Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework
This work addresses scheduling and routing inefficiencies for home repair companies dealing with heterogeneous technician skills and customer demands, but it is incremental as it builds on existing routing and reinforcement learning methods.
The paper tackles the dynamic technician routing problem with rework by modeling it as a sequential decision process and proposing a reinforcement learning-based policy that balances routing efficiency, service urgency, and rework risk. The results show that allowing some non-perfect assignments improves overall service quality and that state-dependent parametrization adds value.
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.