Document Type : Research Paper


Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.


Construction Site Layout Planning (CSLP) is an important problem because of its impact on time, cost, productivity, and safety of the projects. In this paper, CSLP is formulated by the Quadratic Assignment Problem (QAP). At first, two case studies including equal and un-equal area facilities are solved by the simulated annealing optimization algorithm. Then, the obtained results are compared with the other papers. The comparisons show that the proposed Simulated Annealing (SA) is as efficient as ACO, PSO, CBO, ECBO, WOA, WOA-CBO, and ACO-PA which have been proposed by other papers for the same problems. As a result, the comparisons show that SA is as capable as other meta-heuristics of solving the combinatorial optimization problems like CSLP, while the hardware properties and computational times have been compared. Besides the comparisons, the design of experiments shows the relationship between each SA parameter and the computational time of the algorithm. Also, the history of convergence of the proposed SA indicates the high speed of reaching the optimal solution and the artificial intelligence of the proposed SA.


Main Subjects

[1]     Liao, T. W., Egbelu, P. J., Sarker, B. R., & Leu, S. S. (2011). Metaheuristics for project and construction management–A state-of-the-art review. Automation in construction20(5), 491-505.
[2]     Li, H., & Love, P. E. (2000). Genetic search for solving construction site-level unequal-area facility layout problems. Automation in construction9(2), 217-226.
[3]     Li, H., & Love, P. E. (1998). Site-level facilities layout using genetic algorithms. Journal of computing in civil engineering12(4), 227-231.
[4]     Andayesh, M., & Sadeghpour, F. (2014). The time dimension in site layout planning. Automation in construction44, 129-139.
[5]     Sadeghpour, F., & Andayesh, M. (2015). The constructs of site layout modeling: an overview. Canadian journal of civil engineering42(3), 199-212.
[6]     Yeh, I. C. (1995). Construction-site layout using annealed neural network. Journal of computing in civil engineering9(3), 201-208.
[7]     Li, H., & Love, P. E. (2000). Genetic search for solving construction site-level unequal-area facility layout problems. Automation in construction9(2), 217-226.
[8]     Tam, C. M., Tong, T. K., & Chan, W. K. (2001). Genetic algorithm for optimizing supply locations around tower crane. Journal of construction engineering and management127(4), 315-321.
[9]     Tam, C. M., Tong, T. K., Leung, A. W., & Chiu, G. W. (2002). Site layout planning using nonstructural fuzzy decision support system. Journal of construction engineering and management128(3), 220-231.
[10]  Cheung, S. O., Tong, T. K. L., & Tam, C. M. (2002). Site pre-cast yard layout arrangement through genetic algorithms. Automation in Construction11(1), 35-46.
[11]  [11] H. Zhang, J.Y. Wang, Particle swarm optimization for construction site unequal-area layout, Journal of construction engineering and management, 134 (2008) 739-748.
[12]  Lien, L. C., & Cheng, M. Y. (2012). A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization. Expert systems with applications39(10), 9642-9650.
[13]  Gharaie, E., Afshar, A., & Jalali, M. R. (2006, February). Site layout optimization with ACO algorithm. Proceedings of the 5th WSEAS international conference on artificial intelligence, knowledge engineering and data bases (pp. 90-94). World Scientific and Engineering Academy and Society (WSEAS).
[14]  Calis, G., & Yuksel, O. (2015). An improved ant colony optimization algorithm for construction site layout problems. Journal of building construction and planning research3(04), 221.
[15]  Kaveh, A., Khanzadi, M., Alipour, M., & Moghaddam, M. R. (2016). Construction site layout planning problem using two new meta-heuristic algorithms. Iranian journal of science and technology, transactions of civil engineering40(4), 263-275.
[16]  Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science220(4598), 671-680.
[17]  Abbasi, B., Shadrokh, S., & Arkat, J. (2006). Bi-objective resource-constrained project scheduling with robustness and makespan criteria. Applied mathematics and computation180(1), 146-152.
[18]  Park, M. W., & Kim, Y. D. (1998). A systematic procedure for setting parameters in simulated annealing algorithms. Computers & operations research25(3), 207-217.
[19]   Kaveh, A., & Rastegar Moghaddam, M. (2018). A hybrid WOA-CBO algorithm for construction site layout planning problem. Scientia iranica25(3), 1094-1104.