Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling

Majed Hadid (), Adel Elomri, Regina Padmanabhan, Laoucine Kerbache, Oualid Jouini, Abdelfatteh El Omri, Amir Nounou and Anas Hamad
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Majed Hadid: College of Science and Engineering, Hamad bin Khalifa University, Doha 34110, Qatar
Adel Elomri: College of Science and Engineering, Hamad bin Khalifa University, Doha 34110, Qatar
Regina Padmanabhan: College of Science and Engineering, Hamad bin Khalifa University, Doha 34110, Qatar
Laoucine Kerbache: College of Science and Engineering, Hamad bin Khalifa University, Doha 34110, Qatar
Oualid Jouini: Laboratoire Génie Industriel, Université Paris-Saclay, Centrale Supélec, Gif-sur-Yvette, 91190 Paris, France
Abdelfatteh El Omri: Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha 3050, Qatar
Amir Nounou: Pharmacy Department, National Center for Cancer Care & Research, Hamad Medical Corporation, Doha 3050, Qatar
Anas Hamad: Pharmacy Department, National Center for Cancer Care & Research, Hamad Medical Corporation, Doha 3050, Qatar

IJERPH, 2022, vol. 19, issue 23, 1-34

Abstract: Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process’s (OCP’s) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

Keywords: outpatient chemotherapy; cancer; oncology health care; clustering; stochastic simulation-based optimization; multi objectives; planning; scheduling; decision-making metaheuristics; artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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