An integrated fuzzy sustainable supplier evaluation and selection framework for green supply chains in reverse logistics

Abstract

Green supply chain management considers the environmental effects of all activities related to the supply chain, from obtaining raw materials to the final delivery of finished goods. Selecting the right supplier is a critical decision in green supply chain management. We propose a fuzzy green supplier selection model for sustainable supply chains in reverse logistics. We define a novel hierarchical fuzzy best-worst method (HFBWM) to determine the importance weights of the green criteria and sub-criteria selected. The fuzzy extension of Shannon's entropy, a more complex evaluation method, is also used to determine the criteria weights, providing a reference comparison benchmark. Several hybrid models integrating both weighting techniques with fuzzy versions of complex proportional assessment (COPRAS), multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA), and the technique for order of preference by similarity to ideal solution (TOPSIS) are designed to rank the suppliers based on their ability to recycle in reverse logistics. We aggregate these methods' ranking results through a consensus ranking model and illustrate the capacity of relatively simple methods such as fuzzy COPRAS and fuzzy MOORA to provide robust rankings highly correlated with those delivered by more complex techniques such as fuzzy MULTIMOORA. We also find that the ranking results obtained by these hybrid models are more consistent when HFBWM determines the weights. A case study in the asphalt manufacturing industry is presented to demonstrate the proposed methods' applicability and efficacy.

A new approach to determine the reverse logistics-related issues of smart buildings focusing on sustainable architecture

“…Some similar research on RL has been published in recent years. For example, Tavana et al (2021) suggested a fuzzy green supplier selection approach for sustainable RL supply chains. They developed a unique hierarchical fuzzy best-worst method (HFBWM) to calculate the relevance weights of the specified green criterion and sub-criteria.…”

“…The smart city is a multifaceted phenomenon that includes a wide variety of interconnected areas such as transportation, education, healthcare, public security, infrastructure, logistics, information and communication technologies (ICT), and resource utilization (Gu et al, 2021;Richnák and Gubová, 2021;Tavana et al, 2021). These industries influence the daily life of city dwellers.…”

Leagile and sustainable supplier selection problem in the Industry 4.0 era: a case study of the medical devices using hybrid multi-criteria decision making tool

“…They considered the tube industry to show the efficiency of the applied method. Tavana et al (2021) introduced the hierarchical fuzzy BWM to compute indicators' weights in the sustainable SSP. The authors compared their method with several traditional methods to show its reliability and robustness.…”

Demand-driven NEV supplier selection: An integrated method based on ontology–QFD–CBR

“…3) In terms of the supplier selection decision, most of the existing studies retrieve supplier case information through keyword matching and attribute similarity calculation so as to select component suppliers, which may result in problems such as low retrieval efficiency and low retrieval accuracy due to the lack of consideration of the semantic relations among keywords or attributes (Zhao and Yu 2011). 4) In terms of supplier selection criteria and their weight determination, most of the existing studies use the cited frequency or questionnaires to determine selection criteria, failing to fully consider the logic and semantic relationship of selection criteria (for example, product quality and quality are synonymous), which easily affects the efficiency of the supplier selection decision (Tavana et al, 2021). In addition, the weights of selection criteria in the existing studies are mostly derived from the subjective preferences of decision-makers and fail to fully consider the demands of users or objective data information.…”