Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach
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Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach Mona Issabakhsh1 · Seokgi Lee1 · Hyojung Kang2 Received: 10 July 2019 / Accepted: 6 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time. Keywords Operations research · Robust optimization · Adaptive large neighborhood search · Infusion appointment scheduling · Infusion time uncertainty
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Frameworks based on the insights from real data of a cancer center are introduced to schedule cancer infusion appointments and create optimal patient schedule The scheduling system is designed considering the inherent uncertainty of the cancer infusion process; the produced schedule stays valid and feasible even if the infusion time of some patients take longer than the scheduled appointments A mathematical model is designed that can handle infusion time increases of patients beyond the scheduled
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infusion time, and a new heuristic method is designed which can find appointments for a large number of patients in a short time, and produces schedules that will not be disrupted if the infusion times of some patients increase beyond those scheduled The produced schedules by the proposed schemes can help improve both patient and staff satisfaction by accumulating delays caused by prolonged infusion time, and result in a more efficient utilization of the cancer center resources
1 Introduction Seokgi Lee
[email protected] 1
Department of Industr
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