T H I R T E E N T H E D I T I O N QUANTITATIVE ANALYSIS for MANAGEMENT BARRY RENDER • RALPH M STAIR, JR. • MICHAEL E HANNA • TREVOR S HALE T H I R T E E N T H E D I T I O N QUANTITATIVE ANALYSIS for MANAGEMENT BARRY RENDER Charles Harwood Professor Emeritus of Management Science Crummer Graduate School of Business, Rollins College RALPH M STAIR, JR Professor Emeritus of Information and Management Sciences, Florida State University MICHAEL E HANNA Professor of Decision Sciences, University of Houston–Clear Lake TREVOR S HALE Associate Professor of Management Sciences, University of Houston–Downtown New York, NY A01_REND3161_13_AIE_FM.indd 02/11/16 10:04 PM To my wife and sons—BR To Lila and Leslie—RMS To Zoe and Gigi—MEH To Valerie and Lauren—TSH Vice President, Business Publishing: Donna Battista Director, Courseware Portfolio Management: Ashley Dodge Director of Portfolio Management: Stephanie Wall Senior Sponsoring Editor: Neeraj Bhalla Managing Producer: Vamanan Namboodiri M.S 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photocopying, recording, or otherwise For information regarding permissions, request forms, and the appropriate contacts within the Pearson Education Global Rights and Permissions department, please visit www.pearsoned.com/permissions/ Acknowledgments of third-party content appear on the appropriate page within the text Unless otherwise indicated herein, any third-party trademarks, logos, or icons that may appear in this work are the property of their respective owners, and any references to third-party trademarks, logos, icons, or other trade dress are for demonstrative or descriptive purposes only Such references are not intended to imply any sponsorship, endorsement, authorization, or promotion of Pearson’s products by the owners of such marks, or any relationship between the owner and Pearson Education, Inc., or its affiliates, authors, licensees, or distributors Library of Congress Cataloging-in-Publication Data Names: Render, Barry, author | Stair, Ralph M., author | Hanna, Michael E., author | Hale, Trevor S., author Title: Quantitative analysis for management / Barry Render, Charles Harwood Professor of Management Science, Crummer Graduate School of Business, Rollins College; Ralph M Stair, Jr., Professor of Information and Management Sciences, Florida State University; Michael E Hanna, Professor of Decision Sciences, University of Houston/Clear Lake; Trevor S Hale, Associate Professor of Management Sciences, University of Houston/Downtown Description: Thirteenth edition | Boston : Pearson, 2016 | Includes bibliographical references and index Identifiers: LCCN 2016037461| ISBN 9780134543161 (hardcover) | ISBN 0134543165 (hardcover) Subjects: LCSH: Management science—Case studies | Operations research—Case studies Classification: LCC T56 R543 2016 | DDC 658.4/03—dc23 LC record available at https://lccn.loc.gov/ 2016037461 10 9 8 7 6 5 4 3 2 1 ISBN 10: 0-13-454316-5 ISBN 13: 978-0-13-454316-1 A01_REND3161_13_AIE_FM.indd 28/10/16 10:13 AM About the Authors Barry Render is Professor Emeritus, the Charles Harwood Distinguished Professor of Operations Management, Crummer Graduate School of Business, Rollins College, Winter Park, Florida He received his B.S in Mathematics and Physics at Roosevelt University and his M.S in Operations Research and his Ph.D in Quantitative Analysis at the University of Cincinnati He previously taught at George Washington University, the University of New Orleans, Boston University, and George Mason University, where he held the Mason Foundation Professorship in Decision Sciences and was Chair of the Decision Science Department Dr Render has also worked in the aerospace industry for General Electric, McDonnell Douglas, and NASA Dr Render has coauthored 10 textbooks published by Pearson, including Managerial Decision Modeling with Spreadsheets, Operations Management, Principles of Operations Management, Service Management, Introduction to Management Science, and Cases and Readings in Management Science More than 100 articles by Dr Render on a variety of management topics have appeared in Decision Sciences, Production and Operations Management, Interfaces, Information and Management, Journal of Management Information Systems, SocioEconomic Planning Sciences, IIE Solutions, and Operations Management Review, among others Dr Render has been honored as an AACSB Fellow and was named twice as a Senior Fulbright Scholar He was Vice President of the Decision Science Institute Southeast Region and served as software review editor for Decision Line for six years and as Editor of the New York Times Operations Management special issues for five years From 1984 to 1993, Dr Render was President of Management Service Associates of Virginia, Inc., whose technology clients included the FBI, the U.S Navy, Fairfax County, Virginia, and C&P Telephone He is currently Consulting Editor to Financial Times Press Dr Render has taught operations management courses at Rollins College for MBA and Executive MBA programs He has received that school’s Welsh Award as leading professor and was selected by Roosevelt University as the 1996 recipient of the St Claire Drake Award for Outstanding Scholarship In 2005, Dr Render received the Rollins College MBA Student Award for Best Overall Course, and in 2009 was named Professor of the Year by full-time MBA students Ralph Stair is Professor Emeritus at Florida State University He earned a B.S in chemical engineering from Purdue University and an M.B.A from Tulane University Under the guidance of Ken Ramsing and Alan Eliason, he received a Ph.D in operations management from the University of Oregon He has taught at the University of Oregon, the University of Washington, the University of New Orleans, and Florida State University He has taught twice in Florida State University’s Study Abroad Program in London Over the years, his teaching has been concentrated in the areas of information systems, operations research, and operations management Dr Stair is a member of several academic organizations, including the Decision Sciences Institute and INFORMS, and he regularly participates in national meetings He has published numerous articles and books, including Managerial Decision Modeling with Spreadsheets, Introduction to Management Science, Cases and Readings in Management Science, Production and Operations Management: A Self-Correction Approach, Fundamentals of Information Systems, Principles of Information Systems, Introduction to Information Systems, Computers in iii A01_REND3161_13_AIE_FM.indd 28/10/16 10:13 AM iv ABOUT THE AUTHORS Today’s World, Principles of Data Processing, Learning to Live with Computers, Programming in BASIC, Essentials of BASIC Programming, Essentials of FORTRAN Programming, and Essentials of COBOL Programming Dr Stair divides his time between Florida and Colorado He enjoys skiing, biking, kayaking, and other outdoor activities Michael E Hanna is Professor of Decision Sciences at the University of Houston–Clear Lake (UHCL) He holds a B.A in Economics, an M.S in Mathematics, and a Ph.D in Operations Research from Texas Tech University For more than 25 years, he has been teaching courses in statistics, management science, forecasting, and other quantitative methods His dedication to teaching has been recognized with the Beta Alpha Psi teaching award in 1995 and the Outstanding