TY - JOUR
T1 - Scheduling patient appointment in an infusion center
T2 - a mixed integer robust optimization approach
AU - Issabakhsh, Mona
AU - Lee, Seokgi
AU - Kang, Hyojung
N1 - Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Adaptive large neighborhood search
KW - Infusion appointment scheduling
KW - Infusion time uncertainty
KW - Operations research
KW - Robust optimization
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U2 - 10.1007/s10729-020-09519-z
DO - 10.1007/s10729-020-09519-z
M3 - Article
C2 - 33044667
SN - 1386-9620
VL - 24
SP - 117
EP - 139
JO - Health Care Management Science
JF - Health Care Management Science
IS - 1
ER -