Currently, the construction industry is facing the challenge of balancing modularity and personalization needs, and assembly buildings are promoted by various countries for their high efficiency and environmental advantages. In this study, a comprehensive optimization scheme is proposed for the problem of balancing modularity and customization of assembled buildings in an intelligent design environment. Firstly, a modular design and customization demand balance model is constructed to analyze the relationship between standardization and customization, module flexibility and adaptability, and 3D point cloud data segmentation using BIM technology. Secondly, a multi-objective optimization model of “cost-duration-carbon emission” for the assembly building construction process is constructed based on the Improved Gray Wolf Optimization (IGWO) algorithm, and the dynamic weighting method is introduced to solve the optimization problem under different construction process execution modes. Simulation results show that the optimization accuracy of IGWO algorithm on the test function f1(x) reaches 0.0015, which is more than 95% higher than that of GWO algorithm. It was verified that the optimized assembled component combination reduced the duration by 20%, carbon emission by 12.25%, and cost by 0.56% compared to the all-cast-in-place solution in the baseline scenario. It was found that the optimal range of prefabrication rate for assembled buildings should be controlled in the range of 20%-60%, which is determined according to the specific needs of the project, and should not be pursued as a high prefabrication rate. The method provides a feasible way to achieve a balance between modularity and individualization for assembly buildings in an intelligent design environment.