Educator Award in 2006 from the Southwest Decision Sciences Institute (SWDSI) Dr Hanna has authored textbooks in management science and quantitative methods, has published numerous articles and professional papers, and has served on the Editorial Advisory Board of Computers and Operations Research In 1996, the UHCL Chapter of Beta Gamma Sigma presented him with the Outstanding Scholar Award Dr Hanna is very active in the Decision Sciences Institute (DSI), having served on the Innovative Education Committee, the Regional Advisory Committee, and the Nominating Committee He has served on the board of directors of DSI for two terms and also as regionally elected vice president of DSI For SWDSI, he has held several positions, including president, and he received the SWDSI Distinguished Service Award in 1997 For overall service to the profession and to the university, he received the UHCL President’s Distinguished Service Award in 2001 Trevor S Hale is Associate Professor of Management Science at the University of Houston– Downtown (UHD) He received a B.S in Industrial Engineering from Penn State University, an M.S in Engineering Management from Northeastern University, and a Ph.D in Operations Research from Texas A&M University He was previously on the faculty of both Ohio University– Athens and Colorado State University–Pueblo Dr Hale was honored three times as an Office of Naval Research Senior Faculty Fellow He spent the summers of 2009, 2011, and 2013 performing energy security/cyber security research for the U.S Navy at Naval Base Ventura County in Port Hueneme, California Dr Hale has published dozens of articles in the areas of operations research and quantitative analysis in journals such as the International Journal of Production Research, the European Journal of Operational Research, Annals of Operations Research, the Journal of the Operational Research Society, and the International Journal of Physical Distribution and Logistics Management, among several others He teaches quantitative analysis courses at the University of Houston–Downtown He is a senior member of both the Decision Sciences Institute and INFORMS A01_REND3161_13_AIE_FM.indd 28/10/16 10:13 AM Brief Contents CHAPTER Introduction to Quantitative Analysis CHAPTER Probability Concepts and Applications 21 CHAPTER Decision Analysis 63 CHAPTER Regression Models 111 CHAPTER Forecasting 147 CHAPTER Inventory Control Models 185 CHAPTER Linear Programming Models: Graphical and Computer Methods 237 CHAPTER Linear Programming Applications 289 CHAPTER Transportation, Assignment, and Network Models 319 CHAPTER 10 Integer Programming, Goal Programming, and Nonlinear Programming 357 CHAPTER 11 Project Management 387 CHAPTER 12 Waiting Lines and Queuing Theory Models 427 CHAPTER 13 Simulation Modeling 461 CHAPTER 14 Markov Analysis 501 CHAPTER 15 Statistical Quality Control 529 ONLINE MODULES 1 Analytic Hierarchy Process M1-1 2 Dynamic Programming M2-1 3 Decision Theory and the Normal Distribution M3-1 4 Game Theory M4-1 5 Mathematical Tools: Determinants and Matrices M5-1 6 Calculus-Based Optimization M6-1 7 Linear Programming: The Simplex Method M7-1 8 Transportation, Assignment, and Network Algorithms M8-1 v A01_REND3161_13_AIE_FM.indd 28/10/16 10:13 AM Contents PREFACE xiii CHAPTER 1.1 1.2 1.3 Introduction to Quantitative Analysis What Is Quantitative Analysis? Business Analytics The Quantitative Analysis Approach Defining the Problem Developing a Model Acquiring Input Data Developing a Solution Testing the Solution Analyzing the Results and Sensitivity Analysis 6 Implementing the Results The Quantitative Analysis Approach and Modeling in the Real World 1.4 How to Develop a Quantitative Analysis Model CHAPTER 2.1 1.6 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach Possible Problems in the Quantitative Analysis Approach 12 Defining the Problem 12 Developing a Model 13 Acquiring Input Data 14 Developing a Solution 14 Testing the Solution 14 Analyzing the Results 15 1.7 2.2 Revising Probabilities with Bayes’ Theorem 27 General Form of Bayes’ Theorem 28 2.3 2.4 2.5 Further Probability Revisions 29 Random Variables 30 Probability Distributions 32 Probability Distribution of a Discrete Random Variable 32 Expected Value of a Discrete Probability Distribution 32 Variance of a Discrete Probability Distribution 33 Probability Distribution of a Continuous Random Variable 34 2.6 The Binomial Distribution 35 Solving Problems with the Binomial Formula 36 Solving Problems with Binomial Tables 37 2.7 The Normal Distribution 38 Area Under the Normal Curve 40 Using the Standard Normal Table 40 Haynes Construction Company Example 41 The Empirical Rule 44 Implementation—Not Just the Final Step 15 Lack of Commitment and Resistance to Change 16 Lack of Commitment by Quantitative Analysts 16 Summary 16 Glossary 16 Key Equations 17 Self-Test 17 Discussion Questions and Problems 18 Case Study: Food and Beverages at Southwestern University Football Games 19 Bibliography 20 Fundamental Concepts 22 Two Basic Rules of Probability 22 Types of Probability 22 Mutually Exclusive and Collectively Exhaustive Events 23 Unions and Intersections of Events 25 Probability Rules for Unions, Intersections, and Conditional Probabilities 25 The Advantages of Mathematical Modeling Mathematical Models Categorized by Risk 1.5 Probability Concepts and Applications 21 2.8 2.9 The F Distribution 44 The Exponential Distribution 46 Arnold’s Muffler Example 47 2.10 The Poisson Distribution 48 Summary 50 Glossary 50 Key Equations 51 Solved Problems 52 Self-Test 54 Discussion Questions and Problems 55 Case Study: WTVX 61 Bibliography 61 Appendix 2.1: Derivation of Bayes’ Theorem 61 vi A01_REND3161_13_AIE_FM.indd 28/10/16 10:13 AM CONTENTS vii CHAPTER 3.1 3.2 3.3 Decision Analysis 63 The Six Steps in Decision Making 63 Types of Decision-Making Environments 65 Decision Making Under Uncertainty 65 Optimistic 66 Pessimistic 66 Criterion of Realism (Hurwicz Criterion) 67 Equally Likely (Laplace) 67 Minimax Regret 67 3.4 3.5 Using Software for Payoff Table Problems 75 QM for Windows 75 Excel QM 75 3.6 3.7 How Probability Values Are Estimated by Bayesian Analysis 83 4.8 4.9 3.8 4.10 4.11 CHAPTER 4.1 4.2 4.3 Appendix 4.1: Formulas for Regression Calculations 145 CHAPTER 5.1 Forecasting 147 5.2 5.3 5.4 4.4 4.5 5.5 5.6 4.6 Using Computer Software for Regression 122 Excel 2016 122 Excel QM 123 QM for Windows 125 A01_REND3161_13_AIE_FM.indd Adjusting for Seasonal Variations 164 Seasonal Indices 165 Calculating Seasonal Indices with No Trend 165 Calculating Seasonal Indices with Trend 166 5.7 Forecasting Models—Trend, Seasonal, and Random Variations 167 The Decomposition Method 167 Software for Decomposition 170 Using Regression with Trend and Seasonal Components 170 Estimating the Variance 119 Triple A Construction Example 121 The Analysis of Variance (ANOVA) Table 122 Triple A Construction ANOVA Example 122 Forecasting Models—Trend and Random Variations 160 Exponential Smoothing with Trend 160 Trend Projections 163 Assumptions of the Regression Model 117 Testing the Model for Significance 119 Components of a Time-Series 149 Measures of Forecast Accuracy 151 Forecasting Models—Random Variations Only 154 Moving Averages 154 Weighted Moving Averages 154 Exponential Smoothing 156 Using Software for Forecasting Time Series 158 Coefficient of Determination 116 