Generative AI for Sustainable and Efficient Layout Designs

Abstract

Generative Artificial Intelligence (GenAI) is emerging as a transformative tool in industrial design, offering novel pathways to optimize functionality, resource efficiency, and sustainability. This paper explores the application of generative AI in 2D layout optimization through the development and evaluation of a specialized tool: the EcoStorage Architect. EcoStorage Architect leverages a Conditional Tabular GAN (ctGAN) to generate optimized layout configurations that not only enhance spatial efficiency and accessibility but also integrate sustainability constraints from the outset. By embedding eco indicators—such as energy efficiency and resource optimization—directly into the generation process, the model ensures that environmental performance is a core driver of design outcomes. The tool is evaluated on a dedicated dataset, with results demonstrating the feasibility of integrating generative AI into early stages of the industrial design process. Quantitative and qualitative assessments highlight gains not only in layout efficiency but also in key sustainability indicators. This work showcases how generative models can drive more adaptive, sustainable, and intelligent design practices in industrial contexts, and proposes a path forward toward AI-driven optimization in facility planning aligned with circular economy principles.

Keywords: Artificial Intelligence, Sustainable Design, Generative AI, Layout Optimization

DOI: 10.54941/ahfe1006700