Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Optimizing the quality of construction projects based on system reliability theory using improved Min-Max ant colony algorithm

Document Type : Original Article

Authors
Saeed Najafi Zangeneh 1
Naser Shams Gharneh 2
Parnian Azizi 3
Abdolhamid Eshragh 4
1 MSc of Industrial Engineering, Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
2 Associate Professor, Faculty of Industrial Engineering, AmirKabir University of Technology, Tehran, Iran
3 MSc of Industrial Engineeringو Faculty of Engineering, Sharif University of Technology، Tehran, Iran
4 Associate Professor, Faculty of Industrial Engineering , Sharif University of Technologyو Tehran, Iran
10.22065/jsce.2018.126873.1536
Abstract
Construction is one of the most important issues in a society economically, socially, culturally and so on. But the issue of quality in the construction projects requires discussion takes more scientific. The objective of this study is to investigate the optimization of the quality of construction projects and construction costs based on system reliability theory. Also, a real construction project is presented to evaluate the efficiency of the proposed model. In order to achieve this goal, a four-step algorithm is defined and proposed model using two methods M3AS and classic ant colony algorithm is solved. The results of both methods are compared. Parzin five-story residential building located on Zaferanieh Street is studied. The results indicated that M3AS innovative approach through local search is more capable than the classical ant colony algorithm for optimization. Most previous studies have not considered the importance of the quality of project. In this study, quality in the form of reliability at the time of completion of the work is quantified. The proposed model has been so overcome lack of expression and effectively using reliability theory. This model can automatically produce reliability and optimal cost of system with regard to structure function of reliability and non-linear function of cost-reliability. In this study, multi-objective optimization model based on reliability theory extends to decision-makers to determine and choose between cost and quality for construction projects. Since a major part of the annual budget allocated to construction projects, these projects are the most important component of the country's development that must be managed properly in terms of cost and quality and be completed according to the schedule. iT is clear that the status of the construction industry in terms of quality and cost should be much improved.
Keywords
Optimization
System reliability theory
M3AS
Construction
َAnt colony algorithm
Subjects
  • Mills, A. (2001). A systematic approach to risk management for construction. Structural survey, 19(5), p. 245-252.
  • Andrews, J.D. and Moss, T. (1993). Reliability and risk assessment. Longman Scientific & Technical Harlow.
  • Tao, R. and Tam, C.-M. (2013). System reliability theory based multiple-objective optimization model for construction projects. Automation in Construction,31: p. 54-64.
  • Shi, Y.-f. and Li H.-m. and Lu N. (2009). Quality reliability-cost optimization of construction project based on genetic algorithm in Engineering Computation. ICEC'09. International Conference on 2009. IEEE.
  • Ning, X. and Wang, L.-g. (2009). Construction quality-cost trade-off using the Pareto-based ant colony optimization algorithm in Management and Service Science. MASS'09. International Conference on 2009 . IEEE.
  • Lock, M.D. (2012). Project Management in Construction. Gower Publishing, Ltd.
  • Babu, A. and Suresh, N. (1996). Project management with time, cost, and quality considerations. European Journal of Operational Research, 88(2), p. 320-327.
  • Easa, S.M. (1989). Resource leveling in construction by optimization. Journal of Construction Engineering and Management. 115(2), p. 302-316.
  • Hegazy, T. (1999). Optimization of resource allocation and leveling using genetic algorithms. Journal of Construction Engineering and Management, 125(3), 167-175.
  • Gomar, J.E. and Haas, C.T. and Morton, D.P. (2002). Assignment and allocation optimization of partially multiskilled workforce. Journal of Construction Engineering and Management, 128(2), p. 103-109.
  • Kelley Jr, J.E. (1957). Computers and Operations Research in Roadbuilding. Operations Research, Computers and Management Decisions, p. 327-334.
  • Goyal, S. (1975). Note-A Note on “A Simple CPM Time-Cost Tradeoff Algorithm”. Management Science, 21(6), p. 718-722.
  • Falk, J.E. and Horowitz, J.L. (1972). Critical path problems with concave cost-time curves. Management Science, 19(4-part-1), p. 446-455.
  • Burns, S.A. and Liu, L. and Feng, C.-W. (1996). The LP/IP hybrid method for construction time-cost trade-off analysis. Construction Management & Economics, 14(3): , p. 265-276.
  • Feng, C.-W. and Liu, L. and Burns, S.A. (1997). Using genetic algorithms to solve construction time-cost trade-off problems. Journal of computing in civil engineering,. 11(3), 184-189.
