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BA 452 Lesson A.9 Operations Management Applications 1 1ReadingsReadings Chapter 4 Linear Programming Applications in Marketing, Finance, and Operations.

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Presentation on theme: "BA 452 Lesson A.9 Operations Management Applications 1 1ReadingsReadings Chapter 4 Linear Programming Applications in Marketing, Finance, and Operations."— Presentation transcript:

1 BA 452 Lesson A.9 Operations Management Applications 1 1ReadingsReadings Chapter 4 Linear Programming Applications in Marketing, Finance, and Operations Management

2 BA 452 Lesson A.9 Operations Management Applications 2 2OverviewOverview

3 3 3Overview Production Scheduling Problems are Resource Allocation Problems when outputs are fixed and when outputs and inputs occur at different periods in time. The simplest problems consider only two time periods. Production Scheduling Problems with Dynamic Inventory help managers find an efficient low-cost production schedule for one or more products over several time periods (weeks or months). Workforce Assignment Problems are Resource Allocation Problems when labor is a resource with a flexible allocation; some labor can be assigned to more than one work center. Make or Buy Problems are Linear Programming Profit Maximization problems when outputs are fixed and when inputs can be either made or bought. Make or Buy Problems help minimize cost. Product Mix Problems are Resource Allocation Problems when outputs have different physical characteristics. Product Mix Problems thus determine production levels that meet demand requirements. Blending Problems with Weight Constraints help production managers blend resources to produce goods of a specific weight at minimum cost.

4 BA 452 Lesson A.9 Operations Management Applications 4 4 Tool Summary n Do not make integer restrictions, and maybe the variables at an optimum will be integers. First Example: P i = (integer) number of producers in month i. First Example: P i = (integer) number of producers in month i. n Use compound variables: First Example: P i = number of producers in month i First Example: P i = number of producers in month i n Use dynamic or recursive constraints: First Example: Define the constraint that the number of apprentices in a month must not exceed the number of recruits in the previous month: A 2 - R 1 < 0; A 3 - R 2 < 0 First Example: Define the constraint that the number of apprentices in a month must not exceed the number of recruits in the previous month: A 2 - R 1 < 0; A 3 - R 2 < 0 n Constrain one variable to be a proportional to another variable: First Example: Define the constraint that each trainer can train two recruits: 2T 1 - R 1 > 0; 2T 2 - R 2 > 0 First Example: Define the constraint that each trainer can train two recruits: 2T 1 - R 1 > 0; 2T 2 - R 2 > 0 n Use inventory variables: Second Example: P2 + I1–I2 = 150 (production-net inventory = demand) Second Example: P2 + I1–I2 = 150 (production-net inventory = demand)Overview

5 BA 452 Lesson A.9 Operations Management Applications 5 5 Production Scheduling

6 BA 452 Lesson A.9 Operations Management Applications 6 6 Overview Production Scheduling Problems are Resource Allocation Problems when outputs are fixed and when outputs and inputs occur at different periods in time. Production Scheduling Problems thus help managers find an efficient low-cost production schedule for one or more products over several periods in the future (weeks or months). The manager determines the production levels that meet demand requirements, given limitations on production capacity, labor capacity, and storage space, while minimizing total production and storage costs. The simplest problems consider only two time periods. Production Scheduling

7 BA 452 Lesson A.9 Operations Management Applications 7 7 Question: Chip Foose is the owner of Chip Foose Custom Cars. Chip has just received orders for 1,000 standard wheels and 1,250 deluxe wheels next month and for 800 standard and 1,500 deluxe the following month. All orders must be filled. The cost of making standard wheels is $10 and deluxe wheels is $16. Overtime rates are 50% higher. There are 1,000 hours of regular time and 500 hours of overtime available each month. It takes.5 hour to make a standard wheel and.6 hour to make a deluxe wheel. The cost of storing a wheel from one month to the next is $2. Minimize total production and inventory costs for standard and deluxe wheels. Production Scheduling

