强化学习
观点
作业车间调度
计算机科学
调度(生产过程)
工作车间
生产计划
灵活性(工程)
工业工程
数学优化
运筹学
生产(经济)
人工智能
流水车间调度
工程类
数学
嵌入式系统
经济
视觉艺术
艺术
宏观经济学
统计
布线(电子设计自动化)
作者
Jens Popper,William Motsch,Alexander David,Teresa Petzsche,Martin Ruskowski
出处
期刊:2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
日期:2021-10-07
被引量:5
标识
DOI:10.1109/iceccme52200.2021.9590925
摘要
Current trends place great demands on the flexibility and sustainability of modern production facilities. The optimisation of these Flexible Job Shop Scheduling Problems (FJSSP) under multiple objective variables, such as the makespan or the consumed energy, is a great challenge for today's planning systems due to the constantly changing constraints. In this paper, we present a method for multi-criteria dynamic planning of production facilities under both common and sustainable target variables, based on a Multi-Agent Reinforcement Learning (MARL) procedure. This is experimentally applied to a planning problem in a series of trials and compared with common methods. Finally, the results and further research questions are presented.
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