Evaluating project completion time in project networks with discrete random activity durations

Deterministic models for project scheduling suffer from the fact that they assume complete information and neglect random influences, that occur during project execution. A typical consequence is the underestimation of the project duration as frequently observed in practice. This phenomenon occurs even in the absence of resource constraints and has been the subject of extensive research in the scientific community. This paper presents a method for obtaining relevant information about the project makespan for scheduling models, with dependent random processing time available in the form of scenarios.

[1]  Jack C. Hayya,et al.  Technical Note—A Comparison of the Method of Bounding Distributions (MBD) and Monte Carlo Simulation for Analyzing Stochastic Acyclic Networks , 1980 .

[2]  Weng-Ming Chu,et al.  A new approximation algorithm for obtaining the probability distribution function for project completion time , 2007, Comput. Math. Appl..

[3]  Bajis M. Dodin,et al.  Bounding the Project Completion Time Distribution in PERT Networks , 1985, Oper. Res..

[4]  Francesca Guerriero,et al.  A solution approach to find the critical path in a time-constrained activity network , 2010, Comput. Oper. Res..

[5]  Dale F. Cooper,et al.  Heuristics for Scheduling Resource-Constrained Projects: An Experimental Investigation , 1976 .

[6]  Jane N. Hagstrom,et al.  Computing the probability distribution of project duration in a PERT network , 1990, Networks.

[7]  Salah E. Elmaghraby,et al.  On the fallacy of averages in project risk management , 2005, Eur. J. Oper. Res..

[8]  Jane N. Hagstrom,et al.  Computational complexity of PERT problems , 1988, Networks.

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

[10]  John R. Schuyler,et al.  Risk and Decision Analysis in Projects , 2001 .

[11]  Willy Herroelen,et al.  The construction of stable project baseline schedules , 2004, Eur. J. Oper. Res..

[12]  Ali Jaafari,et al.  Management of risks, uncertainties and opportunities on projects: time for a fundamental shift , 2001 .

[13]  J Figueira,et al.  Stochastic Programming , 1998, J. Oper. Res. Soc..

[14]  D. R. Fulkerson Expected Critical Path Lengths in PERT Networks , 1962 .

[15]  H. M. Soroush The Most Critical Path in a PERT Network , 1994 .

[16]  Rainer Kolisch,et al.  PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program , 1997 .

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

[18]  N.-H. Shih Estimating completion-time distribution in stochastic activity networks , 2005, J. Oper. Res. Soc..

[19]  D. Malcolm,et al.  Application of a Technique for Research and Development Program Evaluation , 1959 .

[20]  G. Thompson,et al.  Critical Path Analyses Via Chance Constrained and Stochastic Programming , 1964 .

[21]  Lynn Crawford,et al.  Fundamental uncertainties in projects and the scope of project management , 2006 .

[22]  Kim Wikström,et al.  Defining uncertainty in projects – a new perspective , 2008 .