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Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics 9th edition [Kõva köide]

2.60/5 (5 hinnangut Goodreads-ist)

Cliff Ragsdale (Virginia Polytechnic Institute and State University)

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Ilmumisaeg: 28-Sep-2021
Kirjastus: South-Western College Publishing
ISBN-10: 0357132092
ISBN-13: 9780357132098

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Formaat: Hardback, 864 pages, kõrgus x laius x paksus: 38x210x259 mm, kaal: 1791 g
Ilmumisaeg: 28-Sep-2021
Kirjastus: South-Western College Publishing
ISBN-10: 0357132092
ISBN-13: 9780357132098

Teised raamatud teemal:

Püsilink: https://www.kriso.ee/db/9780357132098.html

Märksõnad:

Master key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 9E, written by respected business analytics innovator Cliff Ragsdale. This edition's clear presentation, realistic examples, fascinating topics and valuable software provide everything you need to become proficient in todays most widely used business analytics techniques using the latest version of Excel® in Microsoft® Office 365 or Office 2019. Become skilled in the newest Excel functions as well as Analytic Solver® and Data Mining add-ins. This edition helps you develop both algebraic and spreadsheet modeling skills. Step-by-step instructions and annotated, full-color screen images make examples easy to follow and show you how to apply what you learn about descriptive, predictive and prescriptive analytics to real business situations. WebAssign online tools and author-created videos further strengthen understanding.

1 Introduction to Modeling and Decision Analysis

(15)

Introduction

(2)

The Modeling Approach to Decision Making

(1)

Characteristics and Benefits of Modeling

(2)

Mathematical Models

(1)

Categories of Mathematical Models

(2)

Business Analytics and the Problem-Solving Process

(2)

Anchoring and Framing Effects

(1)

Good Decisions vs. Good Outcomes

(1)

Summary

(1)

References

(2)

Questions and Problems

(1)

Case

(2)

2 Introduction to Optimization and Linear Programming

(30)

Introduction

(1)

Applications of Mathematical Optimization

(1)

Characteristics of Optimization Problems

(1)

Expressing Optimization Problems Mathematically

(1)

Decisions

(1)

Constraints

(1)

Objective

(1)

Mathematical Programming Techniques

(1)

An Example LP Problem

(1)

Formulating LP Models

(1)

Steps in Formulating an LP Model

(2)

Summary of the LP Model for the Example Problem

(1)

The General Form of an LP Model

(1)

Solving LP Problems: An Intuitive Approach

(1)

Solving LP Problems: A Graphical Approach

(1)

Plotting the First Constraint

(1)

Plotting the Second Constraint

(1)

Plotting the Third Constraint

(1)

The Feasible Region

(2)

Plotting the Objective Function 28 Finding the Optimal Solution Using Level Curves

(1)

Finding the Optimal Solution by Enumerating the Corner Points

(1)

Summary of Graphical Solution to LP Problems

(1)

Understanding How Things Change

(1)

Special Conditions in LP Models

(1)

Alternate Optimal Solutions

(1)

Redundant Constraints

(2)

Unbounded Solutions

(1)

Infeasibility

(1)

Summary

(1)

References

(1)

Questions and Problems

(5)

Case

(2)

3 Modeling and Solving LP Problems in a Spreadsheet

(93)

Introduction

(1)

Spreadsheet Solvers

(1)

Solving LP Problems in a Spreadsheet

(1)

The Steps in Implementing an LP Model in a Spreadsheet

(1)

A Spreadsheet Model for the Blue Ridge Hot Tubs Problem

(1)

Organizing the Data

(1)

Representing the Decision Variables

(1)

Representing the Objective Function

(1)

Representing the Constraints

(1)

Representing the Bounds on the Decision Variables

(1)

How Solver Views the Model

(2)

Using Analytic Solver

(1)

Defining the Objective Cell

(1)

Defining the Variable Cells

(1)

Defining the Constraint Cells

(3)

Defining the Nonnegativity Conditions

(1)

Reviewing the Model

(1)

Other Options

(1)

Solving the Problem

(1)

