Cobot selection using hybrid AHP-TOPSIS based multi-criteria decision making technique for fuel filter assembly...

Area of Science:

• Robotics and Automation
• Industrial Engineering
• Operations Research

Background:

• Selecting collaborative robots (cobots) for industrial processes is complex due to advanced features and cost considerations.
• Effective cobot implementation faces challenges in energy efficiency and overall expense.
• Sophistication of modern cobots increases the difficulty of optimal selection.

Purpose of the Study:

• To propose a hybrid Multi-Criteria Decision-Making (MCDM) technique for cobot selection.
• To integrate Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for cobot evaluation.
• To optimize cobot selection for industrial applications, focusing on cost and capability.

Main Methods:

• A hybrid MCDM technique combining AHP and TOPSIS was developed.
• Analytical Hierarchy Process (AHP) was used to determine criterion weights.
• Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank cobot alternatives.
• A case study involving a diesel fuel filter assembly process was utilized for validation.

Main Results:

• The proposed hybrid MCDM technique successfully selected the most suitable cobot.
• AHP effectively established prospective criterion weights for cobot selection.
• TOPSIS provided a clear ranking of cobot alternatives based on defined criteria.
• The method facilitated the selection of a cobot balancing capability and cost.

Conclusions:

• The hybrid AHP-TOPSIS MCDM technique offers an effective solution for cobot selection in industrial settings.
• This approach addresses the complexities of choosing cobots based on product design, manufacturing, and cost.
• The validated method aids in selecting the optimal cobot for specific tasks, ensuring cost-effectiveness and performance.