Chapter 4: Linear Programming Sensitivity Analysis

Slides:



Advertisements
Similar presentations
LINEAR PROGRAMMING SENSITIVITY ANALYSIS
Advertisements

Chapter 5 Sensitivity Analysis: An Applied Approach
Copyright (c) 2003 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 5 Sensitivity Analysis: An Applied Approach to accompany Introduction to.
Introduction to Sensitivity Analysis Graphical Sensitivity Analysis
Sensitivity Analysis Sensitivity analysis examines how the optimal solution will be impacted by changes in the model coefficients due to uncertainty, error.
1/53 Slide Linear Programming: Sensitivity Analysis and Interpretation of Solution n Introduction to Sensitivity Analysis n Graphical Sensitivity Analysis.
SENSITIVITY ANALYSIS.
LP EXAMPLES.
Chapter 7 Linear Programming Models Part One n Basis of Linear Programming n Linear Program formulati on.
Managerial Decision Modeling with Spreadsheets
Chapter 2 Linear Programming Models: Graphical and Computer Methods © 2007 Pearson Education.
Chapter 3 Linear Programming: Sensitivity Analysis and Interpretation of Solution MT 235.
1 5. Linear Programming 1.Introduction to Constrained Optimization –Three elements: objective, constraints, decisions –General formulation –Terminology.
Linear Programming: Fundamentals
1 Linear Programming Using the software that comes with the book.
LINEAR PROGRAMMING SENSITIVITY ANALYSIS
1 1 Slide LINEAR PROGRAMMING Introduction to Sensitivity Analysis Professor Ahmadi.
QM B Lego Simplex. Scenario You manufacture tables and chairs. Tables and chairs are manufactured from small and large bricks. Small brick Large brick.
LINEAR PROGRAMMING: THE GRAPHICAL METHOD
Spreadsheet Modeling & Decision Analysis:
Linear Programming Models: Graphical Methods 5/4/1435 (1-3 pm)noha hussein elkhidir.
John Loucks Modifications by A. Asef-Vaziri Slides by St. Edward’s
Solver Linear Problem Solving MAN Micro-computers & Their Applications.
1 1 Slide LINEAR PROGRAMMING: THE GRAPHICAL METHOD n Linear Programming Problem n Properties of LPs n LP Solutions n Graphical Solution n Introduction.
Linear Programming Models: Graphical and Computer Methods
1© 2003 by Prentice Hall, Inc. Upper Saddle River, NJ The Wyndor Glass Company Problem (Hillier and Liberman) The Wyndor Glass Company is planning.
© Copyright 2004, Alan Marshall 1 Lecture 1 Linear Programming.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Kerimcan OzcanMNGT 379 Operations Research1 LP: Sensitivity Analysis and Interpretation of Solution Chapter 3.
Chapter 2 Linear Programming Models: Graphical and Computer Methods
3.4 Linear Programming.
Introduction to Mathematical Programming OR/MA 504 Chapter 3.
1 The Role of Sensitivity Analysis of the Optimal Solution Is the optimal solution sensitive to changes in input parameters? Possible reasons for asking.
Chapter 19 Linear Programming McGraw-Hill/Irwin
Special Conditions in LP Models (sambungan BAB 1)
Readings Readings Chapter 3
Sensitivity Analysis What if there is uncertainly about one or more values in the LP model? 1. Raw material changes, 2. Product demand changes, 3. Stock.
Operations Research Assistant Professor Dr. Sana’a Wafa Al-Sayegh 2 nd Semester ITGD4207 University of Palestine.
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.
Managerial Decision Making and Problem Solving
McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., Table of Contents Chapter 5 (What-If Analysis for Linear Programming) Continuing the Wyndor.
Managerial Decision Modeling with Spreadsheets Chapter 4 Linear Programming Sensitivity Analysis.
Table of Contents Chapter 5 (What-If Analysis for Linear Programming) Continuing the Wyndor Case Study (Section 5.2)5.2 Changes in One Objective Function.
THE GALAXY INDUSTRY PRODUCTION PROBLEM -
1 LINEAR PROGRAMMING Introduction to Sensitivity Analysis Professor Ahmadi.
Linear Programming McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
5-1 Wyndor (Before What-If Analysis). 5-2 Using the Spreadsheet to do Sensitivity Analysis The profit per door has been revised from $300 to $200. No.
1 1 Slide © 2009 South-Western, a part of Cengage Learning Slides by John Loucks St. Edward’s University.
Linear Programming: Sensitivity Analysis and Interpretation of Solution Pertemuan 5 Matakuliah: K0442-Metode Kuantitatif Tahun: 2009.
1 The Dual in Linear Programming In LP the solution for the profit- maximizing combination of outputs automatically determines the input amounts that must.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
Professional software packages such as The WinQSB and LINDO provide the following LP information: Information about the objective function: –its optimal.
1 1 Slide © 2005 Thomson/South-Western Simplex-Based Sensitivity Analysis and Duality n Sensitivity Analysis with the Simplex Tableau n Duality.
What-If Analysis for Linear Programming
1 The Geometry of Linear Programs –the geometry of LPs illustrated on GTC Handouts: Lecture Notes February 5, 2002.
Chapter 2 Linear Programming Models: Graphical and Computer Methods
1 Sensitivity Analysis (II). 2 Sensitivity Report.
Spreadsheet Modeling & Decision Analysis A Practical Introduction to Management Science 5 th edition Cliff T. Ragsdale.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Supplement 6 Linear Programming.
Lecture 6 Linear Programming Sensitivity Analysis
Don Sutton Spring LP Basic Properties Objective Function – maximize/minimize profit/cost Resource Constraints – labor, money Decision.
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 7-1 1© 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 7 Linear.
P RIMAL -D UAL LPP. T HE R EDDY M IKKS C OMPANY - PROBLEM Reddy Mikks company produces both interior and exterior paints from two raw materials, M 1 and.
Managerial Decision Modeling with Spreadsheets Chapter 4 Linear Programming Sensitivity Analysis.
1 2 Linear Programming Chapter 3 3 Chapter Objectives –Requirements for a linear programming model. –Graphical representation of linear models. –Linear.
Chapter 2 Linear Programming Models: Graphical and Computer Methods
Linear Programming: Sensitivity Analysis and Duality
Table of Contents Chapter 5 (What-If Analysis for Linear Programming)
Sensitivity.
Presentation transcript:

