A New Interval Type-2 Fuzzy Decision Method with an Extended Relative Preference Relation and Entropy to Project Critical Path Selection

Considering uncertainty in multi-criteria decision making (MCDM) is an important issue in today's business and management problems. In this article, to use advantages of IT2FSs, a novel interval type-2 fuzzy multi-criteria decision method is presented with an extended entropy and relative preference relation. To tackle vagueness and uncertainty of real-world problems, the IT2FSs are used and applied to a modified MCDM method. Furthermore, an entropy method is developed under an IT2F environment and for obtaining the final weight of each criterion, a relative preference relation is hybridized with an entropy method. Also, the weight of each decision maker (DM) is calculated by a new IT2F-order preference method by means of the relative closeness. Finally, an existing example about the project critical path selection by considering effective criteria, such as time, cost, quality and safety, is adopted from the literature and solved to indicate the capability of introduced method.

Soft Computing Based on an Interval Type-2 Fuzzy Decision Model for Project-Critical Path Selection Problem

This article describes how project managers are faced with the conflicting criteria to make their decisions. In many real-world conditions, it may be difficult to get certain information about activities attributes, including time, cost, risk, and quality. In this case, interval type-2 fuzzy sets (IT2FSs) which consider more uncertainty than type-1 fuzzy sets (T1FSs) are used. In this article, a new group multi-criteria analysis model is expressed based on new compromise solution and relative preference relation (RPR) concept under IT2FSs environment. Also, a new version of the evaluation on distance from average solution (EDAS) method is introduced to specify the weight of each expert under IT2FSs. Furthermore, the RPR is more reasonable than the defuzzification approach. In fact, the RPR not only can provide preference degree between two fuzzy numbers but also can keep some information. Finally, an application from literature is adopted and solved to demonstrate the applicability of proposed method.