An intuitionistic fuzzy multi-distance based evaluation for aggregated dynamic decision analysis (IF-DEVADA): Its application to waste disposal location selection

Abstract

Multi-criteria decision-making (MCDM) methods used in solving real-life problems are very useful tools as they allow the evaluation of many qualitative and quantitative factors simultaneously. An increasing number of new methods and approaches are being introduced into the literature to overcome different MCDM problems with many proportional and contradictory features. Since the assessments and judgments made for the addressed MCDM problems may vary depending on the conditions that arise in the future, most of the current static MCDM methods can lead to ineffective and wrong decisions. Therefore, there is a need to develop flexible decision models that will enable to deal with a dynamic decision system using current and future information in the literature. In addition, as the decision process brings with its uncertainties arising from incomplete information, the use of intuitionistic fuzzy sets (IFSs) in the decision process will provide a more accurate representation of data and better handle uncertainties that may arise in decision problems. In this study, the extension of the CRITIC method to IFSs is first developed, which takes into account the objective weights of the criteria in an uncertain environment for weighting the criteria. Then, it is intended to develop the extension of the DEVADA method to IFSs in order to create a dynamic decision system capable of dealing with uncertainties. In addition, a stronger multi-measurement system is proposed by considering Euclidean and cosine distances together. To better demonstrate the feasibility and efficiency of the method, the waste disposal location selection problem, where the evaluations are open to temporal changes, is discussed. A comprehensive sensitivity analysis is then performed to verify the stability and effectiveness of the method. Besides, a comparative analysis is presented with distance-based MCDM methods showing the superiority and advantages of the developed method.