基于上下文赌博机的自适应实时车间调度

传统的多Agent车间调度方法使用单一调度规则, 忽略了生产环境变化对调度规则适用性的影响, 导致调度结果欠佳. 本文针对该问题提出一种自适应实时车间调度方法, 通过上下文赌博机对工件调度过程进行类比建模. 经过若干回合学习的上下文赌博机模型能够依据生产环境制定调度决策, 获得优异的调度结果. 最后, 通过仿真实验验证了提出方法的有效性.

Adaptive Real-time Workshop Scheduling Based on Contextual Bandits

The traditional multi-agent workshop scheduling method uses a single scheduling rule, ignoring the influence of production environment changes on the applicability of scheduling rules and resulting in poor scheduling results. This study proposes an adaptive real-time workshop scheduling method to model the workpiece scheduling process by analogy through the contextual bandits. After several rounds of learning, the contextual bandit model can make scheduling decisions according to the production environment and obtain excellent scheduling results. Finally, simulation experiments verify the effectiveness of the proposed method.