CDAE 266 - Class 12 Oct. 5 Last class: Quiz 3 3. Linear programming and applications Today: Result of Quiz 3 3. Linear programming and applications Next.

Slides:



Advertisements
Similar presentations
1Introduction to Linear ProgrammingLesson 2 Introduction to Linear Programming.
Advertisements

LINEAR PROGRAMMING (LP)
Introduction to Mathematical Programming Matthew J. Liberatore John F. Connelly Chair in Management Professor, Decision and Information Technologies.
LINEAR PROGRAMMING SENSITIVITY ANALYSIS
Lesson 08 Linear Programming
Linear Programming.
Linear Programming Problem
LINEAR PROGRAMMING (LP)
Chapter 2: Modeling with Linear Programming & sensitivity analysis
8/27: Linear Programming Lecture: LP Small Groups Homework.
Chapter 2 Linear Programming Models: Graphical and Computer Methods © 2007 Pearson Education.
© 2008 Prentice-Hall, Inc. Chapter 7 To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created.
19 Linear Programming CHAPTER
Linear Programming Introduction. linear function linear constraintsA Linear Programming model seeks to maximize or minimize a linear function, subject.
Basic LP Problem McCarl and Spreen Chapter 2 LP problem is linear form of Mathematical Program This formulation may also be expressed in matrix notation.
Linear and Integer Programming Models
6s-1Linear Programming CHAPTER 6s Linear Programming.
Linear Programming Econ Outline  Review the basic concepts of Linear Programming  Illustrate some problems which can be solved by linear programming.
Linear Programming Models: Graphical Methods 5/4/1435 (1-3 pm)noha hussein elkhidir.
Linear Programming.
Linear programming. Linear programming… …is a quantitative management tool to obtain optimal solutions to problems that involve restrictions and limitations.
9/1 More Linear Programming Collect homework Roll call Review homework Lecture - More LP Small Groups Lecture - Start using MS Excel Assign Homework.
1-1 Introduction to Optimization and Linear Programming Chapter 1.
Introduction to Quantitative Business Methods (Do I REALLY Have to Know This Stuff?)
3.4 Linear Programming.
Linear Programming Chapter 13 Supplement.
Linear Programming Models Tran Van Hoai Faculty of Computer Science & Engineering HCMC University of Technology Tran Van Hoai.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 6S Linear Programming.
CDAE Class 07 Sept. 18 Last class: Result of Quiz 1 2. Review of economic and business concepts Today: 2. Review of economic and business concepts.
CDAE Class 08 Sept. 20 Last class: 2. Review of economic and business concepts Today: 2. Review of economic and business concepts Quiz 2 (Time value.
1 Additional examples LP Let : X 1, X 2, X 3, ………, X n = decision variables Z = Objective function or linear function Requirement: Maximization of the.
CDAE Class 11 Oct. 3 Last class: Result of Quiz 2 2. Review of economic and business concepts Today: Result of Quiz 2 3. Linear programming and applications.
THE GALAXY INDUSTRY PRODUCTION PROBLEM -
BUSINESS MATHEMATICS & STATISTICS. LECTURE 45 Planning Production Levels: Linear Programming.
1/17: DSCB Getting Started, Linear Programming Administrative Issues –Syllabus –Calendar –Get usernames, addresses, majors Linear Programming.
CDAE Class 18 Oct. 25 Last class: 5. Production functions Today: 5. Production functions 6. Costs Next class: 6.Costs Quiz 5 Important date: Problem.
Last class: Today: Next class: Important dates: Result of Quiz 2
CDAE Class 10 Sept. 28 Last class: Result of problem set 1 2. Review of economic and business concepts Today: Result of Quiz 2 2. Review of economic.
CDAE Class 07 Sept. 19 Last class: Result of Quiz 1 2. Review of economic and business concepts Today: 2. Review of economic and business concepts.
