Role of Evolutionary Algorithms in Construction Projects Scheduling
Keywords:
Evolutionary Algorithms (EAs), Construction Scheduling, Multi-Objective Optimization (MOO)Abstract
Due to the increase in the stakeholders and their objectives the construction projects have significantly been affected by the ongoing demands leading to increase in complexity of scheduling problems, research in the field of Multi-Objective Optimization (MOO) have increased significantly. Through their population-based search methodologies, Evolutionary Algorithms drove attention to their efficiency in addressing scheduling problems involving two or three objectives. Genetic Algorithms (GA) particularly have been used in most of the construction optimization problems due to their ability to provide near-optimal Pareto solutions in a reasonable amount of time for almost all objectives. However, when optimizing more than three objectives, the efficiency of such algorithms degrades and trade-offs among conflicting objectives must be made to obtain an optimal Pareto Frontier. To address that, this paper aims to provide a comparative analysis on four evolutionary algorithms (Genetic algorithms – Memetic algorithms – Particle Swarm – Ant colony) in the field of construction scheduling optimization, gaps are addressed, and recommendations are proposed for future research development.
References
V. To?an and M. A. Eirgash, "Time-Cost Trade-off Optimization of Construction Projects using Teaching Learning Based Optimization," KSCE Journal of Civil Engineering, vol. 23, pp. 10-20, 2019/01/01 2019.
Z. Han, N. Cao, G. Liu, and W. Yan, "MOPSO for BIM: a multi-objective optimization tool using particle swarm optimization algorithm on a BIMbased visual programming platform," 2019.
R. Evins, "A review of computational optimisation methods applied to sustainable building design," Renewable and sustainable energy reviews, vol. 22, pp. 230-245, 2013.
A. Panwar and K. N. Jha, "A many-objective optimization model for construction scheduling," Construction Management and Economics, vol. 37, pp. 727-739, 2019/12/02 2019.
H. Adeli and A. Karim, Construction scheduling, cost optimization and management: CRC Press, 2001.
J. Zhou, P. E. D. Love, X. Wang, K. L. Teo, and Z. Irani, "A review of methods and algorithms for optimizing construction scheduling," Journal of the Operational Research Society, vol. 64, pp. 1091-1105, 2013/08/01 2013.
B. Dasovi?, M. Gali?, and U. Klanšek, "A Survey on Integration of Optimization and Project Management Tools for Sustainable Construction Scheduling," Sustainability, vol. 12, p. 3405, 2020.
M. König and U. Beißert, "Construction scheduling optimization by simulated annealing," in Proc. of the 26th Annual International Symposium on Automation and Robotics in Construction, Texas, USA, 2009.
V. Machairas, A. Tsangrassoulis, and K. Axarli, "Algorithms for optimization of building design: A review," Renewable and sustainable energy reviews, vol. 31, pp. 101-112, 2014.
C. A. C. Coello, G. B. Lamont, and D. A. Van Veldhuizen, Evolutionary algorithms for solving multi-objective problems vol. 5: Springer, 2007.
V. Venkrbec, M. Gali?, and U. Klanšek, "Construction process optimisation–review of methods, tools and applications," Gra?evinar, vol. 70, pp. 593-606, 2018.
P. Ballesteros-Pérez, K. M. Elamrousy, and M. C. González-Cruz, "Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking," Automation in Construction, vol. 97, pp. 229-240, 2019.
M. Løvbjerg, "Improving particle swarm optimization by hybridization of stochastic search heuristics and self-organized criticality," 2002.
E. Elbeltagi, T. Hegazy, and D. Grierson, "Comparison among five evolutionary-based optimization algorithms," Advanced Engineering Informatics, vol. 19, pp. 43-53, 2005/01/01/ 2005.
M. Janga Reddy and D. Nagesh Kumar, "Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review," H2Open Journal, vol. 3, pp. 135-188, 2020.
H. R. Maier, S. Razavi, Z. Kapelan, L. S. Matott, J. Kasprzyk, and B. A. Tolson, "Introductory overview: Optimization using evolutionary algorithms and other metaheuristics," Environmental Modelling & Software, vol. 114, pp. 195-213, 2019/04/01/ 2019.
V. Bhosale, S. Shastri, and M. Khandare, "A review of genetic algorithm used for optimizing scheduling of resource constraint construction projects," International Research Journal of Engineering and Technology, vol. 4, pp. 2869-2872, 2017.
