Abstract
In recent years, the construction industry has rapidly adopted offsite-manufacturing and distributed construction methods. This change brings a variety of challenges requiring innovative solutions, such as the utilization of AI-driven and generative design. Numerous studies have explored the concept of multi-objective generative design with genetic algorithms in construction. However, this paper highlights the challenges and proposes a solution for combining generative design with distributed construction to address the need for agility in design. To achieve this goal, the research delves into the development of a multi-objective generative design optimization using a weighted genetic algorithm based on simulated annealing. The specific design case adopted is an educational complex. The proposed process strives for scalable economic viability, environmental comfort, and operational efficiency by optimizing modular configurations of architectural spaces, facilitating affordable, scalable, and optimized construction. Rhino-Grasshopper and Galapagos design tools are used to create a virtual environment capable of generating architectural configurations within defined boundaries. Optimization factors include adherence to urban regulations, acoustic comfort, and sunlight exposure. A normalized scoring approach is also presented to prioritize design preferences, enabling systematic and data-driven design decision-making. Building Information Modeling (BIM) tools are also used to transform the optimization results into tangible architectural elements and visualize the outcome. The resulting process contributes both to practice and academia. Practitioners in AEC industry could gain benefit through adopting and adapting its features with the unique characteristics of various construction projects while educators and future researchers can modify and enhance this process based on new requirements.