Changing criteria weights to achieve fair VIKOR ranking: a postprocessing reranking approach

Changing criteria weights to achieve fair VIKOR ranking: a postprocessing reranking approach

연구 분야: Verification

학회: Autonomous Agents and Multi-Agent Systems

Ranking is a prerequisite for making decisions, and therefore it is a very responsible and frequently applied activity. This study considers fairness issues in a multi-criteria decision-making (MCDM) method called VIKOR (in Serbian language—VIšekriterijumska optimizacija i KOmpromisno Rešenje, which means Multiple Criteria Optimization and Compromise Solution). The method is specific because of its original property to search for the first-ranked compromise solutions based on the parameter v. The VIKOR method was modified in this paper to rank all the alternatives and find compromise solutions for each rank. Then, the obtained ranks were used to satisfy fairness constraints (i.e., the desired level of disparate impact) by criteria weights optimization. We built three types of mathematical models depending on decision makers’ (DMs’) preferences regarding the definition of the compromise parameter v. Metaheuristic optimization algorithms were explored in order to minimize the differences in VIKOR ranking prior to and after optimization. The proposed postprocessing reranking approach ensures fair ranking (i.e., the ranking without discrimination). The conducted experiments involve three real-life datasets of different sizes, well-known in the literature. The comparisons of the results with popular fair ranking algorithms include a comparative examination of several rank-based metrics intended to measure accuracy and fairness that indicate a high-quality competence of the suggested approach. The most significant contributions include developing automated and adaptive optimization procedures with the possibility of further adjustments following DMs’ preferences and matching fairness metrics with traditional MCDM goals in a comprehensive full VIKOR ranking.