Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection

Area of Science:

• Robotics
• Operations Research
• Decision Sciences

Background:

• Selecting industrial robots for specific applications is increasingly complex due to diverse market options.
• Existing multi-criteria decision-making (MCDM) methods can be computationally intensive or less reliable.

Purpose of the Study:

• To propose a novel hybrid MCDM methodology for industrial robot selection.
• To integrate the Best-Worst Method (BWM) with the Evaluation based on Distance from Average Solution (EDAS) method.

Main Methods:

• The study introduces a hybrid MCDM approach combining the Best-Worst Method for criteria weighting and the EDAS method for ranking alternatives.
• A practical example demonstrates the methodology's application in industrial robot selection.
• The proposed method's results are compared against established techniques like TOPSIS and VIKOR.

Main Results:

• The hybrid Best-Worst EDAS method provides a robust and reliable ranking of industrial robots.
• The methodology requires fewer calculations compared to other MCDM techniques.
• Sensitivity analysis confirms the stability and reliability of the proposed hybrid MCDM approach.

Conclusions:

• The integrated Best-Worst EDAS method is a valid and effective tool for industrial robot selection.
• This hybrid MCDM approach offers a more stable and reliable decision-making process.
• The study validates the robustness and efficiency of the proposed methodology.