Abstract
Efficient resource allocation is crucial in many domains, particularly in senior care, where assigning resources to older adults must consider uncertainties associated with vulnerable populations. In collaboration with Senior Health Facilities (SHFs) and domain experts, this paper presents iFair, a novel framework designed to assist decision-makers in equitably allocating scarce resources to older adults. iFair was prototyped in the context of ongoing work on a data exchange platform, CAREDEX, used for enhancing older adults' resilience during disasters. A key novelty of iFair focuses on aligning resident preferences with resources in urgent situations, expediting care, and enhancing task efficiency. We integrate static and dynamic environmental data, including facility layouts and sensor data, with detailed resident profiles to cater to the individual needs and preferences of residents. While our framework primarily focuses on allocation within facilities, it also extends to a regional scale to support the planning and transfer of seniors to mutual aid facilities. Our experiments adapt data from a real SHF to emulate resource allocation in an emergency fire evacuation setting and highlight the delicate balance that decision-makers can achieve between efficiency and fairness.
| Original language | English |
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| Title of host publication | Proceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 22-30 |
| Number of pages | 9 |
| ISBN (Electronic) | 9798350349948 |
| DOIs | |
| State | Published - 2024 |
| Event | 10th IEEE International Conference on Smart Computing, SMARTCOMP 2024 - Osaka, Japan Duration: Jun 29 2024 → Jul 2 2024 |
Publication series
| Name | Proceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024 |
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Conference
| Conference | 10th IEEE International Conference on Smart Computing, SMARTCOMP 2024 |
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| Country/Territory | Japan |
| City | Osaka |
| Period | 06/29/24 → 07/2/24 |
Funding
The authors thank Andrew Chio, Fangqi Liu, Prof. Shangping Ren, and members of the Distributed Systems Middleware (DSM) Group at UCI for their valuable input to this work. This research is also supported by DARPA under Agreement No. FA8750-16-2-0021, by the U.S. NSF Grants No. 2044107, 2133391, 2008993, 2245372, and the ECR Fellowship. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Government, DARPA, or the National Science Foundation.
Keywords
- decision-making
- fairness
- resource allocation
- senior health care