8 BA 452 Lesson A.9 Operations Management Applications 8 8 Month 1 Month 2 Month 1 Month 2 Wheel Reg. Time Overtime Reg. Time Overtime Standard SR 1 SO 1 SR 2 SO 2 Deluxe DR 1 DO 1 DR 2 DO 2 SI = number of standard wheels held in SI = number of standard wheels held in inventory from month 1 to month 2 inventory from month 1 to month 2 DI = number of deluxe wheels held in DI = number of deluxe wheels held in inventory from month 1 to month 2 inventory from month 1 to month 2 n Define the inventory variables: Determine the inventory quantities for standard and deluxe wheels. n Define the production variables: Determine the regular- time and overtime production quantities in each month for standard and deluxe wheels. Production Scheduling

9 BA 452 Lesson A.9 Operations Management Applications 9 9 Define the objective function: Minimize total production and inventory costs for standard and deluxe wheels. Min (production cost per wheel) Min (production cost per wheel) x (number of wheels produced) x (number of wheels produced) + (inventory cost per wheel) + (inventory cost per wheel) x (number of wheels in inventory) x (number of wheels in inventory) Min 10SR SO SR SO 2 Min 10SR SO SR SO DR DO DR DO DR DO DR DO 2 + 2SI + 2DI + 2SI + 2DI Production Scheduling

10 BA 452 Lesson A.9 Operations Management Applications 10 n Define the production month 1 constraint on (units required) + (units stored). Standard: (1) SR 1 + SO 1 = 1,000 + SI or SR 1 + SO 1 SI = 1,000 (1) SR 1 + SO 1 = 1,000 + SI or SR 1 + SO 1 SI = 1,000Deluxe: (2) DR 1 + DO 1 = 1,250 + DI or DR 1 + DO 1 DI = 1,250 (2) DR 1 + DO 1 = 1,250 + DI or DR 1 + DO 1 DI = 1,250 Production Scheduling

11 BA 452 Lesson A.9 Operations Management Applications 11 n Define the production month 2 constraint on (units required) + (units stored). Standard: (3) SR 2 + SO 2 = 800 SI or SR 2 + SO 2 + SI = 800 (3) SR 2 + SO 2 = 800 SI or SR 2 + SO 2 + SI = 800Deluxe: (4) DR 2 + DO 2 = 1,500 DI or DR 2 + DO 2 + DI = 1,500 (4) DR 2 + DO 2 = 1,500 DI or DR 2 + DO 2 + DI = 1,500 Production Scheduling

12 BA 452 Lesson A.9 Operations Management Applications 12 n Define the constraint Reg. Hrs. Used Month 1 < Reg. Hrs. Avail. Month 1: (5).5SR 1 +.6DR 1 < 1000 (5).5SR 1 +.6DR 1 < 1000 n Define the constraint OT Hrs. Used Month 1 < OT Hrs. Avail. Month 1: (6).5SO 1 +.6DO 1 < 500 (6).5SO 1 +.6DO 1 < 500 n Define the constraint Reg. Hrs. Used Month 2 < Reg. Hrs. Avail. Month 2: (7).5SR 2 +.6DR 2 < 1000 (7).5SR 2 +.6DR 2 < 1000 n Define the constraint OT Hrs. Used Month 2 < OT Hrs. Avail. Month 2: (8).5SO 2 +.6DO 2 < 500 (8).5SO 2 +.6DO 2 < 500 Production Scheduling

13 BA 452 Lesson A.9 Operations Management Applications 13 Interpretation: Total cost $67,500, no storage, production schedule: Month 1 Month 2 Month 1 Month 2 Reg. Time Overtime Reg. Time Overtime Reg. Time Overtime Reg. Time Overtime Standard Deluxe Production Scheduling

14 BA 452 Lesson A.9 Operations Management Applications 14 Production Scheduling with Dynamic Inventory

15 BA 452 Lesson A.9 Operations Management Applications 15 Production Scheduling with Dynamic Inventory Overview Production Scheduling Problems with Dynamic Inventory help managers find an efficient low-cost production schedule for one or more products over several time periods (weeks or months). A production scheduling problem is resource-allocation problem for each of several periods in the future. Complex problems consider more than two time periods, so there are many periods of inventory.