Correlation Coefficient 116 Types of Forecasting Models 147 Qualitative Models 147 Causal Models 148 Time-Series Models 149 Regression Models 111 Scatter Diagrams 112 Simple Linear Regression 113 Measuring the Fit of the Regression Model 114 Nonlinear Regression 131 Cautions and Pitfalls in Regression Analysis 134 Summary 135 Glossary 135 Key Equations 136 Solved Problems 137 Self-Test 139 Discussion Questions and Problems 139 Case Study: North–South Airline 144 Bibliography 145 Utility Theory 86 Measuring Utility and Constructing a Utility Curve 86 Utility as a Decision-Making Criterion 88 Summary 91 Glossary 91 Key Equations 92 Solved Problems 92 Self-Test 97 Discussion Questions and Problems 98 Case Study: Starting Right Corporation 107 Case Study: Toledo Leather Company 107 Case Study: Blake Electronics 108 Bibliography 110 Binary or Dummy Variables 129 Model Building 130 Stepwise Regression 131 Multicollinearity 131 Calculating Revised Probabilities 83 Potential Problem in Using Survey Results 85 Multiple Regression Analysis 126 Evaluating the Multiple Regression Model 127 Jenny Wilson Realty Example 128 Decision Trees 77 Efficiency of Sample Information 82 Sensitivity Analysis 82 4.7 Decision Making Under Risk 69 Expected Monetary Value 69 Expected Value of Perfect Information 70 Expected Opportunity Loss 71 Sensitivity Analysis 72 A Minimization Example 73 5.8 Monitoring and Controlling Forecasts 172 Adaptive Smoothing 174 Summary 174 Glossary 174 Key Equations 175 Solved Problems 176 Self-Test 177 Discussion Questions and Problems 178 Case Study: Forecasting Attendance at SWU Football Games 182 Case Study: Forecasting Monthly Sales 183 Bibliography 184 01/11/16 12:31 PM viii CONTENTS CHAPTER 6.1 Inventory Control Models 185 Importance of Inventory Control 186 Decoupling Function 186 Storing Resources 187 Irregular Supply and Demand 187 Quantity Discounts 187 Avoiding Stockouts and Shortages 187 6.2 6.3 Inventory Decisions 187 Economic Order Quantity: Determining How Much to Order 189 Inventory Costs in the EOQ Situation 189 Finding the EOQ 191 Sumco Pump Company Example 192 Purchase Cost of Inventory Items 193 Sensitivity Analysis with the EOQ Model 194 6.4 6.5 Reorder Point: Determining When to Order 194 EOQ Without the Instantaneous Receipt Assumption 196 Annual Carrying Cost for Production Run Model 196 Annual Setup Cost or Annual Ordering Cost 197 Determining the Optimal Production Quantity 197 Brown Manufacturing Example 198 6.6 6.7 6.8 7.2 7.3 Flair Furniture Company 240 7.4 7.5 7.6 6.11 6.12 7.7 Appendix 6.1: ABC Analysis 214 Dependent Demand: The Case for Material Requirements Planning 214 Inventory Control with QM for Windows 235 Sensitivity Analysis 264 High Note Sound Company 265 Changes in the Objective Function Coefficient 266 QM for Windows and Changes in Objective Function Coefficients 266 Excel Solver and Changes in Objective Function Coefficients 267 Changes in the Technological Coefficients 268 Changes in the Resources or Right-Hand-Side Values 269 QM for Windows and Changes in Right-HandSide Values 270 Excel Solver and Changes in Right-Hand-Side Values 270 Summary 272 Glossary 272 Solved Problems 273 Self-Test 277 Discussion Questions and Problems 278 Case Study: Mexicana Wire Winding, Inc. 286 Bibliography 288 Use of Safety Stock 203 Single-Period Inventory Models 209 Summary 221 Glossary 221 Key Equations 222 Solved Problems 223 Self-Test 225 Discussion Questions and Problems 226 Case Study: Martin-Pullin Bicycle Corporation 234 Bibliography 235 Four Special Cases in LP 261 No Feasible Solution 261 Unboundedness 261 Redundancy 262 Alternate Optimal Solutions 263 Quantity Discount Models 200 Just-In-Time Inventory Control 219 Enterprise Resource Planning 220 Solving Minimization Problems 257 Holiday Meal Turkey Ranch 257 Material Structure Tree 215 Gross and Net Material Requirements Plans 216 Two or More End Products 218 Solving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QM 251 Using QM for Windows 251 Using Excel’s Solver Command to Solve LP Problems 252 Using Excel QM 255 Marginal Analysis with Discrete Distributions 210 Café du Donut Example 210 Marginal Analysis with the Normal Distribution 212 Newspaper Example 212 6.9 6.10 Graphical Solution to an LP Problem 241 Graphical Representation of Constraints 241 Isoprofit Line Solution Method 245 Corner Point Solution Method 248 Slack and Surplus 250 Brass Department Store Example 202 Formulating LP Problems 239 CHAPTER 8.1 Linear Programming Applications 289 Marketing Applications 289 Media Selection 289 Marketing Research 291 8.2 Manufacturing Applications 293 Production Mix 293 Production Scheduling 295 8.3 8.4 Employee Scheduling Applications 299 Labor Planning 299 CHAPTER Linear Programming Models: Graphical and Computer Methods 237 Requirements of a Linear Programming Problem 238 7.1 A01_REND3161_13_AIE_FM.indd Financial Applications 300 Portfolio Selection 300 Truck Loading Problem 303 8.5 Ingredient Blending Applications 305 01/11/16 12:31 PM CONTENTS ix Diet Problems 305 Ingredient Mix and Blending Problems 306 8.6 Other Linear Programming Applications 308 Ranking Goals with Priority Levels 371 Goal Programming with Weighted Goals 371 10.4 Nonlinear Objective Function and Linear Constraints 373 Both Nonlinear Objective Function and Nonlinear Constraints 373 Linear Objective Function with Nonlinear Constraints 374 Summary 375 Glossary 375 Solved Problems 376 Self-Test 378 Discussion Questions and Problems 379 Case Study: Schank Marketing Research 384 Case Study: Oakton River Bridge 385 Bibliography 385 Summary 310 Self-Test 310 Problems 311 Case Study: Cable & Moore 318 Bibliography 318 CHAPTER Transportation, Assignment, and Network Models 319 The Transportation Problem 320 9.1 Linear Program for the Transportation Example 320 Solving Transportation Problems Using Computer Software 321 A General LP Model for Transportation Problems 322 Facility Location Analysis 323 9.2 9.3 CHAPTER 11 11.1 The Transshipment Problem 327 Linear Program for Transshipment Example 327 9.4 9.5 9.6 Maximal-Flow Problem 330 Example 330 Shortest-Route Problem 332 Minimal-Spanning Tree Problem 334 Summary 337 Glossary 338 Solved Problems 338 Self-Test 340 Discussion Questions and Problems 341 Case Study: Andrew–Carter, Inc. 352 Case Study: Northeastern Airlines 353 Case Study: Southwestern University Traffic Problems 354 Bibliography 355 11.2 10.1 Integer Programming, Goal Programming, and Nonlinear Programming 357 11.3 10.2 11.4 10.3 Modeling with 0–1 (Binary) Variables 363 Goal Programming 368 Example of Goal Programming: Harrison Electric Company Revisited 369 Extension to Equally Important Multiple Goals 370 A01_REND3161_13_AIE_FM.indd Project Crashing 405 Other Topics in Project Management 410 Subprojects 410 Milestones 410 Resource Leveling 410 Software 410 Summary 410 Glossary 410 Key Equations 411 Solved Problems 412 Self-Test 414 Discussion Questions and Problems 415 Case Study: Southwestern University Stadium Construction 422 Case Study: Family Planning Research Center of Nigeria 423 Bibliography 424 Integer Programming 358 Capital Budgeting Example 364 Limiting the Number of Alternatives Selected 365 Dependent Selections 365 Fixed-Charge Problem Example 366 Financial Investment Example 367 PERT/Cost 400 General Foundry Example 406 Project Crashing with Linear Programming 407 Harrison