arXiv:2607.02196v1 Announce Type: new Abstract: We study online resource allocation when both rewards and consumption sizes may be continuously distributed. Requests arrive sequentially and must be accepted or rejected irrevocably under fixed resource capacities. Each request belongs to one of finitely many observable types; conditional on an observable request type, both the reward and the scalar size are random, and the realized size scales a fixed type-specific resource-consumption vector. The model allows the deterministic fluid relaxation to be degenerate. We show that additive regret is
The continuous distribution of resource consumption and rewards is a more realistic model for many real-world online allocation problems, making this research timely for practical applications.
This research provides a theoretical framework for online resource allocation problems that better reflects real-world complexities, improving decision-making for systems with continuous and uncertain resource demands.
The understanding of additive regret under degeneracy for continuous random consumption in resource allocation is advanced, allowing for more robust algorithm design in dynamic environments.
- · AI/ML researchers
- · Logistics and supply chain companies
- · Cloud computing providers
- · E-commerce platforms
- · Systems relying on oversimplified allocation models
Improved performance and efficiency of online resource allocation algorithms in systems with continuous and uncertain demands.
Optimized utilization of limited resources in various sectors, leading to cost savings and better service delivery.
Potential for new autonomous resource management systems that dynamically adapt to real-time, complex demand patterns.
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Read at arXiv cs.LG