EURO 2024 Copenhagen
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2390. A stochastic programming approach for chemotherapy planning

Invited abstract in session TA-10: Radiotherapy and chemotherapy planning, stream OR in Health Services (ORAHS).

Tuesday, 8:30-10:00
Room: 11 (building: 116)

Authors (first author is the speaker)

1. Melih Celik
School of Management, University of Bath
2. Gunsu Dagistanli Calli
Industrial Engineering, Middle East Technical University
3. Serhat Gül
TED University

Abstract

Cancer is one of the most prevalent causes of death worldwide, second only to heart disease. Chemotherapy is the most widely used approach to treat cancer patients. There are many challenges for planning of chemotherapy appointments in oncology clinics, including the limited capacity of clinics and the uncertainty of the duration of treatment. For this end, an effective planning of chemotherapy schedules is vitally important.

In this study, we consider the problem of chemotherapy planning, where patients are assigned to days in the planning horizon according to their treatment frequency. Due to the uncertainty of durations, we model the problem as a two-stage stochastic program, with an objective that minimizes a combination of earliness and tardiness of appointment days, as well as idle time and overtime of chemotherapy chairs. As realistically sized instances of the problem are impossible to be solved by commercial solvers, we propose a scenario reduction-based heuristic approach that relies on k-medoids clustering to find the representative scenarios. We test the performance of our approach on a dataset from a chemotherapy clinic and drive managerial insights.

Keywords

Status: accepted

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