Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning

As one of the most fundamental operations in mechanical production, hole-making plays a crucial role. However, existing hole-making sequence optimization models are not suitable for workshops with variable production parameters. To address this issue, a new model, named multi-objective multi-tool hole-making sequence optimization with precedence constraints (MO-MTpcHSO), is proposed in this paper. The model has two objectives: spindle travel distance and tool switching time. To solve MO-MTpcHSO, a customized Q-learning based genetic algorithm (QLGA) is proposed. The adaptive encoding method allows chromosomes to express feasible solutions, the population is considered as the agent, and the states are intervals of the diversity coefficient. Different insertion methods in the crossover operator are set as actions, and the reward is related to the diversity and values of objective functions of the population. The effectiveness of QLGA is validated by comparing it with other algorithms in practical workpieces. Moreover, the reasonability of actions and the necessity of the Q-learning framework in QLGA are validated.