Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach

Mona Issabakhsh, Seokgi Lee () and Hyojung Kang
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Mona Issabakhsh: University of Miami
Seokgi Lee: University of Miami
Hyojung Kang: University of Illinois at Urbana-Champaign

Health Care Management Science, 2021, vol. 24, issue 1, No 7, 117-139

Abstract: 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 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10729-020-09519-z

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