CURLIA: A Streamlined Aggregation Method That Elevates Ranking Accuracy in Complex MCDM Problems - Trung, Do Duc; Trang, Bui Thi Thu; Özçalici, Mehmet; Ersoy, Nazlı (Yugoslav Journal of Operations Research; Online first, 2025)
Multi-Criteria Decision-Making (MCDM) techniques frequently produce inconsistent rankings due to variations among methods, complicating the identification of the most accurate ranking of alternatives. Thus, effectively aggregating these distinct rankings becomes crucial. This research proposes the CURLI-AGGREGATOR (CURLIA) method, a novel aggregation technique developed as an extension of the existing Collaborative Unbiased Rank List integration (CURLI) method. While CURLI ranks alternatives directly based on multiple criteria, CURLIA aggregates rankings previously determined by different MCDM methods. The effectiveness of CURLIA was validated through three case studies, each differing in application domain, alternative count, and MCDM techniques employed. Additionally, an extensive simulation involving 1,000 randomized scenarios was performed to evaluate its robustness. Results from a statistical analysis demonstrated that CURLIA significantly outperformed the widely used COPELAND aggregation method in approximately 78.3% of these scenarios. Thus, the CURLIA method constitutes a reliable and robust aggregation tool, significantly enhancing decision-making accuracy across complex and diverse MCDM environments.