1. Operations Management Linear Programming Module B - Part 2

2. Problem B.23
1. Gross Distributors packages and distributes industrial supplies. A standard shipment can be packaged in a class A container, a class K container, or a class T container. The profit from using each type of container is: $8 for each class A container, $6 for each class K container, and $14 for each class T container. The amount of packing material required by each A, K and T container is 2, 1 and 3 lbs., respectively. The amount of packing time required by each A, K, and T container is 2, 6, and 4 hours, respectively. There is 120 lbs of packing material available each week. Six packers must be employed full time (40 hours per week each). Determine how many containers to pack each week.

3. Problem B.23 Container Profit Packing material (lbs.) time (hrs.) A $81
Amount available K T $6
$14 3 6 4
120
=240

4. Problem B.23
xi = Number of class i containers to pack each week. i=A, K, T :
Maximize: 8xA xK xT
2xA xK xT  (lbs.)
2xA xK xT = (hours)
xA, xK, xT  0

5. Linear Programming Solutions
Unique Optimal Solution.
Multiple Optimal Solutions.
Infeasible (no solution).
x + y  800
x  1000
x, y  0
Unbounded (infinite solution).
Maximize 3x + 2y
x + y  1000

6. Computer Solutions
Optimal values of decision variables and objective function.
Sensitivity information for objective function coefficients.
Sensitivity information for RHS (right-hand side) of constraints and shadow price.

7. Computer Solutions Enter data from formulation in Excel.
1 row for the coefficients of objective.
1 row for coefficients & RHS of each constraint.
1 final row for solution (decision variable) values.
Select Solver from the Tools Menu.

8. Computer Solutions - Spreadsheet

9. Computer Solutions - Spreadsheet

10. Computer Solutions - Spreadsheet

11. Computer Solutions - Solver

12. Computer Solutions - Solver

13. Computer Solutions - Solver Parameters

14. Computer Solutions Set Target Cell: to value of objective function.
Equal To: Max or Min
By Changing Cells: = Sol’n values (decision variable values).
B7:D7
Subject to the Constraints:
Click Add to add each constraint:
LHS =,  ,  RHS

15. Computer Solutions - Adding Constraints
Cell Reference: LHS location
Select sign : <=, =, >=
Constraint: RHS location

16. Computer Solutions - Adding Constraints
1st constraint.
Click Add.
Repeat for second constraint.

17. Computer Solutions
Click Options to set up Solver for LP.

18. Computer Solutions - Solver Options
Check ‘on’ Assume Linear Model and Assume Non-Negative.

19. Computer Solutions
Click Solve to find the optimal solution.

20. Computer Solutions - Solver Results

21. Computer Solutions - Optimal Solution
Optimal solution is to use:
0 A containers
17.14 K containers
34.29 T containers
Maximum profit is $583 per week.
Actually $ … in Excel values are rounded.

22. Computer Solutions Optimal solution is to use:
0 class A containers.
class K containers.
class T containers.
Maximum profit is $ per week.
Select Answer and Sensitivity Reports and click OK.
New pages appear in Excel.

23. Computer Solution - Answer Report

24. Sensitivity Analysis
Projects how much a solution will change if there are changes in variables or input data.
Shadow price (dual) - Value of one additional unit of a resource.

25. Computer Solution - Sensitivity Report

26. Computer Solution - Sensitivity Report
Microsoft Excel 8.0e Sensitivity Report
Worksheet: [probb.23.xls]Sheet1
Report Created: 1/31/01 9:53:27 PM
Adjustable Cells
Final
Reduced
Objective
Allowable
Allowable
Cell
Name
Value
Cost
Coefficient
Increase
Decrease
$B$7
Sol'n values A cont. 8
1E+30
$C$7
Sol'n values K cont. 6 8
1E+30
$D$7
Sol'n values T cont 14
1E+30
1.6
Optimal solution:
class A containers
… class K containers
… class T containers
Profit = 0(8) (6) (14) = $

27. Computer Solution - Sensitivity Report

28. Sensitivity for Objective Coefficients
As long as coefficients are in range indicated, then current solution is still optimal, but profit may change!
Current solution is optimal as long as:
Coefficient of xA is between -infinity and
Coefficient of xK is between -infinity and 14
Coefficient of xT is between 12.4 and infinity

29. Sensitivity for Objective Coefficients
If profit for class K container was 12 (not 6), what is optimal solution?

30. Sensitivity for Objective Coefficients
If profit for class K container was 12 (not 6), what is optimal solution?
xA=0, xK=17.14, xT= (same as before)
profit = (more than before!)

31. Sensitivity for Objective Coefficients
If profit for class K container was 16 (not 6), what is optimal solution?

32. Sensitivity for Objective Coefficients
If profit for class K container was 16 (not 6), what is optimal solution?
Different!
Resolve problem to get solution.

33. Computer Solution - Sensitivity Report

34. Sensitivity for RHS values
Shadow price is change in objective value for each unit change in RHS as long as change in RHS is within range.
Each additional lb. of packing material will increase profit by $ for up to 60 additional lbs.
Each additional hour of packing time will increase profit by $ for up to 480 additional hours.

35. Sensitivity for RHS values
Suppose you can buy 50 more lbs. of packing material for $250. Should you buy it?

36. Sensitivity for RHS values
Suppose you can buy 50 more lbs. of packing material for $250. Should you buy it?
NO. $250 for 50 lbs. is $5 per lb.
Profit increase is only $ per lb.

37. Sensitivity for RHS values
How much would you pay for 50 more lbs. of packing material?

38. Sensitivity for RHS values
How much would you pay for 50 more lbs. of packing material?
$214.28
50 lbs.  $4.2857/lb. = $

39. Sensitivity for RHS values
If change in RHS is outside range (from allowable increase or decrease), then we can not tell how the objective value will change.

40. Extensions of Linear Programming
Integer programming (IP): Some or all variables are restricted to integer values.
Allows “if…then” constraints.
Much harder to solve (more computer time).
Nonlinear programming: Some constraints or objective are nonlinear functions.
Allows wider range of situations to be modeled.

41. { { Integer Programming
1 if we build a factory in St. Louis
0 otherwise. {
1 if we build a factory in Chicago
0 otherwise.
We will build one factory in Chicago or St. Louis.
x1 + x2  1
We will build one factory in either Chicago or St. Louis.
x1 + x2 = 1
If we build in Chicago, then we will not build in St. Louis.
x2  1 - x1

42. Harder Formulation Example
You are creating an investment portfolio from 4 investment options: stocks, real estate, T-bills (Treasury-bills), and cash. Stocks have an annual rate of return of 12% and a risk measure of 5. Real estate has an annual rate of return of 10% and a risk measure of 8. T-bills have an annual rate of return of 5% and a risk measure of 1. Cash has an annual rate of return of 0% and a risk measure of 0. The average risk of the portfolio can not exceed 5. At least 15% of the portfolio must be in cash. Formulate an LP to maximize the annual rate of return of the portfolio.

43. Another Formulation Example
A business operates 24 hours a day and employees work 8 hour shifts. Shifts may begin at midnight, 4 am, 8 am, noon, 4 pm or 8 pm. The number of employees needed in each 4 hour period of the day to serve demand is in the table below. Formulate an LP to minimize the number of employees to satisfy the demand.
Midnight
- 4 am
4 am -
8 am 3 6
8 am -
noon
Noon -
4 pm
4 pm -
8 pm
8 pm -
midnight 12 9 13 15