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Linear Programming
Sensitivity Analysis
Max. Z = $40x1 + $50x2
subject to: 1x1 + 2x2  40
4x1 + 3x2  120
x1, x2  0
Figure: Optimal Solution Point
Beaver Creek Pottery Example
Sensitivity Analysis
Maximize Z = $100x1 + $50x2
subject to: 1x1 + 2x2  40
4x2 + 3x2  120
x1, x2  0
Figure: Changing the x1 Objective Function Coefficient
Beaver Creek Pottery Example
Change x1 Objective Function Coefficient
Maximize Z = $40x1 + $100x2
subject to: 1x1 + 2x2  40
4x1 + 3x2  120
x1, x2  0
Figure: Changing the x2 Objective Function Coefficient
Beaver Creek Pottery Example
Change x2 Objective Function Coefficient
 The sensitivity range for an objective function coefficient is
the range of values over which the current optimal solution
point will remain optimal.
 The sensitivity range for the xi coefficient is designated
as ci.
Objective Function Coefficient
Sensitivity Range
objective function Z = $40x1 + $50x2
sensitivity range for:
x1: 25  c1  66.67
x2: 30  c2  80
Figure: Determining the Sensitivity Range for c1
Objective Function Coefficient
Sensitivity Range for c1 and c2
Minimize Z = $6x1 + $3x2
subject to:
2x1 + 4x2  16
4x1 + 3x2  24
x1, x2  0
sensitivity ranges:
4  c1  
0  c2  4.5
Objective Function Coefficient
Fertilizer Cost Minimization Example
Figure: Fertilizer Cost Minimization Example
Changes in Constraint Quantity Values
Increasing the Labor Constraint
Maximize Z = $40x1 + $50x2
subject to: 1x1 + 2x2  40
4x2 + 3x2  120
x1, x2  0
Figure: Increasing the Labor Constraint Quantity
Changes in Constraint Quantity Values
Sensitivity Range for Labor Constraint
Sensitivity range for:
30  q1  80 hr
Figure: Determining the Sensitivity Range for Labor Quantity
Changes in Constraint Quantity Values
Sensitivity Range for Clay Constraint
Sensitivity range for:
60  q2  160 lb
Figure: Determining the Sensitivity Range for Clay Quantity
 Changing individual constraint parameters
 Adding new constraints
 Adding new variables
Other Forms of Sensitivity Analysis
Topics
Other Forms of Sensitivity Analysis
Changing a Constraint Parameter
Maximize Z = $40x1 + $50x2
subject to: 1x1 + 2x2  40
4x1 + 3x2  120
x1, x2  0
Figure: Changing the x1 Coefficient in the Labor Constraint
Adding a new variable to the Beaver Creek model, x3, a third
product, cups
Maximize Z = $40x1 + 50x2 + 30x3
subject to:
x1 + 2x2 + 1.2x3  40 hr of labor
4x1 + 3x2 + 2x3  120 lb of clay
x1, x2, x3  0
Solving model shows that change has no effect on the original
solution (i.e., the model is not sensitive to this change).
Other Forms of Sensitivity Analysis
Adding a New Variable
 Defined as the marginal value of one additional unit of
resource.
 The sensitivity range for a constraint quantity value is
also the range over which the shadow price is valid.
Shadow Prices (Dual Variable Values)
 Two airplane parts: no.1 and no. 2.
 Three manufacturing stages: stamping, drilling, milling.
 Decision variables: x1 (number of part no.1 to produce)
x2 (number of part no.2 to produce)
 Model: Maximize Z = $650x1 + 910x2
subject to:
4x1 + 7.5x2  105 (stamping,hr)
6.2x1 + 4.9x2  90 (drilling, hr)
9.1x1 + 4.1x2  110 (finishing, hr)
x1, x2  0
Example Problem
Problem Statement

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sensitivity analyses.ppt

  • 2. Max. Z = $40x1 + $50x2 subject to: 1x1 + 2x2  40 4x1 + 3x2  120 x1, x2  0 Figure: Optimal Solution Point Beaver Creek Pottery Example Sensitivity Analysis
  • 3. Maximize Z = $100x1 + $50x2 subject to: 1x1 + 2x2  40 4x2 + 3x2  120 x1, x2  0 Figure: Changing the x1 Objective Function Coefficient Beaver Creek Pottery Example Change x1 Objective Function Coefficient
  • 4. Maximize Z = $40x1 + $100x2 subject to: 1x1 + 2x2  40 4x1 + 3x2  120 x1, x2  0 Figure: Changing the x2 Objective Function Coefficient Beaver Creek Pottery Example Change x2 Objective Function Coefficient
  • 5.  The sensitivity range for an objective function coefficient is the range of values over which the current optimal solution point will remain optimal.  The sensitivity range for the xi coefficient is designated as ci. Objective Function Coefficient Sensitivity Range
  • 6. objective function Z = $40x1 + $50x2 sensitivity range for: x1: 25  c1  66.67 x2: 30  c2  80 Figure: Determining the Sensitivity Range for c1 Objective Function Coefficient Sensitivity Range for c1 and c2
  • 7. Minimize Z = $6x1 + $3x2 subject to: 2x1 + 4x2  16 4x1 + 3x2  24 x1, x2  0 sensitivity ranges: 4  c1   0  c2  4.5 Objective Function Coefficient Fertilizer Cost Minimization Example Figure: Fertilizer Cost Minimization Example
  • 8. Changes in Constraint Quantity Values Increasing the Labor Constraint Maximize Z = $40x1 + $50x2 subject to: 1x1 + 2x2  40 4x2 + 3x2  120 x1, x2  0 Figure: Increasing the Labor Constraint Quantity
  • 9. Changes in Constraint Quantity Values Sensitivity Range for Labor Constraint Sensitivity range for: 30  q1  80 hr Figure: Determining the Sensitivity Range for Labor Quantity
  • 10. Changes in Constraint Quantity Values Sensitivity Range for Clay Constraint Sensitivity range for: 60  q2  160 lb Figure: Determining the Sensitivity Range for Clay Quantity
  • 11.  Changing individual constraint parameters  Adding new constraints  Adding new variables Other Forms of Sensitivity Analysis Topics
  • 12. Other Forms of Sensitivity Analysis Changing a Constraint Parameter Maximize Z = $40x1 + $50x2 subject to: 1x1 + 2x2  40 4x1 + 3x2  120 x1, x2  0 Figure: Changing the x1 Coefficient in the Labor Constraint
  • 13. Adding a new variable to the Beaver Creek model, x3, a third product, cups Maximize Z = $40x1 + 50x2 + 30x3 subject to: x1 + 2x2 + 1.2x3  40 hr of labor 4x1 + 3x2 + 2x3  120 lb of clay x1, x2, x3  0 Solving model shows that change has no effect on the original solution (i.e., the model is not sensitive to this change). Other Forms of Sensitivity Analysis Adding a New Variable
  • 14.  Defined as the marginal value of one additional unit of resource.  The sensitivity range for a constraint quantity value is also the range over which the shadow price is valid. Shadow Prices (Dual Variable Values)
  • 15.  Two airplane parts: no.1 and no. 2.  Three manufacturing stages: stamping, drilling, milling.  Decision variables: x1 (number of part no.1 to produce) x2 (number of part no.2 to produce)  Model: Maximize Z = $650x1 + 910x2 subject to: 4x1 + 7.5x2  105 (stamping,hr) 6.2x1 + 4.9x2  90 (drilling, hr) 9.1x1 + 4.1x2  110 (finishing, hr) x1, x2  0 Example Problem Problem Statement