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Site layout planning is a complicated issue due to the existence of a vast number of trades and inter-related planning constraints. In this paper, artificial neural networks are used to model the non-linear operations of a key site facility: a tower crane — for high-rise public housing construction. Then genetic algorithms are used to determine the locations of the tower crane, supply points and demand points by optimizing the transportation time and costs. The scope of this study confines to a defined area of construction: the structural concrete-frame construction stage of public housing projects. The developed genetic algorithm model for site facility layout and the artificial neural network model for predicting tower-crane operations are evaluated using a practical example. The optimization results of the example are very promising and it demonstrates the application value of the models.Site Layout, Genetic Algorithms, Tower Crane, Public Housing Construction,

Site layout planning can affect productivity and is crucial to project success. However, as construction is heterogeneous in the nature of its organizations, project designs, time constraints, environmental effects, etc., site layout planning for each project becomes unique. Affected by many uncertainties ͑variables͒ and variations, site layout planning is a typical multiobjective problem. To facilitate the decision-making process for these problems, a nonstructural fuzzy decision support system ͑NSFDSS͒ is proposed. NSFDSS integrates both experts' judgment and computer decision modeling, making it suitable for the appraisal of complicated construction problems. The system allows assessments based on pairwise comparisons of alternatives using semantic operators that can provide a reliable assessment result even under the condition of insufficient precise information.