Summary of Learning Payoffs While Routing in Skill-based Queues, by Sanne Van Kempen et al.
Learning payoffs while routing in skill-based queues
by Sanne van Kempen, Jaron Sanders, Fiona Sloothaak, Maarten G. Wolf
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a machine learning algorithm that optimizes the routing of customers to servers in queueing systems to maximize customer-server matches. The algorithm adaptively learns payoff parameters while maximizing total payoff, achieving polylogarithmic regret and asymptotic optimality up to logarithmic terms. The approach leverages basic feasible solutions from a static linear program as the action space. Numerical experiments demonstrate the algorithm’s performance, including an example with time-varying parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to match customers with servers in a system that handles requests one by one. Right now, we don’t know what makes each customer unique or what skills each server has. The goal is to figure out how to pair them up for maximum benefit. Researchers created an algorithm that gets better and better at making these matches as it goes along, while also being efficient with its resources. This algorithm could be used in many different situations where requests need to be handled one by one. |
Keywords
» Artificial intelligence » Machine learning
