An integrated decision-making framework to assess the lean logistics performance of suppliers under complex uncertain environment
Engineering Applications of Artificial Intelligence, cilt.180, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 180
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.engappai.2026.115251
- Dergi Adı: Engineering Applications of Artificial Intelligence
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Grey evaluation based on distance from average solution, Interval-valued spherical fuzzy analytic hierarchy process, Lean logistics, Supplier performance, Vehicle routing
- İstanbul Ticaret Üniversitesi Adresli: Evet
Özet
Lean logistics has become a strategic priority for manufacturing firms seeking to enhance operational efficiency, reduce waste, and achieve sustainable supply chain performance. Nevertheless, many existing supplier evaluation approaches face difficulties in handling the uncertainty, hesitation, and imprecision inherent in expert judgments, particularly within lean logistics environments. Although artificial intelligence (AI) and fuzzy decision-making methods are increasingly applied in logistics, there remains a limited number of comprehensive hybrid frameworks capable of simultaneously addressing both ambiguity and grey uncertainty in supplier performance assessment. To fill this gap, this study proposes a hybrid four-stage decision-making framework that integrates the Interval-Valued Spherical Fuzzy Analytic Hierarchy Process (IVSF-AHP) and Grey Evaluation based on Distance from Average Solution (G-EDAS). The proposed methodology consists of: (1) identifying lean logistics evaluation criteria, (2) determining criteria weights using IVSF-AHP under uncertainty, (3) assessing supplier performance under grey uncertainty through G-EDAS, and (4) validating the robustness of the results via comparative and sensitivity analyses. The framework is implemented in a real-world case study involving 22 key suppliers of an international manufacturing company operating in the lighting industry. The findings reveal that “vehicle routing” and “inventory synchronization” are the most influential criteria in lean logistics performance evaluation. The supplier ranking results exhibit strong robustness when compared with alternative methods. The sensitivity analysis results also demonstrate high ranking stability, with strong Spearman rank correlation coefficients across scenarios, confirming the reliability of the framework. The proposed approach provides managers with a systematic, robust, and uncertainty-aware decision support tool for evaluating supplier performance in lean logistics contexts.