Using Excel's Built-in Solver

(1)

Goals and Guidelines for Spreadsheet Design

(2)

Make vs. Buy Decisions

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(1)

Solving the Problem

(1)

Analyzing the Solution

(1)

An Investment Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(2)

Solving the Problem

(1)

Analyzing the Solution

(1)

A Transportation Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(2)

Heuristic Solution for the Model

(1)

Solving the Problem

(1)

Analyzing the Solution

(1)

A Blending Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Some Observations about Constraints, Reporting, and Scaling

(1)

Re-scaling the Model

(1)

Implementing the Model

(1)

Solving the Problem

(1)

Analyzing the Solution

(2)

A Production and Inventory Planning Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(1)

Implementing the Model

(2)

Solving the Problem

(1)

Analyzing the Solution

(1)

A Multiperiod Cash Flow Problem

(1)

Defining the Decision Variables

(1)

Defining the Objective Function

(1)

Defining the Constraints

(2)

Implementing the Model

(2)

Solving the Problem

100

(1)

Analyzing the Solution

101

(1)

Modifying the Taco-Viva Problem to Account for Risk (Optional)

102

(1)

Implementing the Risk Constraints

103

(2)

Solving the Problem

105

(1)

Analyzing the Solution

106

(1)

Data Envelopment Analysis

106

(1)

Defining the Decision Variables

107

(1)

Defining the Objective

107

(1)

Defining the Constraints

107

(1)

Implementing the Model

108

(2)

Solving the Problem

110

(3)

Analyzing the Solution

113

(1)

Summary

114

(1)

References

115

(1)

Questions and Problems

116

(17)

Case

133

(6)

4 Sensitivity Analysis and the Simplex Method

139

(45)

Introduction

139

(1)

The Purpose of Sensitivity Analysis

140

(1)

Approaches to Sensitivity Analysis

140

(1)

An Example Problem

141

(1)

The Answer Report

142

(1)

The Sensitivity Report

143

(1)

Changes in the Objective Function Coefficients

143

(3)

A Comment about Constancy

146

(1)

Alternate Optimal Solutions

146

(1)

Changes in the RHS Values

146

(1)

Shadow Prices for Nonbinding Constraints

147

(1)

A Note about Shadow Prices

147

(2)

Shadow Prices and the Value of Additional Resources

149

(1)

Other Uses of Shadow Prices

149

(1)

The Meaning of the Reduced Costs

150

(3)

Analyzing Changes in Constraint Coefficients

153

(1)

Simultaneous Changes in Objective Function Coefficients

153

(1)

A Warning about Degeneracy

154

(1)

Ad Hoc Sensitivity Analysis

155

(1)

Creating Spider Plots and Tables

155

(3)

Creating a Solver Table

158

(3)

Comments

161

(1)

Robust Optimization

161

(4)

The Simplex Method

165

(1)

Creating Equality Constraints Using Slack Variables

165

(1)

Basic Feasible Solutions

166

(1)

Finding the Best Solution

167

(1)

Summary

168

(1)

References

169

(1)

Questions and Problems

170

(8)

Case

178

(6)

5 Network Modeling

184

(58)

Introduction

184

(1)

The Transshipment Problem

184

(1)

Characteristics of Network Flow Problems

185

(1)

The Decision Variables for Network Flow Problems

186

(1)

The Objective Function for Network Flow Problems

187

(1)

The Constraints for Network Flow Problems

187

(1)

Implementing the Model in a Spreadsheet

188

(2)

Analyzing the Solution

190

(2)

The Shortest Path Problem

192

(1)

An LP Model for the Example Problem

193

(1)

The Spreadsheet Model and Solution

194

(1)

Network Flow Models and Integer Solutions

195

(1)

The Equipment Replacement Problem

196

(1)

The Spreadsheet Model and Solution

197

(3)

Transportation/Assignment Problems

200

(1)

Generalized Network Flow Problems

201

(1)

Formulating an LP Model for the Recycling Problem

202

(1)

Implementing the Model

203

(2)

Analyzing the Solution

205

(1)

