1 1 Slide Chapter 4 Linear Programming Applications nBlending Problem nPortfolio Planning Problem nProduct Mix Problem.

Slides:



Advertisements
Similar presentations
PowerPoint Slides by Robert F. BrookerCopyright (c) 2001 by Harcourt, Inc. All rights reserved. Linear Programming Mathematical Technique for Solving Constrained.
Advertisements

Readings Readings Chapter 4
Chapter 3: Linear Programming Modeling Applications © 2007 Pearson Education.
1 1 Slide © 2005 Thomson/South-Western Slides Prepared by JOHN S. LOUCKS ST. EDWARD’S UNIVERSITY.
1 1 Slide © 2001 South-Western College Publishing/Thomson Learning Anderson Sweeney Williams Anderson Sweeney Williams Slides Prepared by JOHN LOUCKS QUANTITATIVE.
Planning with Linear Programming
Linear Programming Problem
Linear Programming Models & Case Studies
1 Lecture 3 MGMT 650 Sensitivity Analysis in LP Chapter 3.
Linear Programming Problem Formulation.
Managerial Decision Modeling with Spreadsheets
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Linear Programming Applications in Marketing, Finance and Operations
Chapter 6 Linear Programming: The Simplex Method Section 3 The Dual Problem: Minimization with Problem Constraints of the Form ≥
Chapter 6 Linear Programming: The Simplex Method
Introduction to Management Science
1 Lecture 1 MGMT 650 Management Science and Decision Analysis.
1 Lecture 1 MGMT 650 Management Science and Decision Analysis.
Linear Programming (6S) and Transportation Problem (8S)
Pet Food Company A pet food company wants to find the optimal mix of ingredients, which will minimize the cost of a batch of food, subject to constraints.
1 1 Slide Chapter 14: Goal Programming Goal programming is used to solve linear programs with multiple objectives, with each objective viewed as a "goal".
1 Lecture 2 & 3 Linear Programming and Transportation Problem.
1 Lecture 2 MGMT 650 Linear Programming Applications Chapter 4.
Environmentally Conscious Design & Manufacturing (ME592) Date: May 3, 2000 Slide:1 Environmentally Conscious Design & Manufacturing Class 24: Optimization.
Introduction to Management Science
Linear Programming Econ Outline  Review the basic concepts of Linear Programming  Illustrate some problems which can be solved by linear programming.
1 Project Management Chapter 17 Lecture 5. 2 Project Management  How is it different?  Limited time frame  Narrow focus, specific objectives  Why.
1 Lecture 5 Linear Programming (6S) and Transportation Problem (8S)
Readings Readings Chapter 2 An Introduction to Linear Programming.
1 1 Slides by John Loucks St. Edward’s University Modifications by A. Asef-Vaziri.
Slides by John Loucks St. Edward’s University.
1 1 Slides by John Loucks St. Edward’s University Modifications by A. Asef-Vaziri.
1 1 Slide © 2009 South-Western, a part of Cengage Learning Slides by John Loucks St. Edward’s University.
BA 452 Lesson A.8 Marketing and Finance Applications 1 1ReadingsReadings Chapter 4 Linear Programming Applications in Marketing, Finance, and Operations.
Department of Business Administration
Linear Programming :Applications Pertemuan 6 Matakuliah: K0442-Metode Kuantitatif Tahun: 2009.
Kerimcan OzcanMNGT 379 Operations Research1 Linear Programming: Applications Chapter 4.
Ardavan Asef-Vaziri Systems and Operations Management
Chapter 3: Linear Programming Modeling Applications
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
1 1 Slide © 2005 Thomson/South-Western Chapter 4 Linear Programming Applications n Portfolio Planning Problem n Product Mix Problem n Blending Problem.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Linear Programming.  Linear Programming provides methods for allocating limited resources among competing activities in an optimal way.  Any problem.
To accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Fourth Edition  1996 Addison-Wesley Publishing Company, Inc. All rights.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Managerial Decision Modeling with Spreadsheets
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
PowerPoint Slides by Robert F. BrookerHarcourt, Inc. items and derived items copyright © 2001 by Harcourt, Inc. Managerial Economics in a Global Economy.
Modeling and Solving LP Problems in a Spreadsheet Chapter 3 © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
LINEAR PROGRAMMING APPLICATIONS IN MARKETING, FINANCE, AND OPERATIONS MANAGEMENT (2/3) Chapter 4 MANGT 521 (B): Quantitative Management.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
MANGT 521 (B): Quantitative Management
Chapter 6 Simplex-Based Sensitivity Analysis and Duality
1 1 Slide © 2005 Thomson/South-Western Simplex-Based Sensitivity Analysis and Duality n Sensitivity Analysis with the Simplex Tableau n Duality.
1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.
Linear Programming –Strategic Allocation of Resources Decision Making with Excel Simulation 1.
LINEAR PROGRAMMING MEANING:
LINEAR PROGRAMMING. Linear Programming Linear programming is a mathematical technique. This technique is applied for choosing the best alternative from.
 Marketing Application  Media Selection  Financial Application  Portfolio Selection  Financial Planning  Product Management Application  Product.
1 Introduction To Linear Programming l Today many of the resources needed as inputs to operations are in limited supply. l Operations managers must understand.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 2 Linear Programming Chapter 3 3 Chapter Objectives –Requirements for a linear programming model. –Graphical representation of linear models. –Linear.
Linear Programming Models: Graphical and Computer Methods
Slides Prepared by JOHN LOUCKS
Chapter 3 Linear Programming Applications
John Loucks Modifications by A. Asef-Vaziri Slides by St. Edward’s
3.4b: Linear Programming ~ Application
Slides by John Loucks St. Edward’s University.
Presentation transcript:

