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Post-disaster planning demands swift yet quality-conscious decision-making under extreme time pressure and cognitive load, conditions under which conventional approaches frequently fail. While extensive research addresses site selection through multi-criteria decision analysis and GIS-based methods, a critical gap persists in the computational generation of internal site layouts that algorithmically integrate humanitarian spatial standards from multiple institutional sources. This study develops a generative design framework integrating parametric modelling with multi-objective evolutionary optimisation to address this gap. It translates qualitative standards from the SPHERE Association, UNHCR, and national guidelines into quantitative design parameters for temporary housing areas. The methodology proceeds in three stages: (1) systematic extraction and synthesis of spatial parameters from international (SPHERE, UNHCR) and national (AFAD, Chamber of Urban Planners) sources; (2) parametric modelling in Rhino-Grasshopper® to encode design parameters; (3) multi-objective optimisation using NSGA-II genetic algorithms to balance shelter capacity maximisation and 500-m pedestrian accessibility to service hubs. Applied to Ümraniye National Garden, a pre-designated 15-ha temporary housing site in Istanbul, the framework generated 2500 design alternatives, identifying 50 Pareto-optimal configurations spanning capacity-accessibility trade-offs from high-density solutions (1737 units, 19% accessible within 500 m) to accessibility-optimised layouts (1222 units, 92% accessible). This research contributes a replicable, standards-informed computational workflow that systematically reconciles multi-source humanitarian standards and generates site layouts through multi-objective optimisation, advancing beyond component-level optimisation and evaluation-focused approaches. By providing decision-makers with diverse Pareto-optimal alternatives rather than single predetermined solutions, the framework shifts temporary housing design from static manual drafting toward agile, evidence-based generative processes suitable for crisis decision-making contexts. • Research framework systematically integrates national and international humanitarian standards into algorithmic parameters. • Multi-objective optimisation using NSGA-II balances shelter capacity maximisation with 500-m pedestrian accessibility to essential services. • Pareto-optimal solutions reveal non-linear capacity-accessibility trade-offs, with service hub centrality as the primary performance determinant. • Computational approach enables standards-compliant layout generation within operationally viable timeframes, shifting toward evidence-based generative decision support.
Tak et al. (Thu,) studied this question.