Flexible Workers or Full-Time Employees? On Staffing Systems with a Blended Workforce

Abstract

There are different workforce models in the gig economy. While some on-demand service providers rely strictly on either traditional employees or independent contractors, others rely on a blended workforce which melds a layer of contingent workers with a core of permanent employees. In deciding on the number of right people to staff at the right time, managers must appropriately weigh the pertinent tradeoffs. In this paper, we study cost-minimizing staffing decisions in service systems where the manager must decide on how many flexible (contractors) and/or fixed (full-time) agents to staff in order to effectively balance operating costs, varying customer demand patterns, and supply-side uncertainty, while not compromising on the quality of service offered to customers. We consider a queueing-theoretic framework where the number of servers is random because part of the workforce is flexible. Since the staffing problem with a random number of servers is analytically intractable, we formulate two problem relaxations, based on fluid and stochastic-fluid formulations, and establish their accuracies in large systems by relying on an asymptotic, many-server, mode of analysis. We derive the optimal staffing policy and glean insights into the appropriateness of alternative workforce models in on-demand services. We also shed light on the distinction between demand-side (customer arrival rates) and supply-side (number of servers) uncertainties in queueing systems.