1 1 Slide Chapter 4 Linear Programming Applications nBlending Problem nPortfolio Planning Problem nProduct Mix Problem

2 2 Slide Blending Problem Frederick's Feed Company receives four raw grains from which it blends its dry pet food. The pet food advertises that each 8-ounce can meets the minimum daily requirements for vitamin C, protein and iron. The cost of each raw grain as well as the vitamin C, protein, and iron units per pound of each grain are summarized on the next slide. Frederick's 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.

3 3 Slide Blending Problem 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

4 4 Slide Blending Problem nDefine the decision variables x j = the pounds of grain j ( j = 1,2,3,4) x j = the pounds of grain j ( j = 1,2,3,4) used in the 8-ounce mixture used in the 8-ounce mixture nDefine the objective function Minimize the total cost for an 8-ounce mixture: Minimize the total cost for an 8-ounce mixture: MIN.75 x x x x 4 MIN.75 x x x x 4

5 5 Slide Blending Problem Define the constraints Define the constraints Total weight of the mix is 8-ounces (.5 pounds): (1) x 1 + x 2 + x 3 + x 4 =.5 (1) x 1 + x 2 + x 3 + x 4 =.5 Total amount of Vitamin C in the mix is at least 6 units: (2) 9 x x x x 4 > 6 (2) 9 x x x x 4 > 6 Total amount of protein in the mix is at least 5 units: (3) 12 x x x x 4 > 5 (3) 12 x x x x 4 > 5 Total amount of iron in the mix is at least 5 units: (4) 14 x x x 4 > 5 (4) 14 x x x 4 > 5 Nonnegativity of variables: x j > 0 for all j

6 6 Slide n The Management Scientist Output OBJECTIVE FUNCTION VALUE = OBJECTIVE FUNCTION VALUE = VARIABLE VALUE REDUCED COSTS VARIABLE VALUE REDUCED COSTS X X X X X X X X Thus, the optimal blend is about.10 lb. of grain 1,.21 lb. of grain 2,.09 lb. of grain 3, and.10 lb. of grain 4. The mixture costs Frederick’s 40.6 cents. Blending Problem

7 7 Slide Portfolio Planning Problem Winslow Savings has $20 million available for investment. It wishes to invest over the next four months in such a way that it will maximize the total interest earned over the four month period as well as have at least $10 million available at the start of the fifth month for a high rise building venture in which it will be participating.

8 8 Slide Portfolio Planning Problem For the time being, Winslow wishes to invest only in 2-month government bonds (earning 2% over the 2- month period) and 3-month construction loans (earning 6% over the 3-month period). Each of these is available each month for investment. Funds not invested in these two investments are liquid and earn 3/4 of 1% per month when invested locally.

9 9 Slide Portfolio Planning Problem Formulate a linear program that will help Winslow Savings determine how to invest over the next four months if at no time does it wish to have more than $8 million in either government bonds or construction loans.

10 Slide Portfolio Planning Problem nDefine the decision variables g j = amount of new investment in g j = amount of new investment in government bonds in month j government bonds in month j c j = amount of new investment in construction loans in month j c j = amount of new investment in construction loans in month j l j = amount invested locally in month j, l j = amount invested locally in month j, where j = 1,2,3,4 where j = 1,2,3,4

11 Slide Portfolio Planning Problem nDefine the objective function Maximize total interest earned over the 4-month period. Maximize total interest earned over the 4-month period. MAX (interest rate on investment)(amount invested) MAX (interest rate on investment)(amount invested) MAX.02 g g g g 4 MAX.02 g g g g c c c c l l l l l l l l 4

12 Slide Portfolio Planning Problem nDefine the constraints Month 1's total investment limited to $20 million: Month 1's total investment limited to $20 million: (1) g 1 + c 1 + l 1 = 20,000,000 (1) g 1 + c 1 + l 1 = 20,000,000 Month 2's total investment limited to principle and interest invested locally in Month 1: Month 2's total investment limited to principle and interest invested locally in Month 1: (2) g 2 + c 2 + l 2 = l 1 (2) g 2 + c 2 + l 2 = l 1 or g 2 + c l 1 + l 2 = 0