16 BA 452 Lesson A.9 Operations Management Applications 16 Question: Wilson Sporting Goods produces baseballs. Wilson must decide how many baseballs to produce each month. It has decided to use a 6-month planning horizon. n The forecasted demands for the next 6 months are 10,000; 15,000; 30,000; 35,000; 25,000; and 10,000. n Wilson wants to meet these demands on time, knowing that it currently has 5,000 baseballs in inventory and that it can use a given months production to help meet the demand for that month. n During each month there is enough production capacity to produce up to 30,000 baseballs, and there is enough storage capacity to store up to 10,000 baseballs at the end of the month, after demand has occurred. Production Scheduling with Dynamic Inventory

17 BA 452 Lesson A.9 Operations Management Applications 17 n The forecasted production costs per baseball for the next 6 months are $12.50, $12.55, $12.70, $12.80, $12.85, and $ n The holding cost per baseball held in inventory at the end of the month is figured at 5% of the production cost for that month: $0.625, $0.6275, $0.635, $0.64, $0.6425, and $ n The selling price for baseballs is not considered relevant to the production decision because Wilson will satisfy all customer demand exactly when it occurs – at whatever the selling price. n Therefore, Wilson wants to determine the production schedule that minimizes the total production and holding costs. Production Scheduling with Dynamic Inventory

18 BA 452 Lesson A.9 Operations Management Applications 18 n The decision variables are the production quantities for the 6 months, labeled P1 through P6. To keep quantities small, all quantities are in hundreds of baseballs. n Constraints are easier to understand if we add variables I1 through I6 to be the corresponding end-of-month inventories (after meeting demand). For example, I3 is the number of baseballs left over at the end of month 3. n The following constraints define inventories: P1 – I1 = (production–inventory = net demand) P1 – I1 = (production–inventory = net demand) P2 + I1–I2 = 150 (production-net inventory = demand) P2 + I1–I2 = 150 (production-net inventory = demand) P3 + I2–I3 = 300 (production-net inventory = demand) P3 + I2–I3 = 300 (production-net inventory = demand) P4 + I3–I4 = 350 (production-net inventory = demand) P4 + I3–I4 = 350 (production-net inventory = demand) P5 + I4–I5 = 250 (production-net inventory = demand) P5 + I4–I5 = 250 (production-net inventory = demand) P6 + I5–I6 = 100 (production-net inventory = demand) P6 + I5–I6 = 100 (production-net inventory = demand) Production Scheduling with Dynamic Inventory

19 BA 452 Lesson A.9 Operations Management Applications 19 n There are obvious constraints are on production and inventory storage capacities: Pj 300 and Ij 100 for each month j (j = 1, …, 6). n Finally, production and inventory storage are assumed non-negative. Production Scheduling with Dynamic Inventory

20 BA 452 Lesson A.9 Operations Management Applications 20 n There Production Scheduling with Dynamic Inventory

21 BA 452 Lesson A.9 Operations Management Applications 21 Production Scheduling with Dynamic Inventory

22 BA 452 Lesson A.9 Operations Management Applications 22 Workforce Assignment

23 BA 452 Lesson A.9 Operations Management Applications 23 Overview Workforce Assignment Problems are Resource Allocation Problems when labor is one of the resources, and labor allocation has some flexibility; at least some labor can be assigned to more than one department or work center. Workforce Assignment Problems thus help when employees have been cross-trained on two or more jobs or, for instance, when sales personnel can be transferred between stores. Workforce Assignment

24 BA 452 Lesson A.9 Operations Management Applications 24 Question: National Wing Company (NWC) is gearing up for the new B-48 contract. NWC has agreed to supply 20 wings in April, 24 in May, and 30 in June. Wings can be freely stored from one month to the next. Currently, NWC has 100 fully-qualified workers. A fully qualified worker can either be placed in production or can train new recruits. A new recruit can be trained to be an apprentice in one month. After another month, the apprentice becomes a qualified worker. Each trainer can train two recruits. At the end of June, NWC wishes to have at least 140 fully-qualified workers. (Note: NWC must use firm-specific training. There is no outside market for fully-qualified workers.) The production rate and salary per employee type is listed below. Type of Production Rate Wage Type of Production Rate Wage Employee (Wings/Month) Per Month Employee (Wings/Month) Per Month Production.6 $3,000 Production.6 $3,000 Trainer.3 $3,300 Trainer.3 $3,300 Apprentice.4 $2,600 Apprentice.4 $2,600 Recruit.05 $2,200 Recruit.05 $2,200 How should NWC optimize? Workforce Assignment

