MGSC 1205 Quantitative Methods I

Similar documents
MGSC 1205 Quantitative Methods I

Basic Linear Programming Concepts. Lecture 2 (3/29/2017)

Mid Term Solution. T7, T8, T20 pounds of tomatoes to sell in weeks 7, 8, 20. F7, F8, F20 - pounds of tomatoes to freeze in weeks 7, 8, 20.

A Production Problem

Digital Media Mix Optimization Model: A Case Study of a Digital Agency promoting its E-Training Services

Econ 172A, Fall 2010: Quiz III IMPORTANT

CIS QA LEVEL 2 WEEK 5 TOPIC: LINEAR PROGRAMMING OBJECTIVE AND SHORT ANSWER QUESTIONS

Transshipment. Chapter 493. Introduction. Data Structure. Example Model

Managerial Decision Modeling w/ Spreadsheets, 3e (Balakrishnan/Render/Stair) Chapter 2 Linear Programming Models: Graphical and Computer Methods

Chapter 11. Decision Making and Relevant Information Linear Programming as a Decision Facilitating Tool

S Due March PM 10 percent of Final

Linear Programming. BUS 735: Business Decision Making and Research. Wednesday, November 8, Lecture / Discussion. Worksheet problem.

Linear Programming: Basic Concepts

QUANTITATIVE TECHNIQUES SECTION I

Chapter 1 Mathematical Programming: an overview

CHAPTER 5 SUPPLIER SELECTION BY LEXICOGRAPHIC METHOD USING INTEGER LINEAR PROGRAMMING

THE UNIVERSITY OF BRITISH COLUMBIA Sauder School of Business SAMPLE MIDTERM EXAMINATION

Transfer Pricing Cost Accounting Horngreen, Datar, Foster

DIS 300. Quantitative Analysis in Operations Management. Instructions for DIS 300-Transportation

TRANSPORTATION PROBLEM AND VARIANTS

Analysis of Electricity Markets. Lennart Söder

Homework 1 Fall 2000 ENM3 82.1

Analyzing Optimal Solutions Sensitivity Analysis

MBF1413 Quantitative Methods

Linear Programming. 1 Goals and Agenda. Management 560: Management Science. Tuesday, March 17, 2009

Transportation Problems

Operation and supply chain management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology Madras

Linear Programming. Chapter 2: Basic Concepts. Lee-Anne Johennesse. Advanced Quantitative Methods 7 March 2016

LINEAR PROGRAMMING APPROACHES TO AGGREGATE PLANNING. Linear programming is suitable to determine the best aggregate plan.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Short-Run Manufacturing Problems at DEC 2. In the fourth quarter of 1989, the corporate demand/supply group of Digital

P2 Performance Management

Locational Marginal Pricing (LMP): Basics of Nodal Price Calculation

Operations Research QM 350. Chapter 1 Introduction. Operations Research. University of Bahrain

a. Show the feasible region. b. What are the extreme points of the feasible region? c. Find the optimal solution using the graphical procedure.

Review Article Minimizing Costs Can Be Costly

A Spreadsheet Approach to Teaching Shadow Price as Imputed Worth

Microeconomic Theory -1- Introduction and maximization

1. Spatial Equilibrium Behavioral Hypotheses

Using Excel s Solver

Modeling Linear Programming Problem Using Microsoft Excel Solver

UNIVERSITY OF MORATUWA

UNIVERSITY OF ECONOMICS PRAGUE Faculty of Informatics and Statistics. Management Science. Jan Fábry

Applied Data Analysis (Operations Research)

1) Operating costs, such as fuel and labour. 2) Maintenance costs, such as overhaul of engines and spraying.

Paper P2 Performance Management (Russian Diploma) Post Exam Guide May 2012 Exam. General Comments

Optimizing the supply chain configuration with supply disruptions

Pricing Game under Imbalanced Power Structure

Homework Assignment #2: Answer Sheet

Application of Dynamic Programming Model to Production Planning, in an Animal Feedmills.

Goals and Agenda Linear Programming Shadow Prices. Linear Programming. BUS 735: Business Decision Making and Research. Wednesday, November 8, 2017

EXCEL PROFESSIONAL INSTITUTE. LECTURE 5 Holy

THE CATHOLIC UNIVERSITY OF EASTERN AFRICA A. M. E. C. E. A

P2 Performance Management

Introduction to Management Science, 11e (Taylor) Chapter 3 Linear Programming: Computer Solution and Sensitivity Analysis

ECON 4550 (Summer 2014) Exam 3

9 The optimum of Oligopoly

Department of Economics. Harvard University. Spring Honors General Exam. April 6, 2011

OPERATIONS RESEARCH Code: MB0048. Section-A

Commerce 295 Midterm Answers

Problem Set 4 Duality, Sensitivity, Dual Simplex, Complementary Slackness

Capacity Planning with Rational Markets and Demand Uncertainty. By: A. Kandiraju, P. Garcia-Herreros, E. Arslan, P. Misra, S. Mehta & I.E.