Generalized Network Flow Problems and Feasibility

206

(3)

Maximal Flow Problems

209

(1)

An Example of a Maximal Flow Problem

209

(2)

The Spreadsheet Model and Solution

211

(2)

Special Modeling Considerations

213

(3)

Minimal Spanning Tree Problems

216

(1)

An Algorithm for the Minimal Spanning Tree Problem

217

(1)

Solving the Example Problem

217

(1)

Summary

218

(1)

References

219

(1)

Questions and Problems

220

(16)

Case

236

(6)

6 Integer Linear Programming

242

(79)

Introduction

242

(1)

Integrality Conditions

243

(1)

Relaxation

243

(2)

Solving the Relaxed Problem

245

(2)

Bounds

247

(1)

Rounding

247

(3)

Stopping Rules

250

(1)

Solving ILP Problems Using Solver

250

(2)

Other ILP Problems

252

(2)

An Employee Scheduling Problem

254

(1)

Defining the Decision Variables

255

(1)

Defining the Objective Function

255

(1)

Defining the Constraints

255

(1)

A Note About the Constraints

256

(1)

Implementing the Model

256

(2)

Solving the Model

258

(1)

Analyzing the Solution

259

(1)

Binary Variables

259

(1)

A Capital Budgeting Problem

259

(1)

Defining the Decision Variables

260

(1)

Defining the Objective Function

260

(1)

Defining the Constraints

260

(1)

Setting Up the Binary Variables

260

(1)

Implementing the Model

261

(1)

Solving the Model

262

(1)

Comparing the Optimal Solution to a Heuristic Solution

262

(1)

Binary Variables and Logical Conditions

263

(1)

The Line Balancing Problem

264

(1)

Defining the Decision Variables

265

(1)

Defining the Constraints

265

(1)

Defining the Objective

266

(1)

Implementing the Model

267

(4)

Analyzing the Solution 270 Extension

271

(2)

The Fixed-Charge Problem

273

(1)

Defining the Decision Variables

274

(1)

Defining the Objective Function

274

(1)

Defining the Constraints

275

(1)

Determining Values for "Big M"

275

(1)

Implementing the Model

276

(1)

Solving the Model

277

(1)

Analyzing the Solution

277

(2)

A Comment on IF() Functions

279

(1)

Minimum Order/Purchase Size

280

(1)

Quantity Discounts

281

(1)

Formulating the Model

281

(1)

The Missing Constraints

282

(1)

A Contract Award Problem

282

(1)

Formulating the Model: The Objective Function and Transportation Constraints

283

(1)

Implementing the Transportation Constraints

284

(1)

Formulating the Model: The Side Constraints

285

(1)

Implementing the Side Constraints

286

(1)

Solving the Model

287

(1)

Analyzing

The Solution

287

(2)

The Branch-and-Bound Algorithm (Optional)

289

(1)

Branching

289

(2)

Bounding

291

(1)

Branching Again

292

(1)

Bounding Again

292

(1)

Summary of B&B Example

293

(2)

Summary

295

(1)

References

295

(1)

Questions and Problems

296

(20)

Case

316

(5)

7 Goal Programming and Multiple Objective Optimization

321

(44)

Introduction

321

(1)

Goal Programming

322

(1)

A Goal Programming Example

323

(1)

Defining the Decision Variables

323

(1)

Defining the Goals

323

(1)

Defining the Goal Constraints

323

(1)

Defining the Hard Constraints

324

(1)

GP Objective Functions

325

(1)

Defining the Objective

326

(1)

Implementing the Model

327

(1)

Solving the Model

328

(1)

Analyzing the Solution

329

(1)

Revising the Model

329

(1)

Trade-offs: The Nature of GP

330

(2)

Comments about Goal Programming

332

(1)

Multiple Objective Optimization

333

(1)

An MOLP Example

334

(1)

Defining the Decision Variables

335

(1)

Defining the Objectives

335

(1)

Defining the Constraints

335

(1)

Implementing the Model

336

(1)

Determining Target Values for the Objectives

337

(2)

Summarizing the Target Solutions

339

(1)