25 BA 452 Lesson A.9 Operations Management Applications 25 Answer: n Define the Decision Variables P i = number of producers in month i (where i = 1, 2, 3 for April, May, June) T i = number of trainers in month i (where i = 1, 2 for April, May) A i = number of apprentices in month i (where i = 2, 3 for May, June) R i = number of recruits in month i (where i = 1, 2 for April, May) n Define the objective function Minimize total wage cost for producers, trainers, apprentices, and recruits for April, May, and June: Min 3000P T R P T A R P T A R P T A R P T A R 3 Workforce Assignment

26 BA 452 Lesson A.9 Operations Management Applications 26 n Define the constraint that total production in Month 1 (April) must equal or exceed contract for Month 1: must equal or exceed contract for Month 1: (1).6P 1 +.3T R 1 > 20 n Define the constraint that total production in Months 1-2 (April, May) must equal or exceed total contracts for Months 1-2: (2).6P 1 +.3T R 1 +.6P 2 +.3T 2 +.4A R 2 > 44 n Define the constraint that total production in Months 1-3 (April, May, June) must equal or exceed total contracts for Months 1-3: (3).6P 1 +.3T R 1 +.6P 2 +.3T 2 +.4A R 2 +.6P 3 +.4A 3 > 74 n Define the constraint that the number of producers and trainers in a month (fully qualified workers) must not exceed the initial supply of 100, plus any apprentices employed in a previous month: (4) P 1 + T 1 < 100 (5)P 2 + T 2 < 100 (6)P 3 + T 3 < A 2 Workforce Assignment

27 BA 452 Lesson A.9 Operations Management Applications 27 n Define the constraint that the number of apprentices in a month must not exceed the number of recruits in the previous month that have not already become apprentices: (7)A 2 < R 1 ; (8) A 3 < (R 1 - A 2 ) + R 2 n Note: Constraint (8) allows a recruit from Month 1 to be laid off in Month 2, then rehired as an apprentice in Month 3. n Define the constraint that each trainer can train two recruits: (9)2T 1 - R 1 > 0; (10) 2T 2 - R 2 > 0 n Define the constraint that at the end of June, there are to be at least 140 fully qualified workers: (11) A 2 + A 3 > 140 Workforce Assignment

28 BA 452 Lesson A.9 Operations Management Applications 28 Interpretation: Total wage cost = $1,098,000, using the following workforce assignment: Interpretation: Total wage cost = $1,098,000, using the following workforce assignment: April May June July ProducersTrainersApprenticesRecruits Workforce Assignment

29 BA 452 Lesson A.9 Operations Management Applications 29 Make or Buy

30 BA 452 Lesson A.9 Operations Management Applications 30 Overview Make or Buy Problems are Linear Programming Profit Maximization problems when outputs are fixed and when inputs can be either made or bought. Make or Buy Problems thus help production managers to minimize cost by comparing, for some inputs, the lower cost of manufacturing those inputs (rather then buying them) to the opportunity cost of the scarce resources used in manufacture. Make or Buy

31 BA 452 Lesson A.9 Operations Management Applications 31 Question: The Janders Company markets business and engineering products. Janders is currently preparing to introduce two new calculators: one for the business market called the Financial Manager, and one for the engineering market called the Technician. Each calculator has three components: a base, an electronic cartridge, and a faceplate or top. The same base is used for both calculators, but the cartridges and tops are different. All components can be manufactured by the company or purchased from outside suppliers. Make or Buy

32 BA 452 Lesson A.9 Operations Management Applications 32 Company forecasters indicate that 3000 Financial Manager calculators and 2000 Technician calculators will be demanded. However, manufacturing capacity is limited. The company has 200 hours of regular time manufacturing time and 50 hours of overtime that can be scheduled for the calculators. Overtime involves a premium at the additional cost of $9 per hour. Make or Buy

