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Operations Management Linear Programming Module B - Part 2

Operations Management Linear Programming Module B - Part 2. Problem B.23.

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Operations Management Linear Programming Module B - Part 2

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  1. Operations ManagementLinear ProgrammingModule 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. Packing material (lbs.) Packing time (hrs.) Container Profit $8 2 A 2 6 $6 1 K 4 3 $14 T =240 Amount available 120 Problem B.23

  4. 2xA + xK + 3xT  120 (lbs.) 2xA + 6xK + 4xT = 240 (hours) xA, xK, xT 0 : Maximize: 8xA + 6xK + 14xT Problem B.23 xi = Number of class i containers to pack each week. i=A, K, T

  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. • E3 • 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 $582.857… in Excel values are rounded.

  22. Computer Solutions • Optimal solution is to use: • 0 class A containers. • 17.14 class K containers. • 34.29 class T containers. • Maximum profit is $582.857 per week. • Select Answer and Sensitivity Reports andclick 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. 0 -1.142857143 8 1.142857143 1E+30 $C$7 Sol'n values K cont. 17.14285714 0 6 8 1E+30 $D$7 Sol'n values T cont 34.28571429 0 14 1E+30 1.6 Optimal solution: 0 class A containers 17.14285… class K containers 34.28571… class T containers Profit = 0(8) + 17.14285(6) + 34.28571(14) = $582.857

  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 9.142857 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=34.29 (same as before) • profit = 685.71 (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 $4.2857... for up to 60 additional lbs. • Each additional hour of packing time will increase profit by $0.2857... 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 $4.2857 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. = $214.2857...

  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. • Much harder to solve (more computer time).

  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. Midnight - 4 am 4 am - 8 am 3 6 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. 8 am - noon Noon - 4 pm 4 pm - 8 pm 8 pm - midnight 12 9 13 15

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