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Scheduling with the Probabilistic Coupling Method I (PTCM I) - assuming the continuity of work of working teams

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PL
Harmonogramowanie Probabilistyczną Metodą Sprzężeń Czasowych I (PTCM I) - założenie ciągłości pracy brygad roboczych
Języki publikacji
EN
Abstrakty
EN
Modeling and numerical analysis of the design of building structures, their technology, organization and management methods of construction processes are the subject of the work of many scientists in Poland. Schedule designers try to best reflect the reality of construction projects with the available methods, although this procedure is not always successful. One of the scheduling methods is the Time Coupling Methods (TCM), which can be refined using the predictive durations of the Multivariate Method of Statistical Models (MMSM) construction processes and standard deviations. A new scheduling method in the probabilistic approach was developed - Probabilistic Time Couplings Method I (PTCM I). At PTCM I, work is organized in such a way as to maintain the continuity of work of employees, as downtime of workers is disadvantageous and costly. The total duration of the new investment was forecasted and compared with the other methods of scheduling and with real time after its completion. The results clearly show that the developed methodology can be successfully used in scheduling construction works.
PL
Modelowanie i analiza numeryczna projektowania konstrukcji budowlanych, ich technologii, organizacji i metod zarządzania procesami budowlanymi są przedmiotem pracy wielu naukowców w Polsce. Projektanci harmonogramów starają się jak najlepiej odzwierciedlić realia projektów budowlanych dostępnymi metodami, choć procedura ta nie zawsze jest skuteczna. Jedną z metod planowania jest metoda Time Coupling Methods (TCM), którą można udoskonalić za pomocą predykcyjnych czasów trwania procesów konstrukcji wielowymiarowej metody modeli statystycznych (MMSM) i odchyleń standardowych. Opracowano nową metodę szeregowania w podejściu probabilistycznym - Probabilistic Time Couplings Method I (PTCM I). W PTCM I praca jest zorganizowana w taki sposób, aby zachować ciągłość pracy pracowników, gdyż przestoje pracowników są niekorzystne i kosztowne. Prognozowano łączny czas trwania nowej inwestycji i porównano ją z innymi metodami harmonogramowania oraz z czasem rzeczywistym po jej zakończeniu. Wyniki jednoznacznie pokazują, że opracowaną metodykę można z powodzeniem zastosować w harmonogramowaniu robót budowlanych.
Rocznik
Strony
455--469
Opis fizyczny
Bibliogr. 35 poz., il., tab.
Twórcy
  • Kielce University of Technology, Faculty of Civil Engineering and Architecture, Kielce, Poland
  • Lublin University of Technology, Faculty of Civil Engineering and Architecture, Lublin, Poland
Bibliografia
  • [1] S.A. Assaf and S. Al-Hejji, “Causes of delay in large construction projects”, International Journal of Project Management, vol. 24, no. 4, pp. 349-357, 2006, doi: 10.1016/j.ijproman.2005.11.010.
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  • [3] F.D. Fugar and A.B. Agyakwah-Baah, “Delays in building construction projects in Ghana”, Australasian Journal of Construction Economics and Building, vol. 10, no. 1-2, pp. 103-116, 2010, doi: 10.5130/AJCEB.v10i1-2.1592.
  • [4] R.F. Aziz, “Ranking of delay factors in construction projects after Egyptian revolution”, Alexandria Engineering Journal, vol. 52, no. 3, pp. 387-406, 2013, doi: 10.1016/j.aej.2013.03.002.
  • [5] W. Jing, H.I. Naji, R.N. Zehawi, Z. Ali, N. Al-Ansari, and Z.M. Yaseen, “System dynamics modeling strategy for civil construction projects: the concept of successive legislation periods”, Symmetry, vol. 11, no. 5, art. no. 677, 2019, doi: 10.3390/sym11050677.
  • [6] A. Gondia, A. Siam, W. El-Dakhakhni, and A.H. Nassar, “Machine learning algorithms for construction projects delay risk prediction”, Journal of Construction Engineering and Management, vol. 146, no. 1, 2020, doi: 10.1061/(ASCE)CO.1943-7862.0001736.
  • [7] N. Kokkaew and W. Wipulanusat, “Completion delay risk management: A dynamic risk insurance approach”, KSCE Journal of Civil Engineering, vol. 18, pp. 1599-1608, 2014, doi: 10.1007/s12205-014-1128-4.
  • [8] C. Rhodes, Construction industry: statistics and policy. House of Commons Library, no. 01432, 2019, pp. 1-13. [Online]. Available: https://researchbriefings.files.parliament.uk/documents/SN01432/SN01432.pdf.
  • [9] B. Flyvbjerg, “What you should know about megaprojects and why: An overview”, Project Management Journal, vol. 45, no. 2, pp. 6-19, 2014, doi: 10.1002/pmj.21409.
  • [10] Y. Liang and H. Wang, “Sustainable performance measurements for public-private partnership projects: empirical evidence from China”, Sustainability, vol. 11, no. 13, art. no. 3653, 2019, doi: 10.3390/su11133653.
  • [11] X. Jiang, K. Lu, B. Xia, Y. Liu, and C. Cui, “Identifying significant risks and analyzing risk relationship for construction PPP projects in China using integrated FISM-MICMAC approach”, Sustainability, vol. 11, no. 19, art. no. 5206, 2019, doi: 10.3390/su11195206.
  • [12] Y. Li and X.Y. Wang, “Using fuzzy analytic network process and ISM methods for risk assessment of public-private partnership: A China perspective”, Journal of Civil Engineering and Management, vol. 25, no. 2, pp. 168-183, 2019, doi: 10.3846/jcem.2019.8655.
