An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling
| Year of publication: |
2025
|
|---|---|
| Authors: | Li, Haoran ; Gao, Liang ; Fan, Qingsong ; Li, Xinyu ; Han, Baoan |
| Published in: |
International journal of production research. - London [u.a.] : Taylor & Francis, ISSN 1366-588X, ZDB-ID 1485085-0. - Vol. 63.2025, 12, p. 4368-4388
|
| Subject: | decentralised scheduling framework | deep reinforcement learning | Distributed heterogeneous flowshop | dynamic scheduling | greedy heuristics | Scheduling-Verfahren | Scheduling problem | Heuristik | Heuristics | Algorithmus | Algorithm | Theorie | Theory | Lernprozess | Learning process | Evolutionärer Algorithmus | Evolutionary algorithm |
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