基于深度强化学习的服装缝制过程实时动态调度

Real-time dynamic scheduling for garment sewing process based on deep reinforcement learning

服装缝制生产过程易受动态事件干扰,针对订单实时到达的动态事件,以最小化最大完工周期为目标,提出基于深度强化学习的服装缝制过程实时动态调度方法。首先,建立服装缝制过程的调度优化模型,并将该问题转化为基于马尔科夫决策过程的顺序决策问题。然后,通过定义状态特征、候选动作集、奖励函数、探索与利用策略等方面,并结合DDQN算法训练深度神经网络用以描述状态-动作值,据此在决策节点选择最合适的调度规则。实验结果表明:针对牛仔裤前片缝制过程,所提出的方法相较于遗传算法,在调度目标的达成度方面略逊2.3%,但决策时间大幅减少91.4%,表明针对订单动态到达的调度问题,该方法能够实现有效地实时响应,确保了缝制生产的高效性与连续性。

The garment sewing process is prone to interference caused by dynamic events. With the objective of minimizing the completion cycle, a deep reinforcement learning-based real-time dynamic scheduling method for the garment sewing process was proposed for the dynamic events of real-time order arrival. A scheduling optimization model of garment sewing process was established, and the problem was transformed into a sequential decision problem based on Markov decision process. By defining state features, action set, reward function, exploration and exploitation strategy, combined with the DDQN algorithm to train a deep neural network to describe the state-action value, the most appropriate scheduling rule was selected at the decision node. Experiments show that for the sewing process of the front panel of jeans, the proposed method is 2.3% inferior to the genetic algorithm in achieving the scheduling goal, but the decision-making time is greatly reduced by 91.4%. The method is useful to achieve effective real-time response and ensure the efficiency and continuity of sewing production for the scheduling problem of dynamic order arrival.