & \\ \hline \multirow{3}{*}{[30]} & AHP, House of Safety, and Fuzzy logic & \multirow{3}{*}{Risk Assessment in Mining Sector} & (1) Dependency of multiple events is considered. & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & and Fuzzy logic & & (2) Imprecise and vague & \\  & & & structures are managed. & \\ \hline \multirow{3}{*}{[31]} & AHP and Data & Efficiency scores of & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{3}{*}{[32]} & AHP and Genetic & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } \\  & Algorithms (GAs) & & & & \\  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & Fuzzy AHP and Particle & Professor & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & (2) Solves the optimization & \\ \cline{1-1} \cline{3-5}  & Swarm Optimization (PSO) & prioritization & model as a nonlinear & \\ \cline{1-1} \cline{3-5}  & & & system of equations. & \\ \hline \end{tabular}
\end{table} 
Table 1: Hybrid MCDM approaches and their applications, strengths, and weaknesses. a large number of matrices to compute, i.e., using the hybrid approach, the criteria weights are determined by AHP while the alternatives are evaluated using TOPSIS. In evaluating the alternatives, the TOPSIS approach makes use of the weights computed via the AHP approach. Thus, for instance, if there are 10 alternatives, then the AHP approach alone requires 56 comparison matrices. Using the AHP/TOPSIS approach, this is reduced to two matrices, i.e., one matrix for criteria weight computation using AHP and the other matrix for alternative/candidate evaluation using TOPSIS. A short description of the two approaches is given in the following subsections, while the detailed step-by-step procedure of the proposed hybrid approach for the selection of an optimal site for a new business is presented with the help of a case study in Section 5.