This paper aims to present a generative design case study focused on optimizing outpatient clinic layouts. It addresses complex design challenges involving patient flow, spatial efficiency and operational performance in outpatient clinic facilities. The study aims to create adaptable, data-driven design frameworks that reduce walking distances, enhance patient experiences and improve overall operational efficiency in healthcare settings.
Using graph theory and shape grammar methods – including generative modeling and relational Quadratic Assignment Problem (QAP)-based evaluation – the study tackles a complex healthcare facility design problem with the requirements of local and international standards. These standards are interpreted through a space relationship matrix. The algorithmic framework was developed and implemented using visual programming tools. Design alternatives are generated and evaluated using the method to identify layouts that minimize operational costs.
The case study demonstrates a holistic approach that combines graph-based shape grammar representations, genetic algorithms for generative modeling and a stochastic selection process. The framework functions as an effective preliminary design support tool for architects and healthcare managers. The findings support the efficiency of the generative design model, as the QAP scores decrease with generation compared to precedent projects.
While existing literature often focuses solely on optimizing layout plans to improve patient flow and operational efficiency, this study simulates a generative design process. It incorporates precedent analysis, international standards, norms and design principles using a shape grammar rule set as a digital foundation. Each layout is assigned a unique score via the QAP, enabling objective comparison. As the score decreases, the level of optimization increases. This scoring system simplifies decision-making for selecting optimal outpatient clinic layouts during early design phases.
