A Dynamic Model for Pert Risk Evaluating in Fuzzy Environment
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Authors
Hossein Mehrabadi
- Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
Abstract
In this paper we propose a dynamic model for evaluating of time risk in stochastic network, where the activity durations are exponentionaly distributed random variable and independent. We would like to present a new definition of general risk index for project in each time and connect it to the notation of its activity criticalities. We model such networks as finite-state, absorbing, and continuous Marko chain with upper triangular generator matrices. The state space is related to the network structure. The criticality index for each activity will be computed and then we put forward a fuzzy way of measuring the criticality to computing project states criticality. Then by using the probability of absorption in each state severity of criticality will be computed dynamically. The criticality measure obtained may serve as a measure of risk or of the supervision effort needed by senior management. It also by ranking the states before project initiating is able to forecast the critical states in order and help to the project management to developing a proper guideline for resource planning and allocation.
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ISRP Style
Hossein Mehrabadi, A Dynamic Model for Pert Risk Evaluating in Fuzzy Environment, Journal of Mathematics and Computer Science, 3 (2011), no. 1, 35--52
AMA Style
Mehrabadi Hossein, A Dynamic Model for Pert Risk Evaluating in Fuzzy Environment. J Math Comput SCI-JM. (2011); 3(1):35--52
Chicago/Turabian Style
Mehrabadi, Hossein. "A Dynamic Model for Pert Risk Evaluating in Fuzzy Environment." Journal of Mathematics and Computer Science, 3, no. 1 (2011): 35--52
Keywords
- Dynamic PERT
- dynamic risk management
- fuzzy risk
- markov chain
MSC
References
-
[1]
A. Charnes, W. Cooper, G. Thompson, Critical path analysis via chance constrained and stochastic programming, Operations Research, 12 (1964), 460--470
-
[2]
J. J. Martin, Distribution of the time through a directed acyclic network, Operations Research, 13 (1965), 46--66
-
[3]
C. W. Schmit, I. E. Grossmann, The exact overall time distribution of a project with uncertain task durations, Eur. J. Oper. Res., 126 (2000), 614--636
-
[4]
S. M. T. Fatemi Ghomi, S. Hashemin, A new analytical algorithm and generation of Gaussian quadrature formula for stochastic network, Eur. J. Oper. Res., 114 (1999), 610--625
-
[5]
S. M. T. Fatemi Ghomi, M. Rabbani, A new structural mechanism for reducibility of stochastic PERT networks, Eur. J. Oper. Res., 145 (2003), 394--402
-
[6]
V. Kulkarni, V. Adlakha, Markov and Markov-regenerative PERT networks, Operations Research, 34 (1986), 769--781
-
[7]
S. E. Elmaghraby, On the expected duration of PERT type networks, Management Science, 13 (1967), 299--306
-
[8]
A. Azaron, M. Modarres, Project Completion Time in Dynamic PERT Networks with Generating Projects, Scientia Iranica, 14 (2007), 56--63
-
[9]
A. Azaron, H. Katagiri, K. Kato, M. Sakawa, Longest path analysis in networks of queues: Dynamic scheduling problems, Eur. J. Oper. Res., 174 (2006), 132--149
-
[10]
D. Fulkerson, Expected critical path lengths in PERT networks, Operations Research, 10 (1962), 808--817
-
[11]
P. Robillard, Expected completion time in PERT networks, Operations Research, 24 (1976), 177--182
-
[12]
C. Perry, I. D. Creig, Estimating the mean and variance of subjective distributions in PERT and decision analysis, Management Science, 21 (1975), 1477--1480
-
[13]
P. Zielinski, On computing the latest starting times and floats of activities in a network with imprecise durations, Fuzzy Sets and Systems, 150 (2005), 53--76
-
[14]
A. I. Slyeptsov, T. A. Tyshchuk, Fuzzy critical path method for project network planning and control, Cybernet. Syst. Anal., 3 (1997), 158--170
-
[15]
A. I. Slyeptsov, T. A. Tyshchuk, Fuzzy temporal characteristics of operations for project management on the network models basis, Eur. J. Oper. Res., 147 (2003), 253--265
-
[16]
D. Dubois, H. Fargier, V. Galvagnon, On latest starting times and floats in activity networks with ill-known durations, Eur. J. Oper. Res., 147 (2003), 266--280
-
[17]
S. Chanas, D. Dubois, P. Zielinski, On the sure criticality of tasks in activity networks with imprecise durations, IEEE Trans. Syst. Man Cybernet. Part B: Cybernetics , 32 (2002), 393--407
-
[18]
S. Chanas, P. Zielinski, Critical path analysis in the network with fuzzy activity times, Fuzzy Sets and Systems, 122 (2001), 195--204
-
[19]
S. Chanas, P. Zielinski, The computational complexity of the criticality problems in a network with interval activity times, Eur. J. Oper. Res., 136 (2002), 541--550
-
[20]
S. Chanas, P. Zielinski, On the hardness of evaluating criticality of activities in a planar network with duration intervals, Oper. Res. Lett., 31 (2003), 53--59
-
[21]
D. Kuchta, Use of fuzzy numbers in project risk (criticality) assessment, Int. J. Project Manage, 19 (2001), 305--310
-
[22]
D. L. Mon, C. H. Cheng, H. C. Lu, Application of fuzzy distributions on project management, Fuzzy Sets and Systems, 73 (1995), 227--234
-
[23]
S. H. Nasution, Fuzzy critical path method, IEEE Trans. Syst. Man Cybernet, 24 (1994), 48--57
-
[24]
H. J. Rommelfanger, Network analysis and information flow in fuzzy environment, Fuzzy Sets and Systems, 67 (1994), 119--128
-
[25]
A. Kaufmann, Introduction to the Theory of Fuzzy Subsets, Academic Press, New York (1975)
-
[26]
H. J. Zimmermann, Fuzzy Set Theory and Its Applications, Springer, New York (2001)
-
[27]
S. M. T. Fatemi Ghomi, E. Teimouri, Path critical index and activity critical index in PERT networks, Eur. J. Oper. Res., 141 (2002), 147--152
-
[28]
R. A. Bowman, Efficient Estimation of Arc Criticalities in Stochastic Activity Networks, Management Science, 41 (1995), 58--67
-
[29]
S. E. Elmaghraby, Markov Activity Networks, http://www.ise.ncsu.edu/elmaghraby/Markov.pdf, September (1997)