Modelling Operations Research and Business Analytics

"1. Chapter 1. Inventory Problem 1.1 Introduction 1.2 Inventory Problems 1.3 Inventory and Economic Order Quantity (EOQ) 1.3.1 Inventory Analysis with EOQ formula driven approach 1.3.2 Time Invariant Asphalt EOQ model 1.4 Facility Location with an Oil Rig Location Problem 1.5 Computer Cabling Location of Central Computer 1.6 Exercises 1.7 References 2. Chapter 2 Product Mix: Linear Programming Problems 2.1 Linear Programing Problem Introduction 2.2 Simple Manufacturing Example 2.3 Financial Planning 2.4 Blending Formulation Example 2.5 Production Planning Problem 2.6 Shipping Problem 2.7 Product Mix 2.8 Supply Chain Operations (Gasoline Distribution) 2.9 Product Mix with LINDO 2.10 Exercises 2.11 References and Additional Readings 3. Chapter 3 Transportation and Shipping Problems 3.1 Transportation and Shipping Revisited 3.2 Transportation and Shipping Warehouse Problem 3.2.1 Modification to the Warehouse Problem 3.3 Transportation Network 3.4 Exercises 3.5 References and Additional Readings 4. Chapter 4 Assignment Models 4.1 Training Centers and Offices 4.1.1 Assignment Problem 4.2 Exercises 4.3 References and Additional Readings 5. Chapter 5 Mathematical Programming Methods 5.1 Data Envelopment Analysis (DEA) 5.2 Manufacturing Problem with DEA 5.3 Shortest Path Problems 5.3.1 Network analysis 5.3.2 . Kruskal’s Method for Network Analysis Problem 5.3.3 Prim’s Algorithm 5.3.4 Dijkstra’s Algorithm 5.4 Maximum Flow Problem 5.4.1 Example 5.1. Max Flow through a given network 5.5 Critical Path in Project Plan Network 5.5.1 Example 5.2. CPM 5.6 Minimum Cost Flow Problem 5.6.1 Example 5.3. Min cost flow through a network 5.7 General Integer Linear Programs 5.7.1 Example 5.4. Manufacturing Equipment 5.7.2 Example 5.5. Integer LP Programs by EXCEL 5.8 Mixed Integer Programming Application: ""Either-Or"" Constraints 5.8.1 Conditional Relations Among Constraints 5.8.2 A Case of Discrete Finite Valued Variable 5.8.3 0 - 1 Integer Linear Programs 5.9 Illustrious Example 5.9.1 Example 5.7. Consider the following Knapsack Problem 5.9.2 Example 5.8. Traveling Salesperson Problem 5.9.3 Example 5.9. Capital Budgeting Applications 5.9.4 Example 5.10. Marketing Application 5.9.5 Example 5.11. The Cutting Stock Problem 5.10 An Engineering Application: Mixing Substances 5.11 Exercises 5.12 References and Additional Readings 6. Chapter 6 Resource Allocation Models using Dynamic Programming 6.1 Introduction: Basic Concepts and Theory 6.2 Characteristics of Dynamic Programming 6.2.1 Working Backwards 6.2.2 Example 6.1 A Knapsack Problem. 6.3 Modeling and Applications of Discrete Dynamic Programming 6.3.1 Oil Well Investment DP Application 6.4 Exercises 6.5 References and Suggested Readings 7. Chapter 7 Queuing Models 7.1 Introduction to Queuing Theory 7.1.1 Simple Fast Food Service Queue Example 7.1 7.2 The Multi-server Problems 7.3 Exercises 7.4 References and Suggested Readings 8. Chapter 8 Simulation Models 8.1 Missile Attack 8.2 Gasoline-Inventory simulation 8.3 Queuing model 8.4 R Applied simulation 8.5 Exercises 8.6 References and Additional Readings 9. Chapter 9 System Reliability Modeling 9.1 Introduction to Reliability Modeling 9.2 Modeling Component Reliability 9.2.1 Battery Problem – Reliability Example 9.1 9.2.2 Battery Problem Revisited – Reliability Example 9.2 9.3 Modeling series and parallel components 9.3.1 Modeling Series Systems 9.3.2 Radio Components – Example 9.3 9.3.3 Modeling Parallel Systems (Two Components) 9.3.4 Parallel Bridges – Example 9.4 9.4 Modeling Active Redundant Systems 9.4.1 Manufacturing – Example 9.5 9.5 Modeling Standby Redundant Systems 9.5.1 Battery Problems Revisited for Stand-by – Example 9.6 9.5.2 Stake Out Problem Revisited – Example 9.7 9.6 Models of Large Scale Systems 9.7 Exercises 9.8 References and Suggested Readings 10. Chapter 10 Modeling Decision Making with Multi-Attribute Decision Modeling with Technology 10.1 Introduction 10.2 Delphi Method 10.2.1 Pairwise Comparison by Saaty (AHP) 10.2.2 Entropy Method 10.3 Simple Additive Weights (SAW) Method 10.4 Technique of Order Preference by Similarly to the Ideal Solution (TOPSIS) 10.5 Modeling of Ranking Units using Data Envelopment Analysis (DEA) with Linear Programming 10.6 Technology for Multi-Attribute Decision Making (MADM) 10.6.1 Technology and Simple Additive Weights 10.7 Exercises 10.8 References and Suggested Readings. 11. Chapter 11 Regression Techniques 11.1 Introduction to Regression Techniques 11.1.1 Correlation, covariance, and its misconceptions 11.1.2 Correlation: A Measure of LINEAR relationship 11.1.3 Calculating the Correlation 11.1.4 Correlation for Global Warming Data Example 11.1 11.1.5 Testing the Significance of a Correlation with hypothesis testing 11.2 Model Fitting and Least Squares 11.2.1 Global Warming Example 11.1 11.3 The Different Curve Fitting Criterion 11.3.1 A Least-Squares Fit Explosive Data Example 11.2 11.4 Diagnostics and Interpretations 11.4.1 Fruit Flies Over Time – Example 11.4 11.4.2 Revisit Explosive Problem – Example 11.5 11.4.3 Revisit the Cubic Model – Example 11.6 11.5 Diagnostics and Inferential Statistics 11.5.1 The Spring Mass System Using R 11.5.2 Simple Linear Regression Model with complete explanation summary in R 11.6 Polynomial Regression in R 11.6.1 Recovery Level Versus Time – Example 11.8 11.6.2 Wheat Production Revisited 11.7 Exercises 11.8 References and Suggested Readings 12. Chapter 12 Marketing Strategies and Competition Using Game Theory. 12.1 Total Conflict Games 12.1.1 Market Shares 12.1.2 Hitter-Pitcher Dual – A Conflict Game Example 12.1.3 The Expanded Hitter-Pitcher Dual 12.2 The Partial Conflict Game Analysis without Communication 12.3 Methods to Obtain the Equalizing Strategies 12.3.1 Linear Programming with Two Players and Two Strategies Each 12.4 Nash Arbitration Method 12.4.1 R and the Nash Arbitration Method 12.5 Exercises 12.6 References and Additional Readings 13. Index"