Prescriptive Analytics for a Multi-Shift Staffing Problem

Motivated by the work with a leading maintenance service provider in the aviation<br>industry, this paper examines novel data-driven approaches to solving a certain<br>type of capacity-sizing problem—the multi-shift staffing problem—with uncertain,<br>time varying arrival rates and patient "customers" that do not abandon the queue<br>while waiting for a service, but who must be served by a pre-defined time. Drawing<br>on established methods in both capacity management and prescriptive analytics, we<br>propose to use fluid and stationary approximations to apply tailored prescriptive<br>analytics approaches to determine staffing levels for multiple interrelated shifts. The<br>prescriptive analytics approaches rely on machine learning techniques that incorporate<br>a detailed representation of the non-stationary structure of arrivals and leverage<br>extensive auxiliary data that may be predictive of demand. In particular, we adapt<br>established prescriptive analytics approaches—weighted sample average approximation<br>and kernelized empirical risk minimization—and propose a new optimization<br>prediction approach to solving the multi-shift staffing problem. Using a case study<br>that is based on extensive data from our project partner, the maintenance service<br>provider, we demonstrate the applicability of these approaches, highlight their benefits<br>over traditional "estimate then optimize" approaches, and shed light on their<br>structural properties and performance drivers. In the context of our real-world application,<br>we derive a clear recommendation for the choice of method with which to<br>solve the multi-shift staffing problem.