1. 1 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. MODELING USING LINEAR PROGRAMMING SUPPLEMENTARY CHAPTER C DAVID A. COLLIER AND JAMES R. EVANS OM2

2. 2 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. LO1 Explain how to recognize decision variables, the objective function, and constraints in formulating linear optimization models. LO2 Describe how to use linear optimization models for OM applications. LO3 Explain how to use Excel Solver to solve linear optimization models on spreadsheets. Supplementary Chapter C. Modeling Using Linear Programming l e a r n i n g o u t c o m e s

3. 3 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. aller’s Pub & Brewery is a small restaurant and microbrewery that makes six types of special beers, each having a unique taste and color. Jeremy Haller, one of the family owners who oversees the brewery operations, has become worried about increasing costs of grains and hops that are the principal ingredients and the difficulty they seem to be having in making the right product mix to meet demand and using the ingredients that are purchased under contract in the commodities market. Haller’s buys six different types of grains and four different types of hops. Supplementary Chapter C. Modeling Using Linear Programming h

4. 4 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Each of the beers needs different amounts of brewing time and is produced in 30-keg (4,350-pint) batches. While the average customer demand is 55 kegs per week, the demand varies by type. In a meeting with the other owners, Jeremy stated that Haller’s has not been able to plan effectively to meet the expected demand. “I know there must be a better way of making our brewing decisions to improve our profitability.” Supplementary Chapter C. Modeling Using Linear Programming

5. 5 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Can you identify any examples when you needed to find a better way of planning, designing, or operating some system or process? What do you think? Supplementary Chapter C. Modeling Using Linear Programming

6. 6 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Quantitative models that seek to maximize or minimize some objective function while satisfying a set of constraints are called optimization models. Linear programming (LP) models are used widely for many types of operations design and planning problems that involve allocating limited resources among competing alternatives, and for supply chain management design and operations. Supplementary Chapter C. Modeling Using Linear Programming

7. 7 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Softwater Production Planning Problem Pellets are produced in 40- and 80-pound bags. Company has orders for 20,000 pounds 4,000 pounds are currently in inventory Limited amounts of packaging materials and packaging line time Determine how many bags of each size to produce to maximize profit. Supplementary Chapter C. Modeling Using Linear Programming

8. 8 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Decision Variables A decision variable is a controllable input variable that represents the key decisions a manager must make to achieve an objective. x 1 = number of 40-pound bags produced x 2 = number of 80-pound bags produced Supplementary Chapter C. Modeling Using Linear Programming

9. 9 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Objective Function Suppose that Softwater makes $2 for every 40-lb. bag and $4 for every 80-lb. bag produced and sold. Max total profit = z = 2x 1 + 4x 2 [C.1] The constant terms in the objective function are called objective function coefficients. Supplementary Chapter C. Modeling Using Linear Programming

10. 10 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Solutions Any particular combination of decision variables is referred to as a solution. Solutions that satisfy all constraints are referred to as feasible solutions. Any feasible solution that optimizes the objective function is called an optimal solution. Supplementary Chapter C. Modeling Using Linear Programming

11. 11 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. A Solution for the Softwater Problem Supplementary Chapter C. Modeling Using Linear Programming Suppose that Softwater decided to produce 200 40-pound bags and 300 80- pound bags. The profit would be z = 2(200) + 4(300) = 400 + 1,200 = $1,600

12. 12 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Constraints A constraint is some limitation or requirement that must be satisfied by the solution. Suppose that each 40-pound bag requires 1.2 minutes of packaging time per bag and 80- pound bags require 3 minutes per bag. The total packaging time required is 1.2x 1 + 3x 2 Only 1,500 minutes of packaging time are available, so we have the constraint: 1.2x 1 + 3x 2 ≤ 1,500 Supplementary Chapter C. Modeling Using Linear Programming

13. 13 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Packaging Material Constraint Softwater has 6,000 square feet of packaging materials available; each 40- pound bag requires 6 square feet and each 80-pound bag requires 10 square feet. Since the amount of packaging materials used cannot exceed what is available, we have the constraint: 6x 1 + 10x 2 ≤ 6,000

14. 14 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Aggregate Production Constraint We need to produce a net amount of 16,000 pounds. Because the small bags contain 40 pounds of pellets and the large bags contain 80 pounds, we must impose this aggregate-demand constraint: 40x 1 + 80x 2 ≥ 16,000 Supplementary Chapter C. Modeling Using Linear Programming

15. 15 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Nonnegativity Constraints We must prevent the decision variables from having negative values. Thus, we need the constraints: x 1 and x 2 ≥ 0 Supplementary Chapter C. Modeling Using Linear Programming

