A Benchmark Learning Framework for Multi-Objective Street House Planning Incorporating Architects’ Preferences

Architectural planning often involves balancing multiple and potentially conflicting objectives, such as safety, economy, functionality, and aesthetics. However, conventional benchmarking approaches typically focus on single performance dimensions and provide limited support for multi-objective decision-making. To address this limitation, this study proposes a benchmark learning framework for multi-objective street house planning that explicitly incorporates architects’ planning preferences. The framework integrates fuzzy sets to define preference functions, indifference curves to represent trade-offs and derive preference weights, and utility functions to quantify satisfaction levels. In addition, Data Envelopment Analysis (DEA) and efficient frontier theory are employed to evaluate planning efficiency and identify optimal benchmark cases. Using empirical data from 627 street houses, the results indicate that the proposed approach effectively captures architects’ subjective preferences while providing an objective assessment of planning efficiency. The integration of indifference curves and the efficient frontier enables explicit visualization of trade-offs, whereas the combination of utility functions and efficiency analysis facilitates the identification of benchmark learning cases. The proposed framework provides a systematic approach to multi-objective optimization in architectural planning by bridging subjective decision-making with quantitative performance evaluation. It offers practical guidance for architects and planners and contributes to the advancement of benchmark-based methodologies in complex design environments.