Rappel
ES= earliest start time for a particular activity,
EF= earliest finish time for a particular activity,
where
EF = ES (estimated)+ duration of the activity.
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Earliest Start Time Rule The earliest start time of an activity is equal to the largest of the earliest finish times of its immediate predecessors. In symbols, ES = largest EF of the immediate predecessors |
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LS= latest start time for a particular activity, LF =latest finish time for a particular activity, Latest Finish Time Rule The latest finish time of an activity is equal to the smallest of the latest start times of its immediate successors. In symbols, LF = smallest LS of the immediate successors. |
PERT probabilistic
In reality, there is considerable uncertainty about how much time actually will be needed for each activity
Thus the duration of each activity is a random variable having some probability distribution.
The original version of PERT took this uncertainty into account by using three different types of estimates of the duration of an activity to obtain basic information about
its probability distribution, as described below.
The PERT Three-Estimate Approach
The three estimates to be obtained for each activity are
Most likely estimate (m) = estimate of the most likely value of the duration,
Optimistic estimate (o) =estimate of the duration under the most favorable conditions,
Pessimistic estimate (p) = estimate of the duration under the most unfavorable
conditions.
Steps in PERT Analysis
For each activity k
Obtain a k, m k (mode) and b k
Compute expected activity duration (mean)
Compute expected activity duration (mean) d k = t e
Compute activity variance v k=s 2
Compute expected project duration D=Te using standard CPM algorithm standard CPM algorithm
Compute Project Variance V=S**2 as as sum of critical path activity variance ( path activity variance (this assumes independence! )
In case of multiple critical paths use the one with the largest variance largest variance
Compute probability complete project by time t
Assuming project duration normally distributed
Exemple