Integrating Fuzzy Rough Sets with LMAW and MABAC for Green Supplier Selection in Agribusiness

Abstract

The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green supplier, using the case study of the Semberka Company. The objective is to align the company with customer requirements and market trends. Expert decision making, grounded in linguistic values, was employed to facilitate the transformation of these values into fuzzy numbers and subsequently derive rough number boundaries. Ten economic-environmental criteria were identified, and six suppliers were evaluated against these criteria. The fuzzy rough LMAW (Logarithm Methodology of Additive Weights) method was employed to determine the criteria weights, with emphasis placed on the quality criterion. The fuzzy rough MABAC (Multi-Attributive Border Approximation Area Comparison) method was then utilized to rank the suppliers and identify the top performer. The validity of the results was established through validation techniques and sensitivity analysis. This research contributes a novel approach to green supplier selection, employing the powerful tool of fuzzy rough sets. The flexible nature of this approach suggests its potential application in future investigations. The limitation of this study is more complicated calculations for the decision maker. However, this approach is adapted to human thinking and minimizes ambiguity and uncertainty in decision making, and in future research, it is necessary to combine this approach with other methods of multi-criteria analysis.

Selection of a Green Contractor for the Implementation of a Solar Power Plant Project

“…This was accomplished by applying the defined membership function (Table 1). In this paper, the corrected membership function based on the paper by Pamučar et al is used [9], as well as that by Puška et al [17]. The goal of this membership function is to give the lowest priority to the lowest score for the criteria and the highest priority to the highest score for the alternatives.…”

“…This study investigates the application of the fuzzy-rough (initialism: FR) approach in the process of selecting green construction contractors to promote sustainability in the production of food and beverages through the installation of a solar power plant. The FR approach solves the problems of uncertainty and ambiguity in the data used [17], achieving greater robustness during decision making. The combination of fuzzy logic, which deals with vagueness in data, and the rough set theory, which treats uncertainty, provides a powerful tool for analysis and decision making in the context of selecting an eco-friendly contractor.…”

Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector

“…To check the reliability of the result obtained by using the newly proposed ERUNS method, we compare the ranking outcome with other MCDM models such as MABAC, CRADIS, TOPSIS, COPRAS, CoCoSo and SAW. The comparative analysis of ranking has been generally used in the past studies on MCDM models for ascertaining the reliability (for example, [66][67][68][69][70]). Table 11 shows the result of Spearman's Rank Correlation (SRC) test among the various MCDM models which indicate that the result obtained by using ERUNS method is comparable and maintains statistically significant correlation with others.…”