THE APPLICATION OF ACTOR-CRITIC REINFORCEMENT LEARNING FOR FAB DISPATCHING SCHEDULING

被引：0

作者：

Kim, Namyong ^{[1
]}

Shin, IIayong ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, 291 Daehak Ro, Daejeon 34141, South Korea

来源：

2017 WINTER SIMULATION CONFERENCE (WSC) | 2017年

基金：

新加坡国家研究基金会;

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper applies Actor-Critic reinforcement learning to control lot dispatching scheduling in reentrant line manufacture model. To minimize the Work-In-Process(WIP) and Cycle Time(CT), the lot dispatching policy is directly optimized through Actor-Critic algorithm. The results show that the optimized dispatching policy yields smaller average WIP and CT than traditional dispatching policy such as Shortest Processing Time, Latest-Step-First-Served, and Least-Work-Next-Queue.

引用

页码：4570 / 4571

页数：2

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