33 BA 452 Lesson A.9 Operations Management Applications 33 Determine how many units of each component to manufacture and how many to buy given the cost, purchase price and manufacturing time requirements: Make or Buy Component Manufacture cost (regular time) per unit Purchase cost per unit Manufacturing Time Base$0.50$ minutes Financial cartridge $3.75$ minutes Techniciancartridge$3.30$ minutes Financial top $0.60$ minutes Technician top $0.75$ minutes

34 BA 452 Lesson A.9 Operations Management Applications 34 Answer: Define decision variables BM = number of bases manufactured BP = number of bases purchased FCM = number of Financial cartridges manufactured FCP = number of Financial cartridges purchased TCM = number of Technician cartridges manufactured TCP = number of Technician cartridges purchased FTM = number of Financial tops manufactured FTP = number of Financial tops purchased TTM = number of Technician tops manufactured TTP = number of Technician tops purchased OT = number of overtime hours Make or Buy

35 BA 452 Lesson A.9 Operations Management Applications 35 Define the objective: Min 0.5BM + 0.6BP FCM + 4FCP+ 3.3TCM + 3.9TCP + 0.6FTM FTP TTM TTP + 9OT Make or Buy Component Manufacture cost (regular time) per unit Purchase cost per unit Manufacturing Time Base$0.50$ minutes Financial cartridge $3.75$ minutes Techniciancartridge$3.30$ minutes Financial top $0.60$ minutes Technician top $0.75$ minutes

36 BA 452 Lesson A.9 Operations Management Applications 36 Demand Constraints: BM + BP = 5000 Bases FCM + FCP = 3000 Financial cartridges TCM + TCP = 2000 Technician cartridges FTM + FTP = 3000 Financial tops TTM + TTP = 2000 Technician tops Overtime Resource Constraint: OT < 50 Manufacturing Time Constraint (right-side in minutes): BM + 3FCM + 2.5TCM + FTM + 1.5TTM < OT Make or Buy

37 BA 452 Lesson A.9 Operations Management Applications 37 The Management Scientist can solve this 11-variable, 7- constraint linear program: BM = 5000 BP = 0 FCM = FCP = TCM = TCP = FTM = FTP = TTM = TTP = OT = Make or Buy

38 BA 452 Lesson A.9 Operations Management Applications 38 Product Mix

39 BA 452 Lesson A.9 Operations Management Applications 39 Overview Product Mix Problems are Resource Allocation Problems when outputs have different physical characteristics. Product Mix Problems thus help managers determine the production levels that meet demand requirements, given limitations on production capacity and labor capacity, to maximize profit or minimize cost. Product Mix

40 BA 452 Lesson A.9 Operations Management Applications 40 Question: Floataway Tours has $420,000 that can be used to buy new rental boats for hire during the summer. The boats can be bought from two different manufacturers. Floataway Tours would like to buy at least 50 boats, and would like to buy the same number from Sleekboat as from Racer to maintain goodwill. At the same time, Floataway Tours wishes to have a total seating capacity of at least 200. Maximum Expected Maximum Expected Boat Builder Cost Seating Daily Profit Boat Builder Cost Seating Daily Profit Speedhawk Sleekboat $ $ 70 Silverbird Sleekboat $ $ 80 Catman Racer $ $ 50 Classy Racer $ $110 How should Floataway Tours optimize? Product Mix

41 BA 452 Lesson A.9 Operations Management Applications 41 n Define the Decision Variables x 1 = number of Speedhawks ordered x 1 = number of Speedhawks ordered x 2 = number of Silverbirds ordered x 2 = number of Silverbirds ordered x 3 = number of Catmans ordered x 3 = number of Catmans ordered x 4 = number of Classys ordered x 4 = number of Classys ordered n Define the Objective Function Maximize total expected daily profit: Maximize total expected daily profit: Max (Expected daily profit per unit) Max (Expected daily profit per unit) x (Number of units) x (Number of units) Max 70x x x x 4 Product Mix

