A Hybrid Grey Decision Methodology in Social Sustainable Supplier Selection

Abstract

In the past decade, sustainable supply chain management has received much attention from practitioners and academics due to the heightened emphasis on environmental, economic, and social sustainability by customers, for-profit and non-profit institutions, community organizations, legislation, and government oversight. Evaluating and selecting a suitable supplier is considered a critical choice, crucial in supply chain management. Social sustainability in the supplier selection process is an important factor that has not received sufficient attention from academicians. Social and societal challenges are increasingly impacting supply chains. To tackle this challenge in the agricultural section of nations with emerging economies, this study proposed a new methodology using Grey FUCOM (Full Consistency Method) and Gray PROMTHEE (Preference Ranking Organization Method for Enrichment Evaluation) methods for evaluating the social sustainability of wheat and barley seed production companies. This study assists seed users in identifying the most significant supplier selection criteria and one of the most effective suppliers for ecological sustainability throughout the supply chain while maintaining market competitiveness. The results demonstrated a consistent and dependable rating behavior about the weight coefficients of the criteria. Improving the procedures used to evaluate wheat and barley seed suppliers results in a healthier society. So, the proposed model can efficiently evaluate a critical component of the food supply chain in the early stages.

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

“…Individual methods include AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), DEA (Data Envelopment Analysis), GRA (Grey Relation Analysis), ANN (Artificial Neural Network), GP (Genetic Programming), LP (Linear Programming), MOP (Multi-objective Programming), CBR (Case-based Reasoning), GA (Genetic Algorithm) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) (Chai et al, 2013;Zolghadr-Asli et al, 2021;Lipovetsky, 2023). Hybrid methods include hybrid AHP, hybrid ANP, and hybrid ANN (Nasseri et al, 2023;Sina et al, 2023). Hybrid fuzzy methods include FTOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution), FAHP (Fuzzy Analytic Hierarchy Process), FANP (Fuzzy Analytic Network Process), FQFD (Fuzzy Quality Function Deployment), FART (Fuzzy Adaptive Resonance Theory), FST (Fuzzy Sets Theory), LFPP (Logarithmic Fuzzy Preference Programming), and FMOP (Fuzzy Multi-objective Programming) (Hsu et al, 2010;Afrasiabi et al, 2022;Nguyen and Fayek, 2022).…”