Planning and Scheduling of Chemotherapy Operations
Günsu Dağıstanlı Çallı
With the rising need for chemotherapy treatment exceeding the limited capacities of oncology clinics and hospitals, efficient planning and scheduling becomes crucial. In the first stage (i.e., planning), patients’ multiple treatments are allocated into treatment days within the constraints of their treatment plans. In the second stage daily patient appointment schedules are created to maximize resource utilization and patient satisfaction considering the availability of nurses and chairs.
Based on observations at Hacettepe Oncology Hospital in Ankara, we define two problem settings with stochastic and deterministic infusion durations for the first stage, called as Outpatient Chemotherapy Planning (OCP) problem, and present one model for each: a 2-stage Stochastic Mixed-Integer Programming (SMIP) model and a compact deterministic MixedInteger Programming (MILP) model. The first model, aiming to minimize total penalties while balancing nurse workloads, is solved by a scenario reduction heuristic. The second model accommodates objectives aligned with hospital policies and is further supported by MILP-based replanning models. For any selected objective, our solutions outweigh those currently implemented.
We propose a deterministic MILP model minimizing the total weighted start times of the appointments for the Chemotherapy Appointment Scheduling (CAS) problem. For nurse allocations, we present two problems for the Nurse Workload Balancing (NWB), aiming to balance workloads through two objectives: minimizing the maximum workload and minimizing the squared deviation of workloads from the mean. For the second problem, we propose a Mixed-Integer Non-Linear Programming (MINLP) model, a Mixed-Integer Linear Programming (MILP), and a Constraint Programming (CP) model. Results of our computational study show all NWB models can solve large instances, but the model minimizing the maximum workload outperforms others.
Joint work with Prof. Meral Azizoğlu, Assoc. Prof. Serhat Gül, and Prof. Melih Çelik.
Short Bio
Günsu Dagıstanlı Çallı received her B.Sc. degree in Industrial Engineering from Middle East ˘ Technical University (METU) in 2017 and her M.Eng. degree in Industrial Engineering and Operations Research from the University of California, Berkeley in 2018. She also gained her Ph.D. degree from the Department of Industrial Engineering at METU. Her research interests include healthcare systems optimization, chemotherapy planning and scheduling, stochastic and deterministic mathematical programming, and real-time decision support systems.
Venue
Friday, April 17, 2026, 4.00 pm
IE Building, Halim Doğrusöz Auditorium (IE 03)