# Enhancing Efficiency and Cost-Effectiveness: A Groundbreaking Bi-Algorithm MCDM Approach +
## 1 Introduction
Each is supported by a framework allowing for detailed investigations and tailored outcomes based on specific contexts. It is important to acknowledge that some methodologies are explicitly designed for problem domains and may not be applicable or transferable to situations. Therefore, choosing a decision-making methodology requires the consideration of various terms and factors, as each can significantly influence the effectiveness of the decision-making process. We utilized techniques such as AHP, COPRAS [10], TOPSIS [13], VIKOR [14; 15], ELECTRE [5], DEMATEL [16], ANP [17], WASPAS [18], Entropy [3; 8; 18; 19], DEA [20] assessment and evaluation tools [12; 21; 22] and others to scan the paper database for research papers related to DM methods. The field of processes and machinery selection has always been a topic of interest in multicriteria decision making approaches. Our current work introduces a bifurcated method that cleverly combines the DEX (decision experts) and EDAS (evaluation based on distance from average solution) techniques. This innovative combination aims to provide a solution to the challenges involved in selecting machines for fabrication enterprises. The DEX technique stands out for its focus on involving domain experts. It is a strategy where expert insights come together to calculate weights and compare criteria. By integrating knowledge and experiential expertise, the DEX technique enhances the reliability and robustness of decision making thanks to the involvement of decision experts in the deliberation process. Although not new, we chose the EDAS approach for our study due to its applicability and advantages over other MCDM methods, especially in cost estimation within the domain. Recognized for being user-friendly, effective and efficient in facilitating decisions, we seamlessly incorporate the EDAS method into our framework. This approach provides an accessible solution to selecting machines for fabrication establishments. Our analysis focuses on a cutting-edge approach, explicitly emphasizing the fusion of the DEX technique with the EDAS approach. This fusion adds a dimension to the existing literature on machine selection and allows for a thorough evaluation of welding machines using qualitative and quantitative parameters. Unlike studies comparing existing paradigms, our study provides an evaluative framework. This framework considers capacity, pricing considerations, welding speed and precise workpiece thickness. Although these selection criteria may seem straightforward, they are part of a methodology that effectively combines quantitative elements to address the challenges in selecting industrial machinery. The selection of industrial welding machines (welders) that combine DEX and EDAS [7; 11; 13; 14; 22; 23; 24; 25; 26] has been suggested in this study. The purpose of this study is to present an MCDM methodology for industrial machines selection that is straightforward, dependable and resilient while requiring a reduced number of calculations. No research has ever combined the DEX [8; 19; 27; 28] and EDAS approaches to solve the machines selection challenge. There are three advantages to using the DEX method for calculating weights; (i) it consistently yields results, (ii) it requires bilateral comparisons compared to other MCDM methods and (iii) it simplifies the process for decision makers by facilitating the selection of the best and worst criteria and comparing them with others. The EDAS method was selected for evaluating the machines because it is an integrated approach that finds applications in various fields while being computationally more efficient than other MCDM methods like the CODAS, VIKOR and MOORA techniques that were analyzed, and their ranking outcomes were employed. A sensitivity analysis was conducted on these criteria. The results indicate that alternative options are susceptible to weight assignments and play a role in the selection process (refer to Table 1).