Managing Supply in the On-Demand Economy: Flexible Workers, Full-Time Employees, or Both?

• 成果类型: Article
• 署名作者: Dong, Jing; Ibrahim, Rouba
• 署名单位: Columbia University; University of London; University College London
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2019.1916
• 发表日期: 2020
• 页码: 1238-1264
• 关键词: on-demand workforce; Sharing economy; random capacity; many-server queues

摘要

There are different workforce models in the gig economy. Although some ondemand 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 right 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. Because 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 we 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 alternativeworkforce 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.