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The increasing adoption of Autonomous Mobile Robots (AMRs) necessitates a robust decision-making framework that effectively balances subjective expert evaluations with objective performance metrics. Existing Multi-Criteria Decision-Making (MCDM) methods often struggle to handle cognitive uncertainty and imprecise linguistic assessments. To address this gap, this study proposes an integrated MCDM framework combining Triangular Fuzzy TOPSIS (TF-TOPSIS) for subjective factor evaluation and Picture Fuzzy Combined Compromise Solution (PF-CoCoSo) for objective factor assessment. TF-TOPSIS employs an 8-point triangular fuzzy scale to quantify expert preferences, ensuring stable and interpretable rankings, while PF-CoCoSo leverages Picture Fuzzy Sets to capture positive, neutral, and negative membership grades, enhancing decision reliability. A Mobile Robot Selection Index (RSI) integrates both subjective and objective rankings for comprehensive prioritization. A case study on nine AMRs shows AMR R4 leading in RSI and subjective ranking, while AMR R5 ranks highest in objective evaluation but places fourth in RSI, revealing trade-offs in decision-making. Sensitivity analysis confirms ranking stability across normalization techniques, RSI parameter variations, and criteria weight changes, with Pearson correlation values around 0.9, ensuring robustness. Comparative analysis further validates the approach, with TF-TOPSIS achieving a 0.98 correlation with RSI rankings and PF-CoCoSo showing moderate alignment, demonstrating its effectiveness in balancing deterministic and uncertain factors.
Tarafdar et al. (Tue,) studied this question.