Abstract
The management of construction projects has long emphasized the delicate balance between time and cost, as these factors play a critical role in achieving optimal project outcomes. To address this challenge, stochastic optimization algorithms have emerged as valuable tools. One such algorithm, moth-flame optimization (MFO), leverages its capacity to navigate complex and unknown search spaces. When combined with the tournament selection (TS) method, which is designed to maintain diversity and control the convergence rate by providing equal opportunities for all individuals to be selected, it demonstrates remarkable potential and competitiveness in solving challenging problems with constraints. This research introduces an enhanced version of the MFO model, called TMFO, as an innovative approach to address time–cost trade-off (TCTO) problems in construction project management. To assess its performance, three benchmark test problems are employed, including two case studies involving 7 activities and one case study with 18 activities. The results reveal that TMFO outperforms other optimization algorithms when applied to TCTOs in small-scale projects. These findings underscore the effectiveness and relevance of the TMFO algorithm within the domain of construction project management.
Similar content being viewed by others
Data availability
Upon request and subject to reasonable conditions, the corresponding author can provide the data, model, or code that underlie the findings of the study.
References
Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117
Kaveh A, Laknejadi K (2011) A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization. Expert Syst Appl 38(12):15475–15488
Kayalvizhi S, VK DM (2018) Optimal planning of active distribution networks with hybrid distributed energy resources using grid-based multi-objective harmony search algorithm. Appl Soft Comput 67:387–398
Fatlawi A, Vahedian A, Bachache NK (2018) Optimal camera placement using sine-cosine algorithm. In: 2018 8th international conference on computer and knowledge engineering (ICCKE). IEEE
Abdelshafy AM, Hassan H, Jurasz J (2018) Optimal design of a grid-connected desalination plant powered by renewable energy resources using a hybrid PSO–GWO approach. Energy Convers Manag 173:331–347
Pham VHS, Soulisa FV (2023) A hybrid ant lion optimizer (alo) algorithm for construction site layout optimization. J Soft Comput Civ Eng 7(4):50–71
Pham VHS, Nguyen VN (2023) Cement transport vehicle routing with a hybrid sine cosine optimization algorithm. Adv Civ Eng 2023:2728039
Di Filippo A et al. (2021) Generative design for project optimization (S). In: DMSVIVA
Pham VHS, Nguyen VN, Nguyen Dang NT (2024) Hybrid whale optimization algorithm for enhanced routing of limited capacity vehicles in supply chain management. Sci Rep 14(1):793
Pham VHS, Nguyen Dang NT, Nguyen VN (2024) Geometric mean optimizer for achieving efficiency in truss structural design. Evolut Intell. https://doi.org/10.1007/s12065-023-00895-3
Pham VHS, Nguyen Dang NT, Nguyen VN (2024) Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning. Sci Rep 14(1):694
Pham VHS, Nguyen Dang NT, Nguyen VN (2023) Hybrid sine cosine algorithm with integrated roulette wheel selection and opposition-based learning for engineering optimization problems. Int J Comput Intell Syst 16(1):171
Koo C, Hong T, Kim S (2015) An integrated multi-objective optimization model for solving the construction time–cost trade-off problem. J Civ Eng Manag 21(3):323–333
Cheng M-Y, Tran D-H (2014) Two-phase differential evolution for the multiobjective optimization of time–cost tradeoffs in resource-constrained construction projects. IEEE Trans Eng Manag 61(3):450–461
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Xu Y et al (2019) Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181–203
Afshar A et al (2009) Nondominated archiving multicolony ant algorithm in time–cost trade-off optimization. J Constr Eng Manag 135(7):668–674
Albayrak G (2020) Novel hybrid method in time–cost trade-off for resource-constrained construction projects. Iran J Sci Technol Trans Civ Eng 44(4):1295–1307
Aminbakhsh S, Sonmez R (2017) Pareto front particle swarm optimizer for discrete time–cost trade-off problem. J Comput Civ Eng 31(1):04016040