  • [13] B.K. Sovacool, A. Gilbert, and D. Nugent, “An international comparative assessment of construction cost overruns for electricity infrastructure”, Energy Research and Social Science, vol. 3, pp. 152-160, 2014, doi: 10.1016/j.erss.2014.07.016.
  • [14] O. Awojobi and G.P. Jenkins, “Were the hydro dams financed by the World Bank from 1976 to 2005 worthwhile?”, Energy Policy, vol. 86, pp. 222-232, 2015, doi: 10.1016/J.ENPOL.2015.06.040.
  • [15] A. Ansar, B. Flyvbjerg, A. Budzier, and D. Lunn, “Should we build more large dams? The actual costs of hydropower megaproject development”, Energy Policy, vol. 69, pp. 43-56, 2014, doi: 10.1016/j.enpol.2013.10.069.
  • [16] J.R.S. Cristóbal, “The S-curve envelope as a tool for monitoring and control of projects”, Procedia Computer Science, vol. 121, pp. 756-761, 2017, doi: 10.1016/j.procs.2017.11.097.
  • [17] R. Atkinson, “Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria”, International Journal of Project Management, vol. 17, no. 6, pp. 337-342, 1999, doi: 10.1016/S0263-7863(98)00069-6.
  • [18] M. Rogalska, Wieloczynnikowe modele w prognozowaniu czasu procesów budowlanych. Lublin: Monografie Politechniki Lubelskiej, 2016.
  • [19] P. Kostrzewa-Demczuk and M. Rogalska, “Anticipating the length of employees’ working time”, Symmetry, vol. 12, no. 3, art. no. 413, 2020, doi: 10.3390/sym12030413.
  • [20] P. Kostrzewa and M. Rogalska, “Scheduling construction processes using the probabilistic time coupling method III”, in IOP Conference Series: Materials Science and Engineering, vol. 471, no. 11, art. no. 11207, 2019, doi: 10.1088/1757-899X/471/11/112072.
  • [21] S. Bolotin, A. Dadar, M. Rogalska, and Z. Hejducki, “Harmonogramowanie przedsięwzięć budowlanych z uwzględnieniem modelu czasowo przestrzennego”, Przegląd Budowlany, no. 11, pp. 24-28, 2014.
  • [22] R.M. Choudhry, M.A. Aslam, J. Hinze, and F.M. Arain, “Cost and schedule risk analysis of bridge construction in Pakistan: establishing risk guidelines”, Journal of Construction Engineering and Management, vol. 140, no. 7, 2014, doi: 10.1061/(ASCE)CO.1943-7862.0000857.
  • [23] PMI, A Guide to the Project Management Body of Knowledge. Project Management Institute (PMI), 2017.
  • [24] J.C. Teixeria, J. Kulejewski, M. Krzemiński, and J. Zawistowski, Zarządzanie ryzykiem w budownictwie. Warszawa: Guimaraes, 2011.
  • [25] PMBOK Guide. A Guide to the Project Management Body of Knowledge. Pennsylvania, USA: Project Management Institute (PMI), 2000.
  • [26] M. Vanhoucke, “Schedule risk analysis”, in Project Management with Dynamic Scheduling. Springer, 2013, pp. 81-100, doi: 10.1007/978-3-642-40438-2_5.
  • [27] S. Sackey and B.-S. Kim, “Schedule risk analysis using a proposed modified variance and mean of the original program evaluation and review technique model”, KSCE Journal of Civil Engineering, vol. 23, pp. 1484-1492, 2019, doi: 10.1007/s12205-019-1826-z.
  • [28] D. Nasir, B. Mccabe, and L. Hartono, “Evaluating risk in construction-schedule model (ERIC-S): construction schedule risk model”, Journal of Construction Engineering and Management, vol. 129, no. 5, pp. 518-527, 2003, doi: 10.1061/(ASCE)0733-9364(2003)129:5(518).
  • [29] Ö. Ökmen and O. Öztas, “Construction project network evaluation with correlated schedule risk analysis model”, Journal of Construction Engineering and Management, vol. 134, no. 1, pp. 49-63, 2008, doi: 10.1061/(ASCE)0733-9364(2008)134:1(49).
  • [30] Z. M. Yaseen, et al., “Prediction of risk delay in construction projects using a hybrid artificial intelligence model”, Sustainability, vol. 12, no. 4, art. no. 1514, 2020, doi: 10.3390/su12041514.
  • [31] T.S. Glickman and F. Xu, “The distribution of the product of two triangular random variables”, Statistics and Probability Letters, vol. 78, pp. 2821-2826, 2008.
  • [32] K.E. Fairchild, L. Misra, and Y. Shi, “Using triangular distribution for business and finance simulations in Excel”, Journal of Financial Education, vol. 42, pp. 313-336, 2016.
  • [33] D. Johnson, “Triangular approximations for continuous random variables in risk analysis”, Journal of the Operational Research Society, vol. 53, no. 4, pp. 457-467, 2002, doi: 10.1057/palgrave.jors.2601330.
  • [34] P. Kostrzewa-Demczuk, “Scheduling with TCM methods in the probabilistic approach”, PhD thesis, Kielce University of Technology, Poland, 2022.
  • [35] I.T. Yang, “Impact of budget uncertainty on project time-cost tradeoff”, IEEE Transactions on Engineering Management, vol. 52, no. 2, pp. 167-174, 2005, doi: 10.1109/TEM.2005.844924.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-90ba5b50-5e1f-4fdd-902a-7a094f7b1122
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