Maximum appreciative cross-efficiency in DEA: A new ranking method

• Suggests a cross-efficiency based ranking method in DEA.
• Uses individual appreciativeness to generate maximum appreciative cross-efficiency.
• Combines maximum cross-efficiency, preference voting, and OWA operator, to provide a full ranking.
• Illustrates an application of the proposed method in ranking 15 baseball players.

Ranking decision making units (DMUs) is one of the most important applications of data envelopment analysis (DEA). In this paper, we exploit the power of individual appreciativeness in developing a methodology that combines cross-evaluation, preference voting and ordered weighted averaging (OWA). We show that each stage of the proposed methodology enhances discrimination among DMUs while offering more flexibility to the decision process. Our approach is illustrated through an example involving 15 baseball players.