Document Type


Publication Date


MeSH Terms

Proton Therapy,

Subject: LCSH

Markov processes


Industrial Engineering | Mechanical Engineering


A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no-shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI).


This is the authors' accepted version of the article published in Health Care Management Science. The final publication is available at Springer via



Publisher Citation

Gedik, R., Zhang, S., & Rainwater, C. (2016). Strategic level proton therapy patient admission planning: a Markov decision process modeling approach. Health Care Management Science, 20(2), 286–302.



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