Program Evaluation and Review Technique (PERT) Analysis to Predict Completion Time and Project Risk Using Discrete Event System Simulation Method
DOI:
https://doi.org/10.21512/commit.v18i1.8495Keywords:
Program Evaluation and Review Technique (PERT), Completion Time, Project Risk, Discrete Event System SimulationAbstract
The prediction of project completion time, which is important in project management, is only based on an estimate of three numbers, namely the fastest, slowest, and presumably time. The common practice of applying normal distribution through Monte Carlo simulation in Program Evaluation and Review Technique (PERT) research often fails to accurately represent project activity durations, leading to potentially biased project completion prediction. Based on these problems, a different method is proposed, namely, Discrete Event Simulation (DES). The research aims to evaluate the effectiveness of the simmer package in R in conducting PERT analysis. Specifically, there are three objectives in the research: 1) develop a simulation model to predict how long a project will take and find the critical path, 2) create an R script to simulate discrete events on a PERT network, and 3) explore the simulation output using the simmer package in the form of summary statistics and estimation of project risk. Then, a library research with a descriptive and exploratory method is used for data collection. The hypothetical network is used to obtain the numerical results, which provide the predicted value of the project completion, the critical path, and the risk level. Simulation, including 100 replications, results in a predicted project completion time and a standard deviation of 20.7 and 2.2 weeks, respectively. The DES method has been proven highly effective in predicting the completion time of a project described by the PERT network. In addition, it offers increased flexibility.
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References
A. M. Law, W. D. Kelton, and W. D. Kelton, Simulation modeling and analysis. McGraw-Hill, 2007, vol. 3.
J. Banks, J. S. Carson, B. L. Nelson, and D. M. Nicol, Discrete-event system simulation: Pearson new international edition. Pearson Education, 2013.
V. Vinod and R. Sridharan, “Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system,” International Journal of Production Economics, vol. 129, no. 1, pp. 127–146, 2011.
A. A. B. Pritsker and J. J. O’Reilly, Simulation with visual SLAM and AweSim. John Wiley & Sons, 1999.
A. Ebert, P. Wu, K. Mengersen, and F. Ruggeri, “Computationally efficient simulation of queues: The R package queuecomputer,” 2017. [Online]. Available: https://arxiv.org/abs/1703.02151
I. Ucar, B. Smeets, and A. Azcorra, “Simmer: Discrete-event simulation for R,” Journal of Statistical Software, vol. 90, no. 2, pp. 1–30, 2019.
J. Zuo, W. Q. Meeker, and H. Wu, “A simulation study on the confidence interval procedures of some mean cumulative function estimators,” Journal of Statistical Computation and Simulation, vol. 83, no. 10, pp. 1868–1889, 2013.
H. Wang and Z. J. Zhai, “Advances in building simulation and computational techniques: A review between 1987 and 2014,” Energy and Buildings, vol. 128, pp. 319–335, 2016.
B. Smeets and I. Ucar, “Introduction to simmer,” 2023. [Online]. Available: https://cloud.r-project.org/web/packages/simmer/vignettes/simmer-01-introduction.html
I. G. A. A. Yudistira, “Pengembangan simulasi kejadian diskret berbasis paket simmer pada R,” Engineering, MAthematics and Computer Science (EMACS) Journal, vol. 3, no. 2, pp. 79–85, 2021.
I. Ucar, J. A. Hern´andez, P. Serrano, and A. Azcorra, “Design and analysis of 5G scenarios with simmer: An R package for fast des prototyping,” IEEE Communications Magazine, vol. 56, no. 11, pp. 145–151, 2018.
M. Lu and S. M. AbouRizk, “Simplified CPM/PERT simulation model,” Journal of Construction Engineering and Management, vol. 126, no. 3, pp. 219–226, 2000.
W. N. Shofa, I. Soejanto, and T. Ristyowati, “Penjadwalan proyek dengan penerapan simulasi Monte Carlo pada metode Program Evaluation Review and Technique (PERT),” Opsi, vol. 10, no. 2, pp. 150–157, 2017.
N. Ljiljani´c, Z. Raji´c, and T. Paunovi´c, “Use of PERT (Program Evaluation and Review Technique) and PDM (Precedence Diagramming Method) in organizing modern vegetable seedling production,” Ekonomika Poljoprivrede, vol. 69, no. 1, pp. 119–131, 2022.
M. C. Sachs and E. E. Gabriel, “Event history regression with pseudo-observations: Computational approaches and an implementation in R,” Journal of Statistical Software, vol. 102, pp. 1–34, 2022.
V. Knight and G. Palmer, Applied mathematics with open-source software: Operational research problems with Python and R. CRC Press, 2022.
S. Venturini and R. Piccarreta, “A Bayesian approach for model-based clustering of several binary dissimilarity matrices: The dmbc package in R,” Journal of Statistical Software, vol. 100, pp. 1–35, 2021.
S. J. Eglen, “A quick guide to teaching R programming to computational biology students,” PLoS Computational Biology, vol. 5, no. 8, pp. 1–4, 2009.
A. P. Goldberg and J. R. Karr, “DE-Sim: An object-oriented, discrete-event simulation tool for data-intensive modeling of complex systems in Python,” Journal of Open Source Software, vol. 5, no. 55, pp. 1–7, 2020.
S. P. Millard, A. Kowarik, and M. A. Kowarik, Package ‘EnvStats’, 2018.
I. G. A. A. Yudistira, R. Nariswari, and S. Arifin, “output visualization from result of discrete event system simulation with ‘simmer’ R package,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, pp. 0581–0592, 2023.
X. Dong, L. Castro, and N. Shaikh, “Fastnet: An R package for fast simulation and analysis of large-scale social networks,” Journal of Statistical Software, vol. 96, pp. 1–23, 2020.
P. C. Jim´enez and Y. R. Montoya, “Queueing: A package for analysis of queueing networks and models in R,” R Journal, vol. 9, no. 2, 2017.
P. O. Farayola, S. K. Chaganti, A. O. Obaidi, A. Sheikh, S. Ravi, and D. Chen, “Quantile–quantile fitting approach to detect site to site variations in massive multi-site testing,” in 2020 IEEE 38th VLSI Test Symposium (VTS). San Diego, USA: IEEE, April 5–8, 2020, pp. 1–6.
T. J. Cleophas and A. H. Zwinderman, “Quantilequantile plots, a good start for looking at your medical data (50 cholesterol measurements and 58 patients),” Machine Learning in Medicine–A Complete Overview, pp. 319–327, 2020.
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Copyright (c) 2024 I Gusti Agung Anom Yudistira, Rinda Nariswari, Samsul Arifin, Abdul Azis Abdillah, Puguh Wahyu Prasetyo, Nanang Susyanto
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