Investigating multi-objective time, cost, and risk problems using the Grey Wolf Optimization algorithm

• Autorzy: Yilmaz Mehmet , Dede Tayfun , Grzywiński Maksym

Identyfikatory

DOI

10.17512/bozpe.2023.12.09

• Języki publikacji: EN

Abstrakty

EN

Safety plays a crucial role in construction projects. Safety risks encompass potential hazards such as work accidents, injuries, and security. Consequently, it is important to effectively manage these risks with equal emphasis on time and cost considerations during the project planning phase. Within the scope of this research, the grid and archive-based Grey Wolf Optimizer (GWO) algorithm was employed to investigate multi-objective time-cost-risk problems. By employing the GWO, multiple Pareto solutions were provided to the decisionmaker, facilitating improved decision-making. It was determined that the GWO algorithm yields better results in time-cost-risk problems compared to the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms.

Słowa kluczowe

EN

multi-objective optimization; grey wolf optimization algorithm; time-cost-risk

PL

optymalizacja wielocelowa; algorytm optymalizacji szarego wilka; czas-koszt-ryzyko

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Budownictwo o Zoptymalizowanym Potencjale Energetycznym
• Rocznik: 2023
• Tom: Vol. 12
• Strony: 79--86
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Yilmaz Mehmet

• Erzincan Binali Yıldırım University, Turkey

• https://orcid.org/0000-0001-6164-6233

autor

Dede Tayfun

• Karadeniz Technical University, Turkey

• https://orcid.org/+0000-0001-9672-2232

autor

Grzywiński Maksym

• Czestochowa University of Technology, Poland

• https://orcid.org/0000-0003-4345-3897

Bibliografia

• 1.Aminbakhsh, S. & Sonmez, R. (2017) Pareto front particle swarm optimizer for discrete time-cost trade-off problem. Journal of Computing in Civil Engineering, 31(1).
• 2.Kaveh, A., Rajabi, F. & Mirvalad, S. (2021) Many-objective optimization for construction project scheduling using non-dominated sorting differential evolution algorithm based on reference points. Scientia Iranica, 28(6 A), 3112-3128.
• 3.Knowles, J.D. & Corne, D.W. (2000) Approximating the nondominated front using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2), 149-172.
• 4.Mirjalili, S., Saremi, S., Mirjalili, S.M., & Coelho, L.D.S. (2016) Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.
• 5.Mohammadi, M.O., Dede, T. & Grzywiński, M. (2022) Solving a stochastic time-cost-quality tradeoff problem by mete-heuristic optimization algorithms. Construction of Optimized Energy Potential, 11, 41-48.
• 6.Ozcan-Deniz, G., Zhu, Y. & Ceron, V. (2012) Time, cost, and environmental impact analysis on construction operation optimization using genetic algorithms. Journal of Management in Engineering, 28(3), 265-272.
• 7.Sharma, K., Soni, A. & Trivedi, M.K. (2023) A particle swarm optimization-based model for quality-safety trade-off optimization under constraint duration and cost of construction project. Lecture Notes in Mechanical Engineering, 115-126.
• 8.Sonmez, R. & Bettemir, Ö.H. (2012) A hybrid genetic algorithm for the discrete time-cost trade-off problem. Expert Systems with Applications, 39(13), 11428-11434.
• 9.Yılmaz, M. & Dede, T. (2023) Multi-objective time-cost trade-off optimization for the construction scheduling with Rao algorithms. Structures, 48, 798-808.
• 10.Zhang, H. & Li, H. (2010) Multi‐objective particle swarm optimization for construction time-cost tradeoff problems. Construction Management and Economics, 28(1), 75-88.
• 11.Zheng, D.X.M., Ng, S.T. & Kumaraswamy, M.M. (2005) Applying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization. Journal of Construction Engineering and Management, 131(1), 81-91.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).