16. 16 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Softwater Optimization Model Max z = 2x 1 + 4x 2 (profit) subject to 1.2x 1 + 3x 2 ≤ 1,500 (packaging line) 6x 1 + 10x 2 ≤ 6,000 (materials availability) 40x 1 + 80x 2 ≥16,000 (aggregate production) x1, x2 ≥ 0 (nonnegativity)

17. 17 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Linear Functions A function in which each variable appears in a separate term and is raised to the first power is called a linear function. The objective function and all constraints of the Softwater problem consist of linear functions. This is a requirement for a linear program and its solution procedure. Supplementary Chapter C. Modeling Using Linear Programming

18. 18 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Production Scheduling Bollinger Electronics Company produces two electronic components for an airplane engine manufacturer. Demand for the next three months is:

19. 19 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Decision Variables x im denotes the production volume in units for product i in month m. Here i =1, 2, and m = 1, 2, 3; i = 1 refers to component 322A, i = 2 to component 802B, m = 1 to April, m = 2 to May, and m = 3 to June.

20. 20 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Objective Function Component 322A costs $20 per unit to produce and component 802B costs $10 per unit to produce. The production-cost part of the objective function is: 20x 11 + 20x 12 + 20x 13 + 10x 21 + 10x 22 + 10x 23

21. 21 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Objective Function To incorporate the relevant inventory costs into the model, let I im denote the inventory level for product i at the end of month m. Inventory- holding costs are 1.5 percent of the cost of the product; that is, (.015)($20) = $0.30 per unit for component 322A, and (.015)($10) = $0.15 per unit for component 802B. The inventory-holding cost portion of the objective function can be written as: 0.30I 11 + 0.30I 12 + 0.30I 13 + 0.15I 21 + 0.15I 22 + 0.15I 23

22. 22 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Objective Function To incorporate the costs due to fluctuations in production levels from month to month, we need to define additional decision variables: R m = increase in the total production level during month m compared with month m – 1 D m = decrease in the total production level during month m compared with month m – 1

23. 23 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Complete Objective Function Min 20x 11 + 20x 12 + 20x 13 + 10x 21 + 10x 22 + 10x 23 + 0.30I 11 + 0.30I 12 + 0.30I 13 + 0.15I 21 + 0.15I 22 + 0.15I 23 + 0.50R 1 + 0.50R 2 + 0.50R 3 + 0.20D 1 + 0.20D 2 + 0.20D 3

24. 24 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints First we must guarantee that the schedule meets customer demand. We have the basic equation: Ending inventory from previous month + Current production – Ending inventory for this month = This month’s demand Assume inventories at the beginning of the three- month scheduling period are 500 units for component 322A and 200 units for component 802B.

25. 25 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints Month 1: 500 + x 11 – I 11 = 1000 200 + x 21 – I 21 = 1000 Month 2: I 11 + x 12 – I 12 = 3,000 I 21 + x 22 – I 22 = 500 Month 3: I 12 + x 13 – I 13 = 5,000 I 22 + x 23 – I 23 = 3,000

26. 26 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints Minimum Inventory Level: At least 400 units of component 322A and at least 200 units of component 802B: I 13 ≥ 400 and I 23 ≥ 200

27. 27 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Additional Constraint Data Exhibits C.1 and C.2

28. 28 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints Machine capacity: 0.10x 11 + 0.08x 21 ≤ 400 (month 1) 0.10x 12 + 0.08x 22 ≤ 500 (month 2) 0.10x 13 +1 0.08x 23 ≤ 600 (month 3) Labor capacity: 0.05x 11 + 0.07x21 ≤ 300 (month 1) 0.05x 12 + 0.07x 22 ≤ 300 (month 2) 0.05x 13 + 0.07x 23 ≤ 300 (month 3) Storage capacity: 2I 11 + 3I 21 ≤ 10,000 (month 1) 2I 12 + 3I 22 ≤ 10,000 (month 2) 2I 13 + 3I 23 ≤ 10,000 (month 3)

29. 29 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints We must also guarantee that R m and D m will reflect the increase or decrease in the total production level for month m. Suppose the production levels for March were 1,500 units of component 322A and 1,000 units of component 802B. Then April production – March production = Change x 11 + x 21 – 2,500 = Change x 11 + x 21 – 2,500 = R 1 – D 1 Similar constraints for May and June.

30. 30 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints Production Smoothing Constraints: x 11 + x 21 – R 1 + D 1 = 2,500 – x 11 – x 21 + x 12 + x 22 – R 2 + D 2 = 0 – x 12 – x 22 + x 13 + x 23 – R 3 + D 3 = 0

31. 31 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Blending Problems Grand Strand Oil Company produces regular- grade and premium-grade gasoline products by blending three petroleum components. The gasolines are sold at different prices, and the petroleum components have different costs. The firm wants to determine how to blend the three components into the two products in such a way as to maximize profits.

