Maintaining fairness in stochastic chemotherapy scheduling

Abstract

Chemotherapy scheduling is hard to manage under uncertainty in infusion durations, and focusing on expected performance measure values may lead to unfavorable outcomes for some patients. We aim to design daily patient appointment schedules considering fairness regarding patient waiting times. We propose using a metric that encourages fairness and efficiency in waiting time allocations. To optimize this metric, we formulate a two-stage stochastic mixed-integer nonlinear programming model. We employ a binary search algorithm to identify an optimal schedule, and then propose a modified binary search algorithm (MBSA) to enhance computational capability. Moreover, to address stochastic feasibility problems at each MBSA iteration, we introduce a novel reduce-and-augment algorithm that utilizes scenario set reduction and augmentation methods. We use real data from a major oncology hospital to show the efficacy of MBSA. We compare the schedules identified by MBSA with both the baseline schedules from the oncology hospital and those generated by commonly employed scheduling heuristics. We also compare our metric with a well-known inequity metric (the Gini coefficient) and a Rawlsian-type welfare function. Finally, we highlight the significance of considering uncertainty in infusion durations to maintain fairness while creating appointment schedules.