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10227nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20250409161211.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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250409b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781032735924 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.403 |
| Item number |
FOX |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Fox, William P. |
| 245 ## - TITLE STATEMENT |
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| Title |
Modeling operations research and business analytics |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
Routledge |
| Place of publication, distribution, etc. |
New York |
| Date of publication, distribution, etc. |
2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xix, 254 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
GBP |
| Price amount |
44.99 |
| 490 ## - SERIES STATEMENT |
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| Series statement |
Advances in Applied Mathematics |
| 500 ## - GENERAL NOTE |
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| General note |
Table of contents:<br/>About the Authors<br/><br/>Preface<br/><br/>1. Chapter 1. Inventory Problem<br/><br/>1.1 Introduction<br/><br/>1.2 Inventory Problems<br/><br/>1.3 Inventory and Economic Order Quantity (EOQ)<br/><br/>1.3.1 Inventory Analysis with EOQ formula driven approach<br/><br/>1.3.2 Time Invariant Asphalt EOQ model<br/><br/>1.4 Facility Location with an Oil Rig Location Problem<br/><br/>1.5 Computer Cabling Location of Central Computer<br/><br/>1.6 Exercises<br/><br/>1.7 References<br/><br/>2. Chapter 2 Product Mix: Linear Programming Problems<br/><br/>2.1 Linear Programing Problem Introduction<br/><br/>2.2 Simple Manufacturing Example<br/><br/>2.3 Financial Planning<br/><br/>2.4 Blending Formulation Example<br/><br/>2.5 Production Planning Problem<br/><br/>2.6 Shipping Problem<br/><br/>2.7 Product Mix<br/><br/>2.8 Supply Chain Operations (Gasoline Distribution)<br/><br/>2.9 Product Mix with LINDO<br/><br/>2.10 Exercises<br/><br/>2.11 References and Additional Readings<br/><br/>3. Chapter 3 Transportation and Shipping Problems<br/><br/>3.1 Transportation and Shipping Revisited<br/><br/>3.2 Transportation and Shipping Warehouse Problem<br/><br/>3.2.1 Modification to the Warehouse Problem<br/><br/>3.3 Transportation Network<br/><br/>3.4 Exercises<br/><br/>3.5 References and Additional Readings<br/><br/>4. Chapter 4 Assignment Models<br/><br/>4.1 Training Centers and Offices<br/><br/>4.1.1 Assignment Problem<br/><br/>4.2 Exercises<br/><br/>4.3 References and Additional Readings<br/><br/>5. Chapter 5 Mathematical Programming Methods<br/><br/>5.1 Data Envelopment Analysis (DEA)<br/><br/>5.2 Manufacturing Problem with DEA<br/><br/>5.3 Shortest Path Problems<br/><br/>5.3.1 Network analysis<br/><br/>5.3.2 . Kruskal’s Method for Network Analysis Problem<br/><br/>5.3.3 Prim’s Algorithm<br/><br/>5.3.4 Dijkstra’s Algorithm<br/><br/>5.4 Maximum Flow Problem<br/><br/>5.4.1 Example 5.1. Max Flow through a given network<br/><br/>5.5 Critical Path in Project Plan Network<br/><br/>5.5.1 Example 5.2. CPM<br/><br/>5.6 Minimum Cost Flow Problem<br/><br/>5.6.1 Example 5.3. Min cost flow through a network<br/><br/>5.7 General Integer Linear Programs<br/><br/>5.7.1 Example 5.4. Manufacturing Equipment<br/><br/>5.7.2 Example 5.5. Integer LP Programs by EXCEL<br/><br/>5.8 Mixed Integer Programming Application: "Either-Or" Constraints<br/><br/>5.8.1 Conditional Relations Among Constraints<br/><br/>5.8.2 A Case of Discrete Finite Valued Variable<br/><br/>5.8.3 0 - 1 Integer Linear