Dynamic Lot Size Optimization with Reinforcement Learning

被引：0

作者：

Voss, Thomas ^{[1
]}

Bode, Christopher ^{[1
]}

Heger, Jens ^{[1
]}

机构：

[1] Leuphana Univ, Unive Allee 1, D-21335 Luneburg, Germany

来源：

DYNAMICS IN LOGISTICS (LDIC 2022) | 2022年

关键词：

Simulation; Reinforcement learning; Lot sizing; DECISION-MAKING; NEURAL-NETWORKS;

D O I：

10.1007/978-3-031-05359-7_30

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

Production planning and control has a great influence on the economic efficiency and logistical performance of a company. In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry. Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing. The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5% of costs compared to a human planner and a heuristic approach proposed by Groff.

引用

页码：376 / 385

页数：10

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