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Optimization of site selection for construction and demolition waste recycling plant using genetic algorithm

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • Published:
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Abstract

With regard to the site selection of construction and demolition of waste recycling plants in China, an optimization model for the site selection of a recycling plant was constructed using a genetic algorithm, and an empirical study was conducted with Panyu and Nansha Districts of Guangzhou City as examples. The study shows that the optimal solution obtained on optimizing the site selection of a construction and demolition waste recycling plant using a genetic algorithm conforms to the actual investigation. The optimal solution using the genetic algorithm was obtained after only 200 iterations, at which point the fitness value converges at a stable value of 1.8 × 10−5, which proves the rationality and operability of the site-selection optimization model. However, given the slow evolutionary speed of the genetic algorithm, it is easy to fall into a local optimum. Thus, its improvement using a tabu algorithm is necessary. The research results can provide the government with a theoretical basis for the site selection of construction and demolition waste recycling plants.

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Acknowledgements

The research was supported by the National Natural Science Foundation of China (71501052). The author would like to acknowledge the valuable suggestions of the editor and three anonymous reviewers.

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Authors and Affiliations

  1. Department of Construction Management, School of Management, Guangzhou University, Guangzhou, 510006, China

    Jingkuang Liu, Yanqing Xiao, Dong Wang & Yongshi Pang

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  1. Jingkuang Liu
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  2. Yanqing Xiao
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  3. Dong Wang
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  4. Yongshi Pang
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Correspondence to Yanqing Xiao.

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Liu, J., Xiao, Y., Wang, D. et al. Optimization of site selection for construction and demolition waste recycling plant using genetic algorithm. Neural Comput & Applic 31 (Suppl 1), 233–245 (2019). https://doi.org/10.1007/s00521-018-3730-8

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  • DOI: https://doi.org/10.1007/s00521-018-3730-8

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