32. 32 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Petroleum Component Cost and Supply Data Regular-grade gasoline can be sold for $2.20 per gallon and the premium-grade gasoline for $2.40 per gallon. Current commitments to distributors require Grand Strand to produce at least 10,000 gallons of regular-grade gasoline. Exhibit C.4

33. 33 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Component Specifications for Grand Strand’s Products Data Exhibit C.5

34. 34 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C Modeling Using Linear Programming Decision Variables x 1r = gallons of component 1 in regular gasoline x 2r = gallons of component 2 in regular gasoline x 3r = gallons of component 3 in regular gasoline x 1p = gallons of component 1 in premium gasoline x 2p = gallons of component 2 in premium gasoline x 3p = gallons of component 3 in premium gasoline

35. 35 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Objective Function Max 2.20(x 1r + x 2r + x 3r ) + 2.40(x 1p + x 2p + x 3p ) – 1.00(x 1r + x 1p ) - 1.20(x 2r + x 2p ) - 1.64(x 3r + x 3p ) By combining terms, we can then write the objective function as: Max 1.20x 1r + 1.00x 2r + 0.56x 3r + 1.40x 1p + 1.20x 2p + 0.76x 3p

36. 36 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints Component availability: x 1r + x 1p ≤ 5,000 (component 1) x 2r + x 2p ≤ 10,000 (component 2) x 3r + x 3p ≤ 10,000 (component 3) Regular grade gasoline requirement: x 1r + x 2r +x 3r ≥ 10,000

37. 37 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints Component 1 must account for at most 30 percent of the total gallons of regular gasoline produced: x 1r /(x 1r + x 2r + x 3r ) ≤ 0.30 or x 1r ≤ 0.30(x 1r + x 2r + x 3r ) Rewrite this as: 0.70x 1r - 0.30x 2r - 0.30x 3r ≤ 0 Other specification constraints: – 0.40x 1r + 0.60x 2r – 0.40x 3r ≤0 – 0.20x 1r – 0.20x 2r + 0.80x 3r ≤ 0 – 0.75x 1p – 0.25x 2p – 0.25x 3p ≤ 0 – 0.40x 1p + 0.60x 2p – 0.40x 3p ≤ 0 – 0.30x 1p – 0.30x 2p + 0.70x 3p ≤ 0

38. 38 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Transportation Problem The transportation problem is a special type of linear program that arises in planning the distribution of goods and services from several supply points to several demand locations.

39. 39 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Foster Generators Supply/Demand Data Exhibits C.6 and C.7

40. 40 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Foster Generators Transportation Cost per Unit Foster Generators Cost Data Exhibit C.8

41. 41 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Transportation Table

42. 42 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Transportation LP Model Min total cost = 3 x 11 + 2x 12 + 7x 13 + 6x 14 + 7x 21 + 5x 22 + 2x 23 + 3x 24 + 2x 31 + 5x 32 + 4x 33 + 5x 34 Subject to Cleveland: x 11 + x 12 + x 13 + x 14 = 5,000. Bedford: x 21 + x 22 + x 23 + x 24 = 6,000. York: x 31 + x 32 + x 33 + x 34 = 2,500. Boston: x 11 + x 21 + x 31 = 6,000. Chicago: x 12 + x 22 + x 32 = 4,000 St. Louis: x 13 + x 23 + x 33 = 2,000 Lexington: x 14 + x 24 + x 34 = 1,500

43. 43 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming LP Model for Crashing Decisions

44. 44 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Data

45. 45 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Decision Variables and Objective Function x i = start time of activity i y i = amount of crash time used for activity I Min 2,000y A + 1,000y B + 2,500y C + 1,500y D + 500y E

46. 46 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Constraints For each arc from activity i to activity j in the network, the start time for the following activity must be at least as great as the finish time for each immediate predecessor with crashing applied x j ≥ x i + normal time for activity i - y i

47. 47 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Precedence Constraints x B ≥ x A + 10 - y A x D ≥ x B + 14 - y B x C ≥ x B + 14 - y B x E ≥ x D + 11 - y D x E ≥ x C + 6 - y C x F ≥ x E + 8 - y E

48. 48 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Other Constraints Maximum Crash Times: y A ≤ 3 y B ≤ 4 y C ≤ 2 y D ≤ 2 y E ≤ 4 Project Completion Time: x F = 35

49. 49 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Using Excel Solver – Softwater Spreadsheet Model

50. 50 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Solver Model

51. 51 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Solver Results Dialog Box

52. 52 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Solver Solution

53. 53 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Solver Answer Report

54. 54 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Solver Sensitivity Report

55. 55 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Supplementary Chapter C. Modeling Using Linear Programming Solver Limits Report

56. 56 OM2, Supplementary Ch. C Modeling Using Linear Programming ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Holcomb Candle Case Study Supplementary Chapter C. Modeling Using Linear Programming Formulate an LP model, solve it, and explain what the solution means for the company.