Spreadsheet Modeling & Decision Analysis: A Practical Introduction

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Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, 3e by Cliff Ragsdale 1

Chapter 3 Modeling and Solving LP Problems in a Spreadsheet Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-2

Introduction Solving LP problems graphically is only possible when there are two decision variables Few real-world LP have only two decision variables Fortunately, we can now use spreadsheets to solve LP problems Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-3

Finding out the Max or Min values Global Global Global optima optima optima Local optima Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-4

Spreadsheet Solvers The company that makes the Solver in Excel, Lotus 1-2-3, and Quattro Pro is Frontline Systems, Inc. Check out their web site: http://www.frontsys.com Other packages for solving MP problems: AMPL CPLEX LINDO MPSX Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-5

The Steps in Implementing an LP Model in a Spreadsheet 1. Organize the data for the model on the spreadsheet. 2. Reserve separate cells in the spreadsheet to represent each decision variable in the model. 3. Create a formula in a cell in the spreadsheet that corresponds to the objective function. 4. For each constraint, create a formula in a separate cell in the spreadsheet that corresponds to the lefthand side (LHS) of the constraint. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-6

Let’s Implement a Model for the Blue Ridge Hot Tubs Example. MAX: 350X1 300X2 } profit S.T.: 1X1 1X2 200} pumps 9X1 6X2 1566 } labor 12X1 16X2 2880 } tubing X1, X2 0 } nonnegativity Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-7

Implementing the Model See file Fig3-1.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-8

How Solver Views the Model Target cell - the cell in the spreadsheet that represents the objective function Changing cells - the cells in the spreadsheet representing the decision variables Constraint cells - the cells in the spreadsheet representing the LHS formulas on the constraints Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-9

Let’s go back to Excel and see how Solver works. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-10

Goals For Spreadsheet Design Communication - A spreadsheet's primary business purpose is that of communicating information to managers. Reliability - The output a spreadsheet generates should be correct and consistent. Auditability - A manager should be able to retrace the steps followed to generate the different outputs from the model in order to understand the model and verify results. Modifiability - A well-designed spreadsheet should be easy to change or enhance in order to meet dynamic user requirements. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-11

Spreadsheet Design Guidelines Organize the data, then build the model around the data. Do not embed numeric constants in formulas. Things which are logically related should be physically related. Use formulas that can be copied. Column/rows totals should be close to the columns/rows being totaled. The English-reading eye scans left to right, top to bottom. Use color, shading, borders and protection to distinguish changeable parameters from other model elements. Use text boxes and cell notes to document various elements of the model. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-12

Make vs. Buy Decisions: The Electro-Poly Corporation Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-13

Make vs. Buy Decisions: The Electro-Poly Corporation Electro-Poly is a leading maker of slip-rings. A 750,000 order has just been received. Model 1 Model 2 Model 3 3,000 2,000 900 Hours of wiring/unit 2 1.5 3 Hours of harnessing/unit 1 2 1 Cost to Make 50 83 130 Cost to Buy 61 97 145 Number ordered The company has 10,000 hours of wiring capacity and 5,000 hours of harnessing capacity. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-14

Defining the Decision Variables M1 Number of model 1 slip rings to make in-house M2 Number of model 2 slip rings to make in-house M3 Number of model 3 slip rings to make in-house B1 Number of model 1 slip rings to buy from competitor B2 Number of model 2 slip rings to buy from competitor B3 Number of model 3 slip rings to buy from competitor Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-15

Defining the Objective Function Minimize the total cost of filling the order. MIN: 50M1 83M2 130M3 61B1 97B2 145B3 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-16

Defining the Constraints Demand Constraints M1 B1 3,000 } model 1 M2 B2 2,000 } model 2 M3 B3 } model 3 900 Resource Constraints 2M1 1.5M2 3M3 10,000 } wiring 1M1 2.0M2 1M3 5,000 } harnessing Nonnegativity Conditions M1, M2, M3, B1, B2, B3 0 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-17

Implementing the Model See file Fig3-17.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-18

