Fair and diverse allocation of scarce resources

https://doi.org/10.1016/j.seps.2021.101193Get rights and content


We developed a fair-diverse scarce resource allocation optimization framework.

We considered a trade-off between Geographical Diversity and Social group Fairness.

We proposed a tuning approach to find an optimum range for the trade-off parameter.

We evaluated the performance of our proposed approach using real COVID-19 datasets.

We demonstrated the allocation results for three major segregated cities in the U.S.


We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and create a synergistic intervention strategy that gives vulnerable populations higher priority in medical resource distribution. This prevention-centered strategy seeks a balance between geographical coverage and social group fairness. The proposed principle can be applied to other scarce resources and social benefits allocation.



Dr. Hadis Anahideh is a Research Assistant Professor of the Mechanical and Industrial Engineering Department at University of Illinois at Chicago. She received her Ph.D. degree in Industrial Engineering from the University of Texas at Arlington. She holds a Master's degree in IE and a Bachelor's Degree in Applied Math. Dr. Anahideh's research objectives center around Sequential Optimization, Active Learning, Statistical Learning, and Algorithmic Fairness. She primarily seeks to develop innovative learning and optimization methodologies, which have potential utility for multiple fields within the engineering operations and design, and social systems.

Dr. Lulu Kang is an Associate Professor of the Department of Applied Math at Illinois Institute of Technology. She holds an M.S. in Operations Research and a Ph.D. in Industrial Engineering from Georgia Institute of Technology. Dr. Kang's research focus is data science. Specifically, her research areas include statistical learning, uncertainty quantification, statistical design and analysis of experiments, Bayesian computational statistics, optimization and their application in complex systems in manufacturing, energy, and other engineering fields. Dr. Kang serves as the associate editor for journals SIAM/ASA Journal on Uncertainty Quantification and Technometrics.

Nazanin Nezami is a Ph.D. student in the industrial engineering and operations research program at the University of Illinois at Chicago. She received her B.S. degree in industrial engineering from Sharif University of Technology in 2018. Subsequently, she obtained an M.S. degree from the Industrial and Systems Engineering Department at the University of Minnesota, Twin Cities. She is a research assistant at Optimal Learning and Exploration (OPLEX) Lab under the supervision of Dr. Anahideh. Her main research interests are in data-driven decision making and optimal learning.

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