Pareto-optimal peer evaluation in context-dependent DEA: Pareto-optimal peer

• Published in: Operational research 2025, Vol.25 (1)
• Main Authors: Ghandi, Fatemeh, Davtalab-Olyaie, Mostafa, Asgharian, Masoud, Begen, Mehmet A., Saadatmandi, Abbas
• Format: Article
• Language: English
• Subjects: Business and Management; Computational Intelligence; Management Science; Operations Research; Operations Research/Decision Theory; Original Paper
• ISSN: 1109-2858; 1866-1505
• DOI: 10.1007/s12351-024-00873-2

Context-dependent data envelopment analysis based on cross-efficiency evaluation has been proposed to present more meaningful measures of attractiveness and progress scores, cross-attractiveness, and cross-progress scores, by considering the distance between the decision-making unit (DMU) under evaluation and the entire evaluation context overall. Using an illustrative example, we show that the state of the art existing method does not guarantee to produce a non-dominated cross-attractiveness (cross-progress) scores vector. This raises concerns about the reliability and universal acceptance of the derived scores. Thus, we investigate the Pareto optimality of these peer evaluation scores and introduce a concept of optimality in line with the dominance notion. We subsequently propose a multi-objective model designed to produce all non-dominated cross-attractiveness (cross-progress) score vectors. We introduce two perspectives – a common base and an individualized approach. The common base method determines the same vector of weights for all units in the evaluation context to produce non-dominated scores. In contrast, the individualized based approach empowers each DMU within the evaluation context to assess the attractiveness (progress) score of DMUs under evaluation at a specific level based on their own distinct criteria. This methodology is aligned with the inherent desire of the DMUs within the evaluation context to have the most substantial impact on the evaluation of cross-attractiveness (cross-progress) for the DMUs under the assessment at a specific level. We illustrate our proposed methods with a real-world examples to yield non-dominated cross-attractiveness (cross-progress) scores.