Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 223, Issue 2, Pages 573-584Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2012.06.046
Keywords
Patient scheduling; OR in health services; Markov decision processes; Linear programming; Approximate dynamic programming
Funding
- NSERC [5527-07]
- Canadian Institutes of Health Research (CIHR)
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Seeking to reduce the potential impact of delays on radiation therapy cancer patients such as psychological distress, deterioration in quality of life and decreased cancer control and survival, and motivated by inefficiencies in the use of expensive resources, we undertook a study of scheduling practices at the British Columbia Cancer Agency (BCCA). As a result, we formulated and solved a discounted infinite-horizon Markov decision process for scheduling cancer treatments in radiation therapy units. The main purpose of this model is to identify good policies for allocating available treatment capacity to incoming demand, while reducing wait times in a cost-effective manner. We use an affine architecture to approximate the value function in our formulation and solve an equivalent linear programming model through column generation to obtain an approximate optimal policy for this problem. The benefits from the proposed method are evaluated by simulating its performance for a practical example based on data provided by the BCCA. (C) 2012 Elsevier B.V. All rights reserved.
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