Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/297210 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Logistics Research [ISSN:] 1865-0368 [Volume:] 16 [Issue:] 1 [Article No.:] 6 [Year:] 2023 [Pages:] 1-16
Verlag: 
Bundesvereinigung Logistik (BVL), Bremen
Zusammenfassung: 
We present an approximation method for the hybrid flow shop scheduling problem based on relaxation and machine learning techniques. Our model combines a suitable relaxation of the objective function to a continuous solution space with a self-supervised learning method for neural networks, which does not require any labeled training data. Thereby, we avoid the pre-computation of exact solutions, which is generally not feasible for NP-hard problems such as hybrid flow shop scheduling. In terms of computational effort during the decision process, our approach outperforms other methods with similar approximation accuracy, which suggests that the considered technique of self- supervised learning is well suited for high-performance applications involving the approximate optimization of discrete NP-hard problems.
Schlagwörter: 
scheduling
hybrid flow shop
flowtime
makespan
relaxation
neural networks
self- supervised learning
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
2.02 MB





Publikationen in EconStor sind urheberrechtlich geschützt.