Neural Computing and Applications

, Volume 31, Supplement 1, pp 233–245 | Cite as

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

  • Jingkuang Liu
  • Yanqing XiaoEmail author
  • Dong Wang
  • Yongshi Pang
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


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.


Construction and demolition waste (C&DW) Optimization Genetic algorithm Convergence 



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.


  1. 1.
    Yang H, Xia JQ, Thompson JR, Flower RJ (2017) Urban construction and demolition waste and landfill failure in Shenzhen, China. Waste Manag 63:393–396CrossRefGoogle Scholar
  2. 2.
    Guangzhou Municipal Government (GMG) (2012) Layout planning of Guangzhou construction waste storage plant (2012–2020. Accessed 8 June 2012
  3. 3.
    Wang D, Liu J, Chen Y (2018) Distinguishing investment changes in metro construction project based on a factor space algorithm. Clust Comput.
  4. 4.
    Wang D, Liu J, Wang X et al (2017) Cost-effectiveness analysis and evaluation of a ‘three-old’ reconstruction project based on smart system. Clust Comput.
  5. 5.
    ReVelle CS, Eiselt HA (2005) Location analysis: a synthesis and survey. Eur J Oper Res 165:1–19MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Zhao W, Leeftink RB, Rotter VS (2010) Evaluation of the economic feasibility for recycling of construction and demolition waste in China—the case of Chongqing. Resour Conserv Recycl 54:377–389CrossRefGoogle Scholar
  7. 7.
    Banias G, Achillas C, Vlachokostas C, Moussiopoulos N, Tarsenis S (2010) Assessing multiple criteria for the optimal location of a construction and demolition waste management facility. Build Environ 45:2317–2326CrossRefGoogle Scholar
  8. 8.
    Khadivi MR, Fatemi Ghomi SMT (2012) Solid waste facilities location using of analytical network process and data envelopment analysis approaches. Waste Manag 32:1258–1265CrossRefGoogle Scholar
  9. 9.
    Aragonés-Beltrán P, Pastor-Ferrando JP, García-García F, Pascual-Agulló A (2010) An Analytic Network Process approach for siting a municipal solid waste plant in the metropolitan area of Valencia (Spain). J Environ Manag 91:1071–1086CrossRefGoogle Scholar
  10. 10.
    Zhou D (2010) The research on present situation of China’s circular economy policy, environment and the orientation. J Lanzhou Commer Coll 02:44–48Google Scholar
  11. 11.
    Nian TK, Zheng DF, Luan MT (2004) Fuzzy sets theory for multi-objective system and its application to site selection of municipal solid wastes landfill. Rock Soil Mech 25(4):574–578Google Scholar
  12. 12.
    Lu XF, Cai LN, Qu ZW (2005) The location-routing problem in the municipal solid waste logistics system. Syst Eng Theory Pract 5:89–94Google Scholar
  13. 13.
    Lu XF (2007) Study on fundamental logistics optimization for the construction industry. Harbin Institute of Technology, HarbinGoogle Scholar
  14. 14.
    He B, Yang C, Ren MM (2007) Undesirable facility location problem using a multi-objective evolutionary algorithm. Syst Eng Theory Pract 11:72–78Google Scholar
  15. 15.
    Wang D, Chen Y, Jing X, (2017) Knowledge Management of Web Financial Reporting in Human-Computer Interactive Perspective. EURASIA J Math Sci Technol Educ 13(7):3349–3373Google Scholar
  16. 16.
    Ia CX, Peng XY, Liu GT, Liu CW, Wu X, Deng JJ (2006) Establishment of optmiization model for location of municipal solid waste transfer station and its application. Acta Sci Circumst 26(11):1927–1931Google Scholar
  17. 17.
    Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 2nd edn. MIT Press, CambridgeCrossRefGoogle Scholar
  18. 18.
    Chen Y, Wang D, Bi G (2018) An image edge recognition approach based on multi-operator dynamic weight detection in virtual reality scenario. Clust Comput.