CHAPTER 5 SOCIAL WELFARE MAXIMIZATION FOR HYBRID MARKET

Ch.01 Introduction to Modeling. Management Science / Instructor: Bonghyun Ahn

OPERATIONAL CASE STUDY February 2018 EXAM ANSWERS. Variant 2. The February 2018 exam can be viewed at

Operations Research Models and Methods Paul A. Jensen and Jonathan F. Bard. Inventory Level. Figure 4. The inventory pattern eliminating uncertainty.

Business Mathematics / Quantitative Research Methods I

Inventory Management 101 Basic Principles SmartOps Corporation. All rights reserved Copyright 2005 TeknOkret Services. All Rights Reserved.

COB291, Management Science, Test 3, Fall 2008 DO NOT TURN TO THE NEXT PAGE UNTIL YOU ARE INSTRUCTED TO DO SO!

INSTRUCTIONS. Supply Chain Management and LEAN concept simulation

ECON 8010 (Spring 2013) Exam 3

7,5 ECTS. Industrial Business Economics II IBE2. The exam is given to: Name: (Filled by student) Personal number: (Filled by student)

Performance Management

Linear Programming 1 WENDY DESCRIBES THE CASE

Excel Solver Tutorial: Wilmington Wood Products (Originally developed by Barry Wray)

AN ECONOMIC ANALYSIS OF THE EMISSION REDUCTION MARKET SYSTEM IN CHICAGO. Chao-Ning Liao and Hayri Onal * Long Beach, California, July 28-31, 2002

This is a refereed journal and all articles are professionally screened and reviewed

Programmed to pass. calculate the shadow price of direct labour

Decision making and Relevant Information

Techniques of Operations Research

DEVELOPMENT OF A DYNAMIC PROGRAMMING MODEL FOR OPTIMIZING PRODUCTION PLANNING. the Polytechnic Ibadan, Mechatronics Engineering Department; 3, 4

COMM 290 MIDTERM/FINAL EXAM REVIEW SESSION BY TONY CHEN

Technical Bulletin Comparison of Lossy versus Lossless Shift Factors in the ISO Market Optimizations

Chapters 1 and 2 Trade Without Money and A Model of Money

PRODUCTION MANAGEMENT ANALYSIS USING MONTE CARLO METHOD

Effect of Transportation Model on Organizational Performance: A Case Study of MTN Nigeria, Asaba, Delta State, Nigeria

Modelling Financial Flow of the Supply Chain

The Transportation and Assignment Problems. Hillier &Lieberman Chapter 8

Model Question Paper with Solution

LECTURE 2 SINGLE VARIABLE OPTIMIZATION

OPTIMIZATION OF ROCK STONE SELECTION IN SHORE PROTECTION PROJECTS - CASE STUDY: GAZA BEACH CAMP SHORE PROTECTION PROJECT

MAN256 Introduction to Management Science

Code No: RR Set No. 1

Modeling Using Linear Programming

Check HW: WS Calculator Linear Programming

The Ascending Bid Auction Experiment:

David Simchi-Levi M.I.T. November 2000

You can find the consultant s raw data here:

CHAPTER THREE DEMAND AND SUPPLY

Modeling of competition in revenue management Petr Fiala 1

Transcription:

MGSC 1205 Quantitative Methods I Class Seven Sensitivity Analysis Ammar Sarhan 1

How can we handle changes? We have solved LP problems under deterministic assumptions. find an optimum solution given certain constant parameters (costs, price, time, etc) How well do we know these parameters? Usually not very accurately rough estimates Conditions in most world situations are dynamic & changing prices of raw materials change product supply changes new machinery is bought to replace old employee turnover occurs 2

Sensitivity Analysis Post-optimality analysis: examining changes after the optimal solution has been reached. input data are varied to assess optimal solution sensitivity. Basic Question: How does our solution change as the input parameters change? How much does the objective function change? How much do the optimal values of the decision variables change? Do our results remain valid (If the parameters change...)? 3

Example: High Note Sound Company The company Manufactures quality CD players and stereo receivers. Each CD player sold results in $50 profit, while each receiver yields $120 profit. Each product requires skilled craftsmanship. Each CD player requires: 2 hours electrician s time and 3 hours technician s time Each receiver requires: 4 hours electrician s time and 1 hour technician s time Hours available: 80 for electrician s time, 60 for technician s time Objective: maximize profit 4

