Summary of Learning State-dependent Policy Parametrizations For Dynamic Technician Routing with Rework, by Jonas Stein et al.
Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework
by Jonas Stein, Florentin D Hildebrandt, Barrett W Thomas, Marlin W Ulmer
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles the problem of scheduling home repair and installation services, where technicians with varying skills visit customers to resolve tasks of different complexity. The authors model this as a sequential decision process, where they aim to minimize customer inconvenience due to delay. They propose a state-dependent policy that balances factors like routing efficiency, urgency of service, and risk of rework via reinforcement learning. The results show that taking non-perfect assignments can be beneficial for overall service quality, and a state-dependent parametrization provides additional value. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to schedule home repair services so customers don’t have to wait too long. It’s like trying to match technicians with the right tasks, but technicians are all different and might not always be available. The authors created a system that decides which technician to send to each customer based on things like how important the task is and how likely it is to take a few tries to get it done. They found that sometimes taking a less-than-perfect match can actually make things better overall. |
Keywords
* Artificial intelligence * Reinforcement learning
