An Extended TOPSIS Method for the Multiple Attribute Decision Making Problems Based on double hierarchy linguistic soft sets (DHLSSs)
DOI:
https://doi.org/10.21015/vtm.v13i2.2161Abstract
In this paper, classical soft set theory is extended to develop Double Hierarchy Linguistic Soft Sets (DHLSSs) for addressing multi-attribute decision-making problems. DHLSSs provide an effective framework for handling qualitative information expressed through double hierarchy linguistic terms. Since the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a widely accepted method for multi-criteria decision analysis, this study integrates TOPSIS with DHLSSs to propose an extended decision-making approach. The proposed method is designed to handle situations in which attribute weights and attribute values are unknown and represented in the form of double hierarchy linguistic term elements. Background concepts related to DHLTSs, Hamacher t-norms and t-conorms, as well as fuzzy sets, rough sets, soft sets, and fuzzy soft sets are briefly reviewed. Attribute weights are determined using an entropy-based method, and an improved TOPSIS algorithm is employed to rank alternatives. An illustrative example is presented to demonstrate the feasibility and effectiveness of the proposed approach.
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