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.

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
Introduction to Management Science
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.
Operations Management Linear Programming Module B - Part 2
McGraw-Hill/Irwin © The McGraw-Hill Companies, Inc., Three Classic Applications of LP Product Mix at Ponderosa Industrial –Considered limited.
Sensitivity analysis BSAD 30 Dave Novak
Introduction to Management Science
Chapter 4: Linear Programming Sensitivity Analysis
1 5. Linear Programming 1.Introduction to Constrained Optimization –Three elements: objective, constraints, decisions –General formulation –Terminology.
1 Linear Programming Using the software that comes with the book.
LINEAR PROGRAMMING SENSITIVITY ANALYSIS
Linear Programming: Computer Solution and Sensitivity Analysis
3-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Linear Programming: Computer Solution and Sensitivity Analysis Chapter 3.
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.
Goals and aims To introduce Linear Programming To find a knowledge on graphical solution for LP problems To solve linear programming problems using excel.
Spreadsheet Modeling & Decision Analysis:
John Loucks Modifications by A. Asef-Vaziri Slides by St. Edward’s
Solver Linear Problem Solving MAN Micro-computers & Their 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 Slides by JOHN LOUCKS St. Edward’s University.
Kerimcan OzcanMNGT 379 Operations Research1 LP: Sensitivity Analysis and Interpretation of Solution Chapter 3.
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.
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.
The Theory of the Simplex Method
Presentation: H. Sarper
Operations Research Assistant Professor Dr. Sana’a Wafa Al-Sayegh 2 nd Semester ITGD4207 University of Palestine.
Chapter Topics Computer Solution Sensitivity Analysis
Linear Programming: Basic Concepts
Linear and Integer Programming Models 1 Chapter 2.
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.
We can make Product1 and Product2. There are 3 resources; Resource1, Resource2, Resource3. Product1 needs one hour of Resource1, nothing of Resource2,
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
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.
Table of Contents Chapter 6.8 (Sensitivity Analysis with Solver Table) Continuing the Wyndor Case Study Changes in One Objective Function Coefficient Simultaneous.
THE GALAXY INDUSTRY PRODUCTION PROBLEM -
1 LINEAR PROGRAMMING Introduction to Sensitivity Analysis Professor Ahmadi.
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.
Sensitivity Analysis Consider the CrossChek hockey stick production problem:   Management believes that CrossChek might only receive $120 profit from the.
3-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Linear Programming: Computer Solution and Sensitivity Analysis Chapter 3.
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 The Dual in Linear Programming In LP the solution for the profit- maximizing combination of outputs automatically determines the input amounts that must.
Professional software packages such as The WinQSB and LINDO provide the following LP information: Information about the objective function: –its optimal.
Sensitivity Analysis Consider the CrossChek hockey stick production problem: Management believes that CrossChek might only receive $120 profit from the.
1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.
What-If Analysis for Linear Programming
Spreadsheet Modeling & Decision Analysis A Practical Introduction to Management Science 5 th edition Cliff T. Ragsdale.
Lecture 6 Linear Programming Sensitivity Analysis
Sensitivity Analysis Introduction to Sensitivity Analysis
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.
Operations Research By: Saeed Yaghoubi 1 Graphical Analysis 2.
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 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.
Practice: Given the following Sensitivity Analysis Report
St. Edward’s University
Table of Contents Chapter 5 (What-If Analysis for Linear Programming)
MAN 305 OPERATIONS RESEARCH II Week 4 –Sensitivity Analysis with Spreadsheets DR. KAZIM BARIŞ ATICI.
Sensitivity.
Presentation transcript:

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 change occurs in the optimal solution.

5-3 Using the Spreadsheet to do Sensitivity Analysis The profit per door has been revised from $300 to $500. No change occurs in the optimal solution.

5-4 Using the Spreadsheet to do Sensitivity Analysis The profit per door has been revised from $300 to $1,000. The optimal solution changes.

5-5 Using Solver Table to do Sensitivity Analysis

5-6 Using Solver Table to do Sensitivity Analysis

5-7 Using the Sensitivity Report to Find the Allowable Range

5-8 Graphical Insight into the Allowable Range The two dashed lines that pass through the solid constraint boundary lines are the objective function lines when P D (the unit profit for doors) is at an endpoint of its allowable range, 0 ≤ P D ≤ 750.

5-9 Using the Spreadsheet to do Sensitivity Analysis The profit per door has been revised from $300 to $450. The profit per window has been revised from $500 to $400. No change occurs in the optimal solution.

5-10 Using the Spreadsheet to do Sensitivity Analysis The profit per door has been revised from $300 to $600. The profit per window has been revised from $500 to $300. The optimal solution changes.

5-11 Using Solver Table to do Sensitivity Analysis

5-12 Using Solver Table to do Sensitivity Analysis

5-13 Using Solver Table to do Sensitivity Analysis

5-14 The 100 Percent Rule The 100 Percent Rule for Simultaneous Changes in Objective Function Coefficients: If simultaneous changes are made in the coefficients of the objective function, calculate for each change the percentage of the allowable change (increase or decrease) for that coefficient to remain within its allowable range. If the sum of the percentage changes does not exceed 100 percent, the original optimal solution definitely will still be optimal. (If the sum does exceed 100 percent, then we cannot be sure.)

5-15 Graphical Insight into 100 Percent Rule The estimates of the unit profits for doors and windows change to P D = $525 and P W = $350, which lies at the edge of what is allowed by the 100 percent rule.

5-16 Graphical Insight into 100 Percent Rule When the estimates of the unit profits for doors and windows change to P D = $150 and P W = $250 (half their original values), the graphical method shows that the optimal solution still is (D, W) = (2, 6) even though the 100 percent rule says that the optimal solution might change.

