2. Objective
By the end of this class, we will be able to:
Analyze Graphical Solution of Linear Programming
Models
Recognize Graphical Representation of LP Models
Explore Graphical Representation of Constraints
Learn about Utilization of Feasible Solution Area
Understand Corner Point Property
Analyze Graphical Solutions
Explore LP Characteristics
6. There are different ways to solve these problems.
The problems having two variables can be solved
graphically.
7. Graphical solution is possible with two ‘decision variables’ (may be with three) in a linear programming
model
Graphical Solution - Linear Programming Models
15. Graphical Representation of Constraints
Graphical Representation of Given
Constraints
Maximize Z = 40x1 + 50x2
subject to: 1x1 + 2x2 ≤ 40
4x1 + 3x2 ≤ 120
x1 ≥ 0, x2 ≥0
16. Feasible Solution Area
Feasible Solution Area
Maximize Z = 40x1 + 50x2
subject to: 1x1 + 2x2 ≤ 40
4x1 + 3x2 ≤ 120
x1 ≥ 0, x2 ≥0
17. Corner Point Property
The intersection points of two or more constraints that make up a feasible region are corner points.
18. Graphical Solution of Corner Point Method
Representation of Each Corner
Points
Maximize Z = 40x1 + 50x2
subject to constraints:
1x1 + 2x2 ≤ 40
4x1 + 3x2 ≤ 120
x1 ≥ 0, x2 ≥0
19. The Product Mix Problem Example
Decision to take:
Determine how
much to
manufacture for
more than 2
products
Objective is to:
Maximize profit
Constraints are:
Limited resources
20. Example: Dream Pvt. Ltd.
Two products:
Chocolate and
Biscuit
Decision:
Number of each
product to
manufacture per
month
Objective:
Maximize profit
27. Graphical Solution
Graphical representation of LP Model provides insight into it and their solutions.
It is plotted in two dimension with X-Y axis and making points to plot the graph.
29. LP Characteristics
Feasible Region: The region formed by all
constraints including non-negative constraint
Corner Point Property: One or more corner
points must have an ‘optimal solution’
Optimal Solution: Where objective function
has an ideal (minimum or maximum) value
34. Redundant Constraints - In this the feasible region is not affected
Example: x < 10
x < 12
Special Situation in LP
35. Infeasibility – when there are no points to fulfill
the given constraints (lack of feasible region)
Example: x < 10
x < 15
Special Situation in LP
36. Alternate Optimal Solutions – when there exists
multiple optimal solution
Max 2X1 + 2X2
Subject to:
X1 + X2 < 10
X1 < 5
X2 < 6
X1, X2 > 0
0 5 10
T
C
10
6
0
The points lying on
Red segment depicts
optimal solutions
Special Situation in LP
37. Special Situation in LP
Unbounded Solutions – When the objective
function of a solution is not finite
Maximize 2X1 + 2X2
Subject to:
2X1 + 3X2 > 6
X1, X2 > 0
0 1 2 3 T
C
2
1
0
38. Activity
Maximize Z = 4x + 2y
Match the following
10X + 20Y ≤ 80
x1, x2.x3
X ≥ 0, Y ≥ 0
Non-negative Restriction
Objective Function
Decision Variable
Constraint
39. Maximize Z = 4x + 2y
Match the following
10X + 20Y ≤ 80
x1, x2.x3
X ≥ 0, Y ≥ 0
Non-negative Restriction
Objective Function
Decision Variable
Constraint
Activity