A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem

Most existing distributed hybrid flow-shop scheduling problems (DHFSPs) assume identical shops and lack consideration of heterogeneous shops. This study focuses on energy-efficient heterogeneous DHFSP. A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local sea...

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Veröffentlicht in:	Expert systems with applications Jg. 237; S. 121570
Hauptverfasser:	Zhang, Wenqiang, Li, Chen, Gen, Mitsuo, Yang, Weidong, Zhang, Guohui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2024
Schlagworte:	Energy-efficient Heterogeneous distributed scheduling Hybrid flow-shop scheduling Memetic algorithm Particle swarm optimization Q-learning Memetic algorithm Q-learning Energy-efficient Particle swarm optimization Hybrid flow-shop scheduling Heterogeneous distributed scheduling
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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