Machine learning-driven solutions for sustainable and dynamic flexible job shop scheduling under worker absences and renewable energy variability
Candice Destouet, Houda Tlahig, Belgacem Bettayeb, Bélahcène Mazari
| Year of publication: |
2026
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|---|---|
| Authors: | Destouet, Candice ; Tlahig, Houda ; Bettayeb, Belgacem ; Mazari, Bélahcène |
| Published in: |
Computers & operations research : an international journal. - Amsterdam [u.a.] : Elsevier, ISSN 0305-0548, ZDB-ID 1499736-8. - Vol. 186.2026, Art.-No. 107323, p. 1-19
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| Subject: | Dynamic scheduling | Energy variation | Flexible job-shops | Machine learning | Sustainability | Worker absences | Scheduling-Verfahren | Scheduling problem | Erneuerbare Energie | Renewable energy | Fehlzeit | Work absence | Künstliche Intelligenz | Artificial intelligence | Produktionssteuerung | Production control | Nachhaltigkeit | Flexibles Fertigungssystem | Flexible manufacturing system | Algorithmus | Algorithm |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.cor.2025.107323 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10015552994