Quantitative Methods for Business 12/e【內含註冊碼,經拆除不受退】
暫譯: 商業量化方法 第12版【內含註冊碼,經拆除不受退】

Anderson

Quantitative Methods for Business 12/e【內含註冊碼,經拆除不受退】
  • 出版商: South-Western
  • 出版日期: 2012-04-01
  • 售價: $1,220
  • 貴賓價: 9.8 折 $1,195
  • 語言: 英文
  • 頁數: 908
  • 裝訂: 平裝
  • ISBN: 1133584462
  • ISBN-13: 9781133584469
  • 相關分類: 管理與領導 Management-leadership

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商品描述

•Annotations: Annotations that highlight key points and provide additional insights for the student are a continuing feature of this edition. These annotations, which appear in the margins, are designed to provide emphasis and enhance understanding of the terms and concepts being presented in the text.
•Notes & Comments: At the end of many sections, "Notes & Comments" give additional insights about the methodology being discussed and its application. These include warnings about or limitations of the methodology, recommendations for application, and brief descriptions of additional technical considerations.
•Self-Test Exercises: Certain exercises are identified as self-test exercises. Completely worked-out solutions for these exercises are provided in Appendix G, entitled Self-Test Solutions and Answers to Even-Numbered Problems, located at the end of the book. Students can attempt the self-test problems and immediately check the solutuions to evaluate their understanding of the concepts presented in the chapter. In response to requests from professors using our textbooks, we now provide the answers to even-numbered problems in this same appendix.
•Q.M. in Action: These articles are presented throughout the text and provide a summary of an application of quantitative methods found in business today. Adaptations of materials from Interfaces and OR/MS Today articles and write-ups provided by practitioners provide the basis for the applications in this feature.

New to this edition
•New Chapter 12: Advanced Optimization Applications – A new chapter on optimization applications has been added. Applications include portfolio selection, a nonlinear extension of the RMC problem, and selecting stocks to go into an index mutual fund. This chapter introduces the idea of a nonlinear optimization model, but strictly from an applications standpoint. The Management Scientist cannot be used for nonlinear problems, and LINGO or Premium Solver are required.
•New Documented Solutions – The Management Scientist will not be used in future editions of this book. We encourage adopters of this edition to use either LINGO or Premium Solver when solving optimization problems. To make it easy for new users of LINGO or Excel Premium Solver, we provide both LINGO and Excel files with the model formulation for every optimization problem that appears in the body of the text in Chapters 7 through 12. The model files are well documented and should make it easy for the user to understand the model formulation.
•New Appendix A: Building Spreadsheet Models – This is not a book on spreadsheet modeling. However, spreadsheets are a very valuable modeling tool. This Appendix will prove useful to professors and students wishing to solve optimization models with Premium Solver. The appendix also contains a section on the principles of good spreadsheet modeling and a section on auditing tips. Exercises are also provided.
•Updated Chapter 10: Distribution and Network Models – This replaces the old Chapter 10, "Transportation, Assignment, and Transshipment Problems" from the tenth edition. We have added sections on the shortest route problem and the maximal flow problem. However, in keeping with the theme of the book, we do not burden the student with any algorithms. All of the models in the chapter are presented under the unifying theme of linear programming.
•New Q.M. in Action, Cases, and Problems – Q.M. in Action is the name of the short summaries that describe how the quantitative methods being covered in the chapter have been used in practice. In this edition you will find numerous Q.M. in Action vignettes, cases, and homework problems.

商品描述(中文翻譯)

• 註解：本版持續提供的註解強調關鍵點並為學生提供額外的見解。這些註解出現在邊緣，旨在強調並增強對文本中所呈現的術語和概念的理解。

• 註解與評論：在許多章節的結尾，"註解與評論"提供有關所討論方法論及其應用的額外見解。這些包括對方法論的警告或限制、應用建議，以及對其他技術考量的簡要描述。

• 自我測試練習：某些練習被標識為自我測試練習。這些練習的完整解答在書末的附錄 G 中提供，標題為自我測試解答及偶數題的答案。學生可以嘗試自我測試問題，並立即檢查解答，以評估他們對章節中所呈現概念的理解。應教授們對我們教科書的要求，我們現在在同一附錄中提供偶數題的答案。

• 實務中的量化方法：這些文章在文本中隨處可見，提供當今商業中量化方法應用的摘要。來自《Interfaces》和《OR/MS Today》文章及實務工作者提供的材料改編，構成了此特徵中的應用基礎。

本版新內容

• 新的第 12 章：進階優化應用 - 新增了一章有關優化應用的內容。應用包括投資組合選擇、RMC 問題的非線性擴展，以及選擇股票進入指數共同基金。本章介紹了非線性優化模型的概念，但僅從應用的角度出發。管理科學家無法用於非線性問題，需使用 LINGO 或 Premium Solver。