An Investment Problem Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-19

An Investment Problem: Retirement Planning Services, Inc. A client wishes to invest 750,000 in the following bonds. Return 8.65% Years to Maturity 11 Rating 1-Excellent DynaStar 9.50% 10 3-Good Eagle Vision 10.00% 6 4-Fair Micro Modeling 8.75% 10 1-Excellent OptiPro 9.25% 7 3-Good Sabre Systems 9.00% 13 2-Very Good Company Acme Chemical Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-20

Investment Restrictions No more than 25% can be invested in any single company. At least 50% should be invested in longterm bonds (maturing in 10 years). No more than 35% can be invested in DynaStar, Eagle Vision, and OptiPro. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-21

Defining the Decision Variables X1 amount of money to invest in Acme Chemical X2 amount of money to invest in DynaStar X3 amount of money to invest in Eagle Vision X4 amount of money to invest in MicroModeling X5 amount of money to invest in OptiPro X6 amount of money to invest in Sabre Systems Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-22

Defining the Objective Function Maximize the total annual investment return. MAX: .0865X1 .095X2 .10X3 .0875X4 .0925X5 .09X6 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-23

Defining the Constraints Total amount is invested X1 X2 X3 X4 X5 X6 750,000 No more than 25% in any one investment Xi 187,500, for all i 50% long term investment restriction. X1 X2 X4 X6 375,000 35% Restriction on DynaStar, Eagle Vision, and OptiPro. X2 X3 X5 262,500 Nonnegativity conditions Xi 0 for all i Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-24

Implementing the Model See file Fig3-20.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-25

A scheduling problem: AirExpress Anyone responsible for creating work schedules for a number of employees can appreciate the difficulties in this task. It can be very difficult to develop a feasible schedule, much less than an optimal schedule. LP models have been devised to solve these problems. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-26

A scheduling problem: AirExpress Air-Express is an express shipping service that guarantees overnight delivery of packages anywhere in the continental United states. The Co. has various operations centers, called hubs, at airports in major cities across the country. Packages are received at hubs from other locations and then shipped to intermediate hubs or to their final destinations. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-27

A scheduling problem: AirExpress Day of the week Workers reqd shift Days Off Wage Sunday 18 1 Sunday & Monday 680 Day of the week Workers 2 reqd Monday & Tuesday 705 Monday 27 Sunday 18 3 27Tuesday & Wednesday 705 Monday Tuesday 22 Tuesday 4 22 Wednesday & Thursday 705 Wednesday 26 Wednesday 26 Thursday 5 25 Thursday & Friday 705 Thursday 25 Friday 21 Saturday 6 19Friday & Saturday 680 Friday 21 7 Saturday & Sunday 655 Saturday 19 As per negotiation, Saturday and Sunday are off days and the Co. has to pay extra 25 per day for workers on their Work on these days. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-28

A scheduling problem: AirExpress Model Min: 680 x1 705 x2 705 x3 705 x4 705 x5 680 x6 655 x7 s.t. 0 x1 1x2 1x3 1x4 1x5 1x6 0 x7 18 0 x1 0 x2 1x3 1x4 1x5 1x6 1x7 27 1x1 0 x2 0 x3 1x4 1x5 1x6 1x7 22 1x1 1x2 0 x3 0 x4 1x5 1x6 1x7 26 1x1 1x2 1x3 0 x4 0 x5 1x6 1x7 25 1x1 1x2 1x3 1x4 0 x5 0 x6 1x7 21 1x1 1x2 1x3 1x4 1x5 0 x6 0 x7 19 x1 , x , x , x , x , x , x , x 0 2 3 by4 Cliff5Ragsdale. 6 7 2001 South-Western/Thomson Learning. Spreadsheet Modeling and Decision 1Analysis, 3e, 3-29

A Transportation Problem: Tropicsun Supply 275,000 Groves Distances (in miles) 21 Mt. Dora 1 Processing Plants Capacity Ocala 4 50 200,000 40 35 400,000 30 Eustis 2 Orlando 5 22 600,000 55 300,000 20 Clermont 3 Leesburg 25 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 6 225,000 3-30

