Evaluating the Performance of Subjective Weighting Methods for Multi-Criteria Decision-Making using a novel Weights Similarity Coefficient
Ładowanie...
Data
2023
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Elsevier
Abstrakt
In every decision-making problem which involves two or more criteria, there is to identify the relative importance of those criteria in order to make a proper decision. Very often, a decision-makers employee, for this purpose, subjective weighting methods, such as Analytic Hierarchy Process (AHP) or RANking COMparison (RANCOM). However, there is no simple way to compare the quality of the weights identification using different methods. To address this issue, this paper proposes a simple but efficient Weights Similarity Coefficient, which allows us to evaluate the relative performance of different subjective weighting methods. Furthermore, we propose a framework that utilizes the proposed coefficient in order to provide more complete comparison results.To demonstrate the applicability and effectiveness of the proposed framework, a case study is performed to compare two popular weighting methods: AHP and RANCOM. The results of the comparison prove that proposed coefficient with the combination of the proposed framework is an efficient instrument for weighing methods comparison.
Opis
27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2023)
Słowa kluczowe
Manhattan distance, Criteria weights, SPOTIS, AHP, RANCOM, MCDA
Cytowanie
Shekhovtsov A., (2023). Evaluating the Performance of Subjective Weighting Methods for Multi-Criteria Decision-Making using a novel Weights Similarity Coefficient. Procedia Computer Science. 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2023). Vol. 225, pp. 4785 - 4794. doi 10.1016/j.procs.2023.10.478.