Treffer: Predictive vehicle dispatching method for overhead hoist transport systems in semiconductor fabs.

Title:
Predictive vehicle dispatching method for overhead hoist transport systems in semiconductor fabs.
Authors:
Wan, Jiansong1 (AUTHOR), Shin, Hayong1 (AUTHOR) hyshin@kaist.ac.kr
Source:
International Journal of Production Research. May2022, Vol. 60 Issue 10, p3063-3077. 15p. 6 Diagrams, 5 Charts, 4 Graphs.
Database:
Business Source Elite

Weitere Informationen

We propose to use information regarding the fab's future state for Overhead Hoist Transport (OHT) dispatching, which is named as 'predictive dispatching' in this paper. Unlike conventional dispatching methods, two kinds of information are additionally considered in our proposed methods: the expected arrival time of jobs in the near future and the time needed for occupied vehicles to become idle. We firstly develop Basic Predictive Dispatching (BPD) under the assumption that job arrival time prediction is error-free. We demonstrate that BPD consistently surpasses conventional benchmark dispatching methods, even when job arrival time prediction contains a certain level of error. However, as the level of error increases, the performance of BPD deteriorates. To improve BPD's performance in the environment with prediction error, we take the certainty level of job arrival time prediction into consideration in our second method called Certainty Weighted Predictive Dispatching (CWPD). Both BPD and CWPD formulate the OHT dispatching problem as a linear assignment problem, but two different matching cost functions are employed separately. By conducting experiments on a sample semiconductor fab, we validate the effectiveness of our proposed approaches. The superiority of CWPD over BPD in the environment with prediction error is also verified. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volltext ist im Gastzugang nicht verfügbar.