Existing researches focus on constructing supply-demand matching models and developing solving algo-rithms 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 de-manders 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.
关键词
云制造/共享制造/供需匹配/强化学习/匹配时域
Key words
cloud manufacturing/shared platform/supply-demand matching/reinforcement learning/matching time horizon
维普
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