Electric Company Example of Integer Programming 358 Using Software to Solve the Harrison Integer Programming Problem 360 Mixed-Integer Programming Problem Example 360 PERT/CPM 389 Planning and Scheduling Project Costs: Budgeting Process 400 Monitoring and Controlling Project Costs 403 Appendix 9.1: Using QM for Windows 355 CHAPTER 10 Project Management 387 General Foundry Example of PERT/CPM 389 Drawing the PERT/CPM Network 390 Activity Times 391 How to Find the Critical Path 392 Probability of Project Completion 395 What PERT Was Able to Provide 398 Using Excel QM for the General Foundry Example 398 Sensitivity Analysis and Project Management 399 The Assignment Problem 325 Linear Program for Assignment Example 325 Nonlinear Programming 372 Appendix 11.1: Project Management with QM for Windows 424 CHAPTER 12 Waiting Lines and Queuing Theory Models 427 12.1 Waiting Line Costs 428 12.2 Three Rivers Shipping Company Example 428 Characteristics of a Queuing System 429 Arrival Characteristics 429 Waiting Line Characteristics 430 01/11/16 2:59 PM www.freebookslides.com M8-30 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS Solution a T he cost table has a fourth column to represent New York To balance the problem, we add a dummy row (person) with a zero relocation cost to each city OFFICE HIREE OMAHA MIAMI DALLAS NEW YORK JONES $800 $1,100 $1,200 $1,000 SMITH $500 $1,600 $1,300 $800 WILSON $500 $1,000 $2,300 $1,500 DUMMY 0 0 b S ubtract the smallest number in each row and cover zeros (column subtraction will give the same numbers and therefore is not necessary) OFFICE HIREE OMAHA MIAMI DALLAS NEW YORK JONES 300 400 200 SMITH 1,100 800 300 WILSON 500 1,800 1,000 DUMMY 0 0 c S ubtract the smallest uncovered number (200) from each uncovered number, add it to each square where two lines intersect, and cover all zeros OFFICE HIREE JONES OMAHA MIAMI 100 DALLAS NEW YORK 200 SMITH 900 600 100 WILSON 300 1,600 800 DUMMY 200 0 d S ubtract the smallest uncovered number (100) from each uncovered number, add it to each square where two lines intersect, and cover all zeros OFFICE HIREE Z09_REND3161_13_AIE_M08.indd 30 OMAHA MIAMI DALLAS NEW YORK JONES 0 100 SMITH 800 500 100 WILSON 200 1,500 800 DUMMY 300 0 100 31/10/16 1:58 PM www.freebookslides.com M8.4 Shortest-Route SOLVED PROBLEMS Problem M8-31 e S ubtract the smallest uncovered number (100) from each uncovered number, add it to squares where two lines intersect, and cover all zeros OFFICE HIREE OMAHA MIAMI DALLAS NEW YORK JONES 100 100 SMITH 700 400 WILSON 100 1,400 700 DUMMY 400 0 100 f S ince it takes four lines to cover all zeros, an optimal assignment can be made at zero squares We assign Dummy (no one) to Dallas Wilson to Omaha Smith to New York Jones to Miami Cost = +0 + +500 + +800 + +1,100 = +2,400 Solved Problem M8-4 PetroChem, an oil refinery located on the Mississippi River south of Baton Rouge, Louisiana, is designing a new plant to produce diesel fuel Figure M8.11 shows the network of the main processing centers along with the existing rate of flow (in thousands of gallons of fuel) The management at PetroChem would like to determine the maximum amount of fuel that can flow through the plant, from node to node Solution Using the maximal-flow technique, we arbitrarily choose path 1–5–7, which has a maximum flow of The capacity are then adjusted, leaving the capacity from to at and the capacity from to also at The next path arbitrarily selected is 1–2–4–7, and the maximum flow is When capacities are adjusted, the capacity from to and the capacity from to are 1, and the capacity from to is The next path selected is 1–3–6–7, which has a maximum flow of 1, and the capacity from to is adjusted to The next path selected is 1–2–5–6–7, which has a maximum flow of After this, there are no more paths with any capacity Arc 5–7 has capacity of While arc 4–7 has a capacity of 1, both arc 2–4 and arc 5–4 have a capacity of 0, so no more flow is available through FIGURE M8.11 0 3 5 1 Z09_REND3161_13_AIE_M08.indd 31 4 3 31/10/16 1:58 PM www.freebookslides.com M8-32 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS FIGURE M8.12 00 00 5,000 5,000 1,0 00 1,000 1,00 0 3, 2, 00 4,0 3,00 1,0 00 node Similarly, while arc 6–7 has a capacity of remaining, the capacity for arc 3–6 and the capacity for arc 5–6 are Thus, the maximum flow is 10 15 + + + 12 The flows are shown in Figure M8.12 Solved Problem M8-5 The network of Figure M8.13 shows the highways and cities surrounding Leadville, Colorado Leadville Tom, a bicycle helmet manufacturer, must transport his helmets to a distributor based in Dillon, Colorado To this, he must go through several cities Tom would like to find the shortest way to get from Leadville to Dillon What you recommend? Solution The problem can be solved using the shortest-route technique The nearest node to the origin (node 1) is node Give this a distance of and put this in a box next to node Next, consider nodes 3, 4, and 5, since there is an arc to each of these from either node or node 2, and both of these have their distances established The nearest node to the origin is (coming through node 2), so the distance to put in the box next to node is 14 18 + 62 Then consider nodes 4, 5, and The node nearest the origin is node 4, which has a distance of 18 (directly from node 1) Then consider nodes and The node with the least distance from the origin is node (coming through node 2), and this distance is 22 Next, consider nodes and 7, and node is selected, since the distance is 26 (coming through node 3) Finally, node is considered, and the shortest distance from the origin is 32 (coming through node 6) The route that gives the shortest distance is 1–2–3–6–7, and the distance is 32 See Figure M8.14 for the solution FIGURE M8.13 12 12 Leadville FIGURE M8.14 16 Leadville 16 18 6 Dillon 22 14 12 14 18 Z09_REND3161_13_AIE_M08.indd 32 10 16 18 14 10 12 16 32 6 Dillon 26 31/10/16 1:58 PM www.freebookslides.com DISCUSSION QUESTIONS AND PROBLEMS M8-33 Self-Test ●● ●● ●● Before taking the self-test, refer to the learning objectives at the beginning of the module, the notes in the margins, and the glossary at the end of the module Use the key at the back of the book to correct your answers Restudy pages that correspond to any questions that you answered incorrectly or material you feel uncertain about If the total demand equals the total supply in a transportation problem, the problem is a degenerate b unbalanced c balanced d infeasible In a transportation problem, what indicates that the minimum cost solution has been found? a All improvement indices are negative or zero b All improvement indices are positive or zero c All improvement indices are equal to zero d All cells in the dummy row are empty An assignment problem may be viewed as a transportation problem with a a cost of $1 for all shipping routes b all supplies and demands equal to c only demand constraints d only supply constraints If the number of filled cells in a transportation table does not equal the number of rows plus the number of columns minus 1, then the problem is said to be a degenerate b unbalanced c optimal d a maximization problem If a solution to a transportation problem is degenerate, then a a dummy row or column must be added b it will be impossible to evaluate all empty cells without removing the degeneracy c there will be more than one