# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 5 Case Study
### Stage 3: Rank Alternatives with CINFU-CODAS
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline  & & **A1** & & **A2** & & **A3** & & **A4** & \\ \hline \multirow{4}{*}{C3} & \(\xi\) & \(\mu\) & \(v\) & \(\mu\) & \(v\) & \(\mu\) & \(v\) & \(\mu\) & \(v\) \\ \cline{2-10}  & 0.1 & 0.238 & 0.122 & 0.238 & 0.122 & 0.283 & 0.077 & 0.315 & 0.045 \\  & 0.2 & 0.422 & 0.217 & 0.422 & 0.217 & 0.503 & 0.137 & 0.56 & 0.08 \\  & 0.3 & 0.555 & 0.285 & 0.555 & 0.285 & 0.66 & 0.18 & 0.735 & 0.105 \\  & 0.4 & 0.634 & 0.326 & 0.634 & 0.326 & 0.754 & 0.206 & 0.84 & 0.12 \\  & 0.5 & 0.66 & 0.34 & 0.66 & 0.34 & 0.785 & 0.215 & 0.875 & 0.125 \\  & 0.6 & 0.634 & 0.326 & 0.634 & 0.326 & 0.754 & 0.206 & 0.84 & 0.12 \\  & 0.7 & 0.555 & 0.285 & 0.555 & 0.285 & 0.66 & 0.18 & 0.735 & 0.105 \\  & 0.8 & 0.422 & 0.217 & 0.422 & 0.217 & 0.503 & 0.137 & 0.56 & 0.08 \\  & 0.9 & 0.238 & 0.122 & 0.238 & 0.122 & 0.283 & 0.077 & 0.315 & 0.045 \\ \hline \multirow{4}{*}{C4} & 0.1 & 0.225 & 0.135 & 0.256 & 0.104 & 0.283 & 0.077 & 0.315 & 0.045 \\  & 0.2 & 0.4 & 0.24 & 0.544 & 0.185 & 0.503 & 0.137 & 0.56 & 0.08 \\  & 0.3 & 0.525 & 0.315 & 0.596 & 0.243 & 0.66 & 0.18 & 0.735 & 0.105 \\  & 0.4 & 0.6 & 0.36 & 0.682 & 0.278 & 0.754 & 0.206 & 0.84 & 0.12 \\  & 0.5 & 0.625 & 0.375 & 0.71 & 0.29 & 0.785 & 0.215 & 0.875 & 0.125 \\  & 0.6 & 0.6 & 0.36 & 0.682 & 0.278 & 0.754 & 0.206 & 0.84 & 0.12 \\  & 0.7 & 0.525 & 0.315 & 0.596 & 0.243 & 0.66 & 0.18 & 0.735 & 0.105 \\  & 0.8 & 0.4 & 0.24 & 0.454 & 0.185 & 0.503 & 0.137 & 0.56 & 0.08 \\  & 0.9 & 0.225 & 0.135 & 0.256 & 0.104 & 0.283 & 0.077 & 0.315 & 0.045 \\ \hline \multirow{4}{*}{C5} & 0.1 & 0.315 & 0.045 & 0.27 & 0.09 & 0.225 & 0.135 & 0.18 & 0.18 \\  & 0.2 & 0.56 & 0.08 & 0.48 & 0.16 & 0.4 & 0.24 & 0.32 & 0.32 \\  & 0.3 & 0.735 & 0.105 & 0.63 & 0.21 & 0.525 & 0.315 & 0.42 & 0.42 \\  & 0.4 & 0.84 & 0.12 & 0.72 & 0.24 & 0.6 & 0.36 & 0.48 & 0.48 \\  & 0.5 & 0.875 & 0.125 & 0.75 & 0.25 & 0.625 & 0.375 & 0.5 & 0.5 \\  & 0.6 & 0.84 & 0.12 & 0.72 & 0.24 & 0.6 & 0.36 & 0.48 & 0.48 \\  & 0.7 & 0.735 & 0.105 & 0.63 & 0.21 & 0.525 & 0.315 & 0.42 & 0.42 \\  & 0.8 & 0.56 & 0.08 & 0.48 & 0.16 & 0.4 & 0.24 & 0.32 & 0.32 \\  & 0.9 & 0.315 & 0.045 & 0.27 & 0.09 & 0.225 & 0.135 & 0.18 & 0.18 \\ \hline \multirow{4}{*}{C6} & 0.1 & 0.21 & 0.149 & 0.256 & 0.104 & 0.283 & 0.077 & 0.315 & 0.045 \\  & 0.2 & 0.374 & 0.265 & 0.454 & 0.185 & 0.503 & 0.137 & 0.56 & 0.08 \\  & 0.3 & 0.491 & 0.348 & 0.596 & 0.243 & 0.66 & 0.18 & 0.735 & 0.105 \\  & 0.4 & 0.561 & 0.399 & 0.682 & 0.278 & 0.754 & 0.206 & 0.84 & 0.12 \\  & 0.5 & 0.585 & 0.415 & 0.71 & 0.29 & 0.785 & 0.215 & 0.875 & 0.125 \\  & 0.6 & 0.561 & 0.399 & 0.682 & 0.278 & 0.754 & 0.206 & 0.84 & 0.12 \\  & 0.7 & 0.491 & 0.348 & 0.596 & 0.243 & 0.66 & 0.18 & 0.735 & 0.105 \\  & 0.8 & 0.374 & 0.265 & 0.454 & 0.185 & 0.503 & 0.137 & 0.56 & 0.08 \\  & 0.9 & 0.21 & 0.149 & 0.256 & 0.104 & 0.283 & 0.077 & 0.315 & 0.045 \\ \hline \end{tabular}
\end{table} 
Table 12: _Cont._dissimilarities from the ideal solution are strengthened. For example, at \(\zeta=0.5\), alternative A1 and A3 have Euclidean distances of 0.131 and 0.056, respectively, and Taxicab distances of 0.084 and 0.055, respectively. These distances imply that A3 is closer to the ideal solution than A1. In other words, A3 is a more advantageous alternative at this particular \(\zeta\) value. Table 15 indicates the alternatives' assessment scores and ranking. A higher evaluation score means a better general performance and a more outstanding choice for a particular machine. The machines were ranked in descending order based on their evaluation scores. Although the \(\zeta\) value may affect the ranking, in this application, the alternative ranking stays relatively consistent for different \(\zeta\) values, presenting a robust and reliable outcome. The best machine for inventory tracking was A1, whereas the least desired one was A3 for any \(\zeta\) values.