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

Abstract

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 meta-heuristics 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-learning-based 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. 2024 Elsevier Ltd