Scheduling Multiobjective Dynamic Surgery Problems via Q-Learning-Based Meta-Heuristics

This work addresses multiobjective dynamic surgery scheduling problems with considering uncertain setup time and processing time. When dealing with them, researchers have to consider rescheduling due to the arrivals of urgent patients. The goals are to minimize the fuzzy total medical cost, fuzzy maximum completion time, and maximize average patient satisfaction. First, we develop a mathematical model for describing the addressed problems. The uncertain time is expressed by triangular fuzzy numbers. Then, four meta-heuristics are improved, and eight variants are developed, including artificial bee colony, genetic algorithm, teaching-learning-base optimization, and imperialist competitive algorithm. For improving initial solutions' quality, two initialization strategies are developed. Six local search strategies are proposed for fine exploitation and a $Q$ -learning algorithm is used to choose the suitable strategies among them in the iterative process of the meta-heuristics. The states and actions of $Q$ -learning are defined according to the characteristic of the addressed problems. Finally, the proposed algorithms are tested for 57 instances with different scales. The analysis and discussions verify that the improved artificial bee colony with $Q$ -learning is the most competitive one for scheduling the dynamic surgery problems among all compared algorithms.