Programs<br/><br/>5.9 Illustrious Example<br/><br/>5.9.1 Example 5.7. Consider the following Knapsack Problem<br/><br/>5.9.2 Example 5.8. Traveling Salesperson Problem<br/><br/>5.9.3 Example 5.9. Capital Budgeting Applications<br/><br/>5.9.4 Example 5.10. Marketing Application<br/><br/>5.9.5 Example 5.11. The Cutting Stock Problem<br/><br/>5.10 An Engineering Application: Mixing Substances<br/><br/>5.11 Exercises<br/><br/>5.12 References and Additional Readings<br/><br/>6. Chapter 6 Resource Allocation Models using Dynamic Programming<br/><br/>6.1 Introduction: Basic Concepts and Theory<br/><br/>6.2 Characteristics of Dynamic Programming<br/><br/>6.2.1 Working Backwards<br/><br/>6.2.2 Example 6.1 A Knapsack Problem.<br/><br/>6.3 Modeling and Applications of Discrete Dynamic Programming<br/><br/>6.3.1 Oil Well Investment DP Application<br/><br/>6.4 Exercises<br/><br/>6.5 References and Suggested Readings<br/><br/>7. Chapter 7 Queuing Models<br/><br/>7.1 Introduction to Queuing Theory<br/><br/>7.1.1 Simple Fast Food Service Queue Example 7.1<br/><br/>7.2 The Multi-server Problems<br/><br/>7.3 Exercises<br/><br/>7.4 References and Suggested Readings<br/><br/>8. Chapter 8 Simulation Models<br/><br/>8.1 Missile Attack<br/><br/>8.2 Gasoline-Inventory simulation<br/><br/>8.3 Queuing model<br/><br/>8.4 R Applied simulation<br/><br/>8.5 Exercises<br/><br/>8.6 References and Additional Readings<br/><br/>9. Chapter 9 System Reliability Modeling<br/><br/>9.1 Introduction to Reliability Modeling<br/><br/>9.2 Modeling Component Reliability<br/><br/>9.2.1 Battery Problem – Reliability Example 9.1<br/><br/>9.2.2 Battery Problem Revisited – Reliability Example 9.2<br/><br/>9.3 Modeling series and parallel components<br/><br/>9.3.1 Modeling Series Systems<br/><br/>9.3.2 Radio Components – Example 9.3<br/><br/>9.3.3 Modeling Parallel Systems (Two Components)<br/><br/>9.3.4 Parallel Bridges – Example 9.4<br/><br/>9.4 Modeling Active Redundant Systems<br/><br/>9.4.1 Manufacturing – Example 9.5<br/><br/>9.5 Modeling Standby Redundant Systems<br/><br/>9.5.1 Battery Problems Revisited for Stand-by – Example 9.6<br/><br/>9.5.2 Stake Out Problem Revisited – Example 9.7<br/><br/>9.6 Models of Large Scale Systems<br/><br/>9.7 Exercises<br/><br/>9.8 References and Suggested Readings<br/><br/>10. Chapter 10 Modeling Decision Making with Multi-Attribute Decision Modeling with Technology<br/><br/>10.1 Introduction<br/><br/>10.2 Delphi Method<br/><br/>10.2.1 Pairwise Comparison by Saaty (AHP)<br/><br/>10.2.2 Entropy Method<br/><br/>10.3 Simple Additive Weights (SAW) Method<br/><br/>10.4 Technique of Order Preference by Similarly to the Ideal Solution (TOPSIS)<br/><br/>10.5 Modeling of Ranking Units using Data Envelopment Analysis (DEA) with Linear Programming<br/><br/>10.6 Technology for Multi-Attribute Decision Making (MADM)<br/><br/>10.6.1 Technology and Simple Additive Weights<br/><br/>10.7 Exercises<br/><br/>10.8 References and Suggested Readings.<br/><br/>11. Chapter 11 Regression Techniques<br/><br/>11.1 Introduction to Regression Techniques<br/><br/>11.1.1 Correlation, covariance, and its misconceptions<br/><br/>11.1.2 Correlation: A Measure of LINEAR relationship<br/><br/>11.1.3 Calculating the Correlation<br/><br/>11.1.4 Correlation for Global Warming Data Example 11.1<br/><br/>11.1.5 Testing the Significance of a Correlation with hypothesis testing<br/><br/>11.2 Model Fitting and Least Squares<br/><br/>11.2.1 Global Warming Example 11.1<br/><br/>11.3 The Different Curve Fitting Criterion<br/><br/>11.3.1 A Least-Squares Fit Explosive Data Example 11.2<br/><br/>11.4 Diagnostics and Interpretations<br/><br/>11.4.1 