On uncertainty and sensitivity analyses in project duration based on dependency information
Keywords:
dependency structure matrix, project duration, uncertainty analysis, sensitivity analysis, overlapping, scheduleAbstract
The dependency structure matrix (DSM) shows the interdependency between activities, and it has been shown to be useful in the estimation of complex projects’ durations. The estimate of project durations is based on activity durations, their interrelationships, and the permitted level of overlapping, all of which are represented by DSMs. However, these variables have individual uncertainties that generate overall uncertainty in the project duration. The objective of this work is to show that uncertainty analysis and sensitivity analysis are essential parts of analyzing the uncertainty in project scheduling. Specifically, this work shows how to perform sensitivity analysis in project scheduling using DSM, how to reduce the number of input variables with uncertainty for sensitivity analysis, and how to identify input variables whose control of uncertainty reduces the uncertainty of the project duration. An example is used to explain the methodology, and a case study is used to show the usefulness of sensitivity analysis and uncertainty analysis.
References
Adeyinka, A.L., Applications of Sensitivity Analysis in Petroleum Engineering, Thesis University of Texas at Austin, 2007.
Browning, T.R., Applying the design structure matrix to system decomposition and integration problems: A review and new directions. IEEE Transactions on Engineering management 48 (3) (2001) 292-306. DOI: 10.1109/17.946528
Cacuci, D.G., Ionescu-Bujor, M., A comparative review of Sensitivity and uncertainty analysis of large-scale systems. II: Statistical methods, Nuclear science and engineering 147 (3) (2004) 204-217.
Confalonieri, R., Bellocchi, G., Bregaglio, S., Donatelli, M., Acutis, M., Comparison of sensitivity analysis techniques: a case study with the rice model WARM, Ecological Modelling 221 (2010) 1897 -1906. DOI: 10.1016/j.ecolmodel.2010.04.021
Frey, H.C., Patil, S.R., Identification and review of sensitivity analysis methods. Risk Anal 22(3) (2002) 553-578.
Gálvez, E.D., Capuz-Rizo, S.P., Ordieres, J.B., Study of the uncertainty of task programming using the dependency structure matrix, Información Tecnológica 23(1) (2012) 19-34.
Gálvez, E.D., Ordieres, J.B., Capuz-Rizo, S.F., Evaluation of Project Duration Uncertainty using the Dependency Structure Matrix and Monte Carlo Simulations, Revista de la Construcción, (2015) 14(2), 72-79.
Gálvez, E.D., J.B. Ordieres, S.F. Capuz-Rizo, Analysis of Project Duration Uncertainty using Global Sensitivity Analysis, Journal of Modern Project Management (2015) 2(3), 18-25
Helton, J.C., Johnson, J.D., Sallaberry, C.J., Storlie, C.B., Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engineering & System Safety 91 (2006) 1175-1209. DOI: 10.1016/j.ress.2005.11.017
Herroelen, W., Leus, R., Project scheduling under uncertainty: survey and research potentials, European J. of operational research 165 (2005) 289-306. DOI: 10.1016/j.ejor.2004.04.002
Homma, T., Saltelli, A., Importance measures in global sensitivity analysis of nonlinear models, Reliability Engineering & System Safety 52 (1996) 1-17. DOI: 10.1016/0951-8320(96)00002-6
Ionescu-Bujor, M., Cacuci, D.G., A comparative review of Sensitivity and uncertainty analysis of large-scale systems. I: Deterministic methods, Nuclear science and engineering 147 (3) (2004) 189-203.
Iooss, B., Lemaître, P., A review on global sensitivity analysis methods. In Uncertainty management in Simulation-Optimization of Complex Systems: Algorithms and Applications, C. Meloni and G. Dellino (eds), Springer, 2015
Jansen, M.J.W., Analysis of variance designs for model output, Computer Physics Communication 117 (1999) 35–43. DOI: 10.1016/S0010-4655(98)00154-4
Lamas, P., Demeulemeester, E., A purely proactive scheduling procedure for the resource-constrained project scheduling problem with stochastic activity durations, Journal of Scheduling 19 409-428. DOI: 10.1007/s10951-015-0423-3
Maheswari, J.U., Varghese, K., Project scheduling using dependency structure matrix, International Journal of Project Management 23 (3) (2005) 223-230.
Meyer, M.A., Booker, J.M., Eliciting and Analyzing Expert Judgment: A Practical Guide (ASA-SIAM Series on Statistics and Applied Probability), 2001.
Mckay, M., Beckman, R., Conover, W., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21(2) (1979) 239-245. DOI: 10.2307/1271432
Fu, N., Lau, H.C., Varakantham, P., Robust execution strategies for project scheduling with unreliable resources and stochastic durations, Journal of Scheduling 18 (6) 607-622. DOI: 10.1007/s10951-015-0425-1
O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E., Rakow, T., Uncertain Judgements: Eliciting Experts' Probabilities, John Wiley & Sons Ltd., England, 2006.
Pujol, G., Iooss, B., Janon, A., Sensitivity: Sensitivity Analysis. R package version 1.10.1. http://CRAN.R-project.org/package=sensitivity, 2014.
Perminova, O., Gustafsson, M.,Wikström, K., Defining uncertainty in projects-a new perspective, Int. J. Proy. Management 26 (2007) 73-79.
Ronen, Y., Uncertanty analysis. Boca Raton, FL., CRC Press, Inc., 1988
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/, 2013.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., Global sensitivity Analysis: The primer, John Wiley & Sons Ltd., 2008
Saltelli, A., Chan, K., Scott, E.M., Sensitivity Analysis, Wiley, 2009
Srour, I.M., Abdul-Malak, M.U., Yessine, A.A., Ramadan, M., A methodology for scheduling overlapped design activities based on dependency information, Automation in Construction 29 (2013) 1-11. DOI: 10.1016/j.autcon.2012.08.001
Shi, Q., Blomquist, T., A new approach for project scheduling using fuzzy dependency structure matrix, International Journal of Project Management 30 (2012) 503-510.
Stephens, M.A., EDF Statistics for Goodness of Fit and Some Comparisons, Journal of the American Statistical Association (American Statistical Association) 69 (347) (1974) 730–737.
Sepúlveda, F.D., Cisternas, L.A., Gálvez, E.D., The use of global sensitivity analysis for improving processes: Applications to mineral processing, Computers & Chemical Engineering, 66(4) (2014), 221-232. DOI: 10.1016/j.compchemeng.2014.01.008
Sobol’, I.M., Sensitivity analysis for non-linear mathematical model, Math. Modelling Comput. Exp. 1 (1993) 407–414.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto M., Tarantola, S., Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Computer Physics Communications 181 (2010) 259–270. DOI: 10.1016/j.cpc.2009.09.018
Tufte, E.R., The Visual Display of Quantitative Information, The Graphics Press., 2001.
Tienda, P., Romano, M., A matrix algorithm RUPSP/GRUPSP “no splitting allowed” for production planning under Lean Construction methodology based on production processes, Revista de la Construcción 10 (2) (2011) 90-103
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Edelmira Ahumada Galvez, Joaquin B Ordieres, Salvador F Capuz-Rizo
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
