Interval Estimate of Project Time Completion Using Simulation Approach
Subject Areas : Business ManagementAmin Zeinalzadeh 1 , Jafar Hosseini Dolama 2 , Majid Bagerzadeh Khajeh 3
1 - Master of Industrial Engineering
2 - Graduate Student in Industrial Engineering
3 - Faculty Member, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Keywords: simulation, Discrete Event Simulation Approach, Project Programming,
Abstract :
This paper aims to utilize an approach for computing time completion of project network paths with determined activities that can be used to estimate the time completion of a project. Activity durations are considered to be deterministic in critical path method (CPM). Since it is impossible to predict future events in a deterministic manner, project evaluation and review technique (PERT) is introduced which studies project with probabilistic activity durations. In this technique, large variance of path durations can influence the calculation accuracy, in which case the simulation technique is an appropriate method. In this paper, a sample arrow network is chosen, and a time distribution is fitted for each activity with Input Analyzer Software using some historical data. Mean square error, chi-square and Kolmogorov-Smirnov (K-S) tests are used as three measures to evaluate the goodness-of-fit. In the next stage, a computer model is built for the network in Arena 7.0 software. This model is run 120 times and based on these replications, the minimum required sample size for (1-α) Í100% confidence interval is specified. Finally, a confidence interval is computed for duration of each network path and based on this; the time completion of project is estimated.
Banks, Jerry (1999), Discrete Event Simulation, initially published in the Proceedings of the Winter Simulation Conference, pp. 7-13.
Hajj Shir Mohammadi, A. (1999). Project Management and Control. Isfahan: Industrial University, (In Persian).
Kelton, W. David, Randall P. Sadowski & Deborah A. Sadowski (2001), Simulation with Arena, McGraw-Hill; second edition.
Law, Averill M. & W. David, Kelton (1994), Simulation, Modeling and Analysis, McGraw-Hill Science/Engineering/Math; 3 edition.
Lee, Sang Hyun, Feniosky, Pen˜a-Mora & Moonseo Park (2006), “Dynamic planning and control methodology for strategic and operational construction project management”, Automation in Construction 15, pp. 84 – 97.
Liyanage, K.N.H.P. (2005), Methodologies for Data Collection and Model Documentation in Computer Simulation, Proceedings of the International Conference on Computer and Industrial Management, ICIM, Bangkok, Thailand, pp. 4-1 – 4-6.
Martinez, Julio C. & Photios G. Ioannou (1997), “State-Based Probabilistic Scheduling Using STROBOSCOPE’s CPM Add-On”, Proceedings, Construction Congress V, pp. 438-445.
Raymond, H. Myers & Ronald E. Walpole (1978), Probability and Statistics for Engineering and Scientists, Macmilian Publishing Co; Inc; second eition.
Robinson, S (2005), Discrete-event simulation: from the pioneers to the present, what next?, Journal of the Operational Research Society 56, pp. 619–629.
Sargent, Robert G. (2009), Verification and Validation of Simulation Models, Proceedings of the Winter Simulation Conference, pp. 162-176.
Suri, P.K. & Bhushan, Bharat (2008), Simulator for Optimization of Software Project Cost and Schedule, Journal of Computer Science 4 (12), pp. 1030-1035.
Suri, P.K., Bhushan, Bharat & Ashish, Jolly (2009), Time Estimation for Project Management Life Cycle: A Simulation Approach, International Journal of Computer Science and Network Security, VOL.9 No.5, pp. 211-215.
Tavares, L.V. (2002), A review of the contribution of Operational Research to Project Management, European Journal of Operational Research 136, pp. 1-18.
_||_
Banks, Jerry (1999), Discrete Event Simulation, initially published in the Proceedings of the Winter Simulation Conference, pp. 7-13.
Hajj Shir Mohammadi, A. (1999). Project Management and Control. Isfahan: Industrial University, (In Persian).
Kelton, W. David, Randall P. Sadowski & Deborah A. Sadowski (2001), Simulation with Arena, McGraw-Hill; second edition.
Law, Averill M. & W. David, Kelton (1994), Simulation, Modeling and Analysis, McGraw-Hill Science/Engineering/Math; 3 edition.
Lee, Sang Hyun, Feniosky, Pen˜a-Mora & Moonseo Park (2006), “Dynamic planning and control methodology for strategic and operational construction project management”, Automation in Construction 15, pp. 84 – 97.
Liyanage, K.N.H.P. (2005), Methodologies for Data Collection and Model Documentation in Computer Simulation, Proceedings of the International Conference on Computer and Industrial Management, ICIM, Bangkok, Thailand, pp. 4-1 – 4-6.
Martinez, Julio C. & Photios G. Ioannou (1997), “State-Based Probabilistic Scheduling Using STROBOSCOPE’s CPM Add-On”, Proceedings, Construction Congress V, pp. 438-445.
Raymond, H. Myers & Ronald E. Walpole (1978), Probability and Statistics for Engineering and Scientists, Macmilian Publishing Co; Inc; second eition.
Robinson, S (2005), Discrete-event simulation: from the pioneers to the present, what next?, Journal of the Operational Research Society 56, pp. 619–629.
Sargent, Robert G. (2009), Verification and Validation of Simulation Models, Proceedings of the Winter Simulation Conference, pp. 162-176.
Suri, P.K. & Bhushan, Bharat (2008), Simulator for Optimization of Software Project Cost and Schedule, Journal of Computer Science 4 (12), pp. 1030-1035.
Suri, P.K., Bhushan, Bharat & Ashish, Jolly (2009), Time Estimation for Project Management Life Cycle: A Simulation Approach, International Journal of Computer Science and Network Security, VOL.9 No.5, pp. 211-215.
Tavares, L.V. (2002), A review of the contribution of Operational Research to Project Management, European Journal of Operational Research 136, pp. 1-18.