Q-learning-based multi-objective particle swarm optimization with local search within factories for energy-efficient distributed flow-shop scheduling problem

Given the increasing severity of ecological issues, sustainable development and green manufacturing have emerged as crucial areas of research and practice. The continuous growth of the globalizing economy has led to the prevalence of distributed manufacturing systems. Distributed flow-shop schedulin...

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Veröffentlicht in:	Journal of intelligent manufacturing Jg. 36; H. 1; S. 185 - 208
Hauptverfasser:	Zhang, Wenqiang, Geng, Huili, Li, Chen, Gen, Mitsuo, Zhang, Guohui, Deng, Miaolei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2025 Springer Nature B.V
Schlagworte:	Advanced manufacturing technologies Algorithms Business and Management Clean energy Control Convergence Energy consumption Energy efficiency Evolutionary algorithms Factories Industrial plants Job shop scheduling Learning Machines Manufacturing Mechatronics Multiple objective analysis Optimization Particle swarm optimization Processes Production Robotics Search methods Subgroups Sustainable development Energy consumption Q-learning Distributed flow-shop scheduling problem Variable neighborhood search Particle swarm optimization
ISSN:	0956-5515, 1572-8145
Online-Zugang:	Volltext
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