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.

翻译：现实决策问题中，确定因素对决策属性的影响是首要任务之一。为最大化影响决策属性，在系统中建立因素间的合理层次结构并确定其重要性值变得至关重要。解释结构模型（ISM）是一种广泛使用的层次构建方法，基于专家意见挖掘因素间的相互影响。本文探讨了传统ISM方法在因素间存在密集关联关系的系统中的主要缺陷，我们将此类系统称为"密集系统"。我们提出一种新型迭代层次构建技术——"迭代层次与排序方法"（IHRP），该方法在密集系统中表现优异。为处理专家意见的模糊性，本研究采用直觉模糊语言学。本文提出基于因素在系统中的层次位置进行两阶段相对重要性计算，并据此对因素排序。我们通过收集印度高中行政管理因素的新型数据（通过调研该领域专家获取），开展了学生成绩评估案例研究。将所提方法与常规ISM方法的因素排序结果，与TOPSIS、VIKOR等标准排序方法进行相关性比较分析。结果表明，所提IHRP框架的相关性达85-95%，而常规ISM方法仅为50-60%。这证明了所提方法在确定层次结构（特别是在密集系统中）方面优于传统方法的有效性。