42 BA 452 Lesson A.9 Operations Management Applications 42 n Define the constraint to spend no more than $420,000: (1) 6000x x x x 4 < 420,000 (1) 6000x x x x 4 < 420,000 n Define the constraint to buy at least 50 boats: (2) x 1 + x 2 + x 3 + x 4 > 50 (2) x 1 + x 2 + x 3 + x 4 > 50 n Define the constraint that the number of boats from Sleekboat equals the number of boats from Racer: (3) x 1 + x 2 = x 3 + x 4 or x 1 + x 2 - x 3 - x 4 = 0 (3) x 1 + x 2 = x 3 + x 4 or x 1 + x 2 - x 3 - x 4 = 0 n Define the constraint that seating capacity be at least 200: (4) 3x 1 + 5x 2 + 2x 3 + 6x 4 > 200 (4) 3x 1 + 5x 2 + 2x 3 + 6x 4 > 200 Product Mix

43 BA 452 Lesson A.9 Operations Management Applications 43 Interpretation: Expected daily profit is $5, Buy 28 Speedhawks from Sleekboat; buy 28 Classys from Racer. Buy 28 Speedhawks from Sleekboat; buy 28 Classys from Racer. The minimum number of boats was exceeded by 6 (surplus for constraint #2). The minimum number of boats was exceeded by 6 (surplus for constraint #2). The minimum seating capacity was exceeded by 52 (surplus for constraint #4). The minimum seating capacity was exceeded by 52 (surplus for constraint #4). Product Mix

44 BA 452 Lesson A.9 Operations Management Applications 44 Blending with Weight Constraints

45 BA 452 Lesson A.9 Operations Management Applications 45 Blending with Weight Constraints Overview Blending Problems with Weight Constraints help production managers blend resources to produce goods of a specific weight at minimum cost.

46 BA 452 Lesson A.9 Operations Management Applications 46 Question: The Maruchan Corporation receives four raw grains from which it blends its Maruchan Ramen Noodle Soup. Maruchan advertises that each 8-ounce packet meets the minimum daily requirements for vitamin C, protein and iron. The following is the cost of each raw grain, the vitamin C, protein, and iron units per pound of each grain. Vitamin C Protein Iron Vitamin C Protein Iron Grain Units/lb Units/lb Units/lb Cost/lb Grain Units/lb Units/lb Units/lb Cost/lb Maruchan is interested in producing the 8-ounce mixture at minimum cost while meeting the minimum daily requirements of 6 units of vitamin C, 5 units of protein, and 5 units of iron. Blending with Weight Constraints

47 BA 452 Lesson A.9 Operations Management Applications 47 Answer: n Define the decision variables. x j = the pounds of grain j (j = 1,2,3,4) used in 8-ounce mixture x j = the pounds of grain j (j = 1,2,3,4) used in 8-ounce mixture n Define the objective. Minimize the total cost for an 8-ounce mixture: Min.75x x x x 4 Min.75x x x x 4 Blending with Weight Constraints

48 BA 452 Lesson A.9 Operations Management Applications 48 n Constrain the total weight of the mix to 8-ounces (.5 pounds): (1) x 1 + x 2 + x 3 + x 4 =.5 (1) x 1 + x 2 + x 3 + x 4 =.5 n Constrain the total amount of Vitamin C in the mix to be at least 6 units: (2) 9x x 2 + 8x x 4 > 6 (2) 9x x 2 + 8x x 4 > 6 n Constrain the total amount of protein in the mix to be at least 5 units: (3) 12x x x 3 + 8x 4 > 5 (3) 12x x x 3 + 8x 4 > 5 n Constrain the total amount of iron in the mix to be at least 5 units: (4) 14x x 3 + 7x 4 > 5 (4) 14x x 3 + 7x 4 > 5 Blending with Weight Constraints

49 BA 452 Lesson A.9 Operations Management Applications 49 Interpretation: The mixture costs Fredericks 40.6 cents. Optimal blend: n lb. of grain 1 n lb. of grain 2 n lb. of grain 3 n lb. of grain 4 Blending with Weight Constraints

50 BA 452 Lesson A.9 Operations Management Applications 50 BA 452 Quantitative Analysis End of Lesson A.9


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