An Improvement in Master Surgical Scheduling Using Artificial Neural Network and Fuzzy Programming Approach

In this study, a new mathematical model is presented for the master surgical scheduling (MSS) problem at the tactical level. The capacity of the operating room for each specialty is determined in the previous level and used as an input for the tactical level. In MSS, elective surgeries are often performed in a cycle for a cycle. However, this problem considers both elective and emergency patients. The model of this problem is specifically designed to achieve this tactical plan to provide emergency care, as it provides the possibility of reserving some capacity for emergency patients. The current study, forecast emergency patients by applying an artificial neural network, and reserve capacity for them are based on the demand. Fuzzy chance-constraint programming is employed to handle the uncertainty in the model. The data of a private hospital in Iran is used to solve the problem using GAMS software. The results show that the performance of the proposed method against the solution in the hospital performed better.