Defining the Decision Variables Xij # of bushels shipped from node i to node j Specifically, the nine decision variables are: X14 # of bushels shipped from Mt. Dora (node 1) to Ocala (node 4) X15 # of bushels shipped from Mt. Dora (node 1) to Orlando (node 5) X16 # of bushels shipped from Mt. Dora (node 1) to Leesburg (node 6) X24 # of bushels shipped from Eustis (node 2) to Ocala (node 4) X25 # of bushels shipped from Eustis (node 2) to Orlando (node 5) X26 # of bushels shipped from Eustis (node 2) to Leesburg (node 6) X34 # of bushels shipped from Clermont (node 3) to Ocala (node 4) X35 # of bushels shipped from Clermont (node 3) to Orlando (node 5) Modeling and Decision Analysis, 3e, by from Cliff Ragsdale. 2001 South-Western/Thomson XSpreadsheet bushels shipped Clermont (node 3)Learning. to Leesburg (node 6) 36 # of 3-31

Defining the Objective Function Minimize the total number of bushel-miles. MIN: 21X14 50X15 40X16 35X24 30X25 22X26 55X34 20X35 25X36 A bushel 35.2391 litres Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-32

Defining the Constraints Capacity constraints X14 X24 X34 200,000 } Ocala X15 X25 X35 600,000 } Orlando X16 X26 X36 225,000 } Leesburg Supply constraints X14 X15 X16 275,000 } Mt. Dora X24 X25 X26 400,000 } Eustis X34 X35 X36 300,000 } Clermont Nonnegativity conditions Xij 0 for all i and j Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-33

Implementing the Model See file Fig3-24.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-34

A Blending Problem: The Agri-Pro Company Agri-Pro has received an order for 8,000 pounds of chicken feed to be mixed from the following feeds. Percent of Nutrient in Nutrient Feed 1 Feed 2 Feed 3 Feed 4 Corn 30% 5% 20% 10% Grain 10% 3% 15% 10% Minerals 20% 20% 20% 30% Cost per pound 0.25 0.30 0.32 0.15 The order must contain at least 20% corn, 15% grain, and 15% minerals. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-35

Defining the Decision Variables X1 pounds of feed 1 to use in the mix X2 pounds of feed 2 to use in the mix X3 pounds of feed 3 to use in the mix X4 pounds of feed 4 to use in the mix Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-36

Defining the Objective Function Minimize the total cost of filling the order. MIN: 0.25X1 0.30X2 0.32X3 0.15X4 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-37

Defining the Constraints Produce 8,000 pounds of feed X1 X2 X3 X4 8,000 Mix consists of at least 20% corn (0.3X1 0.5X2 0.2X3 0.1X4)/8000 0.2 Mix consists of at least 15% grain (0.1X1 0.3X2 0.15X3 0.1X4)/8000 0.15 Mix consists of at least 15% minerals (0.2X1 0.2X2 0.2X3 0.3X4)/8000 0.15 Nonnegativity conditions X1, X2, X3, X4 0 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-38

A Comment About Scaling Notice that the coefficient for X2 in the ‘corn’ constraint is 0.05/8000 0.00000625 As Solver solves our problem, intermediate calculations must be done that make coefficients large or smaller. Storage problems may force the computer to use approximations of the actual numbers. Such ‘scaling’ problems sometimes prevents Solver from being able to solve the problem accurately. Most problems can be formulated in a way to minimize scaling errors. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-39

Re-Defining the Decision Variables X1 thousands of pounds of feed 1 to use in the mix X2 thousands of pounds of feed 2 to use in the mix X3 thousands of pounds of feed 3 to use in the mix X4 thousands of pounds of feed 4 to use in the mix Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-40

Re-Defining the Objective Function Minimize the total cost of filling the order. MIN: 250X1 300X2 320X3 150X4 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-41

Re-Defining the Constraints Produce 8,000 pounds of feed X1 X2 X3 X4 8 Mix consists of at least 20% corn (0.3X1 0.5X2 0.2X3 0.1X4)/8 0.2 Mix consists of at least 15% grain (0.1X1 0.3X2 0.15X3 0.1X4)/8 0.15 Mix consists of at least 15% minerals (0.2X1 0.2X2 0.2X3 0.3X4)/8 0.15 Nonnegativity conditions X1, X2, X3, X4 0 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-42

