基于强化学习协同进化算法求解柔性作业车间节能调度问题

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关键词：柔性作业车间调度；Q-learning；改进NSGA-II；多目标优化 中图分类号：TH165 文献标志码：A 文章编号：1001-3695（2025）07-016-2039-09 doi：10.19734/j. issn.1001-3695.2024.11.0479

Abstract：Fortheflexiblejob shopenergy eficientscheduling problem（EEFJSP），thispaperconstructedaFJSPmodel with theoptimizationobjectivesof minimizingthemaximumcompletiontimeand minimizingthetotal energyconsumption.Firstly， it proposedanadaptivealgorithmbasedonreinforcementlearningco-evolutionaryalgorithm（QNSGA-II）to characteriethe problemmodel．Secondly，it introduced theconceptsof state spaceandaction spaces，and designedareward-punishment functionbasedoheoverallaveragefinesandpopulationdiversitytoensuretheefectivenessofthealgoritinteiterative processInorder toimprovetheabilityof theglobal searchandlocalsearch，itproposedanimproved tabusearchalgorithmto updatethepopulationaftercrosoverandmutation.Inordertoimprovetheabilityof globalsearchandlocal search，itproposedanimprovedtaboosearchalgorithmtoupdate thepopulationaftercrosoverandmutation.Finall，itanalyzedtheeffectivenessoftheimproved tabusearch strategyandthe Q-learning parameteradaptationstrategy toverifythealgorithm’seffectiveness andsuperiority，anditcomparedtheproposedQNSGA-Iwithother multi-objectiveoptimizationalgorithmsto verify the superiority of the algorithms in solving the EEFJSP.

Key Words：flexible job shop scheduling；Q-learning；improved NSGA-I ；multi-objective optimization

0 引言

近几十年来，随着环境的不断恶化，政府呼呼节能减排，绿色制造已成为各国增强制造业核心竞争力的战略重点。（剩余20115字）