Probability of Project Completion Using Stochastic Project Scheduling Simulation

This paper introduces a software, Stochastic Project Scheduling Simulation (SPSS), developed to measure the probability to complete a project in a certain time specified by the user. To deliver a project by a completion date committed to in a contract, a number of activities need to be carried out. The time that an entire project takes to complete and the activities that determine total project duration are always questionable because of the randomness and stochastic nature of the activities' durations. Predicting a project completion probability is valuable, particularly at the time of bidding. The SPSS finds the longest path in a network and runs the network a number of times specified by the user and calculates the stochastic probability to complete the project in the specified time. The SPSS can be used by a contractor: (1) to predict the probability to deliver the project in a given time frame and (2) to assess its capabilities to meet the contractual requirement before bidding. The SPSS can also be used by a construction owner to quantify and analyze the risks involved in the schedule. The benefits of the tool to researchers are: (1) to solve program evaluation and review technique problems; (2) to complement Monte Carlo simulation by applying the concept of project network modeling and scheduling with probabilistic and stochastic activities via a web based Java Simulation which is operateable over the Internet, and (3) to open a way to compare a project network having different distribution functions.

[1]  Cliff T. Ragsdale,et al.  Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Management Science , 2003 .

[2]  Jonathan Jingsheng Shi,et al.  ACTIVITY-BASED CONSTRUCTION (ABC) MODELING AND SIMULATION METHOD , 1999 .

[3]  Keith C. Crandall.  Analysis of Schedule Simulations , 1977 .

[4]  Cliff Schexnayder,et al.  Defining a beta distribution function for construction simulation , 1999, WSC '99.

[5]  Daniel W. Halpin,et al.  Simulation of cable-stayed bridges using DISCO , 1994, Proceedings of Winter Simulation Conference.

[6]  Simaan M. AbouRizk,et al.  Simphony: an environment for building special purpose construction simulation tools , 1999, WSC '99.

[7]  Anil Sawhney,et al.  Petri net based simulation of construction schedules , 1997, WSC '97.

[8]  Boyd C. Paulson,et al.  Professional construction management : including C.M., design-construct, and general contracting , 1992 .

[9]  Alfredo Ang H.-S.,et al.  Probability concepts in engineering planning and design, vol i : basic principles , 1979 .

[10]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[11]  Wayne L. Winston,et al.  Practical Management Science Spreadsheet Modeling and Applications , 2002, J. Oper. Res. Soc..

[12]  K. MacCrimmon,et al.  An Analytical Study of the PERT Assumptions , 1964 .

[13]  Hira N. Ahuja,et al.  Simulation Model to Forecast Project Completion Time , 1985 .

[14]  Richard M. Van Slyke,et al.  Letter to the Editor---Monte Carlo Methods and the PERT Problem , 1963 .

[15]  Photios G. Ioannou,et al.  General-purpose systems for effective construction simulation , 1999 .

[16]  Iris D. Tommelein,et al.  Knowledge-based assembly of simulation networks using construction designs, plans, and methods , 1994, Proceedings of Winter Simulation Conference.

[17]  Van Slyke,et al.  MONTE CARLO METHODS AND THE PERT PROBLEM , 1963 .

[18]  David Hoyle.  QS-9000 Quality Systems Handbook , 1993 .

[19]  Boyd C. Paulson.  Interactive Graphics for Simulating Construction Operations , 1978 .

[20]  S. Abourizk,et al.  STATISTICAL PROPERTIES OF CONSTRUCTION DURATION DATA , 1992 .