Using activity sensitivity and network topology information to monitor project time performance

Omega

Volumn 38, Issue 5, 2010, Pages 359-370

  Vanhoucke, Mario ^a,b  

^a GHENT UNIVERSITY   (Belgium)

^b VLERICK BUSINESS SCHOOL   (Belgium)

Author keywords

Project management; Simulation

Indexed keywords

DECISION MAKING; INFORMATION MANAGEMENT; NETWORK TOPOLOGY;

ACCURATE RESPONSE; ACTIVITY NETWORK; ACTIVITY SENSITIVITIES; NETWORK TOPOLOGY; PERFORMANCE; PROJECT MANAGERS; PROJECT OBJECTIVES; RELATIVE SENSITIVITY; SIMULATION; TOPOLOGY INFORMATION;

PROJECT MANAGEMENT;

EID: 77953177108     PISSN: 03050483     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.omega.2009.10.001     Document Type: Article

References (37)

1. Klingel A. Bias in PERT project completion time calculations for a real network. Management Science 1966, 13:B194-B201.
2. Schonberger R. Why projects are "always" late: a rationale based on manual simulation of a PERT/CPM network. Interfaces 1981, 11:65-70.
3. Gutierrez G., Kouvelis P. Parkinson's law and its implications for project management. Management Science 1991, 37:990-1001.
4. Hulett D. Schedule risk analysis simplified. Project Management Network, July 1996.
5. Lipke W. Schedule is different. The Measurable News 2003;Summer:31-34.
6. Vandevoorde S., Vanhoucke M. A comparison of different project duration forecasting methods using earned value metrics. International Journal of Project Management 2006, 24:289-302.
7. Vanhoucke M., Vandevoorde S. A simulation and evaluation of earned value metrics to forecast the project duration. Journal of the Operational Research Society 2007, 58:1361-1374.
8. Lipke W., Zwikael O., Henderson K., Anbari F. Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International Journal of Project Management 2009, 27:400-407.
9. Williams T. Criticality in stochastic networks. Journal of the Operational Research Society 1992, 43:353-357.
10. Elmaghraby S. On criticality and sensitivity in activity networks. European Journal of Operational Research 2000, 127:220-238.
11. Yao M.-J., Chu W.-M. A new approximation algorithm for obtaining the probability distribution function for project completion time. Computers & Mathematics with Applications 2007, 54:282-295.
12. PMBOK. A guide to the project management body of knowledge, 3rd ed. Newtown Square, PA: Project Management Institute, Inc.; 2004.
13. Martin J. Distribution of the time through a directed, acyclic network. Operations Research 1965, 13:46-66.
14. Van Slyke R. Monte Carlo methods and the PERT problem. Operations Research 1963, 11:839-860.
15. Dodin B., Elmaghraby S. Approximating the criticality indices of the activities in PERT networks. Management Science 1985, 31:207-223.
16. Fatemi Ghomi S., Teimouri E. Path critical index and activity critical index in PERT networks. European Journal of Operational Research 2002, 141:147-152.
17. Cho J., Yum B. An uncertainty importance measure of activities in PERT networks. International Journal of Production Research 1997, 35:2737-2758.
18. Tavares L., Ferreira J., Coelho J. A surrogate indicator of criticality for stochastic networks. International Transactions on Operational Research 2004, 11:193-202.
19. Kuchta D. Use of fuzzy numbers in project risk (criticality) assessment. International Journal of Project Management 2001, 19:305-310.
20. Elmaghraby S., Fathi Y., Taner M. On the sensitivity of project variability to activity mean duration. International Journal of Production Economics 1999, 62:219-232.
21. Gutierrez G., Paul A. Analysis of the effects of uncertainty, risk-pooling, and subcontracting mechanisms on project performance. Operations Research 2000, 48:927-938.
22. Demeulemeester E., Vanhoucke M., Herroelen W. A random network generator for activity-on-the-node networks. Journal of Scheduling 2003, 6:13-34.
23. Vanhoucke M., Coelho J.S., Debels D., Maenhout B., Tavares L.V. An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research 2008, 187:511-524.
24. Vanhoucke M., Coelho J., Debels D., Maenhout B., Tavares L. An evaluation of the adequacy of project network generators with systematically sampled networks. European Journal of Operational Research 2008, 187:511-524.
25. Vanhoucke M, Vandevoorde S. Measuring the accuracy of earned value/earned schedule forecasting predictors. The Measurable News 2007;Winter:26-30.
26. Vanhoucke M, Vandevoorde S. Earned value forecast accuracy and activity criticality. The Measurable News 2008;Summer:13-16.
27. Mastor A. An experimental and comparative evaluation of production line balancing techniques. Management Science 1970, 16:728-746.
28. Elmaghraby S. Activity networks: project planning and control by network models 1977, Wiley, New York.
29. Williams T. Towards realism in network simulation. Omega-International Journal of Management Science 1999, 27:305-314.
30. AbouRizk S., Halpin D., Wilson J. Fitting beta distributions based on sample data. Journal of Construction Engineering and Management 1994, 120:288-305.
31. Johnson D. The triangular distribution as a proxy for the beta distribution in risk analysis. Statistician 1997, 46:387-398.
32. Kuhl ME, Lada EK, Steiger NM, Wagner MA, Wilson JR. Introduction to modeling and generating probabilistic input processes for simulation. In: Henderson S, Biller B, Hsieh M, Shortle J, Tew J, Barton R, editors. Proceedings of the 2007 winter simulation conference. New Jersey: Institute of Electrical and Electronics Engineers; 2007. p. 63-76.
33. Hans E., Herroelen W., Leus R., Wullink G. A hierarchical approach to multi-project planning under uncertainty. Omega-International Journal of Management Science 2007, 35:563-577.
34. Huang C.-C., Chu P.-Y., Chiang J.-H. A fuzzy AHP application in government-sponsored R&D project selection. Omega-International Journal of Management Science 2008, 36:1038-1052.
35. Durbach I., Stewart T. Using expected values to simplify decision making under uncertainty. Omega-International Journal of Management Science 2009, 37:312-330.
36. Vanhoucke M. The effect of project schedule adherence and rework on the duration forecast accuracy of earned value metrics. Technical Report, Ghent University, Ghent, Belgium; 2008.
37. Eilat H., Golany B., Shtub A. R&D project evaluation: an integrated DEA and balanced scorecard approach. Omega-International Journal of Management Science 2008, 36:895-912.