MULTI-RESOURCE ALLOCATION AND LEVELING IN MULTI-PROJECT SCHEDULING PROBLEM WITH HYBRID-CHROMOSOME NON-DOMINATED SORTING GENETIC ALGORITHM II
DOI:
https://doi.org/10.9744/duts.10.2.232-251Keywords:
resource allocation, resource leveling, multi-project scheduling problem, optimization, metaheuristic, hybrid-chromosome NSGA-IIAbstract
Multi-resource allocation and leveling in multi-project (MR-AL-MP) scheduling refers to the attempt of producing a project schedule with minimum project duration and maximum resource utilization while complying with all precedence and resource availability constraints in a multi-project environment involving multiple resources. This study proposes a model that integrates both resource allocation and leveling models into a unified framework. This study develops a modified version of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-Chromosome NSGA-II, as the optimization algorithm. For validation purposes, the performance of Hybrid-Chromosome NSGA-II is compared with two benchmark metaheuristic algorithms which are Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Symbiotic Organisms Search (MOSOS) in optimizing a case study. It is shown that the proposed model and algorithm are able to produce a set of non-dominated solutions that represent the feasible trade-off relationships between the objectives. Furthermore, the Hybrid-Chromosome NSGA-II is superior to MOPSO and MOSOS in terms of the quality, spread, and diversity of the solutions.
References
Alcaraz, J., & Maroto, C. (2001). A robust genetic algorithm for resource allocation in project scheduling. Annals of operations Research, 102(1), 83-109.
Chan, W. T., Chua, D. K., & Kannan, G. (1996). Construction resource scheduling with genetic algorithms. Journal of construction engineering and management, 122(2), 125-132.
Chen, P. H., & Shahandashti, S. M. (2009). Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Automation in Construction, 18(4), 434-443.
Coello, C. C., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE.
Cui, Y., Geng, Z., Zhu, Q., & Han, Y. (2017). Multi-objective optimization methods and application in energy saving. Energy, 125, 681-704.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
El-Rayes, K., & Jun, D. H. (2009). Optimizing resource leveling in construction projects. Journal of Construction Engineering and Management, 135(11), 1172-1180.
Guo, Y., Li, N., & Ye, T. (2009). Multiple resources leveling in multiple projects scheduling problem using particle swarm optimization. In 2009 Fifth International Conference on Natural Computation (Vol. 3, pp. 260-264). IEEE.
Hegazy, T. (1999). Optimization of resource allocation and leveling using genetic algorithms. Journal of construction engineering and management, 125(3), 167-175.
Heon Jun, D., & El-Rayes, K. (2011). Multiobjective optimization of resource leveling and allocation during construction scheduling. Journal of construction engineering and management, 137(12), 1080-1088.
Khanzadi, M., Kaveh, A., Alipour, M., & Karimi Aghmiuni, H. (2016). Application of CBO and CSS for resource allocation and resource leveling problem. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(1), 1-10.
Koulinas, G. K., & Anagnostopoulos, K. P. (2013). A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities. Automation in Construction, 31, 169-175.
Lova, A., & Tormos, P. (2001). Analysis of scheduling schemes and heuristic rules performance in resource-constrained multiproject scheduling. Annals of Operations Research, 102(1), 263-286.
Majazi Dalfard, V., & Ranjbar, V. (2012). Multi-projects scheduling with resource constraints & priority rules by the use of simulated annealing algorithm. Tehnički vjesnik, 19(3), 493-499.
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2005). Pareto multi objective optimization. In Proceedings of the 13th international conference on, intelligent systems application to power systems (pp. 84-91). IEEE.
Ponz-Tienda, J. L., Salcedo-Bernal, A., Pellicer, E., & Benlloch-Marco, J. (2017). Improved adaptive harmony search algorithm for the resource leveling problem with minimal lags. Automation in Construction, 77, 82-92.
Popescu, C. M., & Charoenngam, C. (1995). Project planning, scheduling, and control in construction: An encyclopedia of terms and applications. John wiley & Sons.
Son, J., & Mattila, K. G. (2004). Binary resource leveling model: Activity splitting allowed. Journal of construction engineering and management, 130(6), 887-894.
Sörensen, K., & Glover, F. (2013). Metaheuristics. Encyclopedia of operations research and management science, 62, 960-970.
Tran, D. H., Cheng, M. Y., & Prayogo, D. (2016). A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem. Knowledge-Based Systems, 94, 132-145.
Wu, Z., Zhang, L., Wang, Y., & Wang, K. (2008). Optimization for multi-resource allocation and leveling based on a self-adaptive ant colony algorithm. In 2008 International Conference on Computational Intelligence and Security (Vol. 1, pp. 47-51). IEEE.
Zhang, H., Li, H., & Tam, C. M. (2006). Permutation-based particle swarm optimization for resource-constrained project scheduling. Journal of computing in civil engineering, 20(2), 141-149.
Zhang, H., Li, X., Li, H., & Huang, F. (2005). Particle swarm optimization-based schemes for resource-constrained project scheduling. Automation in construction, 14(3), 393-404.
Zhao, S. L., Liu, Y., Zhao, H. M., & Zhou, R. L. (2006). GA-based resource leveling optimization for construction project. In 2006 International Conference on Machine Learning and Cybernetics (pp. 2363-2367). IEEE.
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