| Autor: |
Xinyan Ou, Qing Chang, Jorge Arinez, Jing Zou |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 6, Pp 14699-14709 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2800641 |
| Popis: |
In this paper, a manufacturing work cell utilizing gantries to move between machines for loading and unloading materials/parts is considered. The production performance of the gantry work cell highly depends on the gantry movements in real operation. This paper formulates the gantry scheduling problem as a reinforcement learning problem, in which an optimal gantry moving policy is solved to maximize the system output. The problem is carried out by the Q-learning algorithm. The gantry system is analyzed and its real-time performance is evaluated by permanent production loss and production loss risk, which provide a theoretical base for defining reward function in the Q-learning algorithm. A numerical study is performed to demonstrate the effectiveness of the proposed policy by comparing with the first-comefirst-served policy. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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