Multi-Criteria Decision-Making for Industrial Robot Selection
Summary
Selecting an industrial robot requires balancing multiple conflicting objectives such as payload, reach, precision, cycle time, energy consumption and total cost of ownership. Multi-Criteria Decision-Making (MCDM) frameworks provide structured pathways to formalise these trade-offs, combining quantitative performance data with expert judgements. Traditional approaches often treat weighting and ranking separately, but recent trends embrace hybrid methods that integrate objective and subjective weighting techniques with distance- or compromise-based ranking algorithms. Advances in MCDM now include the incorporation of fuzzy and cloud models to capture uncertainty, group decision-making protocols for stakeholder alignment, and sensitivity analysis to ensure robustness under varying assumptions. Such developments are critical to support the global transition to smart manufacturing and Industry 4.0, where rapid deployment of collaborative and specialised robots must be underpinned by transparent, reproducible decision processes.
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Multi-Criteria Decision-Making for Industrial Robot Selection publication trend
The graph below shows the total number of articles in multi-criteria decision-making for industrial robot selection across all publications each year (not limited to Nature Index journals).
Technical terms
Multi-Criteria Decision-Making (MCDM): A class of techniques for evaluating and ranking alternatives based on multiple, often conflicting, performance criteria.
AHP (Analytical Hierarchy Process): A structured pairwise comparison method that derives numerical weights to reflect the relative importance of decision criteria.
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution): A ranking procedure that identifies solutions closest to an ideal best and furthest from an ideal worst.
Best–Worst Method (BWM): A weighting approach in which decision-makers identify the most and least important criteria and compare all others against these extremes.
EDAS (Evaluation Based on Distance from Average Solution): A method that ranks alternatives by computing their distances from the average performance across all criteria.
References
- Cobot selection using hybrid AHP-TOPSIS based multi-criteria decision making technique for fuel filter assembly process. Heliyon (2024).
- Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. PLOS ONE (2021).
- Application of MEREC in Multi-Criteria Selection of Optimal Spray-Painting Robot. Processes (2022).
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