Determining a GP Objective

340

(1)

The Minimax Objective

341

(1)

Implementing the Revised Model

342

(1)

Solving the Model

343

(1)

Comments on MOLP

344

(2)

Summary

346

(1)

References

346

(1)

Questions and Problems

347

(12)

Case

359

(6)

8 Nonlinear Programming and Evolutionary Optimization

365

(74)

Introduction

365

(1)

The Nature of NLP Problems

366

(1)

Solution Strategies for NLP Problems

367

(1)

Local vs. Global Optimal Solutions

368

(3)

Economic Order Quantity Models

371

(2)

Implementing the Model

373

(1)

Solving the Model

373

(2)

Analyzing the Solution

375

(1)

Comments on the EOQ Model

375

(1)

Location Problems

376

(1)

Defining the Decision Variables

376

(1)

Defining the Objective

377

(1)

Defining the Constraints

378

(1)

Implementing the Model

378

(1)

Solving the Model and Analyzing the Solution

379

(1)

Another Solution to the Problem

380

(2)

Some Comments about the Solution to Location Problems

382

(1)

Nonlinear Network Flow Problem

382

(1)

Defining the Decision Variables

382

(1)

Defining the Objective

382

(1)

Defining the Constraints

383

(1)

Implementing the Model

384

(1)

Solving the Model and Analyzing the Solution

385

(2)

Project Selection Problems

387

(1)

Defining the Decision Variables

387

(1)

Defining the Objective Function

388

(1)

Defining the Constraints

388

(1)

Implementing the Model

389

(1)

Solving the Model

390

(1)

Optimizing Existing Financial Spreadsheet Models

391

(1)

Implementing the Model

392

(1)

Optimizing the Spreadsheet Model

393

(1)

Analyzing the Solution

393

(1)

Comments on Optimizing Existing Spreadsheets

394

(1)

The Portfolio Selection Problem

395

(1)

Defining the Decision Variables

396

(1)

Defining the Objective

397

(1)

Defining the Constraints

397

(1)

Implementing the Model

398

(2)

Analyzing the Solution

400

(2)

Handling Conflicting Objectives in Portfolio Problems

402

(1)

Sensitivity Analysis

403

(3)

Lagrange Multipliers

406

(1)

Reduced Gradients

406

(1)

Solver Options for Solving NLPs

406

(2)

Evolutionary Algorithms

408

(1)

Forming Fair Teams

409

(1)

A Spreadsheet Model for the Problem

410

(1)

Solving the Model

411

(1)

Analyzing the Solution

412

(1)

The Traveling Salesperson Problem

412

(1)

A Spreadsheet Model for the Problem

413

(2)

Solving the Model

415

(1)

Analyzing the Solution

415

(1)

Summary

416

(1)

References

417

(1)

Questions and Problems

418

(16)

Case

434

(5)

9 Regression Analysis

439

(52)

Introduction

439

(1)

An Example

440

(2)

Regression Models

442

(1)

Simple Linear Regression Analysis

443

(1)

Defining "Best Fit"

444

(1)

Solving the Problem Using Solver

444

(3)

Solving the Problem Using the Regression Tool

447

(2)

Evaluating the Fit

449

(2)

The R2 Statistic

451

(1)

Making Predictions

452

(1)

The Standard Error

453

(1)

Prediction Intervals for New Values of Y

453

(3)

Confidence Intervals for Mean Values of Y

456

(1)

Extrapolation

456

(1)

Statistical Tests for Population Parameters

456

(1)

Analysis of Variance

457

(1)

Assumptions for the Statistical Tests

457

(3)

Statistical Tests

460

(1)

Introduction to Multiple Regression

460

(1)

A Multiple Regression Example

461

(1)

Selecting the Model

462

(1)

Models with One Independent Variable

463

(1)

Models with Two Independent Variables

464

(2)

Inflating R2

466

(1)

The Adjusted-R2 Statistic

466

(1)

The Best Model with Two Independent Variables

467

(1)

Multicollinearity

467

(1)

The Model with Three Independent Variables

467

(1)

