An evolution strategies-based reinforcement learning algorithm for multi-objective dynamic parallel machine scheduling problems

•Proposed evolution strategies-based reinforcement learning algorithm.•A multi-agent system to generate scheduling policy for parallel machines.•Action with selecting a job directly to be sequenced is used to avoid sparse reward.•Parallel machine scheduling problem with multi-objective MILP model.•M...

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Veröffentlicht in:	Swarm and evolutionary computation Jg. 95; S. 101944
Hauptverfasser:	Chen, Yarong, Zhang, Junjie, Mumtaz, Jabir, Huang, Shenquan, Zhou, Shengwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2025
Schlagworte:	Energy consumption Evolution strategies Multi-agent, reinforcement learning Multi-objective optimization Parallel machine scheduling problem Energy consumption Multi-objective optimization Evolution strategies Multi-agent, reinforcement learning Parallel machine scheduling problem
ISSN:	2210-6502
Online-Zugang:	Volltext
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