A new genetic algorithm to solve integrated operating room scheduling problem with multiple objective functions

In this paper, a new genetic algorithm is presented to plan and schedule operating rooms at the operational level to minimize completion time, surgeons’ free time window, and operating rooms’ overtime, idle time, and setup time costs. The duration of surgeries is calculated according to a predetermined time plus an allowance related to the uncertainty of the surgery time. Also, the operating rooms’ setup times depend on the sequence of surgeries. The time window constraint involves resource availability such as surgeons and operating rooms. First, a mixed-integer nonlinear mathematical model is proposed to solve the problem. Thereafter, a genetic algorithm is developed to solve the problem inspired from the role model concept in sociology using simulating and differentiating procedures, namely Role Model Genetic Algorithm (RMGA). The performance of the proposed algorithm is examined by comparing it with a conventional genetic algorithm and a hybrid genetic algorithm proposed for the nearest problem in the literature to the current problem. The results shows that RMGA prepares better results.