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This research article presents an integrated intelligent optimization model utilizing Building Information Modeling (BIM) for multi-objective optimization in engineering assembly processes. The model employs a multi-objective particle swarm optimization (MOPSO) algorithm enhanced with a dynamic objective weighting mechanism and incorporates a Deep Q-Network (DQN) for real-time feedback, achieving effective scheduling under varying objectives. Experimental results demonstrate the model

Author
ZJ X
Language
EN

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