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Comparative Analysis of MADM Approaches: ELECTRE, TOPSIS and Multi-level LDM Methodology
There are multiple Multi-Attribute Decision Making methods elaborated for the past years. Those methods are targeted at aggregating assessments provided by the stakeholders of the problematic situation in order to choose the best alternative from the set of given ones. This paper considers ELECTRE, TOPSIS and the Multi-Level Linguistic Decision Making Methodology. In this paper we try to challenge first two methods by comparing it to latter method. One of the biggest challenges of modern Decision Making methods is flexibility to accept not only quantitative assessments but also hybrid ones: qualitative, interval, mixed etc. This brings the necessity for fuzzy computations. Decision Making methods analysis is performed through deep dive in constitution of each method and comparison across the set of elaborated criteria. Key criteria for the Decision Making methods assessment were identified and the comparative analysis on the base of two scenarios of different complexity was elaborated. The conclusion is made that ELECTRE and TOPSIS are well suited for small problems containing only several (less than a dozen) alternatives and criteria while being hardly generalized for the case of poorly structured problems (pollution, hunger, poverty). At the same time, Multi-Level Linguistic Decision Making Methodology excels at analyzing the problem from multiple aspects and considering any number of experts with arbitrary expertise that is beneficial in complex decision making cases.