A self-learning artificial bee colony algorithm based on reinforcement learning for a flexible job-shop scheduling problem

The flexible job-shop scheduling problem (FJSP) is currently one of the most critical issues in process planning and manufacturing. The FJSP is studied with the goal of achieving the shortest makespan. Recently, some intelligent optimization algorithms have been applied to solve FJSP, but the key parameters of intelligent optimization algorithms cannot be dynamically adjusted during the solution process. Thus, the solutions cannot best meet the needs of production. To solve the problems of slow convergence speed and reaching a local optimum with the artificial bee colony (ABC) algorithm, an improved self-learning artificial bee colony algorithm (SLABC) based on reinforcement learning (RL) is proposed. In the SLABC algorithm, the number of updated dimensions of the ABC algorithm can be intelligently selected according to the RL algorithm, which improves the convergence speed and accuracy. In addition, a self-learning model of the SLABC algorithm is constructed and analyzed using Q-learning as the learning method of the algorithm, and the state determination and reward methods of the RL algorithm are designed and included in the environment of the artificial bee colony algorithm. Finally, this article verifies that SLABC has excellent convergence speed and accuracy in solving FJSP through Brandimarte instances.