Optimization of Construction Resource Allocation and Levelling Using the World Hyper-Heuristic Algorithm

Authors

  • Zahra Joorbonyan * Morvarid Intelligent Industrial Systems Research Group, Iran. https://orcid.org/0000-0003-3579-830X
  • Marzieh Lamtar-Gholipoor Department of Chemical Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.

https://doi.org/10.48313/maa.v2i1.28

Abstract

Resource allocation and levelling remain critical challenges in construction management, where project tasks must be synchronized under strict time and resource constraints. Traditional scheduling techniques, such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT), efficiently define timelines but fail to handle fluctuations in resource usage. These fluctuations cause inefficiencies, idle periods, and cost overruns. This paper introduces the World Hyper-Heuristic (WHH) algorithm, an adaptive optimization framework that dynamically selects among multiple metaheuristics using reinforcement learning. WHH integrates Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Imperialist Competitive Algorithm (ICA) into a unified decision-making model. The proposed method is implemented in MATLAB and tested on a real construction project dataset involving 20 activities. Experimental results demonstrate that WHH achieves superior performance in minimizing resource fluctuation and improving levelling smoothness. Compared to GA, PSO, ACO, SA, and ICA, the proposed algorithm exhibits faster convergence, higher stability, and better adaptability to constraint complexity. These findings suggest that WHH provides a practical, intelligent framework for optimizing resource allocation and levelling in modern construction projects.

Keywords:

Resource allocation, Resource levelling, World Hyper-Heuristic, Reinforcement learning, Metaheuristics, MATLAB, Construction optimization

References

  1. [1] Mahmoodi, A., Jasemi Zergani, M., Hashemi, L., & Millar, R. (2022). Analysis of optimized response time in a new disaster management model by applying metaheuristic and exact methods. Smart and resilient transportation, 4(1), 22–42. https://doi.org/10.1108/SRT-01-2021-0002
  2. [2] Alimoradi, M., Azgomi, H., & Asghari, A. (2022). Trees social relations optimization algorithm: A new swarm-based metaheuristic technique to solve continuous and discrete optimization problems. Mathematics and computers in simulation, 194, 629–664. https://doi.org/10.1016/j.matcom.2021.12.010
  3. [3] Khodadadi, N., Gharehchopogh, F. S., Abdollahzadeh, B., & Mirjalili, S. (2022). AMHS: Archive-based multi-objective harmony search algorithm. Proceedings of 7th international conference on harmony search, soft computing and applications (pp. 259–269). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2948-9_25%0A%0A
  4. [4] Got, A., Moussaoui, A., & Zouache, D. (2020). A guided population archive whale optimization algorithm for solving multiobjective optimization problems. Expert systems with applications, 141, 112972. https://doi.org/10.1016/j.eswa.2019.112972
  5. [5] Tejani, G. G., Sharma, S. K., Mousavirad, S. J., & Radwan, A. (2025). The two-archive multi-objective grey wolf optimization algorithm for truss structures. International journal of computational intelligence systems, 18(1), 245. https://doi.org/10.1007/s44196-025-00972-8
  6. [6] Daliri, A., Branch, K., Sheikha, M., Roudposhti, K. K., Branch, L., Alimoradi, M., & Mohammadzadeh, J. (2025). Optimized categorical boosting for gastric cancer classification using heptagonal reinforcement learning and the water optimization algorithm. 2025 7th international conference on pattern recognition and image analysis (IPRIA). IEEE. https://www.researchgate.net/profile/Arman-Daliri/publication/389465487
  7. [7] Daliri, A., Alimoradi, M., Zabihimayvan, M., & Sadeghi, R. (2024). World hyper-heuristic: A novel reinforcement learning approach for dynamic exploration and exploitation. Expert systems with applications, 244, 122931. https://doi.org/10.1016/j.eswa.2023.122931
  8. [8] Khodadadi, N., Abualigah, L., El-Kenawy, E. S. M., Snasel, V., & Mirjalili, S. (2022). An archive-based multi-objective arithmetic optimization algorithm for solving industrial engineering problems. IEEE access, 10, 106673–106698. https://doi.org/10.1109/ACCESS.2022.3212081
  9. [9] Lameesa, A., Hoque, M., Alam, M. S. Bin, Ahmed, S. F., & Gandomi, A. H. (2024). Role of metaheuristic algorithms in healthcare: A comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health. Journal of computational design and engineering, 11(3), 223–247. https://doi.org/10.1093/jcde/qwae046
  10. [10] Daliri, A., Asghari, A., Azgomi, H., & Alimoradi, M. (2022). The water optimization algorithm: A novel metaheuristic for solving optimization problems. Applied intelligence, 52(15), 17990–18029. https://doi.org/10.1007/s10489-022-03397-4%0A%0A
  11. [11] Kuo, R. J., Gosumolo, M., & Zulvia, F. E. (2019). Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining. Neural computing and applications, 31(8), 3559–3572. https://doi.org/10.1007/s00521-017-3278-z
  12. [12] Alimoradi, M., Zabihimayvan, M., Daliri, A., Sledzik, R., & Sadeghi, R. (2022). Deep neural classification of darknet traffic. Artificial intelligence research and development (pp. 105–114). IOS Press. https://doi.org/10.3233/FAIA220323
  13. [13] Tejani, G. G., Mashru, N., Patel, P., Sharma, S. K., & Celik, E. (2024). Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization. Scientific reports, 14(1), 31553. https://doi.org/10.1038/s41598-024-82918-2
  14. [14] Ghanbari, A. A., & Alaei, H. (2021). Meta-heuristic algorithms for resource management in crisis based on OWA approach. Applied intelligence, 51(2), 646–657. https://doi.org/10.1007/s10489-020-01808-y

Downloads

Published

2025-03-18

Issue

Vol. 2 No. 1 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Joorbonyan, Z. ., & Lamtar-Gholipoor, M. . (2025). Optimization of Construction Resource Allocation and Levelling Using the World Hyper-Heuristic Algorithm. Metaheuristic Algorithms With Applications, 2(1), 63-81. https://doi.org/10.48313/maa.v2i1.28

Similar Articles

You may also start an advanced similarity search for this article.