Fruit Flies Over Time – Example 11.4<br/><br/>11.4.2 Revisit Explosive Problem – Example 11.5<br/><br/>11.4.3 Revisit the Cubic Model – Example 11.6<br/><br/>11.5 Diagnostics and Inferential Statistics<br/><br/>11.5.1 The Spring Mass System Using R<br/><br/>11.5.2 Simple Linear Regression Model with complete explanation summary in R<br/><br/>11.6 Polynomial Regression in R<br/><br/>11.6.1 Recovery Level Versus Time – Example 11.8<br/><br/>11.6.2 Wheat Production Revisited<br/><br/>11.7 Exercises<br/><br/>11.8 References and Suggested Readings<br/><br/>12. Chapter 12 Marketing Strategies and Competition Using Game Theory.<br/><br/>12.1 Total Conflict Games<br/><br/>12.1.1 Market Shares<br/><br/>12.1.2 Hitter-Pitcher Dual – A Conflict Game Example<br/><br/>12.1.3 The Expanded Hitter-Pitcher Dual<br/><br/>12.2 The Partial Conflict Game Analysis without Communication<br/><br/>12.3 Methods to Obtain the Equalizing Strategies<br/><br/>12.3.1 Linear Programming with Two Players and Two Strategies Each<br/><br/>12.4 Nash Arbitration Method<br/><br/>12.4.1 R and the Nash Arbitration Method<br/><br/>12.5 Exercises<br/><br/>12.6 References and Additional Readings<br/><br/>13. Index<br/>(https://www.routledge.com/Modeling-Operations-Research-and-Business-Analytics/Fox-Burks/p/book/9781032735924) |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
This book provides sample exercises, techniques, and solutions to employ mathematical modeling to solve problems in Operations Research and Business Analytics. Each chapter begins with a scenario and includes exercises built on realistic problems faced by managers and others working in operations research, business analytics, and other fields employing applied mathematics. A set of assumptions is presented, and then a model is formulated. A solution is offered, followed by examples of how that model can be used to address related issues.<br/><br/>Key elements of this book include the most common problems the authors have encountered over research and while consulting the fields including inventory theory, facilities' location, linear and integer programming, assignment, transportation and shipping, critical path, dynamic programming, queuing models, simulation models, reliability of system, multi-attribute decision-making, and game theory.<br/><br/>In the hands of an experienced professional, mathematical modeling can be a powerful tool. This book presents situations and models to help both professionals and students learn to employ these techniques to improve outcomes and to make addressing real business problems easier. The book is essential for all managers and others who would use mathematics to improve their problem-solving techniques.<br/><br/>No previous exposure to mathematical modeling is required. The book can then be used for a first course on modeling, or by those with more experience who want to refresh their memories when they find themselves facing real-world problems. The problems chosen are presented to represent those faced by practitioners.<br/><br/>The authors have been teaching mathematical modeling to students and professionals for nearly 40 years. This book is presented to offer their experience and techniques to instructors, students, and professionals.<br/><br/>(https://www.routledge.com/Modeling-Operations-Research-and-Business-Analytics/Fox-Burks/p/book/9781032735924) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Operations research |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Business analytics |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Burks, Robert E |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
