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Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project

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Abstract

In this paper, a new cost estimation method based on parallel Monte Carlo simulation and market investigation for the chemical engineering construction project is proposed to consider both the uncertainties of cost estimation and market drivers. The critical items of exerting large impacts on the cost estimation are selected by the market investigation. Then important critical items are chosen by the sensitivity analysis based on the parallel Monte-Carlo simulation combining with the Likert scale method from critical items. The Relative Important Indices and Normalized Important Indices are obtained according to the discipline and procurement experts’ experience in the relative construction market. Then re-rankings of market drivers will be acted as guidelines for carrying out project cost simulations based on the parallel Monte-Carlo method, with inquired information of important critical items with more efficiency. An illustrative example in a petrochemical Engineering Procurement Construction contracting project in Saudi verified the validity and practicability of the proposed method.

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References

  1. Oh, S., Rhodes, J., Strong, R.: Impact of cost uncertainty on pricing decisions under risk aversion. Eur. J. Oper. Res. 253, 144–153 (2016)

    Article  MathSciNet  Google Scholar 

  2. Leyva-Suarez, E., Herrera, G.S., de la Cruz, L.M.: A parallel computing strategy for Monte Carlo simulation using groundwater models. Geofis. Intern. 54, 245–254 (2015)

    Google Scholar 

  3. Rajabi, M.M., Ataie-Ashtiani, B., Simmons, C.T.: Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations. J. Hydrol. 520, 101–122 (2015)

    Article  Google Scholar 

  4. Sousa, V., Almeida, N.M., Luís, A.: Dias. Risk-based management of occupational safety and health in the construction industry-Part 2: quantitative model. Saf. Sci. 74, 184–194 (2015)

    Article  Google Scholar 

  5. Gideon, A.K., Wasek, J.S.: Predicting the likelihood of cost overruns: an empirical examination of major department of defense acquisition programs. J. Cost Anal. paramet. 8, 34–48 (2015)

    Article  Google Scholar 

  6. Mochtar, K., Arditi, D.: Role of marketing intelligence in making pricing policy in construction. J. Manage. Eng. 17(2), 140–148 (2011)

    Google Scholar 

  7. Akintoye, A.S., Skitmore, M.: A conceptual model of construction contractors. Pricing strategies, Proceeding 6th annual conference. Association of researchers in construction management, Standford University, (1990), pp. 31-47

  8. Mochtar, K., Arditi, D.: Alternate pricing strategies in construction. J. Civil Eng. Sci. Appl. 2(1), 56–64 (2000)

    Google Scholar 

  9. David, T.H.: Integrated Cost-Schedule Risk Analysis. Gowering Publishing House, Aldershot (2011)

    Google Scholar 

  10. Salling, K.B., Leleur, S.: Accounting for the inaccuracies in demand forecasts and construction cost estimations in transport project evaluation. Trans. Policy 38(9), 8–18 (2015)

    Article  Google Scholar 

  11. Oztas, A., Okmen, O.: Judgmental risk analysis process development in construction projects. Build. Environ. 40, 1244–1254 (2005)

    Article  Google Scholar 

  12. Cheung, F.K.T., Skitmore, M.: Application of cross validation techniques for modelling construction costs during the very early design stage. Build. Environ. 41(12), 1973–1990 (2006)

    Article  Google Scholar 

  13. Ali, T.: Probabilistic model for cost contingency. J. Constr. Eng. Manag. 129(2), 280–284 (2003)

    Google Scholar 

  14. Yang, I.T.: Simulation-based estimation for correlated cost elements. Int. J. Proj. Manag. 23, 275–282 (2005)

    Article  Google Scholar 

  15. Wang, W.C.: SIM-UTILITY: model for project ceiling price determination. J. Constr. Eng. Manag. 128(1), 76–84 (2002)

    Article  Google Scholar 

  16. Chau, K.W.: The validity of the triangular distribution assumption in Monte Carlo simulation of construction costs: empirical evidence from Hong Kong. Constr. Manag. Econ. 13(10), 15–21 (1995)

    Article  MathSciNet  Google Scholar 

  17. Nie, J.: The quantified management model for science and technology talent based on the ability and its application. Petrol. Petrochem. Today 10(1), 32–36 (2012)

    Google Scholar 

  18. Sanghi, S.: The Handbook of Competency Mapping: Understanding, Designing and Implementing Competency Models in Organizations. Sage Publications Ltd., London (2007)

    Google Scholar 

  19. Lubke, G.H.: B.O. Muthén, applying multigroup confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons. Struct. Eq. Model. 11(3), 514–534 (2004)

    Article  MathSciNet  Google Scholar 

  20. Esselink, K., Loyens, L.D.J.C., Smit, B.: Parallel Monte Carlo simulations. Phys. Rev. E 51, 1560–1568 (1995)

    Article  Google Scholar 

  21. Khan, M.O., Kennedy, G., Chan, D.Y.C.: A scalable Parallel Monte Carlo method for free energy simulations of molecular systems. J. Comput. Chem. 26, 72–77 (2005)

    Article  Google Scholar 

  22. Jiménez, F., Ortiz, C.J.: A GPU-based parallel object kinetic Monte Carlo algorithm for the evolution of defects in irradiated materials. Comput. Mater. Sci. 113, 178–186 (2016)

    Article  Google Scholar 

  23. van der Kaap, N.J., Koster, L.J.A.: Massively parallel kinetic Monte Carlo simulations of charge carrier transport in organic semiconductors. J. Comput. Phys. 307, 321–332 (2016)

    Article  MathSciNet  Google Scholar 

  24. Pandya, T.M., Johnson, S.R., Evans, T.M., Davidson, G.G., Hamilton, S.P., Godfrey, A.T.: Implementation, capabilities, and benchmarking of shift, a massively parallel Monte Carlo radiation transport code. J. Comput. Phys. 308, 239–272 (2016)

    Article  MathSciNet  Google Scholar 

  25. Millán, E.N., Goirán, S.B., Piccoli, M.F., Garino, C.G., Aranibar, J.N., Bringa, E.M.: Monte Carlo simulations of settlement dynamics in GPUs. Cluster Comput. 19(1), 557–566 (2016)

    Article  Google Scholar 

  26. Ye, Jun, Zheng, Xu, Ding, Yong: Secure outsourcing of modular exponentiations in cloud and cluster computing. Clust. Comput. 19(2), 811–820 (2016)

    Article  Google Scholar 

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Acknowledgments

This work is supported by the Key National Natural Science Foundation of China (71433001).

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Correspondence to Zhi-Qiang Geng.

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Zhu, B., Yu, LA. & Geng, ZQ. Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project. Cluster Comput 19, 1293–1308 (2016). https://doi.org/10.1007/s10586-016-0585-6

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  • DOI: https://doi.org/10.1007/s10586-016-0585-6

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