Optimization of Quantitative and Qualitative Indicators of Construction Projects with a Project Management Knowledge Approach (Case study: Qucham Reservoir Dam)

Document Type : Research Article

Authors

Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract

In recent years, the complexity of project implementation, competitive business environment, and limited resources of organizations have shown the need to pay attention to project management in achieving project goals. Therefore, in the implementation process, employers seek to increase quality, reduce execution time, costs, and risk, which are their main goals. In this research, optimization between the components of the survival pyramid including time, cost, quality, and risk in construction projects are done on a case-by-case basis on the Qucham reservoir dam. For this purpose, six Metahioristic optimization algorithms are used, which are three classical algorithms (genetics, Tabu search, and simulated annealing) and three new algorithms (butterfly, cyclical parthenogenesis, and harris hawk). In four cases, each component of the survival pyramid is optimized separately, and finally, all four cases are examined simultaneously. Coding related to objective functions and optimization algorithms has been done in MATLAB software. The results indicate the proper performance of the genetic algorithm. Also, in optimizing the quality index, only the genetic algorithm has given the best optimal answer, and in the combined optimization, considering all the indicators simultaneously, the genetic algorithms and the Harris hawk have given the best solution.

Keywords

Main Subjects


  1. Ahmad Abdullahi, Ali Khozin "Using Genetic Algorithm to Optimize Balance, Time, Price, Quality and Risk in Construction Projects and Investment Projects" - Journal of Accounting and Auditing Studies, Issue 20, 2016, pp. 104-123.
  2. Afshar, A., et al. (2007). "Multi-objective optimization of time-cost-quality using multi-colony ant algorithm."
  3. Ebrahimnezhad, S., et al. (2013). "Time-cost-quality trade-off in a CPM1 network using fuzzy logic and genetic algorithm." International Journal of Industrial Engineering & Production Management 24(3): 361-376.
  4. Nguyen, A.-T., et al. (2014). "A review on simulation-based optimization methods applied to building performance analysis." Applied Energy 113: 1043-1058.
  5. Aziz, R. F., et al. (2014). "Smart optimization for mega construction projects using artificial intelligence." Alexandria Engineering Journal 53(3): 591-606.
  6. Vahid Alikhanzadeh, Mustafa Kazemi, Mohammad Legions "Optimizing the Balance of Cost, Time and Quality in Construction Projects with an Approach to Investigating the Effect of Materials and Workforce Selection" International Conference on Management and Industrial Engineering - March 4, 2015.
  7. Salimi, S., et al. (2018). "Performance analysis of simulation-based optimization of construction projects using high performance computing." Automation in Construction 87: 158-172.
  8. Mehrdad Far dad "Thesis on the Role of Knowledge Management in Reducing the Time of Construction Projects by Case Study of Kayson Company Projects" Islamic Azad University, Noor Branch, Winter 2016.
  9. Rosłon, J. and J. Zawistowski (2016). "Construction projects’ indicators improvement using selected metaheuristic algorithms." Procedia Engineering 153: 595-598.
  10. Mathew, J., et al. (2016). "Multi objective optimization for scheduling repetitive projects using GA." Procedia Technology 25: 1072-1079.
  11. Amer M. Fahmy "Optimization Algorithms in Project Scheduling" Intec Open Limited, Chapter 8, September 21st 2016.
  12. Sina Fard Moradi nia, Ali Reza Khademy, “Resource-Constrained Project Scheduling Optimization by Genetic Algorithm", ASAS Scientific Research Quarterly, Volume 20 (2018) 58-73.
  13. Si, B., et al. (2019). "Performance assessment of algorithms for building energy optimization problems with different properties." Sustainability 11(1): 18.
  14. Acar Yildirim, H. and C. Akcay (2019). "Time-cost optimization model proposal for construction projects with genetic algorithm and fuzzy logic approach." Revista de la construction 18(3): 554-567.
  15. Hesham A. Abdelkhalek, Hesham S. Refaie, Remon F. Aziz. "Optimization of time and cost through learning curve analysis". Ain Shams Engineering Journal, 26 December 2019.
  16. Pornima M.Kashid&Manisha Jamgade. "Time and Cost Optimization of Construction Projects: A Review". International Journal of Engineering Sciences & Research Technology, 8(4), April, 2019.
  17. Holland, J. H. (1992). "Genetic algorithms." Scientific American 267(1): 66-73.
  18. Glover, F. and M. Laguna (1998). Tabu search. Handbook of combinatorial optimization, Springer: 2093-2229.
  19. Van Laarhoven, P. J., & Arts, E. H. Simulated annealing. In simulated annealing: Theory and applications (pp. 7-15). (1987), Springer, Dordrecht.
  20. Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, (2019) 23(3), 715-734.
  21. Kaveh, A. and A. Zolghadr (2017). "Cyclical parthenogenesis algorithm: A new meta-heuristic algorithm."
  22. Heidari, A. A., et al. (2019). "Harris hawks optimization: Algorithm and applications." Future Generation Computer Systems 97: 849-872.