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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Vydané v:	Swarm and evolutionary computation Ročník 95; s. 101944
Hlavní autori:	Chen, Yarong, Zhang, Junjie, Mumtaz, Jabir, Huang, Shenquan, Zhou, Shengwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2025
Predmet:	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
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