optimal solution d the problem has no feasible solution If the total demand is greater than the total capacity in a transportation problem, then a the optimal solution will be degenerate b a dummy source must be added c a dummy destination must be added d both a dummy source and a dummy destination must be added The Hungarian method is a used to solve assignment problems b a way to develop an initial solution to a transportation problem c also called Vogel’s approximation method d used only for problems in which the objective is to maximize profit In an assignment problem, it may be necessary to add more than one row to the table a False b True When using the Hungarian method, an optimal assignment can always be made when every row and every column has at least one zero a False b True 10 The first step of the maximal-flow technique is to a select any node b pick any path from the start to the finish with some flow c pick the path with the maximum flow d pick the path with the minimal flow e pick a path where the flow going into each node is greater than the flow coming out of each node 11 In which technique you connect the existing solution to the nearest node that is not currently connected? a maximal tree b shortest route c maximal flow d minimal flow 12 In the shortest-route technique, the objective is to determine the route from an origin to a destination that passes through the fewest number of other nodes a True b False 13 Adjusting the flow capacity numbers on a path is an important step in which technique? a minimal flow b maximal flow c shortest route Discussion Questions and Problems Discussion Questions M8-1 What is a balanced transportation problem? Describe the approach you would use to solve an unbalanced problem M8-2 The stepping-stone method is being used to solve a transportation problem The smallest quantity in a cell with a minus sign is 35, but two different cells with Z09_REND3161_13_AIE_M08.indd 33 minus signs have 35 units in them What problem will this cause, and how should this difficulty be resolved? M8-3 The stepping-stone method is being used to solve a transportation problem There is only one empty cell having a negative improvement index, and this index is -2 The stepping-stone path for this cell indicates that the smallest quantity for the cells with minus 31/10/16 1:58 PM www.freebookslides.com M8-34 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS Is this necessary? Why? What will the new optimal cost be? M8-8 Sue Simmons’s marketing research firm has local representatives in all but five states She decides to expand to cover the whole United States by transferring five experienced volunteers from their current locations to new offices in each of the five states Simmons’s goal is to relocate the five representatives at the least total cost Consequently, she sets up a * relocation cost table and prepares to solve it for the best assignments by use of the Hungarian method At the last moment, Simmons recalls that although the first four volunteers did not pose any objections to being placed in any of the five new cities, the fifth volunteer did make one restriction That person absolutely refused to be assigned to the new office in Tallahassee, Florida—fear of southern roaches, the representative claimed! How should Sue alter the cost matrix to ensure that this assignment is not included in the optimal solution? M8-9 Describe the steps of the maximal-flow technique M8-10 What are the steps of the shortest-route technique? M8-11 D escribe a problem that can be solved by the shortest-route technique M8-12 Is it possible to get alternate optimal solutions with the shortest-route technique? Is there an automatic way of knowing if you have an alternate optional solution? signs is 80 units If the total cost for the current solution is $900, what will the total cost be for the improved solution? What can you conclude about how much the total cost will decrease when developing each new solution for any transportation problem? M8-4 Explain what happens when the solution to a transportation problem does not have m + n - occupied squares (where m = number of rows in the table and n = number of columns in the table) M8-5 What is the enumeration approach to solving assignment problems? Is it a practical way to solve a row * column problem? A * problem? Why? M8-6 How could an assignment problem be solved using the transportation approach? What condition will make the solution of this problem difficult? M8-7 You are the plant supervisor and are responsible for scheduling workers to jobs on hand After estimating the cost of assigning each of five available workers in your plant to five projects that must be completed immediately, you solve the problem using the Hungarian method The following solution is reached, and you post these job assignments: Jones to project A Smith to project B Thomas to project C Gibbs to project D Heldman to project E The optimal cost was found to be $492 for these assignments The plant general manager inspects your original cost estimates and informs you that increased employee benefits mean that each of the 25 numbers in your cost table is too low by $5 He suggests that you immediately rework the problem and post the new assignments Problems M8-13 The management of the Executive Furniture Corporation decided to expand the production capacity at its Des Moines factory and to cut back production at its other factories It also recognizes a shifting market for its desks and revises the Data for Problem M8-13 NEW WAREHOUSE REQUIREMENTS NEW FACTORY CAPACITIES Albuquerque (A) 200 desks Des Moines (D) 300 desks Boston (B) 200 desks Evansville (E) 150 desks Cleveland (C) 300 desks Fort Lauderdale (F) 250 desks Table for Problem M8-13 TO FROM ALBUQUERQUE BOSTON CLEVELAND DES MOINES EVANSVILLE FORT LAUDERDALE Note: means the problem may be solved with QM for Windows; means the problem may be solved with Excel QM; and means the problem may be solved with QM for Windows and/or Excel QM Z09_REND3161_13_AIE_M08.indd 34 31/10/16 1:58 PM www.freebookslides.com DISCUSSION QUESTIONS AND PROBLEMS M8-35 Data for Problem M8-14 TO FROM PROJECT A PROJECT B PROJECT C PLANT CAPACITIES PLANT $10 $4 $11 70 PLANT 12 50 PLANT 30 40 50 60 150 PROJECT REQUIREMENTS Table for Problem M8-16 TO FROM MILL CAPACITY (TONS) SUPPLY HOUSE SUPPLY HOUSE SUPPLY HOUSE PINEVILLE $3 $3 $2 25 OAK RIDGE 40 MAPLETOWN 3 30 30 30 35 95 SUPPLY HOUSE DEMAND (TONS) requirements at its three warehouses (See the data table on the previous page.) (a) Use the northwest corner rule to establish an initial feasible shipping schedule and calculate its cost (See the transportation table on the previous page.) (b) Use the stepping-stone method to test whether an improved solution is possible (c) Explain the meaning and implications of an improvement index that is equal to What decisions might management make with this information? Exactly how is the final solution affected? M8-14 The Hardrock Concrete Company has plants in three locations and is currently working on three major construction projects, each located at a different site The shipping costs per truckload of concrete, daily plant capacities, and daily project requirements are provided in the table on this page (a) Formulate an initial feasible solution to Hardrock’s transportation problem using the northwest corner