Abstract
ABSTRACTChemotherapy infusion treatments for cancer have significant and unpredictable variability in duration. This variability can have negative impact on operations – both patient wait time and staff overtime – if not managed well. From an appointment scheduling optimization perspective, this problem has a unique structure because a single server (a nurse) attends to multiple customers (patients) at one time. Based on our observations at the University of Michigan Comprehensive Cancer Center (UMCCC) and collaborations with clinicians there, we present a two-stage stochastic integer program for designing patient appointment schedules under uncertainty in treatment times. The objective is to minimize a trade-off between expected patient wait times and expected total time required to treat patients. We show that solving this optimization problem exactly requires a prohibitive computational time, so we develop a heuristic algorithm to find approximate solutions. We also present an approach to compute lower bounds on the optimal objective value that we use to analyze the performance of our algorithm. Computational experiments based on real-world data are presented and used to draw managerial insights.
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