arXiv:2606.18679v1 Announce Type: cross Abstract: We study the problem of fair online resource allocation, motivated by applications such as refugee resettlement and airline scheduling, where agents arrive sequentially and must be assigned to facilities with limited capacities. We introduce a model that maximizes the overall welfare subject to resource constraints and a Lipschitz fairness requirement, which ensures that similar agents arriving in the same batch receive similar expected outcomes. We first analyze the offline problem, proving that the value of the optimal fair allocation is at l
The increasing complexity and scale of resource allocation problems in various sectors, coupled with advancements in AI and optimization techniques, drive the need for more sophisticated and fair online allocation models.
This research provides a foundational model for fair online resource allocation, directly impacting high-stakes applications such as refugee resettlement and airline scheduling by ensuring equitable and efficient distribution given real-time constraints.
The explicit inclusion of a Lipschitz fairness requirement in online resource allocation models introduces a quantifiable standard for equitable outcomes, moving beyond simple efficiency metrics.
- · Logistics and supply chain companies
- · Government agencies (e.g., immigration)
- · AI/Optimization software developers
- · Organizations managing sequential resource distribution
- · Systems relying on purely greedy or non-fair allocation algorithms
- · Entities whose operational models are disrupted by fairness constraints
More equitable and efficient allocation of limited resources in real-time scenarios becomes technically feasible.
Public trust and social stability may improve in systems where fairness in resource distribution is demonstrably applied.
The development of standardized 'fairness-as-a-service' modules could become a new sub-sector within AI-driven optimization.
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Read at arXiv cs.LG