5-17 Using the Spreadsheet to do Sensitivity Analysis The hours available in plant 2 have been increased from 12 to 13. The total profit increases by $150 per week.

5-18 Using the Spreadsheet to do Sensitivity Analysis The hours available in plant 2 have been further increased from 13 to 18. The total profit increases by $750 per week ($150 per hour added in plant 2).

5-19 Using the Spreadsheet to do Sensitivity Analysis The hours available in plant 2 have been further increased from 18 to 20. The total profit does not increase any further.

5-20 Using Solver Table to do Sensitivity Analysis

5-21 Using the Sensitivity Report

5-22 Graphical Interpretation of the Allowable Range

5-23 Using the Spreadsheet to do Sensitivity Analysis One available hour in plant 3 has been shifted to plant 2. The total profit increases by $50 per week.

5-24 Using Solver Table to do Sensitivity Analysis

5-25 The 100 Percent Rule The 100 Percent Rule for Simultaneous Changes in Right-Hand Sides: The shadow prices remain valid for predicting the effect of simultaneously changing the right-hand sides of some of the functional constraints as long as the changes are not too large. To check whether the changes are small enough, calculate for each change the percentage of the allowable change (decrease or increase) for that right-hand side to remain within its allowable range. If the sum of the percentage changes does not exceed 100 percent, the shadow prices definitely will still be valid. (If the sum does exceed 100 percent, then we cannot be sure.)

5-26 A Production Problem Weekly supply of raw materials: 6 Large Bricks Products: Table Profit = $20 / Table Chair Profit = $15 / Chair 8 Small Bricks

5-27 Sensitivity Analysis Questions With the given weekly supply of raw materials and profit data, how many tables and chairs should be produced? What is the total weekly profit? What if one more large brick were available. How much would you be willing to pay for it? What if an additional two large bricks were available (to make a total of 9). How much would you be willing to pay for these two additional bricks? What if the profit per table were now $25. (Assume now there are only 6 large bricks again.) How many tables and chairs should now be produced? What if the profit per table were now $35. How many tables and chairs should now be produced?

5-28 Graphical Solution (Original Problem) Maximize Profit = ($20)T + ($15)C subject to 2T + C ≤ 6 large bricks 2T + 2C ≤ 8 small bricks and T ≥ 0, C ≥ 0.

Large Bricks Maximize Profit = ($20)T + ($15)C subject to 2T + C ≤ 7 large bricks 2T + 2C ≤ 8 small bricks and T ≥ 0, C ≥ 0.

Large Bricks Maximize Profit = ($20)T + ($15)C subject to 2T + C ≤ 9 large bricks 2T + 2C ≤ 8 small bricks and T ≥ 0, C ≥ 0.

5-31 $25 Profit per Table Maximize Profit = ($25)T + ($15)C subject to 2T + C ≤ 6 large bricks 2T + 2C ≤ 8 small bricks and T ≥ 0, C ≥ 0.

5-32 $35 Profit per Table Maximize Profit = ($35)T + ($15)C subject to 2T + C ≤ 6 large bricks 2T + 2C ≤ 8 small bricks and T ≥ 0, C ≥ 0.

5-33 Generating the Sensitivity Report After solving with Solver, choose “Sensitivity” under reports:

5-34 The Sensitivity Report

5-35 The Sensitivity Report The solution Allowable range (Solution stays the same) Usage of the resource (Left-hand-side of constraint) Allowable range (Shadow price is valid) Increase in objective function value per unit increase in right-hand-side (RHS) ∆Z = (shadow price)(∆RHS)

5-36 $35 Profit per Table

Large Bricks

Large Bricks

% Rule for Simultaneous Changes in the Objective Coefficients For simultaneous changes in the objective coefficients, if the sum of the percentage changes does not exceed 100%, the original solution will still be optimal. (If it does exceed 100%, we cannot be sure—it may or may not change.) Examples: (Does solution stay the same?) Profit per Table = $24 & Profit per Chair = $13 Profit per Table = $25 & Profit per Chair = $12 Profit per Table = $28 & Profit per Chair = $18

% Rule for Simultaneous Changes in the Right-Hand-Sides For simultaneous changes in the right-hand-sides, if the sum of the percentage changes does not exceed 100%, the shadow prices will still be valid. (If it does exceed 100%, we cannot be sure—they may or may not be valid.) Examples: (Are the shadow prices valid? If so, what’s the new total profit?) (+1 Large Brick) & (+2 Small Bricks) (+1 Large Brick) & (–1 Small Brick)

5-41 Summary of Sensitivity Report for Changes in the Objective Function Coefficients Final Value –The value of the decision variables (changing cells) in the optimal solution. Reduced Cost –Increase in the objective function value per unit increase in the value of a zero- valued variable (for small increases)—may be interpreted as the shadow price for the nonnegativity constraint. Objective Coefficient –The current value of the objective coefficient. Allowable Increase/Decrease –Defines the range of the coefficients in the objective function for which the current solution (value of the decision variables or changing cells in the optimal solution) will not change.

5-42 Summary of Sensitivity Report for Changes in the Right-Hand- Sides Final Value –The usage of the resource (or level of benefit achieved) in the optimal solution— the left-hand side of the constraint. Shadow Price –The change in the value of the objective function per unit increase in the right- hand-side of the constraint (RHS): ∆Z = (Shadow Price)(∆RHS) (Note: only valid if change is within the allowable range—see below.) Constraint R.H. Side –The current value of the right-hand-side of the constraint. Allowable Increase/Decrease –Defines the range of values for the RHS for which the shadow price is valid and hence for which the new objective function value can be calculated. (NOT the range for which the current solution will not change.)