An approximate dynamic programming method for the multi-period technician scheduling problem with experience-based service times and stochastic customers

In this paper, we study how an organization can recognize that individuals learn when assigning employees to tasks. By doing so, an organization can meet current demands and position the capabilities of their workforce for the yet unknown demands in future days. Specifically, we study a variant of the technician and task scheduling problem in which the tasks to be performed in the current day are known, but there is uncertainty regarding the tasks to be performed in subsequent days. To solve this problem, we present an Approximate Dynamic Programming-based approach that incorporates into daily assignment decisions estimates of the long-term benefits associated with experience accumulation. We benchmark this approach against an approach that only considers the impact of experience accumulation on just the next day's productivity and show that the ADP approach outperforms this one-step lookahead approach. Finally, based on the results from an extensive computational study we derive insights into how an organization can schedule their employees in a manner that enables meeting both near and long-term demands.