B5:C5 D6 D8:D9 5

Answer Analysis Target Cell (Max) Cell Name Original Value Final Value $D$6 Profit $0.00 $2,400.00 Cell Name Original Value Final Value $B$5 Solution value CD players 0.00 0.00 $C$5 Solution value Stereo receivers 0.00 20.00 This column indicates whether a constraint is exactly satisfied (LHS=RHS) Cell Name Cell Value Formula Status Slack $D$8 Electricians' Time 80.00 $D$8<=$F$8 Binding 0.00 $D$9 Audio Technicians' Time 20.00 $D$9<=$F$9 Not Binding 40.00 6

Answer Report Target Cell (Max) Cell Name Original Value Final Value $D$6 Profit $0.00 $2,400.00 Cell Name Original Value Final ValueThis column indicates $B$5 Solution value CD players 0.00 0.00 the amount of unused $C$5 Solution value Stereo receivers 0.00 20.00 resource Cell Name Cell Value Formula Status Slack $D$8 Electricians' Time 80.00 $D$8<=$F$8 Binding 0.00 $D$9 Audio Technicians' Time 20.00 $D$9<=$F$9 Not Binding 40.00 7

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 This table presents information regarding the impact of changes to the OFCs (i.e., the unit profits of $50 & $120) Allowable Increases & Allowable Decreases: they are the range of values for which we can change the OFCs, and still have current Corner Point remain as Optimal Solution This is the whole point of doing the analysis! 8

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Allowable Increases & Allowable Decreases: they are the range of values for which we can change the OFCs, and still have current Corner Point remain as Optimal Solution 9

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 This table presents information on impact of changes in RHS. Final Value: the values of how much of each resource (constraint) is used up in reaching the optimal solution - Electricians time is binding - Technicians time is non-binding with Slack = 40. Constraint RHS: the value input for the RHS of each constraint equation 10

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Changes in RHS usually affect the size of the feasible region. - If the size of feasible region increases, optimal objective function improves - If the size of feasible region decreases, optimal objective function worsens Relationship expressed as Shadow Price. Impact of changes in RHS values is measured by the Shadow Price. 11

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Shadow Price is the change in optimal objective function value for one unit increase in RHS. Electrician s time is binding constraint; If Electrician s time increases, more products can be made, and profit will go up The amount of profit will change by for each additional unit of the binding resource is equal to the Shadow Price. For each additional hour of Electrician s time that firm can increase will increase the profit by $30. 12

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Allowable Increases & Allowable Decreases: Validity range for the shadow price. - For what level of increase of RHS value of the electricians time constraint is the shadow price of $30 valid? - The shadow price is valid only as long as the change in the RHS is within the Allowable Increase & Allowable Decrease values. 13

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Question: Assume we have an opportunity to get 50 additional hours of electricians time. However, this time will cost us an extra $20 per hour. Should we take it? Question: Can you solve the problem If we have 100 Electricians time If we have 60 Electricians time If we have 240 Electricians time If we have 0 Electricians time 14

Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Technicians time has 40 unused hours. No interest in acquiring additional hours of resource. Shadow price for audio technicians time is zero. Allowable increase for RHS value is infinity. Once 40 hours is lost (current unused portion, or slack) of technicians time, resource also becomes binding. Any additional loss of time will clearly have adverse effect on profit. 15

Changes in a Right-Hand Side Receivers 60 50 40 30 20 10 Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 Technician s time = 60 (3C+ S= 60) (2C+4S = 80) Electricians time increases from 80 to 100 (2C+4S = 100) 0 20 40 60 80 CD players 16

Changes in a Right-Hand Side 60 Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease 50 $B$5 Solution value CD players 0.00-10.00 50.00 10.00 1E+30 $C$5 Solution value Stereo receivers 20.00 0.00 120.00 1E+30 20.00 Receivers 40 30 20 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $D$8 Electricians' Time 80.00 30.00 80.00 160.00 80.00 $D$9 Audio Technicians' Time 20.00 0.00 60.00 1E+30 40.00 10 Electricians time decreases from 80 to 60 0 20 40 60 80 CD players 17

Changes in a Right-Hand Side Receivers 60 50 40 30 20 Summary in changes in RHS Changes in RHS usually affect the size of the feasible region. The location of the optimal corner point changes There is a range of values for each RHS in which the shadow price remains unchanged. 10 Electricians time = 80 0 20 40 60 80 CD players 18

Manufacturing Application Anderson Electronics is considering producing four potential products: VCR Stereo TV DVD Supply Elct. Components 3 4 4 3 4,700 Non-Elct. Components 2 2 4 3 4,500 Assembly time 1 1 3 2 2,500 Selling price $70 $80 $150 $110 Cost $41 $48 $78 $56 Profit $29 $32 $72 $54 Objective: Maximize profit 19