• 新的文檔解答 - 本書未來版本將不再使用管理科學家。我們鼓勵本版的採用者在解決優化問題時使用 LINGO 或 Premium Solver。為了方便 LINGO 或 Excel Premium Solver 的新用戶，我們提供了每個出現在第 7 章至第 12 章文本中的優化問題的模型公式的 LINGO 和 Excel 檔案。這些模型檔案有良好的文檔，應能使用戶輕鬆理解模型公式。

• 新的附錄 A：建立電子表格模型 - 這不是一本關於電子表格建模的書。然而，電子表格是一種非常有價值的建模工具。這個附錄將對希望使用 Premium Solver 解決優化模型的教授和學生非常有用。附錄還包含有關良好電子表格建模原則的部分和審核提示的部分，並提供練習題。

• 更新的第 10 章：分配與網路模型 - 這取代了第十版的舊第 10 章 "運輸、分配和轉運問題"。我們新增了最短路徑問題和最大流問題的部分。然而，為了符合本書的主題，我們不會讓學生負擔任何算法。章節中的所有模型都在線性規劃的統一主題下呈現。

• 新的實務中的量化方法、案例和問題 - 實務中的量化方法是描述本章所涵蓋的量化方法在實踐中如何使用的短摘要的名稱。在本版中，您將找到許多實務中的量化方法小品、案例和作業問題。

作者簡介

Dr. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He earned his B.S., M.S., and Ph.D. degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. In addition, he was the coordinator of the College’s first Executive Program. At the University of Cincinnati, Professor Anderson has taught introductory statistics for business students as well as graduate-level courses in regression analysis, multivariate analysis, and management science. He has also taught statistical courses at the Department of Labor in Washington, D.C. He has been honored with numerous nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 leading textbooks in the areas of statistics, management science, linear programming, and production and operations management. He is an active consultant in the field of sampling and statistical methods.

Dr. Dennis J. Sweeney is Professor of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. He earned a B.S.B.A. degree from Drake University and his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has served as visiting professor at Duke University. Professor Sweeney has also served as Head of the Department of Quantitative Analysis and as Associate Dean of the College of Business Administration at the University of Cincinnati. Professor Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger, and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences, and other journals. Professor Sweeney has co-authored 10 leading texts in the areas of statistics, management science, linear programming, and production and operations management.

Dr. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology. He earned his B.S. degree at Clarkson University. He complete his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the undergraduate program in Information Systems and then served as its coordinator. At RIT he was the first chairman of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis. Professor Williams is the co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics. He has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models.

Dr. Jeffrey D. Camm is Professor of Quantitative Analysis, Head of the Department of Operations and Business Analytics, and College of Business Research Fellow in the College of Business at the University of Cincinnati. Dr. Camm holds a B.S. from Xavier University and a Ph.D. from Clemson University. He has served at the University of Cincinnati since 1984 and has been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 30 papers in the general area of optimization applied to problems in operations management. He has published his research in Science, Management Science, Operations Research, Interfaces and other professional journals. At the University of Cincinnati, he was named the Dornoff Fellow of Teaching Excellence and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005-2010 he served as editor-in-chief of Interfaces, and is currently on the editorial board of INFORMS Transactions on Education.

Dr. Kipp Martin is Professor of Operations Research and Computing Technology at the Graduate School of Business, University of Chicago. Born in St. Bernard, Ohio, he earned a B.A. in Mathematics, an MBA, and a Ph.D. in Management Science from the University of Cincinnati. While at the University of Chicago, Professor Martin has taught courses in Management Science, Operations Management, Business Mathematics, and Information Systems. Research interests include incorporating Web technologies such as XML, XSLT, XQuery, and Web Services into the mathematical modeling process; the theory of how to construct good mixed integer linear programming models; symbolic optimization; polyhedral combinatorics; methods for large scale optimization; bundle pricing models; computing technology and database theory. Dr. Martin has published in INFORMS Journal of Computing, Management Science, Mathematical Programming, Operations Research, The Journal of Accounting Research, and other professional journals. He is also the author of The Essential Guide to Internet Business Technology (with Gail Honda) and Large Scale Linear and Integer Optimization.