rule Then evaluate each unused shipping route by computing all improvement indices Is this solution optimal? Why? (b) Is there more than one optimal solution to this problem? Why? M8-15 H ardrock Concrete’s owner has decided to increase the capacity at his smallest plant (see Problem M8-14) Instead of producing 30 loads of concrete per day at plant 3, that plant’s capacity is doubled to 60 loads Find the new optimal Z09_REND3161_13_AIE_M08.indd 35 solution using the northwest corner rule and stepping-stone method How has changing the third plant’s capacity altered the optimal shipping assignment? Discuss the concepts of degeneracy and multiple optimal solutions with regard to this problem M8-16 The Saussy Lumber Company ships pine flooring to three building supply houses from its mills in Pineville, Oak Ridge, and Mapletown Determine the best transportation schedule for the data given in the table on this page Use the northwest corner rule and the stepping-stone method M8-17 The Krampf Lines Railway Company specializes in coal handling On Friday, April 13, Krampf had empty cars at the following towns in the quantities indicated: TOWN SUPPLY OF CARS Morgantown 35 Youngstown 60 Pittsburgh 25 By Monday, April 16, the following towns will need coal cars as follows: TOWN DEMAND FOR CARS Coal Valley 30 Coaltown 45 Coal Junction 25 Coalsburg 20 31/10/16 1:58 PM www.freebookslides.com M8-36 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS Table for Problem M8-17 TO FROM COAL VALLEY COALTOWN MORGANTOWN 50 COAL JUNCTION 30 COALSBURG 60 70 YOUNGSTOWN 20 80 10 90 PITTSBURGH 100 40 80 30 Using a railway city-to-city distance chart, the dispatcher constructs a mileage table for the preceding towns The result is shown in the table on this page Minimizing total miles over which cars are moved to new locations, compute the best shipment of coal cars M8-18 An air conditioning manufacturer produces room air conditioners at plants in Houston, Phoenix, and Memphis These are sent to regional distributors in Dallas, Atlanta, and Denver The shipping costs vary, and the company would like to find the leastcost way to meet the demands at each of the distribution centers Dallas needs to receive 800 air conditioners per month, Atlanta needs 600, and Denver needs 200 Houston has 850 air conditioners available each month, Phoenix has 650, and Memphis has 300 The shipping cost per unit from Houston to Dallas is $8, to Atlanta is $12, and to Denver is $10 The cost per unit from Phoenix to Dallas is $10, to Atlanta is $14, and to Denver is $9 The cost per unit from Memphis to Dallas is $11, to Atlanta is $8, and to Denver is $12 How many units should be shipped from each plant to each regional distribution center? What is the total cost for this? M8-19 Finnish Furniture manufactures tables in facilities located in three cities—Reno, Denver, and Pittsburgh The tables are then shipped to three retail stores located in Phoenix, Cleveland, and Chicago Management wishes to develop a distribution schedule that will meet the demands at the lowest possible cost The shipping cost per unit from each of the sources to each of the destinations is shown in the following table: TO FROM PHOENIX CLEVELAND CHICAGO RENO 10 16 19 DENVER 12 14 13 PITTSBURGH 18 12 12 The available supplies are 120 units from Reno, 200 units from Denver, and 160 units from Pittsburgh Phoenix has a demand of 140 units, Cleveland has a demand of 160 units, and Chicago has a demand of 180 units How many units should be shipped from each manufacturing facility to each of the retail stores if cost is to be minimized? What is the total cost? M8-20 Finnish Furniture has experienced a decrease in the demand for tables in Chicago; the demand has fallen to 150 units (see Problem M8-19) What special condition would exist? What is the minimum-cost solution? Will there be any units remaining at any of the manufacturing facilities? M8-21 Consider the transportation table on this page Find an initial solution using the northwest corner rule What special condition exists? Explain how you will proceed to solve the problem M8-22 The B Hall Real Estate Investment Corporation has identified four small apartment buildings in which it would like to invest Mrs Hall has Table for Problem M8-21 TO DESTINATION A DESTINATION B DESTINATION C SUPPLY SOURCE $8 $9 $4 72 SOURCE 38 SOURCE 46 SOURCE 19 FROM DEMAND Z09_REND3161_13_AIE_M08.indd 36 110 34 31 175 31/10/16 1:58 PM www.freebookslides.com DISCUSSION QUESTIONS AND PROBLEMS M8-37 Table for Problem M8-22 PROPERTY (INTEREST RATES) (%) SAVINGS AND LOAN COMPANY HILL ST BANKS ST PARK AVE DRURY LANE MAXIMUM CREDIT LINE ($) FIRST HOMESTEAD 8 10 11 80,000 COMMONWEALTH 10 12 10 100,000 WASHINGTON FEDERAL 11 10 9 120,000 $60,000 $40,000 $130,000 $70,000 LOAN REQUIRED TO PURCHASE BUILDING approached three savings and loan companies regarding financing Because Hall has been a good client in the past and has maintained a high credit rating in the community, each savings and loan company is willing to consider providing all or part of the mortgage loan needed on each property Each loan officer has set differing interest rates on each property (rates are affected by the neighborhood of the apartment building, condition of the property, and desire by the individual savings and loan to finance various-size buildings), and each loan company has placed a maximum credit ceiling on how much it will lend Hall in total This information is summarized in the table on this page Each apartment building is equally attractive as an investment to Hall, so she has decided to purchase all buildings possible at the lowest total payment of interest From which savings and loan companies should she borrow to purchase which buildings? More than one savings and loan can finance the same property M8-23 The J Mehta Company’s production manager is planning for a series of 1-month production periods for stainless steel sinks The demand for the next months is as follows: MONTH DEMAND FOR STAINLESS STEEL SINKS 120 160 240 100 The Mehta firm can normally produce 100 stainless steel sinks in a month This is done during regular production hours at a cost of $100 per sink If demand in any month cannot be satisfied by regular production, the production manager has three other choices: (1) he can produce up to 50 more sinks per month in overtime but at a cost of $130 per sink; (2) he can purchase a limited number of sinks from a friendly competitor for resale (the maximum number of outside purchases over Z09_REND3161_13_AIE_M08.indd 37 the 4-month period is 450 sinks, at a cost of $150 each); or (3) he can fill the demand from his onhand inventory The inventory carrying cost is $10 per sink per month Back orders are not permitted Inventory on hand at the beginning of month is 40 sinks Set up this “production smoothing” problem as a transportation problem to minimize cost Use the northwest corner rule to find an initial level for production and outside purchases over the 4-month period M8-24 In a job shop operation, four jobs may be performed on any of four machines The hours required for each job on each machine are presented in the following table The plant supervisor would like to assign jobs so that total time is minimized Find the best solution MACHINE JOB W X Y Z A12 10 14 16 13 A15 12 13 15 12 B2 12 12 11 B9 14 16 18 16 M8-25 Four automobiles