CDAE Class 17 Oct. 23 Last class: Result of Quiz 4 3. Linear programming and applications Today: 3. Linear programming and applications Review for.
Linear Programming with Excel Solver.  Use Excel’s Solver as a tool to assist the decision maker in identifying the optimal solution for a business decision.
1 Max 8X 1 + 5X 2 (Weekly profit) subject to 2X 1 + 1X 2  1000 (Plastic) 3X 1 + 4X 2  2400 (Production Time) X 1 + X 2  700 (Total production) X 1.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 6S Linear Programming.
CDAE Class 23 Nov. 13 Last class: Result of Quiz 6 7. Profit maximization and supply Today: 7. Profit maximization and supply 8. Perfectly competitive.
CDAE Class 25 Nov 28 Last class: Result of Quiz 7 7. Profit maximization and supply Today: 7. Profit maximization and supply 8. Perfectly competitive.
CDAE Class 19 Oct. 31 Last class: Result of the midterm exam 5. Production Today: 5. Production 6. Costs Quiz 6 (Sections 5.1 – 5.7) Next class:
1 Linear Programming (LP) 線性規劃 - George Dantzig, 1947.
CDAE Class 13 Oct. 10 Last class: Result of Quiz 3 3. Linear programming and applications Class exercise 5 Today: 3. Linear programming and applications.
CDAE Class 11 Oct. 2 Last class: 2. Review of economic and business concepts Today: 2. Review of economic and business concepts 3. Linear programming.
Introduction to linear programming:- - Linear programming (LP) applies to optimization models in which the objective and constraints functions are strictly.
1 A Linear Programming model seeks to maximize or minimize a linear function, subject to a set of linear constraints. The linear model consists of the.
Monday WARM-UP: TrueFalseStatementCorrected Statement F 1. Constraints are conditions written as a system of equations Constraints are conditions written.
Chapter 2 Linear Programming Models: Graphical and Computer Methods
CDAE Class 12 Oct. 4 Last class: 2. Review of economic and business concepts Today: 3. Linear programming and applications Quiz 3 (sections 2.5 and.
LINEAR PROGRAMMING 3.4 Learning goals represent constraints by equations or inequalities, and by systems of equations and/or inequalities, and interpret.
Linear Programming (LP) Problems MAX (or MIN): c 1 X 1 + c 2 X 2 + … + c n X n Subject to:a 11 X 1 + a 12 X 2 + … + a 1n X n
Adeyl Khan, Faculty, BBA, NSU 1 Introduction to Linear Programming  A Linear Programming model seeks to maximize or minimize a linear function, subject.
CDAE Class 15 Oct. 16 Last class: Result of group project 1 3. Linear programming and applications Class Exercise 7 Today: 3. Linear programming.
Introduction to Quantitative Business Methods (Do I REALLY Have to Know This Stuff?)
CDAE Class 16 Oct. 18 Last class: 3. Linear programming and applications Quiz 4 Today: Result of Quiz 4 3. Linear programming and applications Group.
CDAE Class 20 Nov 2 Last class: 5. Production 6. Costs Quiz 6 (Sections 5.1 – 5.7) Today: Results of Quiz 5 6. Costs Next class: 6. Costs Important.
Don Sutton Spring LP Basic Properties Objective Function – maximize/minimize profit/cost Resource Constraints – labor, money Decision.
6s-1Linear Programming William J. Stevenson Operations Management 8 th edition.
1 2 Linear Programming Chapter 3 3 Chapter Objectives –Requirements for a linear programming model. –Graphical representation of linear models. –Linear.
1 Linear Programming 2 A Linear Programming model seeks to maximize or minimize a linear function, subject to a set of linear constraints. The linear.
Chapter 2 Linear Programming Models: Graphical and Computer Methods
Exam 1 Review/Instructions
Linear Programming Introduction.
Optimization Theory Linear Programming
Linear Programming Introduction.
Presentation transcript:

