The critical path method, or critical path analysis, is an algorithm for scheduling a set of project activities. It is commonly used in conjunction with the program evaluation and review technique.
This is a python program that
takes an activity table with each activity's predecessors and duration and stores it into a graph
using a modified Breadth First Search algorithm to traverse the graph and determine the correct path
calculates number of attributes for each activity following the selected path, these attributes are :
DU = Activity Duration
ES = Early Start Time
EF = Early Finish Time
LS = Late Start Time
LF = Late Finish Time
SK = Slack Value
draws a plot showing the the graph, attributes for each node and critical path (green nodes),
Here is Some Sample Tests And It's Output
Activity
Predecessors
Duration
A
-
7
B
A
9
C
A
3
D
B
8
E
C
5
F
C
4
G
D E F
2
H
G
1
Activity
Predecessors
Duration
A
-
5
B
-
4
C
A
7
D
A
3
E
B
2
F
D
9
G
B C
8
Activity
Predecessors
Duration
A
-
3
B
A
4
C
A
2
D
B
5
E
C
1
F
C
2
G
D E
4
H
F G
3
Activity
Predecessors
Duration
A
-
3
B
A
60
C
A
5
D
A
15
E
B
6
F
D
40
G
C E
10
H
F
7
I
G
6
J
H I
12
Activity
Predecessors
Duration
A
-
7
B
A
7
C
B
14
D
A
14
E
D
14
F
D
14
G
E
21
H
F
7
I
G
7
J
C H
1
K
E
7
L
E
7
M
L
7
N
I J K M
7
Activity
Predecessors
Duration
A
-
2
B
A
4
C
B
3
D
B
2
E
C D
10
F
B
4
Activity
Predecessors
Duration
A
-
16
B
-
20
C
-
30
D
B
15
E
B
10
F
D
3
G
D
16
H
A
15
I
E F
12
Activity
Predecessors
Duration
A
-
8
B
-
2
C
A
5
D
B
4
E
C D
6
F
E
7
G
D
7
H
G
5
I
E
6
Calculating Duration From Optimistic, Most probable and Pessimistic Time
Activity
Predecessors
Optimistic Time
Most probable Time
Pessimistic Time
A
-
6
9
12
B
A
3
4
11
C
A
2
5
14
D
B C
4
6
8
E
C
1
1.5
5
F
E
5
6
7
G
D
7
8
15
H
B
1
2
3
| Variance | 7.22222222 |
| Standard Deviation | 2.68741925 |
| Propability on 26 day | 4.9% |
Used Tools:
Python -obviously-
Pandas: to import the data from csv files and store it in adjacency list for the graph
Networkx: to make the graph, nodes, edges, colors, arrows and everything
Graphviz: to calculate the positions for the graph nodes by "dot" engine
