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Shehadeh propose a new framework that unifies different fairness measures into a general, parameterized class of convex fairness measures. They introduce a unified framework for optimization problems with a convex fairness measure objective or constraint, including unified reformulations and solution methods. Additionally, they establish mechanisms for quantifying the impact of employing different convex fairness measures on the optimal solutions to the resulting fairness-promoting optimization problem. Numerical experiments, including applications to resource allocation and facility location, demonstrate the computational efficiency of the unified framework over traditional ones.<\/jats:p>","DOI":"10.1287\/opre.2023.0301","type":"journal-article","created":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T16:23:48Z","timestamp":1752078228000},"page":"1087-1103","source":"Crossref","is-referenced-by-count":1,"title":["A Unified Framework for Analyzing and Optimizing a Class of Convex Fairness Measures"],"prefix":"10.1287","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0965-118X","authenticated-orcid":false,"given":"Man Yiu","family":"Tsang","sequence":"first","affiliation":[{"name":"Department of Industrial, Manufacturing, and Systems Engineering, Texas Tech University, Lubbock, Texas 79409"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7842-0951","authenticated-orcid":false,"given":"Karmel S.","family":"Shehadeh","sequence":"additional","affiliation":[{"name":"Daniel J. 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