SimAUD 2018 June 05-07 Delft, the Netherlands © 2018 Society for Modeling & Simulation International (SCS) Generative Urban Design: Integrating Financial and Energy Goals for Automated Neighborhood Layout Danil Nagy, Lorenzo Villaggi, and David Benjamin The Living, an Autodesk Studio New York, NY, USA danil.nagy@autodesk.com ABSTRACT This paper demonstrates an application of Generative Design to an urban scale through the design of a real-world residential neighborhood development project in Alkmaar, Netherlands. Problems in urban design can benefit greatly from the Generative Design framework due to their complexity and the presence of many stakeholders with various and potentially conflicting demands. We demonstrate this potential complexity by optimizing for two important goals: the profitability of the project for the developer and the potential for energy generation of solar panels placed on the roofs of the buildings. This paper points to further research into the application of the Generative Design framework to solve design problems at an urban scale. Author Keywords Generative Design, urban design, optimization, genetic algorithm, parametric modelling, solar analysis, profit optimization ACM Classification Keywords I.6.5 SIMULATION AND MODELING - Model Development 1 INTRODUCTION Generative Design allows designers to tap into the power of computation to explore large design spaces and derive design solutions which are both novel and high-performing relative to a chosen set of goals. This process relies on a set of technologies including parametric design software for modeling the space of all possible solutions, simulation software for deriving metrics to evaluate each potential design, and optimization solvers such as the Genetic Algorithm (GA) which can automatically search through the design space to find the most optimal designs. In recent years, this type of workflow has become widely used to solve design problems in a variety of domains such as engineering, industrial design, and architecture. Urban design problems tend to be very complex, involving a multitude of stakeholders, each with their own complex and competing goals for the project. These complex goals are difficult to resolve through a traditional design process, forcing designers to rely on intuition and prior experience which can limit potentials for novel design solutions. The Generative Design methodology can help urban designers navigate complex design spaces and a multitude of competing goals across different stakeholder domains. Such applications, however, have not been widely explored. This paper describes a novel application of the Generative Design methodology at an urban scale through the design of a residential neighborhood of 7,000 sqm in Alkmaar, Netherlands. To show the utility of this workflow, we consider two important and competing goals, each representing the desires of different stakeholders in the project. The first is the cost and revenue of the development project, which is important for the developer. The second is the potential energy generation of solar panels attached to the roofs of each building. This is important not only for minimizing the environmental impact of the development but also for the future homeowners who will benefit financially from the energy being generated. Through this example, we demonstrate how the Generative Design process can help reveal the potential tradeoffs between competing design goals and help urban designers discover designs which solve these goals in novel ways. 2 LITERATURE REVIEW While the application of evolutionary algorithms for optimization of design spaces is well known within the manufacturing industry [1], they are under-explored in the architectural and urban design domains. Prior work by the authors [2] explored the application of this workflow to the interior layout of an office space. Calixto and Celani [3] also described over 15 years of work exploring applications of evolutionary computing for spatial layouts. However, these studies were mostly theoretical and did not show the feasibility of applying such a process to a real-world design project. Furthermore, none of these studies were applied at an urban scale nor dealt with profitability or energy generation requirements.