Chapter 4: Linear Programming Sensitivity Analysis © 2007 Pearson Education

What if there is uncertainly about one or more values in the LP model? Sensitivity analysis allows us to determine how “sensitive” the optimal solution is to changes in data values. This includes analyzing changes in: An Objective Function Coefficient (OFC) A Right Hand Side (RHS) value of a constraint

Graphical Sensitivity Analysis We can use the graph of an LP to see what happens when: An OFC changes, or A RHS changes Recall the Flair Furniture problem

Flair Furniture Problem Max 7T + 5C (profit) Subject to the constraints: 3T + 4C < 2400 (carpentry hrs) 2T + 1C < 1000 (painting hrs) C < 450 (max # chairs) T > 100 (min # tables) T, C > 0 (nonnegativity)

Objective Function Coefficient (OFC) Changes What if the profit contribution for tables changed from $7 to $8 per table? 8 Max 7 T + 5 C (profit) Clearly profit goes up, but would we want to make more tables and less chairs? (i.e. Does the optimal solution change?) X

Characteristics of OFC Changes There is no effect on the feasible region The slope of the level profit line changes If the slope changes enough, a different corner point will become optimal

Optimal Corner (T=320, C=360) Still optimal 500 400 300 200 100 Original Objective Function 7T + 5 C = $4040 Optimal Corner (T=320, C=360) Still optimal Revised Objective Function 8T + 5 C = $4360 Feasible Region 0 100 200 300 400 500 T

Both have new optimal corner points 1000 600 450 What if the OFC became higher? Or lower? 11T + 5C = $5500 Optimal Solution (T=500, C=0) 3T + 5C = $2850 (T=200, C=450) Both have new optimal corner points Feasible Region 0 100 500 800 T

There is a range for each OFC where the current optimal corner point remains optimal. If the OFC changes beyond that range a new corner point becomes optimal. Excel’s Solver will calculate the OFC range.

Right Hand Side (RHS) Changes What if painting hours available changed from 1000 to 1300? 1300 2T + 1C < 1000 (painting hrs) This increase in resources could allow us to increase production and profit. X

Characteristics of RHS Changes The constraint line shifts, which could change the feasible region Slope of constraint line does not change Corner point locations can change The optimal solution can change

Feasible region becomes larger Old optimal corner point (T=320,C=360) Profit=$4040 C 500 400 300 200 100 Feasible region becomes larger New optimal corner point (T=560,C=180) Profit=$4820 Original Feasible Region 2T + 1 C = 1000 2T + 1 C = 1300 0 100 200 300 400 500 600 T

Effect on Objective Function Value New profit = $4,820 Old profit = $4,040 Profit increase = $780 from 300 additional painting hours $2.60 in profit per hour of painting Each additional hour will increase profit by $2.60 Each hour lost will decrease profit by $2.60

Shadow Price The change is the objective function value per one-unit increase in the RHS of the constraint. Will painting hours be worth $2.60 per hour regardless of many hours are available ?

Range of Shadow Price Validity Beyond some RHS range the value of each painting hour will change. While the RHS stays within this range, the shadow price does not change. Excel will calculate this range as well as the shadow price.