  19. 19.
    Wang D, Chen Y, Chen D (2018) Efficiency optimization and simulation to manufacturing and service systems based on manufacturing technology Just-In-Time. Pers Ubiquit Comput.
  20. 20.
    Yue CY (2003) Decision theory and methods. The Science Press, BeijingGoogle Scholar
  21. 21.
    Onwubolu GC (2002) Emerging optimization techniques in production planning and control. Comput Aided Des 6:747–757MathSciNetzbMATHGoogle Scholar
  22. 22.
    Liu H, Chen Y, Zha Y et al (2018) The effect of satisfaction on loyalty in consumption and service industry based on meta-analysis and it’s algorithm. Wirel Pers Commun.
  23. 23.
    Liu JK, Ma YL, Pang YS, Zhou WS, Nie YP (2017) The optimization of the site selection of the companies majored in resourceful disposal of construction waste based on ELECTRE III—a case study of Guangzhou Panyu and Nansha Districts. Guangzhou Archit 145(2):12–17Google Scholar
  24. 24.
    Liu JK, Nie YP, Pang YS, Deng ZT (2017) An analytic network process approach for the optimal location of a construction and demolition waste management facility. Proj Manag Technol 15(12):19–25Google Scholar
  25. 25.
    Wang D, Zha Y, Bi G et al (2018) A meta-analysis of satisfaction-loyalty relationship in e-commerce: sample and measurement characteristics as moderators. Wirel Pers Commun.
  26. 26.
    Giannikos L (1998) A multi-objective programming model for locating treatment sites and routing hazardous wastes. Eur J Oper Res 104:333–342CrossRefzbMATHGoogle Scholar
  27. 27.
    Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley Press, ChichesterzbMATHGoogle Scholar
  28. 28.
    Tan KC, Lee TH, Khor EF (2001) Evolutionary algorithm with dynamic population size and local exploration for multi-objective optimization. IEEE Trans Evol Comput 5(6):565–588CrossRefGoogle Scholar
  29. 29.
    Xie T, Chen HW, Kang LS (2003) Evolutionary algorithms of multi-objective optimization problem. Chin J Comput 26(8):997–1003MathSciNetGoogle Scholar
  30. 30.
    Garrido E, Calvo F, Ramos AF, Zamorano M (2005) Methodology of environmental diagnosis for construction and demolition waste landfills: a tool for planning and making decisions. Environ Technol 26(11):1231–1242CrossRefGoogle Scholar
  31. 31.
    Wang W, Zmeureanu R, Rivard H (2005) Applying multi-objective genetic algorithms in gree building design optimization. Build Environ 40:1512–1525CrossRefGoogle Scholar
  32. 32.
    Buenrostro-Delgado O, Ortega-Rodriguez JM, Clemitshaw KC, González-Razo C, Hernández-Paniagua IY (2015) Use of genetic algorithms to improve the solid waste collection service in an urban area. Waste Manag 41:20–27CrossRefGoogle Scholar
  33. 33.
    Constantinos C, Dimitrios K (2012) A methodology to optimally site and design municipal solid waste transfer stations using binary programming. Resour Conserv Recycl 60:89–98CrossRefGoogle Scholar
  34. 34.
    Lv XY, Zheng SY (2017) application of fuzzy clustering genetic algorithm to military equipment logistics center location. Comput Systems Appl 26(12):170–174Google Scholar
  35. 35.
    Hu DW, Chen C (2007) Application of GA and TS in logistics distribution center location and routing problem. Syst Eng Theory Pract 9:171–176Google Scholar
  36. 36.
    Yuan Q, Zou Y (2016) Selection of cold chain logistics distribution center location based on improved hybrid genetic algorithm. J Shanghai Jiao Tong Univ 50(11):1795–1800MathSciNetzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Jingkuang Liu
    • 1
  • Yanqing Xiao
    • 1
    Email author
  • Dong Wang
    • 1
  • Yongshi Pang
    • 1
  1. 1.Department of Construction Management, School of ManagementGuangzhou UniversityGuangzhouChina

Personalised recommendations