A Comment About Scaling Earlier the largest coefficient in the constraints was 8,000 and the smallest is 0.05/8 0.00000625. Now the largest coefficient in the constraints is 8 and the smallest is 0.05/8 0.00625. The problem is now more evenly scaled. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-43

The Assume Linear Model Option The Solver Options dialog box has an option labeled “Assume Linear Model”. When you select this option Solver performs some tests to verify that your model is in fact linear. These test are not 100% accurate & often fail as a result of a poorly scaled model. If Solver tells you a model isn’t linear when you know it is, try solving it again. If that doesn’t work, try re-scaling your model. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-44

Implementing the Model See file Fig3-28.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-45

A Production Planning Problem: The Upton Corporation Upton is planning the production of their heavy-duty air compressors for the next 6 months. Month 1 2 3 4 5 6 Unit Production Cost 240 250 265 285 280 260 Units Demanded 1,000 4,500 6,000 5,500 3,500 4,000 Maximum Production 4,000 3,500 4,000 4,500 4,000 3,500 Minimum Production 1,750 2,000 2,250 2,000 1,750 2,000 Beginning inventory 2,750 units Safety stock 1,500 units Unit carrying cost 1.5% of unit production cost Maximum warehouse capacity 6,000 units Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-46

Defining the Decision Variables Pi number of units to produce in month i, i 1 to 6 Bi beginning inventory month i, i 1 to 6 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-47

Defining the Objective Function Minimize the total cost production & inventory costs. MIN: 240P1 250P2 265P3 285P4 280P5 260P6 3.6(B1 B2)/2 3.75(B2 B3)/2 3.98(B3 B4)/2 4.28(B4 B5)/2 4.20(B5 B6)/2 3.9(B6 B7)/2 Note: The beginning inventory in any month is the same as the ending inventory in the previous month. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-48

Defining the Constraints Production levels 2,000 P1 4,000 } month 1 1,750 P2 3,500 } month 2 2,000 P3 4,000 } month 3 2,250 P4 4,500 } month 4 2,000 P5 4,000 } month 5 1,750 P6 3,500 } month 6 Ending Inventory (EI BI P - D) 1,500 B1 P1 - 1,000 6,000 } month 1 1,500 B2 P2 - 4,500 6,000 } month 2 1,500 B3 P3 - 6,000 6,000 } month 3 1,500 B4 P4 - 5,500 6,000 } month 4 1,500 B5 P5 - 3,500 6,000 } month 5 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-49

Defining the Constraints (cont’d) Beginning Balances B1 2750 B2 B1 P1 - 1,000 B3 B2 P2 - 4,500 B4 B3 P3 - 6,000 B5 B4 P4 - 5,500 B6 B5 P5 - 3,500 B7 B6 P6 - 4,000 Notice that the Bi can be computed directly from the Pi. Therefore, only the Pi need to be identified as changing cells. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-50

Implementing the Model See file Fig3-31.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-51

A Multi-Period Cash Flow Problem: The Taco-Viva Sinking Fund - I Taco-Viva needs to establish a sinking fund to pay 800,000 in building costs for a new restaurant in the next 6 months. Payments of 250,000 are due at the end of months 2 and 4, and a final payment of 300,000 is due at the end of month 6. The following investments may be used. Investment A B C D Available in Month Months to Maturity Yield at Maturity 1, 2, 3, 4, 5, 6 1 1.8% 1, 3, 5 2 3.5% 1, 4 3 5.8% 1 6 11.0% Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-52

Summary of Possible Cash Flows Investment A1 B1 C1 D1 A2 A3 B3 A4 C4 A5 B5 A6 Cash Inflow/Outflow at the Beginning of Month 1 2 3 4 5 6 7 -1 1.018 -1 1.035 -1 1.058 -1 1.11 -1 1.018 -1 1.018 -1 1.035 -1 1.018 -1 1.058 -1 1.018 -1 1.035 -1 1.018 Req’d Payments 0 (in 1,000s) 0 250 0 250 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 0 300 3-53

