Abstract
This paper presents a time-cost-carbon emissions trade-off problem with a hybrid genetic algorithm, to a large-scale construction project in China. The objective functions in this paper are to minimize the total project time, to minimize the total penalty cost, and to minimize carbon emissions. After describing the working procedure of the problem in the project, it gives the mathematical formulation model. Furthermore, because traditional optimization techniques could not cope with the problem effectively, we present a new approach based on the hybrid genetic algorithm (hGA). An adaptive hybrid genetic algorithm is developed for finding feasible solutions. Finally, a large scale construction projects is used as a practical example to demonstrate the efficiency and practicality of the model. Results are presented to highlight the performances of the optimization method, which is very efficient and effective as compared to other algorithms.
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Acknowledgements
The humanities and Social Science Youth Fund Project of the Ministry of Education (14XJC630011); Chongqing science and Technology Commission foundation and frontier research project project (cstc2016jcyjA0442); Science and technology research project of the Chongqing Municipal Education Committee (KJ1600608); The research project of the humanities and social sciences of the Chongqing Municipal Education Committee (16SKJD32); Central group of collaborative innovation of urban industrial development in Chengyu economic zone of Chongqing technology and business university (Research on multi objective Tradeoff Problem of large-scale construction project scheduling based on multi-level decision making).
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Zheng, H., Bai, H. (2019). Multi-Objective Optimization Problem for Hydroelectric Construction Project and Strategy Analysis Based on Carbon Emissions. In: Xu, J., Cooke, F., Gen, M., Ahmed, S. (eds) Proceedings of the Twelfth International Conference on Management Science and Engineering Management. ICMSEM 2018. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93351-1_40
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DOI: https://doi.org/10.1007/978-3-319-93351-1_40
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