Making Predictions

468

(1)

Other Model Selection Issues

469

(1)

Binary Independent Variables

470

(1)

Statistical Tests for the Population Parameters

471

(1)

Polynomial Regression

472

(1)

Expressing Nonlinear Relationships Using Linear Models

472

(5)

Summary of Nonlinear Regression

477

(1)

Summary

477

(1)

References

477

(1)

Questions and Problems

478

(9)

Case

487

(4)

10 Data Mining

491

(60)

Introduction

491

(1)

Data Mining Overview

492

(2)

Classification

494

(1)

A Classification Example

495

(7)

Classification Data Partitioning

502

(2)

Discriminant Analysis

504

(2)

Discriminant Analysis Example

506

(5)

Logistic Regression

511

(2)

Logistic Regression Example

513

(3)

K-Nearest Neighbor

516

(1)

K-Nearest Neighbor Example

517

(2)

Classification Trees

519

(2)

Classification Tree Example

521

(3)

Neural Networks

524

(2)

Neural Network Example

526

(2)

Naive Bayes

528

(2)

Naive Bayes Example

530

(4)

Comments on Classification

534

(1)

Combining Classifications with Ensemble Methods

534

(1)

The Role of Test Data

534

(1)

Prediction

534

(1)

Association Rules (Affinity Analysis)

535

(2)

Association Rules Example

537

(1)

Cluster Analysis

538

(1)

Cluster Analysis Example

539

(1)

K-Mean Clustering Example

540

(2)

Hierarchical Clustering Example

542

(2)

Time Series

544

(1)

Summary

544

(1)

References

545

(1)

Questions and Problems

546

(3)

Case

549

(2)

11 Time Series Forecasting

551

(69)

Introduction

551

(1)

Time Series Methods

552

(1)

Measuring Accuracy

553

(1)

Stationary Models

553

(2)

Moving Averages

555

(1)

Forecasting with the Moving Average Model

556

(1)

Weighted Moving Averages

557

(3)

Forecasting with the Weighted Moving Average Model

560

(1)

Exponential Smoothing

560

(1)

Forecasting with the Exponential Smoothing Model

561

(2)

Seasonality

563

(1)

Stationary Data with Additive Seasonal Effects

564

(4)

Forecasting with the Model

568

(1)

Stationary Data with Multiplicative Seasonal Effects

569

(2)

Forecasting with the Model

571

(1)

Trend Models

572

(1)

An Example

573

(1)

Double Moving Average

574

(1)

Forecasting with the Model

575

(1)

Double Exponential Smoothing (Holt's Method)

576

(4)

Forecasting with Holt's Method

580

(1)

Holt-Winter's Method for Additive Seasonal Effects

580

(4)

Forecasting with Holt-Winter's Additive Method

584

(1)

Holt-Winter's Method for Multiplicative Seasonal Effects

584

(4)

Forecasting with Holt-Winter's Multiplicative Method

588

(1)

Modeling Time Series Trends Using Regression

588

(1)

Linear Trend Model

589

(2)

Forecasting with the Linear Trend Model

591

(1)

Quadratic Trend Model

592

(1)

Forecasting with the Quadratic Trend Model

592

(2)

Modeling Seasonality with Regression Models

594

(1)

Adjusting Trend Predictions with Seasonal Indices

594

(1)

Computing Seasonal Indices

595

(2)

Forecasting with Seasonal Indices

597

(1)

Refining the Seasonal Indices

598

(2)

Seasonal Regression Models

600

(1)

The Seasonal Model

601

(3)

Forecasting with the Seasonal Regression Model

604

(1)

Combining Forecasts

604

(1)

Summary

605

(1)

References

605

(1)

Questions and Problems

606

(10)

Case

616

(4)

12 Introduction to Simulation Using Analytic Solver

620

(82)

Introduction

620

(1)

Random Variables and Risk

621

(1)

Why Analyze Risk?