Solver’s Sensitivity Report When Excel Solver is used to find an optimal solution, the option of generating the “Sensitivity Report” is available. Go to file 4-1.xls

Constraint RHS Changes If the change in the RHS value is within the allowable range, then: The shadow price does not change The change in objective function value = (shadow price) x (RHS change) If the RHS change goes beyond the allowable range, then the shadow price will change.

Objective Function Coefficient (OFC) Changes If the change in OFC is within the allowable range, then: The optimal solution does not change The new objective function value can be calculated

Anderson Electronics Example Decision: How many of each of 4 products to make? Objective: Maximize profit Decision Variables: V = number of VCR’s S = number of stereos T = number of TV’s D = number of DVD players

Max 29V + 32S + 72T + 54D (in $ of profit) Subject to the constraints: 3V + 4S + 4T + 3D < 4700 (elec. components) 2V + 2S + 4T + 3D < 4500 (nonelec. components) V + S + 3T + 2D < 2500 (assembly hours) V, S, T, D > 0 (nonnegativity) Go to file 4-2.xls

RHS Change Questions What if the supply of nonelectrical components changes? What happens if the supply of electrical components increased by 400 (to 5100)? increased by 4000 (to 8700)?

What if we could buy an additional 400 elec What if we could buy an additional 400 elec. components for $1 more than usual? Would we want to buy them? What if would could get an additional 250 hours of assembly time by paying $5 per hour more than usual? Would this be profitable?

Decision Variables That Equal 0 We are not currently making any VCR’s (V=0) because they are not profitable enough. How much would profit need to increase before we would want to begin making VCR’s?

Reduced Cost of a Decision Variable (marginal contribution to the obj. func. value) - (marginal value of resources used) = Reduced Cost marginal profit of a VCR = $29 - marginal value of resources = ? Reduced Cost of a VCR = - $1.0

Reduced Cost is: The minimum amount by which the OFC of a variable should change to cause that variable to become non-zero. The amount by which the objective function value would change if the variable were forced to change from 0 to 1.

OFC Change Questions For what range of profit contributions for DVD players will the current solution remain optimal? What happens to profit if this value drops to $50 per DVD player?

Alternate Optimal Solutions May be present when there are 0’s in the Allowable Increase or Allowable Decrease values for OFC values.

Simultaneous Changes All changes discussed up to this point have involved only 1 change at a time. What if several OFC’s change? Or What if several RHS’s change? Note: they cannot be mixed

The 100% Rule ∑ (change / allowable change) < 1 RHS Example Electrical components decrease 500 500 / 950 = 0.5263 Assembly hours increase 200 200 / 466.67 = 0.4285 0.9548 The sensitivity report can still be used

Pricing New Variables Suppose they are considering selling a new product, Home Theater Systems (HTS) Need to determine whether making HTS’s would be sufficiently profitable Producing HTS’s would take limited resources away from other products

To produce one HTS requires: 5 electrical components 4 nonelectrical components 4 hours of assembly time Can shadow prices be used to calculate reduction in profit from other products? (check 100% rule) 5/950 + 4/560 + 4/1325 = 0.015 < 1

Required Profit Contribution per HTS elec cpnts 5 x $ 2 = $10 nonelec cpnts 4 x $ 0 = $ 0 assembly hrs 4 x $24 = $96 $106 Making 1 HTS will reduce profit (from other products) by $106 Shadow Prices

Need (HTS profit contribution) > $106 Cost to produce each HTS: elec cpnts 5 x $ 7 = $35 nonelec cpnts 4 x $ 5 = $20 assembly hrs 4 x $10 = $40 $95 (HTS profit contribution) = (selling price) - $95 So selling price must be at least $201

Is HTS Sufficiently Profitable? Marketing estimates that selling price should not exceed $175 Producing one HTS will cause profit to fall by $26 ($201 - $175) Go to file 4-3.xls

Sensitivity Analysis for a Minimization Problem Burn-Off makes a “miracle” diet drink Decision: How much of each of 4 ingredients to use? Objective: Minimize cost of ingredients

Data Units of Chemical per Ounce of Ingredient A B C D X 3 4 8 10 Requirement A B C D X 3 4 8 10 > 280 units Y 5 6 > 200 units Z 25 20 40 < 1050 units $ per ounce of ingredient $0.40 $0.20 $0.60 $0.30

A + B + C + D > 36 (min daily ounces) Min 0.40A + 0.20B + 0.60C + 0.30D ($ of cost) Subject to the constraints A + B + C + D > 36 (min daily ounces) 3A + 4B + 8C + 10D > 280 (chem x min) 5A + 3B + 6C + 6D > 200 (chem y min) 10A + 25B + 20C + 40D < 280 (chem z max) A, B, C, > 0 Go to file 4-5.xls