  • Li, H. and Love, P. (1997). Using improved genetic algorithms to facilitate time-cost optimization. Journal of Construction Engineering and Management,. 123(3), 233-237.
  • Li, H. and Cao, J.-N. and Love, P. (1999). Using machine learning and GA to solve time-cost trade-off problems. Journal of Construction Engineering and Management, 125(5), p. 347-353.
  • Feng, C.-W. and Liu, L. and Burns, S.A. (2000). Stochastic construction time-cost trade-off analysis. Journal of computing in civil engineering, 14(2), p. 117-126.
  • Zheng, D.X. and Ng, S.T. and Kumaraswamy, M.M. (2004). Applying a genetic algorithm-based multiobjective approach for time-cost optimization. Journal of Construction Engineering and Management, 130(2), p. 168-176.
  • Zheng, D.X. and Ng, S.T. and 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), p. 81-91.
  • Khang, D.B. and Myint, Y.M. (1999). Time, cost and quality trade-off in project management: a case study. International Journal of Project Management, 17(4), 249-256.
  • El-Rayes, K. and Kandil, (2005). Time-cost-quality trade-off analysis for highway construction. Journal of Construction Engineering and Management, 131(4), p. 477-486.
  • Nabipoor Afruzi, E. and et al. (2014). A Multi-Objective Imperialist Competitive Algorithm for solving discrete time, cost and quality trade-off problems with mode-identity and resource-constrained situations. Computers & Operations Research, 50, 80-96.
  • Tavana, M. and Abtahi, A. and K. Khalili-Damghani. (2014). A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems. Expert Systems with Applications, 41(4), p. 1830-1846.
  • Monghasemi, S. and et al. (2014). A Novel Multi Criteria Decision Making Model for Optimizing Time-Cost-Quality Trade-off Problems in Construction Projects. Expert Systems with Applications.
  • Cheng, M.-Y. and Tran, D.-H. and Cao, M.-T. (2014). Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem. Knowledge-Based Systems.
  • Beasley, J.E. (2005). Advances in linear and integer programming. 1996, Clarendon Press Oxford.
  • Karlof, J.K. Integer programming: theory and practice. CRC Press.
  • Jünger, M. and et al. (2009). 50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-art, Springer.
  • Fondahl, J.W. (1962). A non-computer approach to the critical path method for the construction industry.
  • Prager, W. (1963). A structural method of computing project cost polygons. Management Science,.9(3), 394-404.
  • Siemens, N. (1971). A simple CPM time-cost tradeoff algorithm. Management Science,17(6), p. B-354-B-363.
  • Moselhi, O. (1993). Schedule compression using the direct stiffness method. Canadian Journal of Civil Engineering, 20(1), p. 65-72.
  • Chan, W.-T. and Chua, D.K. and Kannan, G. (1996). Construction resource scheduling with genetic algorithms. Journal of Construction Engineering and Management, 122(2), p. 125-132.
  • Leu, S.-S. and Yang, C.-H. (1999). GA-based multicriteria optimal model for construction scheduling. Journal of Construction Engineering and Management, 125(6), p. 420-427.
  • Elazouni, A.M. and Gab-Allah, A. (2004). Finance-based scheduling of construction projects using integer programming. Journal of Construction Engineering and Management, 130(1), p. 15-24
  • Liu, L. and Burns, S.A. and Feng, C.-W. (1995). Construction time-cost trade-off analysis using LP/IP hybrid method. Journal of Construction Engineering and Management, 121(4), p. 446-454.
  • Eberhart, R.C. and Shi, Y. (2001). Particle swarm optimization: developments, applications and resources. in Evolutionary Computation. Proceedings of the 2001 Congress on IEEE.
  • Bo, Z. and Yi-jia, C. (2005). Multiple objective particle swarm optimization technique for economic load dispatch. Journal of Zhejiang University SCIENCE A, 6(5), p. 420-427.
  • Dorigo, M. and Birattari, M. and Stutzle, T. (2006). Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4), p. 28-39.
  • Ya-ping, K. and Ying, X. (2006). Construction time-cost trade-off analysis using ant colony optimization algorithm. in Management Science and Engineering,. ICMSE'06. 2006 International Conference on IEEE.
  • Ng, T. and Zhang, Y. (2008). Optimizing construction time and cost using ant colony optimization approach. Journal of Construction Engineering and Management, 134(9), p. 721-728.
  • Hui-min, L. and Wang, Z.-f. (2009). Applying self-adaptive ant colony optimization for construction time-cost optimization. in Management Science and Engineering, 2009. ICMSE 2009. International Conference on 2009. IEEE.
  • Afshar, A. and et al. (2009). Nondominated archiving multicolony ant algorithm in time–cost trade-off optimization. Journal of Construction Engineering and Management, 135(7), p. 668-674.
  • Dorigo, M. and Birattari, M. and Stutzle, T. (2006). Ant colony optimization. Computational Intelligence Magazine. IEEE, 1(4), p. 28-39.
  • Stützle, T. and Hoos, H.H. (2000). MAX–MIN ant system. Future generation computer systems,. 16(8) 889-914.

  • Receive Date 21 April 2018
  • Revise Date 26 August 2018
  • Accept Date 28 November 2018