A hybrid WOA-CBO algorithm for construction site layout planning problem

Document Type : Article


1 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of ‎Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran‎

2 School of Civil Engineering, Iran University of Science and Te b 16846 - 13114 , Iran


The whale optimization algorithm (WOA) is a recently developed swarm-based optimization algorithm inspired by the hunting behavior of humpback whales. This study attempts to enhance the original formulation of the WOA by hybridizing it with some concepts of the colliding bodies optimization (CBO) in order to improve solution accuracy, reliability and convergence speed. The new method, called WOA-CBO algorithm, is applied to construction site layout planning problem. To show the efficiency and performance of the WOA and WOA-CBO in construction site layout problems, three case studies are selected. First case is a discrete and equal area facility layout problem that every facility could assign to any location. Second case is an unequal area version of discrete facility layout problem with more constraints and the last case is a continuous model of construction site layouts. These cases are studied by WOA, CBO and WOA-CBO, and the results are compared with each other.


Main Subjects

1. Ning, X., Lam, K.C., and Lam, M.C.K. \A decisionmaking
system for construction site layout planning",
Automat. Construct., 20(4), pp. 459-473 (2011).
2. Adrian, A.M. Utamima, A., and Wang, K.-J. \A
comparative study of GA, PSO and ACO for solving
construction site layout optimization", KSCE J. Civil
Eng., 19(3), pp. 520-527 (2014).
3. Sadeghpour, F. Moselhi, O., and Alkass, S.T.
\Computer-aided site layout planning", J. Construct.
Eng. Manag., 132(2), pp. 143-151 (2006).
4. Kaveh, A. and Shakouri Mahmud Abadi, A. \An
adapted harmony search based algorithm for facility
layout optimization", Int. J. Civil Eng., 10(1), pp. 1-6
5. Cheng, M.-Y. and Connor, J.T.O. \Site layout of
construction temporary facilities using an enhancedgeographic
information system (GIS)", Automat. Construct.,
3(1), pp. 11-19 (1994).
6. Yeh, I.-C. \Architectural layout optimization using annealed
neural network", Automat. Construct., 15(4),
pp. 531-539 (2006).
7. Tate, D.M. and Smith, A.E. \Unequal-area facility
layout by genetic search", IIE Trans., 27(4), pp. 465-
472 (1995).
8. Wang, M.J. Hu, M.H., and Ku, M.Y. \A solution to
the unequal area facilities layout problem by genetic algorithm",
Comput. Indust., 56(2), pp. 207-220 (2005).
9. Azarbonyad, H. and Babazadeh, R. \ A genetic
algorithm for solving quadratic assignment problem
(QAP)", arXiv:1405.5050, pp. 2-5 (2014).
10. Yeh, I.-C. \Construction-site layout using annealed
neural network", J. Comput. Civil Eng., 9, pp. 201-
208 (July 1995).
11. Sadeghpour, F. and Andayesh, M. \The constructs of
site layout modeling: an overview", Canadian J. Civil
Eng., 42(3), pp. 199-212 (2015).
12. Li, H. and Love, P.E. \Genetic search for solving construction
site-level unequal-area facility layout problems",
Automat. Construct., 9(2), pp. 217-226 (2000).
13. Cheung, S.O., Tong, T.K.L., and Tam, C.M. \Site
pre-cast yard layout arrangement through genetic algorithms",
Automat. Construct., 11, pp. 35-46 (2002).
14. Osman, H.M., Georgy, M.E., and Ibrahim, M.E. \A
hybrid CAD-based construction site layout planning
system using genetic algorithms", Automat. Construct.,
12(6), pp. 749-764 (2003).
15. El-rayes, K., Asce, M., and Khalafallah, A. \Trade-o
between safety and cost in planning construction site
layouts", J. Construct. Eng. Manag., pp. 1186-1195
(November 2005).
16. Lien, L.-C. and Cheng, M.-Y. \A hybrid swarm intelligence
based particle-bee algorithm for construction
site layout optimization", Expert Syst. Appl., 39(10),
pp. 9642-9650 (2012).
17. Zhang, H. and Wang, J.Y. \Particle swarm optimization
for construction site unequal-area layout", J.
Construct. Eng. Manag., 134(9), pp. 739-748 (2008).
18. Xu, J. and Li, Z. \Multi-objective dynamic construction
site layout planning in fuzzy random environment",
Automat. Construct., 27, pp. 155-169 (2012).
1104 A. Kaveh and M. Rastegar Moghaddam/Scientia Iranica, Transactions A: Civil Engineering 25 (2018) 1094{1104
19. Lam, K., Ning, X., and Ng, T. \The application of
the ant colony optimization algorithm to the constructionsite
layout planning problem", Construct. Manag.
Economics, 25(4), pp. 359-374 (2007).
20. Gharaie, E., Afshar, A., and Jalali, M. \Site layout
optimization with ACO algorithm", Proceedings of the
5th WSEAS, pp. 90-94 (2006).
21. Calis, G. and Yuksel, O. \An improved ant colony
optimization algorithm for construction site layout
problems", J. Build. Construct. Plan. Res., 3, pp. 221-
232 (2015).
22. Ning, X., Lam, K.-C., and Lam, M.C.-K. \Dynamic
construction site layout planning using max-min
ant system", Automat. Construct., 19(1), pp. 55-65
23. Wong, C.K., Fung, I.W.H., and Tam, C.M. \Comparison
of using mixed-integer programming and genetic
algorithms for construction site facility layout planning",
J. Construct. Eng. Manag., 136(10), pp. 1116-
1128 (2010).
24. Liang, L.Y. and Chao, W.C. \The strategies of tabu
search technique for facility layout optimization", Automat.
Construct., 17(6), pp. 657-669 (2008).
25. Kaveh, A., Khanzadi, M., Alipour, M., and Rastegar
Moghaddam, M. \Construction site layout planning
problem using two new meta-heuristic algorithms",
Iranian J. Sci. Technol.; Trans. Civil Eng., 40(4), pp.
263-275 (2016).
26. Mirjalili, S.A. and Lewis, A. \The whale optimization
algorithm", Adv. Eng. Softw., 95, pp. 51-67 (2016).
27. Kaveh, A. and Mahdavi, V.R. \Colliding bodies optimization:
A novel meta-heuristic method", Comput.
Struct., 139, pp. 18-27 (July 2014).
28. Kaveh, A. and Ilchi Ghazaan, M. \Enhanced colliding
bodies optimization for design problems with continuous
and discrete variables", Adv. Eng. Softw., 77, pp.
66-75 (2014).
29. Li, H. and Love, P.E.D. \Site-level facilities layout
using genetic algorithms", J. Comput. Civil Eng., pp.
227-231 (October 1998).