Defining the Decision Variables Ai amount (in 1,000s) placed in investment A at the beginning of month i 1, 2, 3, 4, 5, 6 Bi amount (in 1,000s) placed in investment B at the beginning of month i 1, 3, 5 Ci amount (in 1,000s) placed in investment C at the beginning of month i 1, 4 Di amount (in 1,000s) placed in investment D at the beginning of month i 1 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-54

Defining the Objective Function Minimize the total cash invested in month 1. MIN: A1 B1 C1 D 1 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-55

Defining the Constraints Cash Flow Constraints 1.018A1 – 1A2 0 } month 2 1.035B1 1.018A2 – 1A3 – 1B3 250 } month 3 1.058C1 1.018A3 – 1A4 – 1C4 0 } month 4 1.035B3 1.018A4 – 1A5 – 1B5 250 } month 5 1.018A5 –1A6 0 } month 6 1.11D1 1.058C4 1.035B5 1.018A6 300 } month 7 Nonnegativity Conditions Ai, Bi, Ci, Di 0, for all i Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-56

Implementing the Model See file Fig3-35.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-57

Risk Management: The Taco-Viva Sinking Fund - II Assume the CFO has assigned the following risk ratings to each investment on a scale from 1 to 10 (10 max risk) Investment Risk Rating A B C D 1 3 8 6 The CFO wants the weighted average risk to not exceed 5. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-58

Defining the Constraints Risk Constraints 1A1 3B1 8C1 6D1 5 A1 B1 C1 D1 } month 1 1A2 3B1 8C1 6D1 5 A2 B1 C1 D1 } month 2 1A3 3B3 8C1 6D1 5 A3 B3 C1 D1 } month 3 1A4 3B3 8C4 6D1 5 A4 B3 C4 D1 } month 4 1A5 3B5 8C4 6D1 5 A5 B5 C4 D1 } month 5 1A6 3B5 8C4 6D1 5 A6 B5 C4 D1 } month 6 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-59

An Alternate Version of the Risk Constraints Equivalent Risk Constraints -4A1 - 2B1 3C1 1D1 0 } month 1 – 2B1 3C1 1D1 – 4A2 0 } month 2 3C1 1D1 – 4A3 – 2B3 0 } month 3 1D1 – 2B3 – 4A4 3C4 0 } month 4 1D1 3C4 – 4A5 – 2B5 0 } month 5 1D1 3C4 – 2B5 – 4A6 0 } month 6 Note that each coefficient is equal to the risk factor for the investment minus 5 (the maximum allowable weighted average risk). Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-60

Implementing the Model See file Fig3-38.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-61

Data Envelopment Analysis (DEA): Steak & Burger Steak & Burger needs to evaluate the performance (efficiency) of 12 units. Outputs for each unit (Oij) include measures of: Profit, Customer Satisfaction, and Cleanliness Inputs for each unit (Iij) include: Labor Hours, and Operating Costs The “Efficiency” of unit i is defined as follows: nO Weighted sum of unit i’s outputs Weighted sum of unit i’s inputs nO Oij w j j 1 nI I v j 1 ij j Oij w j j 1 nI I ij v j j 1 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-62

Defining the Decision Variables wj weight assigned to output j vj weight assigned to input j A separate LP is solved for each unit, allowing each unit to select the best possible weights for itself. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-63

Defining the Objective Function Maximize the weighted output for unit i : nO MAX: Oij w j nO Oij w j j 1 j 1 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-64

Defining the Constraints Efficiency cannot exceed 100% for any unit nO nI j 1 j 1 Okj w j I kj v j , k 1 to the number of units Sum of weighted inputs for unit i must equal 1 nO n I nI I ij v j 1 j 1 j 1 I ij v j 1 j 1 Nonnegativity Conditions wj, vj 0, for all j Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-65

Important Point When using DEA, output variables should be expressed on a scale where “more is better” and input variables should be expressed on a scale where “less is better”. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-66

Implementing the Model See file Fig3-41.xls Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-67

Assignments Page 114 14, 15, 18, 21, 22, 24, 25, 29, 30, 35, 38, 46. Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-68

End of Chapter 3 Spreadsheet Modeling and Decision Analysis, 3e, by Cliff Ragsdale. 2001 South-Western/Thomson Learning. 3-69