Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources
This problem arises in peer reviewer assignment, skill-based call routing, rideshare matching, emergency dispatch, and crowdsourcing: Tasks arrive according to a geometric process and must be immediately assigned to servers that become temporarily busy, yielding rewards that depend on the task-server pair.
ProofPaper ↗
Key points
- We study online task assignment with reusable resources.
- We cast the problem as a contextual restless multi-armed bandit (CRMAB).
- We derive an occupancy-measure LP (OCC-LP) that upper-bounds the reward of any work-conserving policy.
- We introduce a scaling regime in which the number of copies of each server and the number of arrivals per step grow together.
Sources (1)
- [1]Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable ResourcesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:28 PM
This problem arises in peer reviewer assignment, skill-based call routing, rideshare matching, emergency dispatch, and crowdsourcing: Tasks arrive according to a geometric process and must be immediately assigned to servers that become temporarily busy, yielding rewards that depend on the task-server pair.
We study online task assignment with reusable resources.
Extractive summary: sentences quoted from the sources.