Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center

• 成果类型: Article
• 署名作者: Mandelbaum, Avishai; Momcilovic, Petar; Trichakis, Nikolaos; Kadish, Sarah; Leib, Ryan; Bunnell, Craig A.
• 署名单位: Technion Israel Institute of Technology; Texas A&M University System; Texas A&M University College Station; Massachusetts Institute of Technology (MIT); Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2018.3218
• 发表日期: 2020
• 页码: 243-270
• 关键词: Appointment Scheduling; appointment book; data-driven decision making; scheduling under uncertainty

摘要

Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means-and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single-servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center's infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%-40% consistently, under a wide range of experimental setups.