Motivated by the work with an industry partner, this paper proposes and examines novel data-driven approaches to solve a certain type of capacity-sizing problem, which we term the multi-shift staffing problem (MSSP). In our MSSP, a company has to staff multiple shifts for each workday in the presence of uncertain arrival rates that vary throughout the day and patient “customers” that do not abandon the queue while waiting for a service, but who must be served by some pre-defined time. Drawing on established methods in both capacity management and prescriptive analytics, we propose to use fluid and stationary approximations of the demand arrival process to apply tailored prescriptive analytics approaches to determine staffing levels for multiple interrelated shifts. The prescriptive analytics approaches rely on machine learning techniques that incorporate a detailed representation of the non-stationary structure of arrivals and leverage extensive auxiliary data. In particular, we adapt established prescriptive analytics approaches—weighted sample average approximation and kernelized empirical risk minimization—and propose a new optimization prediction approach to solving the multi-shift staffing problem. Using a case study that is based on extensive data from our project partner, the maintenance service provider, we demonstrate the applicability of these approaches, highlight their benefits over traditional “estimate then optimize” approaches, and shed light on their structural properties and performance drivers.

受与行业合作伙伴合作的启发，本文提出并研究了新的数据驱动方法来解决某种类型的容量规模问题，我们称之为多班次人员配备问题 (MSSP)。在我们的 MSSP 中，一家公司必须在每个工作日安排多个班次，因为存在全天变化的不确定到达率以及耐心的“客户”，他们在等待服务时不会放弃排队，但必须由一些人服务预定义的时间。借鉴能力管理和规范分析中的既定方法，我们建议使用需求到达过程的流动和静止近似来应用定制的规范分析方法来确定多个相互关联的班次的人员配备水平。规范性分析方法依赖于机器学习技术，这些技术结合了到达的非平稳结构的详细表示并利用了广泛的辅助数据。特别是，我们采用了已建立的规范分析方法——加权样本平均近似和核化经验风险最小化——并提出了一种新的解决多班次人员配备问题的优化预测方法。使用基于来自我们的项目合作伙伴、维护服务提供商的大量数据的案例研究，我们展示了这些方法的适用性，突出了它们相对于传统“估计然后优化”方法的优势，并阐明了它们的结构特性和性能驱动因素.