Skip to main content

Construction Site Layout Planning Using Colliding Bodies Optimization and Enhanced Colliding Bodies Optimization

  • Chapter
  • First Online:

Abstract

In this chapter, two recently developed metaheuristic algorithms, so-called CBO and ECBO, are employed for construction site layout planning. Results show that both of these algorithms have the capability of solving this kind of problem. Two case studies are presented to show the applicability and performance of the utilized methods [1].

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Kaveh A, Khanzadi M, Alipour M, Rastegar Moghaddam M (2016) Application of two new meta-heuristic algorithms for construction site layout planning problem. Iran J Sci Technol 40(4):263–275

    Google Scholar 

  2. Wong CK, Fung IWH, Tam CM (2010) Comparison of using mixed-integer programming and genetic algorithms for construction site facility layout planning. J Constr Eng Manag 136(10):1116–1128

    Article  Google Scholar 

  3. Adrian AM, Utamima A, Wang KJ (2014) A comparative study of GA, PSO and ACO for solving construction site layout optimization. KSCE J Civil Eng 19(3):520–527

    Article  Google Scholar 

  4. Lam KC, Tang CM, Lee WC (2005) Application of the entropy technique and genetic algorithms to construction site layout planning of medium-size projects. Constr Manag Econ 23(2):127–145

    Article  Google Scholar 

  5. Yeh IC (2006) Architectural layout optimization using annealed neural network. Autom Constr 15(4):531–539

    Article  Google Scholar 

  6. Said H, El-Rayes K (2013) Performance of global optimization models for dynamic site layout planning of construction projects. Autom Constr 36:71–78

    Article  Google Scholar 

  7. Ning X, Lam KC, Lam MCK (2010) Dynamic construction site layout planning using max–min ant system. Autom Constr 19(1):55–65

    Article  Google Scholar 

  8. Tate DM, Smith AE (1995) Unequal-area facility layout by genetic search. IIE Trans 27(4):465–472

    Article  Google Scholar 

  9. Azarbonyad H, Babazadeh R (2012) A genetic algorithm for solving quadratic assignment problem (QAP). In Proceeding of 5th International Conference of Iranian Operations Research Society (ICIORS), Tabriz, Iran, pp 2–5

    Google Scholar 

  10. Yeh IC (1995) Construction-site layout using annealed neural network. J Comput Civil Eng 9(July):201–208

    Article  Google Scholar 

  11. Sanad HM, Ammar MA, Ibrahim ME (2008) Optimal construction site layout considering safety and environmental aspects. J Constr Eng Manag 134(7):536–544

    Article  Google Scholar 

  12. Tommelein ID, Levitt RE, Hayes-Roth B (1992) Site-layout modeling: how can artificial intelligence help? J Constr Eng Manag ASCE 118(3):594–611

    Article  Google Scholar 

  13. Garey MR, Johnson DS (1979) A guide to the theory of NP-completeness. A series of books in the mathematical sciences. W. H. Freeman, New York

    Google Scholar 

  14. Cheung SO, Tong TKL, Tam CM (2002) Site pre-cast yard layout arrangement through genetic algorithms. Autom Constr 11:35–46

    Article  Google Scholar 

  15. Li H, Love PED (1998) Site-level facilities layout using genetic algorithms. J Comput Civil Eng 12(October):227–231

    Article  Google Scholar 

  16. Li H, Love EDP (2000) Genetic search for solving construction site-level unequal-area facility layout problems. Autom Constr 9(2):217–226

    Article  Google Scholar 

  17. Zouein PP, Harmanani H, Hajar A (2002) Genetic algorithm for solving site layout problem with unequal-size and constrained facilities. J Comput Civil Eng 16(2):143

    Article  Google Scholar 

  18. Mavadesley MJ, Al-Jibouri SH, Yang H (2002) Genetic algorithms for construction site layout in project planning. J Constr Eng Manag 128(October):418–426

    Article  Google Scholar 

  19. Mavadesley MJ, Al-Jibouri SH (2003) Proposed genetic algorithms for construction site layout. Eng Appl Artif Intell 16(5–6):501–509

    Article  Google Scholar 

  20. Osman HM, Georgy ME, Ibrahim ME (2003) A hybrid CAD-based construction site layout planning system using genetic algorithms. Autom Constr 12(6):749–764

    Article  Google Scholar 

  21. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan

    Google Scholar 

  22. Zhang H, Wang JY (2008) Particle swarm optimization for construction site unequal-area layout. J Constr Eng Manag 134(9):739–748

    Article  Google Scholar 

  23. Xu J, Li Z (2012) Multi-objective dynamic construction site layout planning in fuzzy random environment. Autom Constr 27:155–169

    Article  Google Scholar 

  24. Lien LC, Cheng MY (2012) A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization. Expert Syst Appl 39(10):9642–9650

    Article  Google Scholar 

  25. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26:29–41

    Article  Google Scholar 

  26. Lam K, Ning X, Ng T (2007) The application of the ant colony optimization algorithm to the construction site layout planning problem. Constr Manag Econ 25(4):359–374

    Article  Google Scholar 

  27. Gharaie E, Afshar A, Jalali M (2006) Site layout optimization with ACO algorithm. In: Proceedings of the 5th WSEAS international conference on artificial intelligence, pp 90–94

    Google Scholar 

  28. Kaveh A, Mahdavi VR (2014) Colliding bodies optimization: a novel meta-heuristic method. Comput Struct 139:18–27

    Article  Google Scholar 

  29. Kaveh A, Ilchi Ghazaan M (2014) Enhanced colliding bodies optimization for design problems with continuous and discrete variables. Adv Eng Softw 77:66–75

    Article  Google Scholar 

  30. Tam CM, Tong KL, Chan Wilson KW (2001) Genetic algorithm for optimizing supply locations around tower crane. Constr Eng Manag 127(4):315–321

    Article  Google Scholar 

  31. Hegazy T, Elbeltagl E (1999) EvoSite: evolution-based model for site layout planning. J Comput Civil Eng 13(3):198–206

    Article  Google Scholar 

  32. Zouein PP, Tommelein ID (1999) Dynamic layout planning using a hybrid incremental solution method. J Constr Eng Manag 5(January):1–16

    Google Scholar 

  33. Kaveh A (2014) Advances in metaheuristic algorithms for optimal design of structures. Springer International Publishing, Switzerland

    Book  MATH  Google Scholar 

  34. Zhang H, Li H, Tam CM (2006) Permutation-based particle swarm optimization for resource-constrained project scheduling. Delay 24(1):83–92

    Google Scholar 

  35. Liang LY, Chao WC (2008) The strategies of tabu search technique for facility layout optimization. Autom Constr 17(6):657–669

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kaveh, A. (2017). Construction Site Layout Planning Using Colliding Bodies Optimization and Enhanced Colliding Bodies Optimization. In: Applications of Metaheuristic Optimization Algorithms in Civil Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-48012-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48012-1_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48011-4

  • Online ISBN: 978-3-319-48012-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics