Ensemble meta-heuristics and Q-learning for staff dissatisfaction constrained surgery scheduling and rescheduling

In this study, we investigate the multi-objective surgery scheduling and rescheduling problems with considering medical staff dissatisfaction and fuzzy surgery time. Rescheduling is activated when emergency patients arrive. First, a multi-objective mathematical model is established for maximizing the average patient satisfaction, and minimizing the fuzzy maximum completion time and total medical cost, simultaneously. Second, five metaheuristics are employed and improved to solve the concerned problems. Five heuristic rules are developed to improve the diversity and quality of initial solutions. For improving the performance of meta-heuristics, six local search operators are designed and two Q-learning-based strategies are developed to select optimal ones intelligently. Finally, 29 instances with different scales are used to verify the performance of the proposed algorithms. Compared with the basic meta-heuristics, the average performance of the algorithms with the second Q-learningbased strategy is improved by 62.5%, 62.1%, 50%, 70.7%, and 70.7%, respectively. Through the Friedman test, the asymptotic significance values of both metrics (0.034 and 0.000) are less than 0.05, indicating that there is a significant performance gap among five algorithms with the second Q-learning-based strategy. The average rank values of the teaching-learning-based optimization with the second Q-learning strategy are 3.7069 and 2.0690 for two metrics, which are better than the compared ones.