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

TL;DR

This paper introduces a reinforcement learning-based approach for dynamic technician routing that accounts for skill mismatches, rework risks, and service urgency to improve overall service quality in home repair services.

Contribution

It proposes a novel state-dependent policy parametrization using reinforcement learning to optimize technician routing with rework considerations in dynamic service environments.

Findings

Non-perfect technician-task matches can improve service quality.

State-dependent parametrization enhances routing efficiency.

Reinforcement learning effectively balances rework risk and urgency.

Abstract

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.