Questions Answer Report Anderson Electronics Answer Report Target Cell (Max) Cell Name Original Value Final Value $F$8 Profit $0.00 $69,400.00 Cell Name Original Value Final Value $B$5 Solution value VCR 0.00 0.00 $C$5 Solution value Stereo 0.00 380.00 $D$5 Solution value TV 0.00 0.00 $E$5 Solution value DVD 0.00 1060.00 Cell Name Cell Value Formula Status Slack $F$10 Electronic comp 4700.00 $F$10<=$H$10 Binding 0.00 $F$11 Non-electronic comp 3940.00 $F$11<=$H$11 Not Binding 560.00 $F$12 Assembly time 2500.00 $F$12<=$H$12 Binding 0.00 Question 1: What s the optimal production strategy? Question 2: What s maximum profit for A.E.? Question 3: Which resource is fully used up? Which constrain is exactly satisfied? 20

Questions Sensitivity Report Anderson Electronics Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value VCR 0.00-1.00 29.00 1.00 1E+30 $C$5 Solution value Stereo 380.00 0.00 32.00 40.00 1.67 $D$5 Solution value TV 0.00-8.00 72.00 8.00 1E+30 $E$5 Solution value DVD 1060.00 0.00 54.00 10.00 5.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $F$10 Electronic comp 4700.00 2.00 4700.00 2800.00 950.00 $F$11 Non-electronic comp 3940.00 0.00 4500.00 1E+30 560.00 $F$12 Assembly time 2500.00 24.00 2500.00 466.67 1325.00 Question 1: What s the Shadow Price of electronic components? Question 2:What s the Allowable Range of RHS value of electronic components? Question 3: What s the impact on profit if we could increase the supply of non-electronic components by 400 units (to a total 4,900 units)? 21

Questions Sensitivity Report Anderson Electronics Sensitivity Report Final Reduced Objective Allowable Allowable Cell Name Value Cost Coefficient Increase Decrease $B$5 Solution value VCR 0.00-1.00 29.00 1.00 1E+30 $C$5 Solution value Stereo 380.00 0.00 32.00 40.00 1.67 $D$5 Solution value TV 0.00-8.00 72.00 8.00 1E+30 $E$5 Solution value DVD 1060.00 0.00 54.00 10.00 5.00 Final Shadow Constraint Allowable Allowable Cell Name Value Price R.H. Side Increase Decrease $F$10 Electronic comp 4700.00 2.00 4700.00 2800.00 950.00 $F$11 Non-electronic comp 3940.00 0.00 4500.00 1E+30 560.00 $F$12 Assembly time 2500.00 24.00 2500.00 466.67 1325.00 Question 4: What s the impact on profit if we could increase the supply of electronic components by 400 units (to a total 5,100 units)? Question 5: What would happen if we could increase the supply of electronic components by 4000 units (to a total 8,700 units)? 22

Questions Sensitivity Report Question 6: For the question about getting an additional 400 units of electronic components, what would happen if the supplier of these 400 units wants $8 per unit (rather than the current cost of $7 per unit)? Question 7: Assume we have an opportunity to get 250 additional hours of assembly time. However, this time will cost us $15 per hour (rather than the current cost of $10 per hour). Should we take it? Question 8: If we force the production of VCR, what would be the impact on profit? Alternatively, how much profit must VCRs become before the firm should consider producing them? Question 9: Assume that there is uncertainty in the price of DVD. For what range of prices will the current production be optimal? Question 10: If DVD actually sold for less and profit per unit drops to $50, what would be the firm s new total profit? 23

Simultaneous Changes In Parameter Values Previous assumption: analyzing only one change at a time Possible to analyze impact of simultaneous changes on optimal solution only under specific condition 100% Rule: Σ (Change / Allowable change) 1 If sum does not exceed 1, information provided in sensitivity report is valid to analyze impact of changes. 24

Sensitivity Analysis Sensitivity analysis is used by management to answer a series of what if questions about inputs to LP model. Over what ranges can prices change without affecting the optimality of the present solution? Will the present solution remain the optimum solution if the amount of raw materials, production time, or storage space is suddenly changed? The amount of each type of resources needed to produce one unit of each type of product may vary slightly. Will such changes affect the optimal solution? 25

Sensitivity Analysis Sensitivity analysis is used to determine effects on the optimal solution within specified ranges for the objective function coefficients (OFCs), and right hand side (RHS) values. Basic Question: How does our solution change as the input parameters change? Allowable Range for OFCs Shadow prices Allowable Range for RHS values 26