13 Slide Portfolio Planning Problem nDefine the constraints (continued) Month 3's total investment amount limited to principle and interest invested in government bonds in Month 1 and locally invested in Month 2: (3) g 3 + c 3 + l 3 = 1.02 g l 2 (3) g 3 + c 3 + l 3 = 1.02 g l 2 or g 1 + g 3 + c l 2 + l 3 = 0

14 Slide Portfolio Planning Problem nDefine the constraints (continued) Month 4's total investment limited to principle and interest invested in construction loans in Month 1, goverment bonds in Month 2, and locally invested in Month 3: (4) g 4 + c 4 + l 4 = 1.06 c g l 3 (4) g 4 + c 4 + l 4 = 1.06 c g l 3 or g 2 + g c 1 + c l 3 + l 4 = 0 $10 million must be available at start of Month 5: (5) 1.06 c g l 4 > 10,000,000 (5) 1.06 c g l 4 > 10,000,000

15 Slide Portfolio Planning Problem nDefine the constraints (continued) No more than $8 million in government bonds at any time: (6) g 1 < 8,000,000 (6) g 1 < 8,000,000 (7) g 1 + g 2 < 8,000,000 (7) g 1 + g 2 < 8,000,000 (8) g 2 + g 3 < 8,000,000 (8) g 2 + g 3 < 8,000,000 (9) g 3 + g 4 < 8,000,000 (9) g 3 + g 4 < 8,000,000

16 Slide Portfolio Planning Problem nDefine the constraints (continued) No more than $8 million in construction loans at any time: (10) c 1 < 8,000,000 (10) c 1 < 8,000,000 (11) c 1 + c 2 < 8,000,000 (11) c 1 + c 2 < 8,000,000 (12) c 1 + c 2 + c 3 < 8,000,000 (12) c 1 + c 2 + c 3 < 8,000,000 (13) c 2 + c 3 + c 4 < 8,000,000 (13) c 2 + c 3 + c 4 < 8,000,000 Nonnegativity: g j, c j, l j > 0 for j = 1,2,3,4

17 Slide Problem: Floataway Tours Floataway Tours has $420,000 that may be used to purchase new rental boats for hire during the summer. The boats can be purchased from two different manufacturers. Floataway Tours would like to purchase at least 50 boats and would like to purchase 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. Pertinent data concerning the boats are summarized on the next slide. Formulate this problem as a linear program. Pertinent data concerning the boats are summarized on the next slide. Formulate this problem as a linear program.

18 Slide Problem: Floataway Tours nData 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

19 Slide Problem: Floataway Tours nDefine 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 nDefine 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: 70 x x x x 4

20 Slide Problem: Floataway Tours nDefine the constraints (1) Spend no more than $420,000: 6000 x x x x 4 < 420, x x x x 4 < 420,000 (2) Purchase at least 50 boats: (2) Purchase at least 50 boats: x 1 + x 2 + x 3 + x 4 > 50 x 1 + x 2 + x 3 + x 4 > 50 (3) Number of boats from Sleekboat equals number of boats from Racer: (3) Number of boats from Sleekboat equals number of boats from Racer: x 1 + x 2 = x 3 + x 4 or x 1 + x 2 - x 3 - x 4 = 0 x 1 + x 2 = x 3 + x 4 or x 1 + x 2 - x 3 - x 4 = 0

21 Slide Problem: Floataway Tours nDefine the constraints (continued) (4) Capacity at least 200: 3 x x x x 4 > x x x x 4 > 200 Nonnegativity of variables: Nonnegativity of variables: x j > 0, for j = 1,2,3,4 x j > 0, for j = 1,2,3,4

22 Slide Problem: Floataway Tours nComplete Formulation Max 70 x x x x 4 s.t x x x x 4 < 420, x x x x 4 < 420,000 x 1 + x 2 + x 3 + x 4 > 50 x 1 + x 2 + x 3 + x 4 > 50 x 1 + x 2 - x 3 - x 4 = 0 x 1 + x 2 - x 3 - x 4 = 0 3 x x x x 4 > x x x x 4 > 200 x 1, x 2, x 3, x 4 > 0 x 1, x 2, x 3, x 4 > 0

23 Slide Problem: Floataway Tours nThe Management Science Output OBJECTIVE FUNCTION VALUE = OBJECTIVE FUNCTION VALUE = Variable Value Reduced Cost Variable Value Reduced Cost x x x x x x x x Constraint Slack/Surplus Dual Price Constraint Slack/Surplus Dual Price

24 Slide Problem: Floataway Tours nSolution Summary Purchase 28 Speedhawks from Sleekboat.Purchase 28 Speedhawks from Sleekboat. Purchase 28 Classy’s from Racer.Purchase 28 Classy’s from Racer. Total expected daily profit is $5, Total expected daily profit is $5, 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).

25 Slide Problem: Floataway Tours nSensitivity Report

26 Slide Problem: Floataway Tours nSensitivity Report