Utilizing online stochastic optimization on scheduling of intensity-modulate radiotherapy therapy (IMRT)

According to Ministry of Health and Welfare of Taiwan, cancer has been one of the major causes of death in Taiwan since 1982. The Intensive-Modulated Radiation Therapy (IMRT) is one of the most important radiotherapies of cancers, especially for Nasopharyngeal cancers, Digestive system cancers and Cervical cancers. For patients, if they can receive the treatment at the earliest possibility while diagnosed with cancers, their survival rate increases. However, the discussion of effective patient scheduling models of IMRT to reduce patients' waiting time is still limited in literature. This study proposed a mathematical model to improve the efficiency of patient scheduling. The research was composed of two stages. In the first stage, the online stochastic algorithm was proposed to improve the performance of present scheduling system. In the second stage the impact of future treatment to reduce patients' waiting time was considered. A genetic algorithm (GA) was then proposed to solve the online stochastic scheduling problem. This research collected data from a practical medical institute and the proposed model was validated with real data. It contributes to both theory and practice by proposing a practical model to assist the medical institute in implementing patient scheduling in a more efficient manner.