621

(1)

Methods of Risk Analysis

622

(1)

Best-Case/Worst-Case Analysis

622

(1)

What-If Analysis

623

(1)

Simulation

624

(1)

A Corporate Health Insurance Example

624

(1)

A Critique of the Base Case Model

625

(1)

Spreadsheet Simulation Using Analytic Solver

626

(1)

Starting Analytic Solver

627

(1)

Random Number Generators

627

(3)

Discrete vs. Continuous Random Variables

630

(1)

Preparing the Model for Simulation

630

(2)

Alternate RNG Entry

632

(2)

Running the Simulation

634

(1)

Selecting the Output Cells to Track

634

(1)

Selecting the Number of Replications

635

(1)

Selecting What Gets Displayed on the Worksheet

636

(1)

Running the Simulation

636

(1)

Data Analysis

637

(1)

The Best Case and the Worst Case

638

(1)

The Frequency Distribution of the Output Cells

638

(1)

The Cumulative Distribution of the Output Cells

639

(1)

Obtaining Other Cumulative Probabilities

640

(1)

Sensitivity Analysis

640

(1)

The Uncertainty of Sampling

641

(1)

Constructing a Confidence Interval for the True Population Mean

642

(1)

Constructing a Confidence Interval for a Population Proportion

643

(1)

Sample Sizes and Confidence Interval Widths

644

(1)

Interactive Simulation

644

(2)

The Benefits of Simulation

646

(1)

Additional Uses of Simulation

647

(1)

A Reservation Management Example

647

(1)

Implementing the Model

647

(2)

Details for Multiple Simulations

649

(1)

Running the Simulations

649

(1)

Data Analysis

650

(2)

An Inventory Control Example

652

(1)

Creating the RNGs

653

(1)

Implementing the Model

654

(3)

Replicating the Model

657

(1)

Optimizing the Model

658

(7)

Analyzing the Solution

665

(1)

Other Measures of Risk

666

(2)

A Project Selection Example

668

(1)

A Spreadsheet Model

668

(1)

Solving and Analyzing the Problem with Analytic Solver

669

(2)

Considering Another Solution

671

(2)

A Portfolio Optimization Example

673

(1)

A Spreadsheet Model

674

(2)

Solving the Problem with Analytic Solver

676

(2)

Summary

678

(1)

References

679

(1)

Questions and Problems

680

(13)

Case

693

(9)

13 Queuing Theory

702

(31)

Introduction

702

(1)

The Purpose of Queuing Models

703

(1)

Queuing System Configurations

704

(1)

Characteristics of Queuing Systems

705

(1)

Arrival Rate

705

(1)

Service Rate

706

(2)

Kendall Notation

708

(1)

Queuing Models

708

(2)

The M/M/s Model

710

(1)

An Example

710

(1)

The Current Situation

710

(2)

Adding a Server

712

(1)

Economic Analysis

712

(1)

The M/M/s Model with Finite Queue Length

713

(1)

The Current Situation

714

(1)

Adding a Server

714

(1)

The M/M/s Model with Finite Population

715

(1)

An Example

716

(1)

The Current Situation

716

(2)

Adding Servers

718

(1)

The M/G/1 Model

719

(1)

The Current Situation

719

(1)

Adding the Automated Dispensing Device

720

(2)

The M/D/1 Model

722

(1)

Simulating Queues and the Steady-State Assumption

722

(1)

Summary

723

(1)

References

723

(2)

Questions and Problems

725

(6)

Case

731

(2)

14 Decision Analysis

733

(74)

Introduction

733

(1)

Good Decisions vs. Good Outcomes

734

(1)

Characteristics of Decision Problems

734

(1)

An Example

735

(1)

The Payoff Matrix

736

(1)

Decision Alternatives

736

(1)

States of Nature

736

(1)

The Payoff Values

737

(1)

Decision Rules

738

(1)

Nonprobabilistic Methods

738

(1)

The Maximax Decision Rule

738

(1)

The Maximin Decision Rule

739

(1)

The Minimax Regret Decision Rule

740

(2)

Probabilistic Methods

742

(1)

Expected Monetary Value

742

(2)

Expected Regret

744

(1)

Sensitivity Analysis

744

(2)

The Expected Value of Perfect Information

746

(2)

