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This dissertation, A Fuzzy Genetic Algorithms (GAs) Model for Time-cost Optimization in Construction by Xinmin, Zheng, 鄭新敏, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled A FUZZY GENETIC ALGORITHMS (GAs) MODEL FOR TIME-COST OPTIMIZATION IN CONSTRUCTION Submitted by Zheng Xinmin for the degree of Master of Philosophy at The University of Hong Kong in June, 2003 Time-cost optimization (TCO) is one of the greatest challenges in a competitive construction environment, because the necessary trade-off between project time and cost usually leads to the optimization of one normally cost at the expense of the other. This in turn requires the contracting organizations to carefully evaluate various alternatives to attain an optimal time-cost equilibrium. With the increasing popularity of alternative construction procurement methods, iiitraditional time-cost models that seek to minimize total cost, while relating the factor of time into feasible constraints have come under criticism. In some cases, such as highway construction projects involving major inconvenience to the public, completion time is a major priority in bidding, and it can be converted to a kind of saving/loss through incentive/disincentive mechanisms. The aim of this study was to develop a TCO model based on artificial intelligence (AI), which could absorb and exploit relevant information from previous generations and balance the the competing demands in an optimal manner. It also sought to explore the potentials of AI and some economic tools for simulating the uncertainties inherent in the construction environment and the fuzzy decision-making process of human beings. This study used genetic algorithms (GAs) to assign adaptive weights to time and cost according to their performance in previous generations, in order to achieve the appropriate balance. It also incorporated fuzzy sets (FSs) theory, to model project managers' predictions of time and cost in stochastic scenarios, since the duration and cost of projects can change dynamically because so many variables are involved. FSs theory ivwas also used to establish optimal time-cost profiles under different risk levels. Besides these AI techniques, the economic analysis tools of utility theory and opportunity cost were also integrated into the model to approximate the rational decision-making process of project managers. The robustness of the TCO model was tested with two simple cases under deterministic and stochastic conditions. The results compared favorably with the findings of other previously published models. The new model can therefore provide managers with the flexibility to analyze their decisions in a more realistic manner. DOI: 10.5353/th_b2751083 Subjects: Construction industry - Cost control - Data processingConstruction industry - Management - Data processingGenetic algorithms

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