Naseri H, Ghasbeh MAE (2018) Time–cost trade off to compensate delay of project using genetic algorithm and linear programming. Int J Innov Manag Technol 9(6):285–290
Zhang L, Zou X, Qi J (2015) A trade-off between time and cost in scheduling repetitive construction projects. J Ind Manag Optim 11(4):1423
Toğan V, Berberoğlu N, Başağa HB (2020) New adaptive weight formulations for time–cost optimization. Structures. Elsevier
Kaveh A et al (2015) CBO and CSS algorithms for resource allocation and time–cost trade-off. Period Polytech Civ Eng 59(3):361–371
Son PVH, Nguyen Dang NT (2023) Optimizing time and cost simultaneously in projects with multi-verse optimizer. Asian J Civ Eng 24:1–7
Son PVH, Nguyen Dang NT (2023) Solving large-scale discrete time–cost trade-off problem using hybrid multi-verse optimizer model. Sci Rep 13(1):1987
Said SS, Haouari M (2015) A hybrid simulation-optimization approach for the robust discrete time/cost trade-off problem. Appl Math Comput 259:628–636
Bettemir ÖH, Talat Birgönül M (2017) Network analysis algorithm for the solution of discrete time–cost trade-off problem. KSCE J Civ Eng 21(4):1047–1058
Toğan V, Eirgash MA (2019) Time–cost trade-off optimization of construction projects using teaching learning based optimization. KSCE J Civ Eng 23:10–20
Eirgash MA, Toğan V, Dede T (2019) A multi-objective decision making model based on TLBO for the time–cost trade-off problems. Struct Eng Mech 71(2):139–151
Tran DH (2020) Optimizing time–cost in generalized construction projects using multiple-objective social group optimization and multi-criteria decision-making methods. Eng Constr Archit Manag 27(9):2287–2313
Son PVH, Dang NTN (2024) A modified sine cosine algorithm for time–cost trade-off problem. Springer, Singapore
Pham VHS, Vo Duy P, Nguyen Dang NT (2023) Optimizing time, cost, and carbon in construction: grasshopper algorithm empowered with tournament selection and opposition-based learning. Sci Rep 13(1):22212
Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. Foundations of genetic algorithms. Elsevier, pp 69–93
Gupta K, Deep K (2016) Tournament selection based probability scheme in spider monkey optimization algorithm. In: Harmony search algorithm: proceedings of the 2nd international conference on harmony search algorithm (ICHSA2015). Springer
Kılıç H, Yüzgeç U (2019) Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling. Comput Ind Eng 132:166–186
Sokolov A, Whitley D (2005) Unbiased tournament selection. In: Proceedings of the 7th annual conference on genetic and evolutionary computation
Burns SA, Liu L, Feng C-W (1996) The LP/IP hybrid method for construction time–cost trade-off analysis. Constr Manag Econ 14(3):265–276
Feng C-W, Liu L, Burns SA (1997) Using genetic algorithms to solve construction time–cost trade-off problems. J Comput Civ Eng 11(3):184–189
Hegazy T (1999) Optimization of construction time–cost trade-off analysis using genetic algorithms. Can J Civ Eng 26(6):685–697
Bettemir ÖH (2009) Optimization of time–cost-resource trade-off problems in project scheduling using meta-heuristic algorithms
Zheng DX, Ng ST, Kumaraswamy MM (2005) Applying Pareto ranking and niche formation to genetic algorithm-based multiobjective time–cost optimization. J Constr Eng Manag 131(1):81–91
Ng ST, Zhang Y (2008) Optimizing construction time and cost using ant colony optimization approach. J Constr Eng Manag 134(9):721–728
Zhang Y, Thomas Ng S (2012) An ant colony system based decision support system for construction time–cost optimization. J Civ Eng Manag 18(4):580–589
Acknowledgements
We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.
Funding
This research did not receive any specific funding from public, commercial, or not-for-profit sector grant agencies.
Author information
Authors and Affiliations
Contributions
All authors, including VHSP, NTND, and VNN, jointly contributed to the writing of the main manuscript, preparation of all figures and tables, and reviewed and approved the final version prior to submission.
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Pham, V.H.S., Nguyen Dang, N.T. & Nguyen, V.N. Achieving improved performance in construction projects: advanced time and cost optimization framework. Evol. Intel. (2024). https://doi.org/10.1007/s12065-024-00918-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12065-024-00918-7