# Enhancing Efficiency and Cost-Effectiveness: A Groundbreaking Bi-Algorithm MCDM Approach +
## 2 Methods and Experiment  
The ranking alternative involves selecting the best option from a list. It is fundamental to engineering, computer science and operation research that many MCDM methods have unique properties. Mathematical programming, gradient-based methods and meta-heuristic algorithms [2; 15; 21; 29; 30; 31; 32; 33] are popular. Optimization in decision making began with 19 th century mathematicians and engineers, and calculus-based optimal approaches were first employed in engineering and physics [9; 16; 18; 22; 25; 34]. They were not used in economics [19; 35; 36; 37; 38; 39] or computer science [3; 5; 12; 40; 41; 42] until the 20 th century. Several types of MCDM or decision-making methods [1; 6; 7; 10; 11; 16; 23; 24; 43; 44; 45; 46] are presented in Table 2, each of which exhibits a distinct set of features. The following \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Group 1: Arc Welding Techniques**} & \multicolumn{1}{c}{**Group 2: Gas Welding**} & \multicolumn{1}{c}{**Group 3: Resistance**} & \multicolumn{1}{c}{**Group 4: Specialized**} \\  & \multicolumn{1}{c}{**Techniques**} & \multicolumn{1}{c}{**Welding Techniques**} & \multicolumn{1}{c}{**Welding Processes**} & \multicolumn{1}{c}{**Welding Processes**} \\ \hline \hline \multicolumn{1}{c}{} & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{Plasma} \\ \multicolumn{1}{c}{} & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \multicolumn{1}{c}{} & & & & & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Terms and welding methods. are examples of the most common types: (i) Mathematical programming: This approach uses a mathematical model to represent the problem and the constraints to solve the problem [24; 44; 47; 48; 44]. After that, the responses can be obtained by working through the mathematical equations. (ii) Techniques based on gradients: These procedures are predicated on using gradients, which are the directions of the most steeply ascending or descending slopes. Moving along the direction of the gradient is to find a local or global optimal [2] solution. (iii) The algorithm of metaheuristics: This approach is predicated on using metaheuristics, or rules of thumb, to direct the search for a solution [3; 11; 13; 18; 40; 49; 50]. These approaches are known as the metaheuristic algorithm. Iteratively enhancing an initial solution over time leads to the discovery of the solution. In this study, our proposed method is presented in Figures 1-3 (DEX stage), and Figure 4 shows the flowchart of the whole proposed study process. This investigation shows a dual-MCDM approach that includes DEX--decision experts--and the EDAS method that are popularly employed to solve decision-making problems in academic and practical industries.