Research on Multi-Objective Optimization of Project Scheduling based on NSGA-III: Trade-off among Project Duration, Resource Allocation, and Robustness

Authors

  • Zhengyang Ling
  • Ying Chen

DOI:

https://doi.org/10.6919/ICJE.202504_11(4).0049

Keywords:

Prefabricated Construction; Resource-Constrained Project Scheduling Problem; NSGA-III.

Abstract

As an important approach to promoting the greening and modernization of the construction industry, prefabricated buildings have been widely applied in recent years. However, current construction project management still faces issues such as decision-making reliance on experience, unreasonable resource allocation, project delays, and insufficient ability to cope with uncertainties, which affect construction efficiency and widespread adoption. Therefore, multi-objective optimization research on project duration, resource supply balance, and robustness is of great significance.This study constructs a three-objective optimization model for prefabricated building construction, comprehensively considering the impact of project duration on economic benefits, the regulatory role of resource supply balance in construction efficiency, and the criticality of robustness in addressing construction risks. The NSGA-III algorithm is employed to optimize project duration, resource balance, and robustness, with a reference point strategy incorporated to enhance algorithm efficiency. Through validation with real-world case studies, the results demonstrate that the proposed method effectively balances the three objectives and improves construction project management.

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References

[1] Naber A, Kolisch R, Bianco L, et al. The resource-constrained project scheduling model of Bianco and Caramia: clarifications and an alternative model formulation[J]. Flexible Services and Manufacturing Journal, 2014, 26: 454-459.

[2] Wang H, Li T, Lin D. Efficient genetic algorithm for resource-constrained project scheduling problem[J]. Transactions of Tianjin University, 2010, 16: 376-382.

[3] Koné O, Artigues C, Lopez P, et al. Comparison of mixed integer linear programming models for the resource-constrained project scheduling problem with consumption and production of resources[J]. Flexible Services and Manufacturing Journal, 2013, 25: 25-47.

[4] Siu M F F, Lu M, AbouRizk S, et al. Quantitative assessment of budget sufficiency and resource utilization for resource-constrained project schedules[J]. Journal of Construction Engineering and Management, 2016, 142(6): 04016003.

[5] Tran D H, Cheng M Y, Cao M T. Solving resource-constrained project scheduling problems using hybrid artificial bee colony with differential evolution[J]. Journal of Computing in Civil Engineering, 2016, 30(4): 04015065.

[6] Kim J L. Improved genetic algorithm for resource-constrained scheduling of large projects[J]. Canadian journal of civil engineering, 2009, 36(6): 1016-1027.

[7] Isah M A, Kim B S. Integrating schedule risk analysis with multi-skilled resource scheduling to improve resource-constrained project scheduling problems[J]. Applied Sciences, 2021, 11(2): 650.

[8] Yao G, Li R, Yang Y. An improved multi-objective optimization and decision-making method on construction sites layout of prefabricated buildings[J]. Sustainability, 2023, 15(7): 6279.

[9] Song Y, Wang J, Lu J, et al. Research on collaborative scheduling of multiple projects of prefabricated building based on the niche genetic-raccoon family optimization algorithm[J]. Alexandria Engineering Journal, 2023, 64: 1015-1033.

[10] Abbasi S, Noorzai E. The BIM-Based multi-optimization approach in order to determine the trade-off between embodied and operation energy focused on renewable energy use[J]. Journal of Cleaner Production, 2021, 281: 125359.

[11] Wang Q, Xu X, Ding X, et al. Multi objective optimization and evaluation approach of prefabricated component combination solutions using NSGA-II and simulated annealing optimized projection pursuit method[J]. Scientific Reports, 2024, 14(1): 16688.

[12] Yuan Y, Ye S, Lin L, et al. Multi-objective multi-mode resource-constrained project scheduling with fuzzy activity durations in prefabricated building construction[J]. Computers & Industrial Engineering, 2021, 158: 107316.

[13] Peng J, Feng Y, Zhang Q, et al. Multi-objective integrated optimization study of prefabricated building projects introducing sustainable levels[J]. Scientific Reports, 2023, 13(1): 2821.

[14] McAllister R D, Rawlings J B, Maravelias C T. The inherent robustness of closed-loop scheduling[J]. Computers & Chemical Engineering, 2022, 159: 107678.

[15] Grumbach F, Müller A, Reusch P, et al. Robustness prediction in dynamic production processes—a new surrogate measure based on regression machine learning[J]. Processes, 2023, 11(4): 1267.

[16] Salido M A, Escamilla J, Barber F, et al. Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems[J]. AI EDAM, 2016, 30(3): 300-312.

[17] Zahid T, Agha M H, Schmidt T. Investigation of surrogate measures of robustness for project scheduling problems[J]. Computers & Industrial Engineering, 2019, 129: 220-227. DOI: 10.1016/j.cie.2019.01.041.

[18] Liu H, Chen P, Ouyang X, et al. Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution[J]. Future Generation Computer Systems, 2023, 146: 18-33.

[19] Palacios J J, Gonzalez-Rodriguez I, Vela C R, et al. Robust multiobjective optimisation for fuzzy job shop problems[J]. Applied Soft Computing, 2017, 56: 604-616.

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Published

2025-03-19

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Section

Articles

How to Cite

Ling, Zhengyang, and Ying Chen. 2025. “Research on Multi-Objective Optimization of Project Scheduling Based on NSGA-III: Trade-off Among Project Duration, Resource Allocation, and Robustness”. International Core Journal of Engineering 11 (4): 413-22. https://doi.org/10.6919/ICJE.202504_11(4).0049.