A Case Study on Dynamic Reservation Systems Using Unconstraining Methods for Censored Data in Healthcare

• Main Authors: Chiao-Ju Hung, 洪巧茹
• Other Authors: 吳政鴻
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/93728957604977308525

碩士 === 國立臺灣大學 === 工業工程學研究所 === 101 === In this research, we endeavor to streamline the demand management by developing a better scheduling system. This modified decision-making system would present decisions on how to use resource protection levels to increase profits in the long term. Specifically, we observed that the National Taiwan University Hospital’s Health Management Center (NTUH-HMC) does not reserve any MRI installations for precise comprehensive health examination (PCHE) customers and that it ends up being booked at capacity. Considering different types of customers’ demand, cancellations, and no-shows, setting up the dynamic booking limit could help improve the center’s income by satisfying the potential future PCHE customer, who wants to be examined more extensively. This kind of policy adjustment is called dynamic booking limit policy. By implementing these policies it will also improve the management of the center’s resource usage and increase its revenue. However, the historical data we obtained were partially censored. Lacking in unconstrained demand data makes it difficult to make better customer sets and capacity allocation decisions. Therefore, we also demonstrate several unconstrained methods and adapt a few models in order to be more precise to predict the true demand distribution. Our method of setting protection levels to incorporate a dynamic booking limit policy could also apply to different reservation systems, especially for perishable production and service industries, such as restaurants, hotels, car rental businesses, and travel agencies.