H. Al?Tabtabai and A. P. Alex, "Using genetic algorithms to solve optimization problems in construction," Engineering, Construction and Architectural Management, vol. 6, pp. 121-132, 1999.
S. Shabir and R. Singla, "A comparative study of genetic algorithm and the particle swarm optimization," International Journal of electrical engineering, vol. 9, pp. 215-223, 2016.
S. Katoch, S. S. Chauhan, and V. Kumar, "A review on genetic algorithm: past, present, and future," Multimedia Tools and Applications, vol. 80, pp. 8091-8126, 2021/02/01 2021.
M. Marseguerra, E. Zio, and S. Martorell, "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering & System Safety, vol. 91, pp. 977-991, 2006/09/01/ 2006.
T. Point. (MAY). Genetic Algorithms - Parent Selection. Available: https://www.tutorialspoint.com/genetic_algorithms/genetic_algorithms_parent_selection.htm#
V. Faghihi, K. F. Reinschmidt, and J. H. Kang, "Construction scheduling using Genetic Algorithm based on Building Information Model," Expert Systems with Applications, vol. 41, pp. 7565-7578, 2014/11/15/ 2014.
J. E. Baker, "Reducing bias and inefficiency in the selection algorithm," in Proceedings of the second international conference on genetic algorithms, 1987, pp. 14-21.
H. F. Rahman, R. K. Chakrabortty, and M. J. Ryan, "Memetic algorithm for solving resource constrained project scheduling problems," Automation in Construction, vol. 111, p. 103052, 2020.
S. M. Lim, A. B. M. Sultan, M. N. Sulaiman, A. Mustapha, and K. Y. Leong, "Crossover and mutation operators of genetic algorithms," International journal of machine learning and computing, vol. 7, pp. 9-12, 2017.
M. Hamdan, "On the disruption-level of polynomial mutation for evolutionary multi-objective optimisation algorithms," Computing and Informatics, vol. 29, pp. 783-800, 2010.
L. S. MOOI, "Crossover and mutation operators of real coded genetic algorithms for global optimization problems," 2016.
J. C. Ferreira, C. M. Fonseca, and A. Gaspar-Cunha, "Methodology to select solutions from the pareto-optimal set: a comparative study," in Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007, pp. 789-796.
K. Deb and J. Sundar, "Reference point based multi-objective optimization using evolutionary algorithms," in Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006, pp. 635-642.
K. Miettinen, "Nonlinear multiobjective optimization kluwer academic publishers," Boston, Massachusetts, 1999.
K. Sharma and M. K. Trivedi, "Development of Multi-Objective Scheduling Model for Construction Projects Using Opposition-Based NSGA III," Journal of The Institution of Engineers (India): Series A, vol. 102, pp. 435-449, 2021/06/01 2021.
P. Merz and B. Freisleben, "A Genetic Local Search Approach to the Quadratic Assignment Problem," Proceedings of the 7th international conference on genetic algorithms, 06/23 1997.
P. Moscato, L. Plata, and M. Norman, "A "Memetic" Approach for the Traveling Salesman Problem Implementation of a Computational Ecology for Combinatorial Optimization on Message-Passing Systems," vol. 1, 07/22 1999.
S. Singh, A. Chand, and S. Lal, Improving Spam Detection Using Neural Networks Trained by Memetic Algorithm, 2013.
J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95-international conference on neural networks, 1995, pp. 1942-1948.
R. Poli, J. Kennedy, and T. Blackwell, "Particle swarm optimization," Swarm intelligence, vol. 1, pp. 33-57, 2007.
H. Zhang and F. Xing, "Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction," Automation in Construction, vol. 19, pp. 1067-1075, 2010.
H. Zhao, H. Zhou, and G. Yang, "Research on Global Path Planning of Artificial Intelligence Robot Based on Improved Ant Colony Algorithm," Journal of Physics: Conference Series, vol. 1744, p. 022032, 02/01 2021.
L. Zuo, L. Shu, S. Dong, C. Zhu, and T. Hara, "A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing," IEEE Access, vol. 3, pp. 2687-2699, 2015.
Y. Li and Y. He, "Multi-objective optimization of construction project based on improved ant colony algorithm," Tehni?ki vjesnik, vol. 27, pp. 184-190, 2020.
A. Slowik and H. Kwasnicka, "Evolutionary algorithms and their applications to engineering problems," Neural Computing and Applications, vol. 32, pp. 12363-12379, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 International Journal of Sciences: Basic and Applied Research (IJSBAR)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