have entered Bubba’s Repair Shop for various types of work, ranging from a transmission overhaul to a brake job The experience level of the mechanics is quite varied, and Bubba would like to minimize the time required to complete all of the jobs He has estimated the time in minutes for each mechanic to complete each job Billy can complete job in 400 minutes, job in 90 minutes, job in 60 minutes, and job in 120 minutes Taylor will finish job in 650 minutes, job in 120 minutes, job in 90 minutes, and job in 180 minutes Mark will finish job in 480 minutes, job in 120 minutes, job in 80 minutes, and job in 180 minutes John will complete job in 500 minutes, job in 110 minutes, job in 90 minutes, and job in 150 minutes Each mechanic should be assigned to just one of these jobs What is the minimum total time required to finish the four jobs? Who should be assigned to each job? 31/10/16 1:58 PM www.freebookslides.com M8-38 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS M8-26 Baseball umpiring crews are currently in four cities where three-game series are beginning When these are finished, the crews are needed to work games in four different cities The distances (miles) from each of the cities where the crews are currently working to each of the the cities where the new games will begin are shown in the following table: M8-28 The hospital administrator at St Charles General must appoint head nurses to four newly established departments: urology, cardiology, orthopedics, and obstetrics In anticipation of this staffing problem, she had hired four nurses: Hawkins, Condriac, Bardot, and Hoolihan Believing in the quantitative analysis approach to problem solving, the administrator has interviewed each nurse, considered his or her background, personality, and talents, and developed a cost scale ranging from to 100 to be used in the assignment A for Nurse Bardot being assigned to the cardiology unit implies that she would be perfectly suited to that task A value close to 100, on the other hand, would imply that she is not at all suited to head that unit The accompanying table gives the complete set of cost figures that the hospital administrator felt represented all possible assignments Which nurse should be assigned to which unit? TO FROM KANSAS CITY CHICAGO Seattle 1,500 1,730 1,940 2,070 Arlington 460 810 1,020 1,270 Oakland 1,500 1,850 2,080 X Baltimore 960 610 400 330 DETROIT TORONTO The X indicates that the crew in Oakland cannot be sent to Toronto Determine which crew should be sent to each city to minimize the total distance traveled How many miles will be traveled if these assignments are made? To see how much better this solution is than the assignments that might have been made, find the assignments that would give the maximum distance traveled M8-27 Roscoe Davis, chairman of a college’s business department, has decided to apply a new method in assigning professors to courses next semester As a criterion for judging who should teach each course, Professor Davis reviews the past two years’ teaching evaluations (which were filled out by students) Since each of the four professors taught each of the four courses at one time or another during the two-year period, Davis is able to record a course rating for each instructor These ratings are shown in the following table Find the best assignment of professors to courses to maximize the overall teaching rating DEPARTMENT NURSE UROLOGY CARDIOLOGY ORTHOPEDICS5 OBSTETRICS5 Hawkins 28 18 15 75 Condriac 32 48 23 38 Bardot 51 36 24 36 Hoolihan 25 38 55 12 M8-29 The Fix-It Shop (see Section M8.2) has added a fourth repairman, Davis Solve the cost table on this page for the new optimal assignment of workers to projects Why did this solution occur? PROJECT WORKER Adams $11 $14 $6 Brown 10 11 Cooper 12 10 13 Davis COURSE PROFESSOR STATISTICS MANAGEMENT FINANCE ECONOMICS Anderson 90 65 95 40 Sweeney 70 60 80 75 Williams 85 40 80 60 McKinney 55 80 65 55 M8-30 T he XYZ Corporation is expanding its market to include Texas Each salesperson is assigned to potential distributors in one of five different areas It is anticipated that the salesperson will spend about to weeks in the assigned area A Table for Problem M8-30 AUSTIN/SAN ANTONIO DALLAS/FT WORTH EL PASO/WEST TEXAS HOUSTON/ GALVESTON CORPUS CHRISTI/RIO GRANDE VALLEY Erica 3 Louis 4 2 Maria 3 Paul 4 Orlando 5 Z09_REND3161_13_AIE_M08.indd 38 31/10/16 1:58 PM www.freebookslides.com DISCUSSION QUESTIONS AND PROBLEMS M8-39 FIGURE M8.15 Network for Problem M8-31 3 2 5 2 2 13 14 11 2 FIGURE M8.17 Network for Problem M8-33 100 10 50 100 1 120 13 40 New Office 60 50 40 2 70 10 100 13 200 20 100 Old Office 100 statewide marketing campaign will begin once the product has been delivered to the distributors The five salespeople who will be assigned to these areas (one person for each area) have rated the areas on the desirability of the assignment as shown in the table on the previous page The scale is (least desirable) to (most desirable) Which assignments should be made if the total of the ratings is to be maximized? M8-31 Bechtold Construction is in the process of installing power lines to a large housing development Steve Bechtold wants to minimize the total length of wire used, which will minimize his costs The housing development is shown as a network in Z09_REND3161_13_AIE_M08.indd 39 10 FIGURE M8.16 Network for Problem M8-32 12 4 100 10 11 100 50 12 Figure M8.15 Each house has been numbered, and the distances between houses are given in hundreds of feet What you recommend? M8-32 The city of New Berlin is considering making several of its streets one-way What is the maximum number of cars per hour that can travel from east to west? The network is shown in Figure M8.16 M8-33 Transworld Moving has been hired to move the office furniture and equipment of Cohen Properties to its new headquarters What route you recommend? The network of roads is shown in Figure M8.17 M8-34 Because of a sluggish economy, Bechtold Construction has been forced to modify its plans for 31/10/16 1:59 PM www.freebookslides.com M8-40 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS FIGURE M8.18 Network for Problem M8-35 56 26 75 65 41 50 48 37 23 53 FIGURE M8.19 Network for Problem M8-36 110 127 70 40 32 160 55 45 200 FIGURE M8.20 Network for Problem M8-37 10 15 8 15 10 11 10 the housing development in Problem M8-31 The result is that the path from node to now has a distance of What impact does this have on the total length of wire needed to install the power lines? M8-35 The director of security wants to connect security video cameras to the main control site from five potential trouble locations Ordinarily, cable would simply be run from each location to the main control site However, because the environment is potentially explosive, the cable must be run in a special conduit that is continually air purged This conduit is very expensive but large enough to handle five cables (the maximum that might be needed) Use the minimal-spanning tree technique to find a minimum distance route for the conduit between the locations noted in Z09_REND3161_13_AIE_M08.indd 40 14 Figure M8.18 (Note that it makes no difference which one is the main control site.) M8-36 One of our best customers has had a major plant breakdown and wants us to make as many widgets for him as possible during the next few days, until he gets the