Response Surface Methodology using desirability functions for multiobjective optimization to minimize indoor overheating hours and maximize useful daylight illuminance

Improving thermal comfort often impacts daylight, creating trade-offs that remain underexplored, particularly in tropical dwellings. Overheating metrics-essential for assessing thermal conditions in warm regions-are entirely absent from daylight performance analysis. Response Surface Methodology (RSM) and desirability functions were employed to optimize the thermal and daylight performance of a typical low-rise tropical housing typology. Specifically, this approach simultaneously optimized Indoor Overheating Hours (IOH) and Useful Daylight Illuminance (UDI) metrics through an Overall Desirability (D). Each response required only 138 simulation runs (~ 30 h: 276 runs) to determine optimal values for passive strategies: window-to-wall ratio (WWR) and roof overhang depth across four orientations (eight factors). Initial screening based on [Formula: see text] fractional factorial design, identified four key factors using stepwise and Lasso regression, narrowed down to three: roof overhang depth on the south and west, WWR on the west, and WWR on the south. Then, RSM optimization yielded an optimal solution (west/south roof overhang: 3.78 m, west WWR: 3.76%, south WWR: 29.3%) with a D of 0.625 (IOH: 8.33%, UDI: 79.67%). Finally, robustness analysis with 1,000 bootstrap replications provided 95% confidence intervals for the optimal values. This study balances thermal comfort and daylight with few experiments using a computationally-efficient multiobjective approach.