CDAE Class 12 Oct. 5 Last class: Quiz 3 3. Linear programming and applications Today: Result of Quiz 3 3. Linear programming and applications Next class: 3. Linear programming and applications Reading: Linear Programming

CDAE Class 12 Oct. 5 Important date: Problem set 2 due Tuesday, Oct. 10

Result of Quiz 3 N = 49 Range = 4 – 10Average = Derivatives 2. Relations among Q, P, TR, TC, Profit and marginal profit 3. MC < MR  increase production MC > MR  decrease production 4. Profit function  Q* that maximizes total profit 5. TC and TR  Break-even

3. Linear programming & applications 3.1. What is linear programming (LP)? 3.2. How to develop a LP model? 3.3. How to solve a LP model graphically? 3.4. How to solve a LP model in Excel? 3.5. How to do sensitivity analysis? 3.6. What are some special cases of LP?

3.2. How to develop a LP model? Major components of a LP model: (1) A set of decision variables. (2) An objective function. (3) A set of constraints Major assumptions of LP: (1) Variable continuity (2) Parameter certainty (3) Constant return to scale (4) No interactions between decision variables

3.2. How to develop a LP model? Major steps in developing a LP model: (1) Define decision variables (2) Express the objective function (3) Express the constraints (4) Complete the LP model Three examples: (1) Furniture manufacturer (2) Galaxy industrials (3) A farmer in Iowa

Table A (example 1): Unit requirements Resources Amount TableChairavailable Wood ( board feet ) Labor ( hours ) ===================================== Unit profit ($)

Develop the LP model Step 1. Define the decision variables Two variables: T = number of tables made C = number of chairs made Step 2. Express the objective function Step 3. Express the constraints Step 4. Complete the LP model

Example 2. Galaxy Industries (a toy manufacturer) 2 products: Space ray and zapper 2 resources: Plastic & time Resource requirements & unit profits (Table B) Additional requirements (constraints): (1) Total production of the two toys should be no more than 800. (2) The number of space ray cannot exceed the number of zappers plus 450.

Table B (example 2): Unit requirements Resources Amount Space ray Zapper available Plastic (lb.) 2 1 1,200 Labor (min.) 3 4 2,400 ===================================== Unit profit ($)

Example 3. A farmer in Iowa has 500 acres of land which can be used to grow corn and/or soybeans. The per acre net profit is $20 for soybeans and $18 for corn. In addition to the land constraint, the farmer has limited labor resources: 200 hours for planting and 160 hours for cultivation and harvesting. Labor required for planting is 0.6 hour per acre for corn and 0.5 hour per acre for soybean. Labor required for cultivation and harvesting is 0.8 hour per acre for corn and 0.3 hour per acre for soybeans. If the farmer’s objective is to maximize the total profit, develop a LP model that can be used to determine how many acres of soy and how many acres of corn to be planted.

Class Exercise 5 (Thursday, Oct. 5) Best Brooms is a small company that produces two difference brooms: one with a short handle and one with a long handle. Suppose each short broom requires 1 hour of labor and 2 lbs. of straw and each long broom requires 0.8 hour of labor and 3 lbs. of straws. We also know that each short broom brings a profit of $10 and each long broom brings a profit of $8 and the company has a total of 500 hours of labor and 1500 lbs of straw. Develop a LP model for the company to maximize its total profit.

3.3. How to solve a LP model graphically? Review of some basic math techniques: (1) How to plot a linear equation? e.g., Y = X 2X + 3Y = 6 X = 3 Y = 4 X = 0 Y = 0

3.3. How to solve a LP model graphically? Review of some basic math techniques: (2) How to plot an inequality e.g., 2X + 3Y < 12 3X < 15 4Y > 8 X > 0 Y > 0

3.3. How to solve a LP model graphically? Review of some basic math techniques: (3) How to solve a system of two equations? e.g., 30X + 20Y = 300 5X + 10 Y = 110

3.3. How to solve a LP model graphically? Major steps of solving a LP model graphically: (1) Plot each constraint (2) Identify the feasible region (3) Plot the objective function (4) Move the objective function to identify the “optimal point” (most attractive corner) (5) Identify the two constraints that determine the “optimal point” (6) Solve the system of 2 equations (7) Calculate the optimal value of the objective function.

3.3. How to solve a LP model graphically? Example 1 -- Furniture Co. X T = Number of tables X C = Number of chairs Maximize P = 6X T + 8X C subject to: 30X T + 20X C < 300 (wood) 5X T + 10X C < 110 (labor) X T > 0 X C > 0

3.3. How to solve a LP model graphically? Example 1 (1) Plot each constraint (a) X T > 0 (b) X C > 0 (c) 30X T + 20X C < 300 (wood) (d) 5X T + 10X C < 110 (labor) (2) Find the feasible region (3) Plot the objective function (4) Move the objective function to identify the optimal point (most attractive corner)

3.3. How to solve a LP model graphically? Example 1 (5) Identify the two constraints that determine the “optimal point” (6) Solve the system of 2 equations 30X T + 20X C = 300 (wood) 5X T + 10X C = 110 (labor) Solution: X T =, X C = (7) Calculate the optimal value of the objective function. P = 6X T + 8X C =

3.3. How to solve a LP model graphically? Example 2 -- Galaxy Industries X S = Number of space ray X Z = Number of zappers Maximize P = 8X S + 5X Z subject to 2X S + 1X Z < 1200 (plastic) 3X S + 4X Z < 2400 (labor) X S + X Z < 800 (total) X S < X Z (mix) X S > 0 X Z > 0

Take-home exercise Solve the following LP model graphically: X T = Number of tables X C = Number of chairs Maximize P = 6X T + 8X C subject to: 40X T + 20X C < 280 (wood) 5X T + 10X C < 95 (labor) X T > 0 X C > 0 X T = ? X C = ? P = ?