Introduction As cancer has become the leading cause of death, the demand for services in cancer establishments has continued to increase in recent years. Some researchers have reported that patients are spending significantly more time waiting, either to schedule an appointment or to wait at cancer facilities. Therefore, the article “Improving Patient Access to Chemotherapy Treatment at Duke Cancer Institute” (Woodall, etc., 2013) aims to improve the flow of patients in their institution, which also focuses on its application in d other cancer establishments. First, the authors obtain some basic information about the Duke Cancer Institute about the flow of information between different departments. Departments include Clinic, Radiology, Central Laboratory, Oncology Treatment Center (OTC), and Pharmacy; Nursing types include full-time and part-time. In order to optimize and simulate processes to meet patient demand and allocate resources, researchers propose three models to achieve their goals. As these three models are analyzed step by step and the last model is based on the previous results. The first model is a “discrete event simulation model,” which aims to predict patient waiting time and acquire information about resource utilization among patients. different departments. Nonetheless, researchers identified that the most serious bottleneck is in over-the-counter services, as nurses are not available during the treatment process. So the researchers decided to focus primarily on over-the-counter care and made assumptions about the maximum number of patients for each nurse. Consequently, as OTC is subject to temporal variability, improving nurses' work schedules and working hours will be the best way to match the supply of nurses with patient demand. Based on the first mode, they then opted for the use of “mixed integers”. programming model” for the change of nurses, in order to release the bottleneck in over-the-counter services. Shifting schedules include daily, weekly, and monthly schedules, with nursing type including full-time and part-time. This method is used to focus on predetermining the number of nurses according to weekly and monthly schedules. The full-time nurse type will include 10 hours and 8 hours per day. Nurses' weekly and monthly allocation is based on daily patient demand. As a result, these analysts decide to replace one or two full-time nurses with nurses at the same skill levels as part-time nurses, which is more suited to over-the-counter peak hour demand and reduces overuse of nurses. resources when there are not too many patients. The final model is based on the previous model to further reduce the bottleneck by optimizing the start time and end time of the daily shift of nurses. This method focuses on the daily movement of the nurse.