A Deep Reinforcement Learning Approach for Smart Coordination Between Production Planning and Scheduling

被引：0

作者：

Gomez-Gasquet, Pedro ^{[1
]}

Boza, Andres ^{[1
]}

Perez Perales, David ^{[1
]}

Esteso, Ana ^{[1
]}

机构：

[1] Univ Politecn Valencia, Ctr Invest Gest & Ingn Prod CIGIP, Camino Vera S-N, Valencia 46022, Spain

来源：

ENTERPRISE INTEROPERABILITY X, EI 2022 | 2024年 / 11卷

关键词：

Planning; Scheduling; Production; Interoperability; DQN; Agent; INTEGRATION;

D O I：

10.1007/978-3-031-24771-2_17

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The hierarchical approach of the production planning and control system proposes to divide decisions into various levels. Some data used in the planning level are based on predictions that anticipate the behavior of the workshop; nevertheless, these predictions can be adjusted at the schedule level. Feedback between both levels would allow better coordination; however, this feedback is not implemented due to interoperability problems and the complexity of the problem. This paper proposes an agent-based system that implements deep reinforcement learning to generate solutions based on artificial intelligence.

引用

页码：195 / 206

页数：12

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