Q学习模因算法求解可重入置换流水车间调度问题OA

The reentrant permutation flow-shop scheduling problem,as a classic NP-hard problem in the field of production scheduling,presents a challenging irregular and large solution space,which poses difficulties for the search of common intelligent optimization algorithms.To tackle this problem,this paper designs a Q-learning-based memetic algorithm that can adaptively select global search operators to minimize the maximum completion time.In the Q-learning-based memetic algorithm,we first design four global search operators tailored to the problem characteristics;second,the integration of the Q-learning mechanism enables the algorithm to adaptively select the most suitable global search operators during the itera-tive process,thereby enhancing the algorithm's efficiency;finally,in the population update phase,we adopt a population update mechanism based on quality and distance,which not only ensures the quality of the solution but also maintains the diversity of the population.Through simulation experiments and comparative experiments with other algorithms,we verify the effectiveness and robustness of the Q-learning-based memetic algorithm proposed in solving the RPFSP problem.