摘要
传统的企业资源计划(Enterprise resource planning,ERP)采用静态化的业务流程设计理念,忽略了人的关键作用,且很少涉及系统性的过程模型,因此难以应对现代企业资源计划的复杂性要求.为实现现代企业资源计划的新范式,本文在ACP(人工社会(Artificial societies)、计算实验(Computational experiments)、平行执行(Parallel execution))方法框架下,以大数据为驱动,融合深度强化学习方法,构建基于平行管理的企业ERP系统.首先基于多Agent构建ERP整体建模框架,然后针对企业ERP的整个流程建立序贯博弈模型,最后运用基于深度强化学习的神经网络寻找最优策略,解决复杂企业ERP所面临的不确定性、多样性和复杂性.
Traditional enterprise resource planning (ERP) usually adopts static business processes design and does not take the key role of "human" into consideration. It rarely involves the systematic process modeling, which makes it impossible to tackle the management complexity of modern enterprises. Considering the big data driven environment of modern enterprises, we utilize the ACP (Artificial societies, computational experiments, parallel execution) theory integrated with deep reinforcement learning approaches to establish a parallel management system for modern ERP management. We first propose a framework for ERP systems based on multi-agent technology where a sequential game model is included. Then, we seek for the optimal strategy using a deep reinforcement learning based neural network. Our proposed framework and approaches can well deal with uncertainty, diversity and complexity of modern ERP systems.
出处
《自动化学报》
EI
CSCD
北大核心
2017年第9期1588-1596,共9页
Acta Automatica Sinica
基金
国家自然科学基金(71702182
71472174
71232006
61533019
61233001
71402178)
复杂系统管理与控制国家重点实验室优秀人才基金(Y6S9011F4E
Y6S9011F4H)资助~~
关键词
企业资源计划
深度强化学习
ACP理论
平行管理
多AGENT建模
Enterprise resource planning (ERP), deep reinforcement learning, ACP theory, parallel management, multiagent technology