The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a district, evaluate multiple objectives, and make informed selections from many Pareto-efficient solutions. UDO distinguishes itself from other forms of design optimisation by addressing the challenges of incorporating a wide range of planning goals, managing the complex interactions among various urban datasets, and considering the social–technical aspects of urban planning involving multiple stakeholders. Previous reviews focusing on specific topics within UDO do not sufficiently address these challenges. This PRISMA systematic literature review provides an overview of research on topics related to UDO from 2012 to 2022, with articles analysed across seven descriptive categories. This paper presents a discussion on the state-of-the-art and identified gaps present in each of the seven categories. Finally, this paper argues that additional research to improve the socio-technical understanding and usability of UDO would require: (i) methods of optimisation across multiple models, (ii) interfaces that address a multiplicity of stakeholders, (iii) exploration of frameworks for scenario building and backcasting, and (iv) advancing AI applications for UDO, including generalizable surrogates and user preference learning.

Extreme heat events, exacerbated by climate change, are intensified in cities by the urban heat island (UHI) effect. A primary cause of UHI is the replacement of natural lands with buildings and roads, trapping heat in the city. Singapore, a high-rise high-density city state in Southeast Asia, has taken actions to mitigate UHI including the expansion of urban green spaces. Increasing urban greenery to mitigate UHI is one of many strategies being tested in the emerging field of climate-responsive city planning. To justify UHI mitigation planning strategies to city stakeholders, however, requires an ability to estimate effectiveness and efficiency in measures. This case study implements a generative urban model to evaluate UHI across a range of urban density scenarios, testing the impacts of both green space and building design strategies in three stages: (i) urban generative modeling, (ii) UHI prediction simulation, and (iii) urban data analysis. The study conducts urban generative modeling for a 100-ha site, using a model created to specifically reflect Singapore’s development control guidelines. Across eight selected design parameters, an extensive set of design solutions (9,000) is obtained via a one-factor-at-a-time sampling method. The Urban Weather Generator tool is used to evaluate the UHI performance per solution. Design space exploration of the urban model and analysis of results identify best-performing UHI mitigation strategies, correlation among model parameters, and parameter significance. These results permit discussion of effective city planning and design strategies for UHI mitigation. Readers engaging with this case study will gain an understanding of the application of urban data analytics to climate-resilient city planning supported by generative urban models and UHI performance simulation.