Abstract: Existing researches focus on constructing supply-demand matching models and developing solving algorithms for cloud manufacturing platforms,with insufficient attention to the impact of batch matching time horizon in uncertain environments on platform operations.Aiming at the complex scenario where capacity suppliers and demanders randomly arrive and may depart anytime in cloud manufacturing platforms,a Markov Decision Model (MDP) was established based on dynamic bipartite graphs and a Q-learning dynamic time horizon decision-making method utilizing state and action reshaping techniques was proposed.According to the aggregated information from platform orders and shared capacities,this method adaptively determined the matching time horizon,and the stable matching solutions considering the preferences of suppliers and demanders were generated.Numerical experiments demonstrated that the comprehensive platform operational indicators of the proposed algorithm were better than the commonly used random-event-triggered and fixed matching time horizon methods.The experimental results provided management insights for the operation of supply-demand matching in cloud manufacturing platforms.

Key words: cloud manufacturing, shared platform, supply-demand matching, reinforcement learning, matching time horizon

摘要： 鉴于现有研究侧重于构建云制造平台供需匹配模型并开发求解算法,批处理匹配时域长度在不确定环境下对云制造平台运营的影响关注不足,针对云制造平台产能供需双方随机到达并可随时离开的复杂情景,建立了基于动态二部图的Markov决策模型,并提出基于状态和动作重塑技术的Q-learning动态时域匹配决策方法。该方法根据平台订单和共享产能的聚合信息,自适应地决策匹配时域长度,并产生考虑了供需双方偏好的稳定匹配方案。数值实验表明,在多种情景和问题参数下,该方法的综合平台运营指标优于常用的随机事件触发和固定匹配时域方法。实验结果为云制造平台供需匹配运营提供了管理启示。

关键词: 云制造, 共享制造, 供需匹配, 强化学习, 匹配时域