Answered: TIME (DAYS) IMMEDIATE PREDECESSOR(S)…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Question

The estimated times and immediate predecessors for the activities in a project a t George Kyparis's retinal scanning company are given in the following table. Assume that the activity
times are independent. a) Calculate the expected time and variance for each activity.
b) What is the expected completion time of the critical path?
What is the expected completion time of the other path in the network?
c) What is the variance of the critical path? Wha t is the variance of the other path in the network?
d) If the time to complete path A-C is normally distributed, what is the probability that this path will be fini shed in 22 weeks or less?
e) If the time to complete path B- D is normally distributed, what is the probability that this path will be finished in 22 weeks or less?
f) Explain why the probability that the critical path will be finished in 22 weeks or less is not necessarily the probability that the project will be fi nished in 22 weeks or less?

TIME (DAYS)
IMMEDIATE
PREDECESSOR(S)
АCTIVITY
a
m
A
3
8
B
4
4
1
3
D
8
C
4
B, D
А, Е
А, Е
a10
14
G
1
4
6.
9.
10
11
12
G
14
16
20
KM
8
10
H, I
6.
6.
2.
7.
2.
2.
2.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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