necessary repairs done With our general-purpose equipment, there are several ways to make widgets (ignoring costs) Any sequence of activities that takes one from node to node in Figure M8.19 will produce a widget How many widgets can we produce per day? Quantities given are number of widgets per day M8-37 The road system from the hotel complex on International Drive (node 1) to Disney World (node 11) in Orlando, Florida, is shown in the network of Figure M8.20 The numbers by the nodes 31/10/16 1:59 PM www.freebookslides.com DISCUSSION QUESTIONS AND PROBLEMS M8-41 FIGURE M8.21 Network for Problem M8-39 4 2 1 3 6 FIGURE M8.22 Network for Problem M8-41 1 10 10 11 16 12 11 14 13 18 18 12 16 17 10 13 10 16 18 14 11 20 18 14 16 25 15 represent the traffic flow in hundreds of cars per hour What is the maximum flow of cars from the hotel complex to Disney World? M8-38 A road construction project would increase the road capacity around the outside roads from International Drive to Disney World by 200 cars per hour (see Problem M8-37) The two paths affected would be 1–2–6–9–11 and 1–5–8–10–11 What impact would this have on the total flow of cars? Would the total flow of cars increase by 400 cars per hour? M8-39 Solve the maximal-flow problem presented in the network of Figure M8.21 The numbers in the network represent thousands of gallons per hour as they flow through a chemical processing plant M8-40 Two terminals in the chemical processing plant, represented by nodes and 7, require emergency repair (see Problem M8-39) No material can flow into or out of these nodes What impact does this have on the capacity of the network? M8-41 Solve the shortest-route problem presented in the network of Figure M8.22, going from node to node 16 All numbers represent kilometers between German towns near the Black Forest M8-42 D ue to bad weather, the roads going through nodes and have been closed (see Problem M841) No traffic can get onto or off of these roads Z09_REND3161_13_AIE_M08.indd 41 10 22 12 12 15 20 0 15 12 15 Describe the impact that this will have (if any) on the shortest route through this network M8-43 Grey Construction would like to determine the least expensive way of providing houses it is building with cable TV It has identified 11 possible branches or routes that could be used to connect the houses The cost in hundreds of dollars and the branches are summarized in the following table (a) What is the least expensive way to run cable to the houses? BRANCH START NODE END NODE COST ($100s) Branch 1 Branch Branch Branch 5 Branch Branch Branch 7 Branch 8 Branch Branch 10 Branch 11 31/10/16 1:59 PM www.freebookslides.com M8-42 MODULE 8 • TRANSPORTATION, ASSIGNMENT, AND NETWORK ALGORITHMS (b) After reviewing cable and installation costs, Grey Construction would like to alter the costs for providing cable TV to its houses The first branches need to be changed The changes are summarized in the following table What is the impact on total costs? BRANCH START NODE END NODE START NODE END NODE DISTANCE (IN HUNDREDS OF MILES) Branch 1 Branch Branch Branch COST ($100s) Branch 5 BRANCH Branch 1 Branch Branch Branch 7 Branch Branch Branch Branch Branch 10 Branch Branch Branch 7 Branch 8 Branch Branch 10 Branch 11 M8-44 In going from Quincy to Old Bainbridge, there are 10 possible roads that George Olin can take Each road can be considered a branch in the shortestroute problem (a) Determine the best way to get from Quincy (node 1) to Old Bainbridge (node 8) that will minimize total distance traveled All distances are in hundreds of miles START NODE END NODE DISTANCE (IN HUNDREDS OF MILES) Branch 1 Branch Branch Branch Branch 5 Branch 6 Branch 7 Branch Branch Branch 10 BRANCH (b) George Olin made a mistake in estimating the distances from Quincy to Old Bainbridge The new distances are in the following table What impact does this have on the shortest route from Quincy to Old Bainbridge? Z09_REND3161_13_AIE_M08.indd 42 M8-45 South Side Oil and Gas, a new venture in Texas, has developed an oil pipeline network to transport oil from exploration fields to the refinery and other locations There are 10 pipelines (branches) in the network The oil flow in hundreds of gallons and the network of pipelines are given in the following table (a) What is the maximum that can flow through the network? START END REVERSE BRANCH NODE NODE CAPACITY CAPACITY FLOW Branch 1 10 4 10 Branch 8 2 5 Branch 12 1 10 Branch 6 6 0 Branch 5 8 1 5 Branch 6 10 2 10 Branch 10 10 0 Branch 5 5 5 Branch 10 1 10 Branch 10 10 1 5 (b) South Side Oil and Gas needs to modify its pipeline network flow patterns The new data are in the following table What impact does this have on the maximum flow through the network? START END REVERSE BRANCH NODE NODE CAPACITY CAPACITY FLOW Branch 1 10 4 10 Branch 8 2 5 Branch 12 1 10 Branch 0 0 0 Branch 5 8 1 5 Branch 6 10 2 10 Branch 10 10 0 Branch 5 5 5 Branch 10 1 10 Branch 10 10 1 5 31/10/16 1:59 PM www.freebookslides.com BIBLIOGRAPHY M8-43 10 15 14 8 11 12 15 M8-46 Northwest University is in the process of completing a computer bus network that will connect computer facilities throughout the university The prime objective is to string a main cable from one end of the campus to the other (nodes 1–25) through underground conduits These conduits are shown in the network of Figure M8.23; the distance between nodes is in hundreds of feet Fortunately, these underground conduits have remaining capacity through which the bus cable can be placed (a) Given the network for this problem, how far (in hundreds of feet) is the shortest route from node to node 25? (b) In addition to the computer bus network, a new phone system is being planned The phone 18 6 19 20 16 13 17 17 22 15 10 1 10 23 10 24 25 20 15 20 FIGURE M8.23 Network for Problem M8-46 10 21 system would use the same underground conduits If the phone system were installed, the following paths along the conduit would be at capacity and would not be available for the computer bus network: 6–11, 7–12, and 17–20 What changes (if any) would you have to make to the path used for the computer bus if the phone system were installed? (c) The university did decide to install the new phone system before the cable for the computer network Because of unexpected demand for computer networking facilities, an additional cable is needed for node to node 25 Unfortunately, the cable for the first or original network has completely used up the capacity along its path Given this situation, what is the best path for the second network cable? Cases See Chapter for cases relevant to this module Bibliography See Chapter for references relevant to this module Z09_REND3161_13_AIE_M08.indd 43 31/10/16 1:59 PM www.freebookslides.com This page intentionally left blank ... QUANTITATIVE ANALYSIS for MANAGEMENT BARRY RENDER Charles Harwood Professor Emeritus of Management Science Crummer Graduate School of Business, Rollins College RALPH M STAIR, JR Professor Emeritus of Information... Operations Management, Principles of Operations Management, Service Management, Introduction to Management Science, and Cases and Readings in Management Science More than 100 articles by Dr Render. .. Render on a variety of management topics have appeared in Decision Sciences, Production and Operations Management, Interfaces, Information and Management, Journal of Management Information Systems,