Decision-analytics-based green performance evaluation in the railway transportation industry – An integrated hesitant fuzzy approach

Abstract

Green performance evaluation at the industry level is of great importance in logistics literature. This evaluation can assist in more effectively allocating government resources to support companies with superior environmental performance. Many criteria used to evaluate the environmental performance of companies involve uncertainty and ambiguity inherent in expert judgments. To address this issue, the present research aims to develop a new hybrid method for group decision-making in a hesitant fuzzy environment. The proposed method investigates companies’ green performance in Iran’s passenger rail transport industry. For this purpose, by combining the field experts’ knowledge and literature review, the framework of the decision-making problem is determined. The paper’s main contribution is to extend a hybrid MADM technique into interval type-2 hesitant fuzzy (IT2HF) environment. In this hybrid approach, the IT2HF SWARA (Stepwise Weight Assessment Ratio Analysis) is utilized to determine the weight of criteria, and IT2HF MARCOS (Measurement of Alternatives and Ranking according to the Compromise Solution) is applied to evaluate the alternatives. Moreover, the proposed approach provides a systematic way to aggregate the opinions of a group of experts through the use of hesitant fuzzy sets. At the same time, each of these experts can explain their opinions in hesitant terms. The data used in this research is obtained from a group of qualified field experts. According to their opinions, the performance of eight major companies that own more than 80% of Iran’s private railway fleet has been evaluated. To validate the results, sensitivity and comparative analysis are carried out. The results indicate the stability of the ranking achieved with the proposed approach.