作者簡介(中文翻譯)

Dr. **David R. Anderson** 是辛辛那提大學商業管理學院的定量分析教授。他在普渡大學獲得了學士、碩士和博士學位。Anderson教授曾擔任定量分析與運營管理系主任及商業管理學院副院長。此外，他還是該學院首個高階管理課程的協調人。在辛辛那提大學，Anderson教授教授商業學生的入門統計課程，以及回歸分析、多變量分析和管理科學的研究生課程。他還在華盛頓特區的勞工部教授統計課程。他因教學卓越和對學生組織服務的卓越表現而獲得多項提名和獎項。Anderson教授共同編寫了10本在統計、管理科學、線性規劃和生產與運營管理領域的領先教科書。他在抽樣和統計方法領域是一位活躍的顧問。

Dr. **Dennis J. Sweeney** 是辛辛那提大學定量分析教授及生產力改善中心的創始人。他在德雷克大學獲得商業管理學士學位，並在印第安納大學獲得MBA和DBA學位，期間他是NDEA獎學金獲得者。Sweeney教授曾在寶潔公司的管理科學小組工作，並擔任杜克大學的訪問教授。他還曾擔任辛辛那提大學定量分析系主任及商業管理學院副院長。Sweeney教授在管理科學和統計領域發表了30多篇文章和專著。美國國家科學基金會、IBM、寶潔公司、聯合百貨、克羅格和辛辛那提燃氣與電力公司資助了他的研究，並在《管理科學》、《運營研究》、《數學規劃》、《決策科學》等期刊上發表。Sweeney教授共同編寫了10本在統計、管理科學、線性規劃和生產與運營管理領域的領先教科書。

Dr. **Thomas A. Williams** 是羅切斯特理工學院商學院的管理科學教授。他在克拉克森大學獲得學士學位，並在倫斯勒理工學院完成研究生學業，獲得碩士和博士學位。在加入RIT商學院之前，Williams教授在辛辛那提大學商業管理學院擔任了七年的教職，期間他開發了資訊系統的本科課程並擔任其協調人。在RIT，他是決策科學系的首任系主任。他教授管理科學和統計課程，以及回歸和決策分析的研究生課程。Williams教授是管理科學、統計、生產與運營管理及數學領域11本領先教科書的共同作者。他曾為多家《財富》500強公司擔任顧問，並參與從數據分析到大型回歸模型開發的各類項目。

Dr. **Jeffrey D. Camm** 是辛辛那提大學商學院的定量分析教授、運營與商業分析系主任及商學院研究員。Camm博士擁有西維吉尼亞大學的學士學位和克萊姆森大學的博士學位。他自1984年以來一直在辛辛那提大學任教，並曾在斯坦福大學擔任訪問學者，以及在達特茅斯學院塔克商學院擔任商業管理的訪問教授。Camm博士在應用於運營管理問題的優化領域發表了30多篇論文。他的研究發表在《科學》、《管理科學》、《運營研究》、《Interfaces》和其他專業期刊上。在辛辛那提大學，他被評為Dornoff教學卓越獎獲得者，並於2006年獲得INFORMS運營研究實踐教學獎。他堅信實踐所教，曾為多家公司和政府機構擔任運營研究顧問。從2005年到2010年，他擔任《Interfaces》的主編，目前是INFORMS教育事務的編輯委員會成員。

Dr. **Kipp Martin** 是芝加哥大學商學研究所的運營研究與計算技術教授。他出生於俄亥俄州的聖伯納德，獲得數學學士學位、MBA和辛辛那提大學的管理科學博士學位。在芝加哥大學期間，Martin教授教授管理科學、運營管理、商業數學和資訊系統的課程。研究興趣包括將Web技術（如XML、XSLT、XQuery和Web服務）納入數學建模過程；構建良好的混合整數線性規劃模型的理論；符號優化；多面體組合學；大規模優化方法；捆綁定價模型；計算技術和數據庫理論。Martin博士在INFORMS計算期刊、管理科學、數學規劃、運營研究、會計研究期刊及其他專業期刊上發表過文章。他還是《互聯網商業技術必備指南》（與Gail Honda合著）和《大規模線性與整數優化》的作者。

目錄大綱

1. Introduction.
2. Introduction to Probability.
3. Probability Distributions.
4. Decision Analysis.
5. Utility and Game Theory.
6. Forecasting.
7. Introduction to Linear Programming.
8. Linear Programming: Sensitivity Analysis and Interpretation of Solution.
9. Linear Programming Applications in Marketing, Finance, and Operations Management.
10. Distribution and Network Models.
11. Integer Linear Programming.74
12. Advanced Optimization Applications
13. Project Scheduling: PERT/CPM.
14. Inventory Models.
15. Waiting Line Models.
16. Simulation.
17. Markov Processes.

目錄大綱(中文翻譯)

1. Introduction.

2. Introduction to Probability.

3. Probability Distributions.

4. Decision Analysis.

5. Utility and Game Theory.

6. Forecasting.

7. Introduction to Linear Programming.

8. Linear Programming: Sensitivity Analysis and Interpretation of Solution.

9. Linear Programming Applications in Marketing, Finance, and Operations Management.

10. Distribution and Network Models.

11. Integer Linear Programming.74

12. Advanced Optimization Applications

13. Project Scheduling: PERT/CPM.

14. Inventory Models.

15. Waiting Line Models.

16. Simulation.

17. Markov Processes.

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