Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Tytuł pozycji:

ADAPTIVE SCHEDULING THROUGH MACHINE LEARNING-BASED PROCESS PARAMETER PREDICTION.

Tytuł:
ADAPTIVE SCHEDULING THROUGH MACHINE LEARNING-BASED PROCESS PARAMETER PREDICTION.
Autorzy:
Frye, M.
Gyulai, D.
Bergmann, J.
Schmitt, R. H.
Temat:
DISCRETE systems
PRODUCTION scheduling
DISCRETE event simulation
MANUFACTURING processes
SCHEDULING
TARDINESS
Źródło:
MM Science Journal; 2019 Special Issue, p3060-3066, 7p
Czasopismo naukowe
Detailed manufacturing process data and sensor signals are typically disregarded in production scheduling. However, they have strong relations since a longer processing time triggers a change in schedule. Although promising approaches already exist for mapping the influence of manufacturing processes on production scheduling, the variability of the production environment, including changing process conditions, technological parameters and the status of current orders, is usually ignored. For this reason, this paper presents a novel, data-driven approach that adaptively refines the production schedule by applying Machine Learning (ML)-models during the manufacturing process in order to predict the process-dependent parameters that influence the schedule. With the proper prediction of these parameters based on the process conditions, the production schedule is proactively adjusted to changing conditions not only to ensure the sufficient product quality but also to reduce the negative effects and losses that delayed rescheduling would cause. The proposed approach aims on minimizing the overall lateness by utilizing an active data exchange between the scheduling system and the predictive MLmodels on the process level. The efficiency of the solution is demonstrated by a realistic case study using discrete event simulation. [ABSTRACT FROM AUTHOR]
Copyright of MM Science Journal is the property of MM Science Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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.)

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies