Interpretive Structural Modelling Using Fuzzy Linguistic Information

In this work a fuzzy set based interpretive structural modelling (ISM) has been proposed. The proposed technique can be assumed to be an extension to the Boolean logic-based ISM. In the existing ISM technique, the influence of one criterion on other criteria is Boolean in nature i.e. a criterion will either be either influencing or not influencing other criteria. It is well established that ISM is very effectively used to recognize and draw relationship among the criteria associated with a decision-making problem. It is a well-known fact that the linguistic terms better represent human cognition as compared to binary numerals 0 and 1. Therefore, the use of linguistic terms in place of 0 and 1 to capture the influence of one criterion on another criterion will be more pragmatic and effective to address the uncertainty involved in human decision making. Different from the existing ISM, in the proposed fuzzy linguistic interpretive structural modelling (FLISM) the respective linguistic terms will be assigned appropriate triangular fuzzy numbers (TFNs). The proposed FLISM is the integration of the concept of ISM and fuzzy logic. Herein, the experts (decision makers) use linguistic terms perfect (P), very high (VH), high (H), medium (M) and low (L) to scale the impact a criterion leaves upon another criteria. The proposed FLISM has been explained with an example to recognize the relationship among the pivotal criteria pertaining to Business analytics methodology (BAM).