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Chemotherapy operations planning and scheduling in oncology clinics is a complex problem due to several factors such as the cyclic nature of chemotherapy treatment plans, the high variability in resource requirements (treatment time, nurse time, pharmacy time) and the multiple clinic resources involved. Treatment plans are made by oncologists for each patient according to existing chemotherapy protocols or clinical trials. It is important to strictly adhere to the patient’s optimal treatment plan to achieve the best health outcomes. However, it is typically difficult to attain strict adherence for every patient due to side effects of chemotherapy drugs and limited resources in the clinics. In this study, our aim is to develop operations planning and scheduling methods for chemotherapy patients with the objective of minimizing the deviation from optimal treatment plans due to limited availability of clinic resources (beds/chairs, nurses, pharmacists). Mathematical programming models are developed to solve the chemotherapy operations planning and scheduling problems. A two-stage rolling horizon approach is used to solve these problems sequentially. Real-size problems are solved to demonstrate the effectiveness of the proposed algorithms in terms of solution quality and computational times.
Türkcan et al. (Sun,) studied this question.