Iterative Hierarchy and Ranking Process (IHRP): A Novel Effective Hierarchy Method for Densely Connected Systems and Case Study in Student Performance Assessment

TL;DR

This paper introduces IHRP, a new hierarchy-building method for densely interconnected systems, validated through a student performance case study showing significantly improved correlation with standard ranking methods.

Contribution

The paper presents a novel iterative hierarchy and ranking process (IHRP) that effectively handles dense systems, improving upon conventional ISM by incorporating intuitionistic fuzzy linguistics.

Findings

IHRP achieves 85-95% correlation with standard outranking methods.

IHRP outperforms conventional ISM in dense systems.

Case study on student performance demonstrates practical applicability.

Abstract

In real-life decision-making problems, determining the influences of the factors on the decision attribute is one of the primary tasks. To affect the decision attribute most, finding a proper hierarchy among the factors and determining their importance values in the system becomes quite important. Interpretive structural modeling (ISM) is a widely used hierarchy-building method that mines factor inter-influences based on expert opinions. This paper discusses one of the main drawbacks of the conventional ISM method in systems where the factors are densely interrelated. We refer to such systems as "dense systems". We propose a novel iterative hierarchy-building technique, called 'Iterative Hierarchy and Ranking Process'(IHRP) which performs effectively in such dense systems. To take the vagueness of the expert opinions into account, intuitionistic fuzzy linguistics has been used in the research work. In this paper, we propose a two-stage calculation of the relative importance of the factors in the system based on their hierarchical positions and rank the factors accordingly. We have performed a case study on student performance assessment by taking up novel Indian high-school administrative factors' data collected by surveying the experts in this field. A comparative study has been conducted in terms of the correlation of the factor ranking achieved by the proposed method and conventional ISM method with that of standard outranking methods like TOPSIS, and VIKOR. Our proposed IHRP framework achieves an 85-95% correlation compared to a 50-60% correlation for the conventional ISM method. This proves the effectiveness of the proposed method in determining a better hierarchy than the conventional method, especially in dense systems.