A Hybrid Triangular Fuzzy MCDM Model for Evaluating and Selecting the Optimal Industrial Robot for Manufacturing Plants Based on Fuzzy AHP and Fuzzy ARAS - Tran, Ngoc Tien; Trinh, Van Long; Nguyen, Tien Sy; Tran, Viet Hoi (Engineering, Technology & Applied Science Research, 2025)
Selecting the optimal industrial robot is a crucial factor in enhancing production efficiency and reducing operational costs. However, conflicts among evaluation criteria make this process challenging, requiring a structured decision-making approach. The present study proposes a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Additive Ratio Assessment (FARAS). The model consists of six main steps: constructing the pairwise contribution matrix from experts regarding the relationships between criteria, developing the fuzzy pairwise comparison matrix for weights, determining the fuzzy weights of each criterion, obtaining expert evaluations of the alternatives, normalizing the data, and ranking the alternatives. Industrial robot selection must achieve a balance between performance and cost considerations. High-performance robots offer superior accuracy and reliability but come with high investment costs, whereas low-cost options may not meet technical requirements. The proposed approach utilizes fuzzy set theory to address the uncertainties inherent in expert evaluations. FAHP determines the criterion weights, whereas FARAS ranks robots based on aggregated performance scores. A case study involving ten industrial robots was conducted to validate the model’s effectiveness. The results indicate that Robot 1 is the optimal choice, whereas Robot 6 ranks the lowest. The findings of the study demonstrate that the hybrid fuzzy MCDM approach not only improves accuracy but also provides a comprehensive decision-support tool, enabling manufacturing organizations to select suitable robots based on scientifically grounded data.