Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
Michal Bouška, Přemysl Šůcha, Antonín Novák, Zdeněk Hanzálek
| Year of publication: |
2023
|
|---|---|
| Authors: | Bouška, Michal ; Šůcha, Přemysl ; Novak, Antonin ; Hanzálek, Zdeněk |
| Published in: |
European journal of operational research : EJOR. - Amsterdam : Elsevier, ISSN 0377-2217, ZDB-ID 243003-4. - Vol. 308.2023, 3 (1.8.), p. 990-1006
|
| Subject: | Deep neural networks | Machine learning | Scheduling | Single machine | Total tardiness | Scheduling-Verfahren | Scheduling problem | Neuronale Netze | Neural networks | Künstliche Intelligenz | Artificial intelligence | Algorithmus | Algorithm | Theorie | Theory | Produktionssteuerung | Production control | Durchlaufzeit | Lead time | Evolutionärer Algorithmus | Evolutionary algorithm |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.ejor.2022.11.034 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014311872