A Q-learning based artificial bee colony algorithm for solving surgery scheduling problems with setup time

With the increasing demand for surgeries, surgery scheduling become an important problem in hospital management. Efficient surgery scheduling can enhance the optimal use of surgical resources, leading to high efficiency of surgery assignments. This work addresses surgery scheduling problems with surgical resources setup time. A mathematical model is established to describe the considered problems with the objective of minimizing the maximum completion time of the surgeries (makespan). Second, a modified artificial bee colony (ABC) algorithm is proposed, named QABC. Six local search operators are developed based on the characteristics of the problem, aiming to strengthen the local search capability of the algorithm. To further improve the performance of the algorithm, this study combines a Q-learning strategy with ABC algorithm. During each iteration of the algorithm, the Q-learning strategy is employed to guide the selection of search operators. Finally, the effectiveness of the local search operators and Q-learning based local search selection is verified by solving 20 cases with varying scales. And the results obtained by the Gurobi solver are compared with the proposed QABC. Furthermore, the proposed QABC is compared with the state-of-the-art algorithms. The experimental results and comparisons show that QABC is more effective than its peers for solving the surgery scheduling problems with setup time.