Practical Management Science.2nd ed.实用管理科学
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ISBN：9780534424350
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简介
"Prose is casual and understandable; not stuffy or full of jargon. Problems are realistic and motivating for students, but solvable. Working through problems within a spreadsheet provided by the authors along side text discussion is invaluable for the learning experience." "More simplistic and to the point. Not to much extra material above and beyond the stuent’s level. I also enjoyed the more focused approach toward simulations!! Good business examples are used with nice introductory paragraphs for each chapter. The CD that accompanies the book contains wonderful solution sets and useful supplemental programs." --This text refers to the Hardcover edition. In the Second Edition of their popular text, Wayne Winston and Chris Albright continue to build on their highly successful approach of teaching by example while using spreadsheets to model a wide variety of business problems. The authors show the relevance of topics through numerous examples of real-world implementation of management science. The ideal solution for people who want to teach by example and who want to solve real problems with spreadsheets and professional spreadsheet add-ins, this text is always interesting, in part due to the useful cases added to this edition.
目录
CHAPTER1 Introduction to Modeling
1.1 Introduction
1.2 A Waiting—Line Example
1.3 Modeling versus Models
1.4 The Seven—Step Modeling Process
1.5 Successful Management Science Applications
1.6 Why Study Management Science?
1.7 Software Included in This Book
1.8 Conclusion
CHAPTER 2 Introductory Spreadsheet Modeling
2.1 Introduction
2.2 Basic Spreadsheet Modeling Concepts
2.3 Modeling Examples
2.4 Conclusion
APPENDIX ripsfor Editing and Documenting Spreadsheets
CHAPTER 3 Introduction to Optimization Modeling
3.1 Introduction
3.2 A Brief History of Linear Programming
3.3 Introduction to LP Modeling
3.4 Sensitivity Analysis and the SolverTable Add—In
3.5 The Linear Assumptions
3.6 Graphical Solution Method
3.7 Infeasibility and Unboundedness
3.8 A Multiperiod Production Problem
3.9 A Decision Support System
3.10 Conclusion
APPENDIX Information on Solvers
CASE 3.1 Shelby Shelving
CHAPTER 4 Linear Programming Models
4.1 Introduction
4.2 Static Workforce Scheduling Models
4.3 Aggregate Planning Models
4.4 Dynamic Workforce Planning Models
4.5 Blending Models
4.6 Production Process Models
4.7 Dynamic Financial Models
4.8 Data Envelopment Analysis DEA
4.9 Conclusion
CASE 4.1 Lakefield Corporation’S Oil Trading Desk
CASE 4.2 Foreign CurrencyTrading
CHAPTER 5 Network Models
5.1 IntrOduction
5.2 Transportation Models
5.3 More General Logistics M0dels
5.4 Non.Logistics Network Models
5.5 Prqject Scheduling Models
5.6 Conclusion
CASE 5.1 Optimized Motor Carrier Selection at WesVaco
CHAPTER 6 Linear Optimization Models with Integer Variables
6.1 Introduction
6.2 Approaches to Optimization with Integer Variables
6.3 Capital Budgeting Models
6.4 Fixed—Cost Models
6.5 Lockbox Modds
6.6 Plant and Warehouse Location Models
6.7 Set—Covering M0dels
Contents
……
CHAPTER 7 Nonlinear Optimization Models with Integer Variables
CHAPTER 8 Evolutionary Solver:An Alternative Optimization Procedure
CHAPTER 9 Multi-Objective Decision Making
CHAPTER 10 Decision Making Under Uncertainty
CHAPTER 11 Introdection to Simulation Modeling
CHAPTER 12 Simulation Models
CHAPTER 13 Inventory Models
CHAPTER 14 Queueing Models
CHAPTER 15 Regression Analsis
CHAPTER 16 Time Series Analysis and Forecating
References
Index
1.1 Introduction
1.2 A Waiting—Line Example
1.3 Modeling versus Models
1.4 The Seven—Step Modeling Process
1.5 Successful Management Science Applications
1.6 Why Study Management Science?
1.7 Software Included in This Book
1.8 Conclusion
CHAPTER 2 Introductory Spreadsheet Modeling
2.1 Introduction
2.2 Basic Spreadsheet Modeling Concepts
2.3 Modeling Examples
2.4 Conclusion
APPENDIX ripsfor Editing and Documenting Spreadsheets
CHAPTER 3 Introduction to Optimization Modeling
3.1 Introduction
3.2 A Brief History of Linear Programming
3.3 Introduction to LP Modeling
3.4 Sensitivity Analysis and the SolverTable Add—In
3.5 The Linear Assumptions
3.6 Graphical Solution Method
3.7 Infeasibility and Unboundedness
3.8 A Multiperiod Production Problem
3.9 A Decision Support System
3.10 Conclusion
APPENDIX Information on Solvers
CASE 3.1 Shelby Shelving
CHAPTER 4 Linear Programming Models
4.1 Introduction
4.2 Static Workforce Scheduling Models
4.3 Aggregate Planning Models
4.4 Dynamic Workforce Planning Models
4.5 Blending Models
4.6 Production Process Models
4.7 Dynamic Financial Models
4.8 Data Envelopment Analysis DEA
4.9 Conclusion
CASE 4.1 Lakefield Corporation’S Oil Trading Desk
CASE 4.2 Foreign CurrencyTrading
CHAPTER 5 Network Models
5.1 IntrOduction
5.2 Transportation Models
5.3 More General Logistics M0dels
5.4 Non.Logistics Network Models
5.5 Prqject Scheduling Models
5.6 Conclusion
CASE 5.1 Optimized Motor Carrier Selection at WesVaco
CHAPTER 6 Linear Optimization Models with Integer Variables
6.1 Introduction
6.2 Approaches to Optimization with Integer Variables
6.3 Capital Budgeting Models
6.4 Fixed—Cost Models
6.5 Lockbox Modds
6.6 Plant and Warehouse Location Models
6.7 Set—Covering M0dels
Contents
……
CHAPTER 7 Nonlinear Optimization Models with Integer Variables
CHAPTER 8 Evolutionary Solver:An Alternative Optimization Procedure
CHAPTER 9 Multi-Objective Decision Making
CHAPTER 10 Decision Making Under Uncertainty
CHAPTER 11 Introdection to Simulation Modeling
CHAPTER 12 Simulation Models
CHAPTER 13 Inventory Models
CHAPTER 14 Queueing Models
CHAPTER 15 Regression Analsis
CHAPTER 16 Time Series Analysis and Forecating
References
Index
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