Decision Trees

748

(1)

Rolling Back a Decision Tree

749

(2)

Creating Decision Trees with Analytic Solver

751

(1)

Adding Event Nodes

752

(3)

Determining the Payoffs and EMVs

755

(1)

Other Features

755

(1)

Multistage Decision Problems

756

(1)

A Multistage Decision Tree

757

(1)

Developing a Risk Profile

758

(1)

Sensitivity Analysis

759

(1)

Tornado Charts

760

(3)

Strategy Tables

763

(2)

Strategy Charts

765

(2)

Using Sample Information in Decision Making

767

(1)

Conditional Probabilities

768

(1)

The Expected Value of Sample Information

769

(1)

Computing Conditional Probabilities

770

(2)

Bayes' Theorem

772

(1)

Utility Theory

772

(1)

Utility Functions

773

(1)

Constructing Utility Functions

773

(3)

Using Utilities to Make Decisions

776

(1)

The Exponential Utility Function

777

(1)

Incorporating Utilities in Decision Trees

778

(2)

Multicriteria Decision Making

780

(1)

The Multicriteria Scoring Model

780

(3)

The Analytic Hierarchy Process

783

(1)

Pairwise Comparisons

784

(1)

Normalizing the Comparisons

785

(1)

Consistency

786

(1)

Obtaining Scores for the Remaining Criteria

787

(1)

Obtaining Criterion Weights

788

(1)

Implementing the Scoring Model

789

(1)

Summary

789

(1)

References

790

(1)

Questions and Problems

791

(11)

Case

802

(5)

15 Project Management

807

(50)

Introduction

807

(1)

An Example

808

(1)

Creating the Project Network

808

(2)

Start and Finish Points

810

(1)

CPM: An Overview

811

(1)

The Forward Pass

812

(2)

The Backward Pass

814

(2)

Determining the Critical Path

816

(2)

A Note on Slack

818

(1)

Project Management Using Spreadsheets

818

(4)

Important Implementation Issue

822

(1)

Gantt Charts

823

(2)

Project Crashing

825

(1)

An LP Approach to Crashing

826

(1)

Determining the Earliest Crash Completion Time

827

(1)

Implementing the Model

828

(1)

Solving the Model

829

(1)

Determining a Least Costly Crash Schedule

830

(1)

Crashing as an MOLP

831

(1)

Pert: An Overview

832

(3)

The Problems with PERT 833 Implications

835

(1)

Simulating Project Networks

835

(1)

An Example

836

(1)

Generating Random Activity Times

836

(1)

Implementing the Model

837

(1)

Running the Simulation

838

(1)

Analyzing the Results

839

(1)

Microsoft Project

840

(2)

Summary

842

(1)

References

843

(1)

Questions and Problems

844

(10)

Case

854

(3)

Index

857

A leading innovator in spreadsheet instruction and highly regarded pioneer in business analytics, Dr. Cliff Ragsdale is the Bank of America Professor of Business Information Technology and Academic Director of the Center for Business Intelligence and Analytics in the Pamplin College of Business at Virginia Tech, where he has taught since 1990. Dr. Ragsdale received his Ph.D. in management science and information technology from the University of Georgia. He also holds an M.B.A. in Finance and B.A. in psychology from the University of Central Florida. Before pursuing his Ph.D., he supervised benefit finance and qualified plans at the international headquarters of Red Lobster, Inc. He has served as an information systems and statistical consultant for a variety of companies and as an expert witness in the area of spreadsheet forensics. Dr. Ragsdale's primary areas of research interest include applications of artificial intelligence, mathematical programming and applying statistics to business problems. His research has appeared in numerous publications, including Decision Sciences; Naval Research Logistics; Omega: The International Journal of Management Science; Computers & Operations Research; Operations Research Letters and Personal Financial Planning. He has received both the Pamplin Award for excellence in teaching and the Outstanding Doctoral Educator Award from the Pamplin College of Business Administration at Virginia Tech. Dr. Ragsdale is a fellow of the Decision Sciences Institute (DSI) and active member of DSI and INFORMS.

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