# Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework

arXiv: https://arxiv.org/abs/2409.01815 · HTML: https://mlsanity.com/paper/2409.01815/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions](https://mlsanity.com/paper/1912.03513/) (2019)
- [Workload Equity in Vehicle Routing Problems: A Survey and Analysis](https://mlsanity.com/paper/1605.08565/) (2016)
- [Dynamic Home Care Routing and Scheduling with Uncertain Number of Visits per Referral](https://mlsanity.com/paper/2403.11410/) (2024)

## Abstract (arXiv)

Home repair and installation services require technicians to visit customers and resolve tasks of different complexity. Technicians often have heterogeneous skills and working experiences. The geographical spread of customers makes achieving only perfect matches between technician skills and task requirements impractical. Additionally, technicians are regularly absent due to sickness. With non-perfect assignments regarding task requirement and technician skill, some tasks may remain unresolved and require a revisit and rework. Companies seek to minimize customer inconvenience due to delay. We model the problem as a sequential decision process where, over a number of service days, customers request service while heterogeneously skilled technicians are routed to serve customers in the system. Each day, our policy iteratively builds tours by adding "important" customers. The importance bases on analytical considerations and is measured by respecting routing efficiency, urgency of service, and risk of rework in an integrated fashion. We propose a state-dependent balance of these factors via reinforcement learning. A comprehensive study shows that taking a few non-perfect assignments can be quite beneficial for the overall service quality. We further demonstrate the value provided by a state-dependent parametrization.