Development of an integrated intelligent BIM-based model for multi-objective optimization in engineering assembly processes

To enhance construction efficiency and economic performance in prefabricated building projects under limited resource conditions, this study proposes an integrated intelligent optimization model based on Building Information Modeling (BIM) semantic representation. The model is designed to generate optimal assembly plans under multi-objective trade-offs, achieving a balanced compromise between shortened construction periods, reduced costs, and minimized resource conflicts. The study begins by constructing an assembly semantic model using the publicly available BuildingNet dataset, extracting key components’ geometric structures and spatial topology to establish a data foundation suitable for multi-objective scheduling modeling. A multi-objective particle swarm optimization (MOPSO) algorithm enhanced with a dynamic objective weighting mechanism is then introduced. By allowing flexible prioritization of construction duration, budget cost, and resource usage, the model generates a diverse solution set and provides multiple candidate optimization schemes. Furthermore, a Deep Q-Network (DQN)-based reinforcement learning strategy is integrated to provide real-time feedback on each solution’s performance during simulated scheduling, enabling continuous policy updates and adaptive evolution. Experiments conducted on 100 standardized assembly tasks demonstrate the model’s effectiveness, producing feasible solution sets under varying objective weights. For a representative configuration, the model achieves an average construction period of 85.2 days, a budget cost of USD 1.486 million, and fewer than 1.7 resource conflict events. Compared with rule-based scheduling models, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and static MOPSO without feedback mechanisms, the proposed approach outperforms in terms of objective coverage, convergence speed, and solution diversity. It achieves superior results in key metrics, including hypervolume (HV = 0.683), solution spread (Spread = 0.227), and inverted generational distance (IGD = 0.017), validating its robustness and adaptability in complex trade-off scenarios. The findings indicate that integrating semantic modeling, evolutionary optimization, and learning-based feedback offers significant potential for dynamic multi-objective construction optimization, providing effective support for BIM practices oriented toward benefit–schedule–resource coordination.