l12a project simulation
TRANSCRIPT
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DECISION RULE (b)
100 sl ips of paper marked 00 - 99
Activity bProbab ility 0 .70 0 .30
Sl ips marked 00 - 69 70 - 99durat ion 7 9
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DECISION RULE - ( c)
100 sl ips of paper marked 00 - 99
Activity cProbab ility 0 .20 0 .6 0 0 .20Sl ips marked 00 - 19 20 - 79 80 - 99
durat ion 2 4 6
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SAMPLE PROJECTJ ob des c ription prede c essors time
A Order m/ c -- 4, 5, 6 (0 .3,0 .5,0 .2)
B Prepare s ite -- 4C Re ce ive m/ c A 2, 4, 6
(0 .2,0 .6 ,0 .2)
D Ele c tr ical conn e c tions A 2E Install m/ c B, C 7, 9
(0 .7,0 .3)
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PROJECT NET WORK
1 34
2
A C D
B E
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RAN DOM NUMBER TABLE
03 68 93 30 90 43 4 6 59 67 89 5 6 68 83 We w ill use these ra ndom numbers to
ge nerate the t imes for the proje c ta c tivities . Ac tivity A C E
Ra ndom No . 03 68 93 Durat ion 4 4 9(Us ing the probab ility d istr ibut ion data)
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PROJECT NET WORK
(1st Real izat ion)
1 34
2
A 4 C D
B E
4
94
2
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COMPILING SIMULATION
RESULTSS .No . A B C D E cr path1 4 * 4 4 * 2 9 * 17
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RAN DOM NUMBER TABLE II
03 68 93 30 90 43 4 6 59 67 89 5 6 68 83 We w ill use these ra ndom numbers to
ge nerate the t imes for the proje c ta c tivities . Ac tivity A C E
Ra ndom No . 30 90 43 Durat ion 5 6 7
(Us ing the probab ility d istr ibut ion data)
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PROJECT NET WORK
(2nd Real izat ion)
1 34
2
A 5 6 C 2 D
B E
47
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COMPILING SIMULATION
RESULTSS .No . A B C D E cr path1 4 * 4 4 * 2 9 * 17
2 5 * 4 6* 2 7* 1 8
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RAN DOM NUMBER TABLE
03 68 93 30 90 43 4 6 59 67 89 5 6 68 83 We w ill use these ra ndom numbers to
ge nerate the t imes for the proje c ta c tivities . Ac tivity A C E
Ra ndom No . 46 59 67 Durat ion 5 4 7(Us ing the probab ility d istr ibut ion data)
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PROJECT NET WORK
(3rd Real izat ion)
1 3 4
2
A C D
B E
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COMPILING SIMULATION
RESULTSS .No . A B C D E cr path1 4 * 4 4 * 2 9 * 17
2 5 * 4 6* 2 7* 1 83 5* 4 4* 2 7* 1 6
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PROJECT DISTRIBUTION,
MEAN & VARIANCE
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CRITICALITY IN DICES
Cr itical ity index of a n a c tivity =Probab ility that the a c tivity be comes
cr itical =Number of t imes the a c tivity was cr itical/Number of s imulat ion runs
[Th is is a conce pt t hat was totall y missing in conventional PER T]
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SUMMARY - I
Proje c t s imulat ion as a powerful tool toha ndle u nc erta in a c tivity durat ions .
Bas ic methodolo gy of s imulat ion involvesrepl icat ing the behav iour of the network bysampl ing a c tivity t imes us ing Mon te Carlos imulat ion.Mon te Carlo s imulat ion to ge nerate
d is crete a nd con tinuous d istr ibut ions .
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SUMMARY - II
A small ma ch ine installat ion proje c t take n up to illustrate the bas ic approa ch of proje c t s imulat ion.Comp ilat ions at the e nd of s imulat ion usedto obta in parameters l ike Proje c t expe c ted durat ion Var ia nc e of proje c t durat ion Cr itical ity ind ices of a c tivities
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SUMMARY - III
S imulat ion results ca n be used to showthe errors in the PERT a nalys is .
A lar ge number of s imulat ion runs isneeded to obta in reaso nably rel iableest imates .(Stat ist ical infere nc e is used to determ inethe number of s imulat ion runs to est imatethe proje c t parameters w ith a pre spe cif iedpre cis ion a nd con f ide nc e)
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Entrepreneurship Oxford University Press 2008 All rights reserved