An Introduction To Management Science: Quantitative Approaches To Decision Making


David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann Publisher: Cengage Learning


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Chapter 1: Introduction 1.1: Problem Solving and Decision Making 1.2: Quantitative Analysis and Decision Making 1.3: Quantitative Analysis 1.4: Models of Cost, Revenue, and Profit 1.5: Management Science Techniques 1: Exercises (13) 1: Case Problems 1: Test Bank (1) Chapter 2: An Introduction to Linear Programming 2.1: A Simple Maximization Problem 2.2: Graphical Solution Procedure 2.3: Extreme Points and the Optimal Solution 2.4: Computer Solution of the Par, Inc., Problem 2.5: A Simple Minimization Problem 2.6: Special Cases 2.7: General Linear Programming Notation 2: Exercises (35) 2: Case Problems 2: Test Bank (1) Chapter 3: Linear Programming: Sensitivity Analysis and Interpretation of Solution 3.1: Introduction to Sensitivity Analysis 3.2: Graphical Sensitivity Analysis 3.3: Sensitivity Analysis: Computer Solution 3.4: Limitations of Classical Sensitivity Analysis 3.5: The Electronic Communications Problem 3: Exercises (18) 3: Case Problems 3: Test Bank (1) Chapter 4: Linear Programming Applications in Marketing, Finance, and Operations Management 4.1: Marketing Applications 4.2: Financial Applications 4.3: Operations Management Applications 4: Exercises (13) 4: Case Problems 4: Test Bank (2) Chapter 5: Advanced Linear Programming Applications 5.1: Data Envelopment Analysis 5.2: Revenue Management 5.3: Portfolio Models and Asset Allocation 5.4: Game Theory 5: Exercises (9) 5: Test Bank (1) Chapter 6: Distribution and Network Models 6.1: Supply Chain Models 6.2: Assignment Problem 6.3: Shortest-Route Problem 6.4: Maximal Flow Problem 6.5: A Production and Inventory Application 6: Exercises (20) 6: Case Problems 6: Test Bank (1) Chapter 7: Integer Linear Programming 7.1: Types of Integer Linear Programming Models 7.2: Graphical and Computer Solutions for an All-Integer Linear Program 7.3: Applications Involving 0-1 Variables 7.4: Modeling Flexibility Provided by 0-1 Integer Variables 7: Exercises (14) 7: Case Problems 7: Test Bank (2) Chapter 8: Nonlinear Optimization Models 8.1: A Production Application—Par, Inc., Revisited 8.2: Constructing an Index Fund 8.3: Markowitz Portfolio Model 8.4: Blending: The Pooling Problem 8.5: Forecasting Adoption of a New Product 8: Exercises (22) 8: Case Problems Chapter 9: Project Scheduling: PERT/CPM 9.1: Project Scheduling Based on Expected Activity Times 9.2: Project Scheduling Considering Uncertain Activity Times 9.3: Considering Time-Cost Trade-Offs 9: Exercises (9) 9: Case Problems 9: Test Bank (1) Chapter 10: Inventory Models 10.1: Economic Order Quantity (EOQ) Model 10.2: Economic Production Lot Size Model 10.3: Inventory Model with Planned Shortages 10.4: Quantity Discounts for the EOQ Model 10.5: Single-Period Inventory Model with Probabilistic Demand 10.6: Order-Quantity, Reorder Point Model with Probabilistic Demand 10.7: Periodic Review Model with Probabilistic Demand 10: Exercises (18) 10: Case Problems Chapter 11: Waiting Line Models 11.1: Structure of a Waiting Line System 11.2: Single-Server Waiting Line Model with Poisson Arrivals and Exponential Service Times 11.3: Multiple-Server Waiting Line Model with Poisson Arrivals and Exponential Service Times 11.4: Some General Relationships for Waiting Line Models 11.5: Economic Analysis of Waiting Lines 11.6: Other Waiting Line Models 11.7: Single-Server Waiting Line Model with Poisson Arrivals and Arbitrary Service Times 11.8: Multiple-Server Model with Poisson Arrivals, Arbitrary Service Times, and No Waiting Line 11.9: Waiting Line Models with Finite Calling Populations 11: Exercises (23) 11: Case Problems Chapter 12: Simulation 12.1: What-If Analysis 12.2: Simulation of Sanotronics Problem 12.3: Inventory Simulation 12.4: Waiting Line Simulation 12.5: Simulation Considerations 12: Exercises (8) 12: Case Problems Chapter 13: Decision Analysis 13.1: Problem Formulation 13.2: Decision Making Without Probabilities 13.3: Decision Making With Probabilities 13.4: Risk Analysis and Sensitivity Analysis 13.5: Decision Analysis with Sample Information 13.6: Computing Branch Probabilities with Bayes” Theorem 13.7: Utility Theory 13: Exercises (28) 13: Case Problems Chapter 14: Multicriteria Decisions 14.1: Goal Programming: Formulation and Graphical Solution 14.2: Goal Programming: Solving More Complex Problems 14.3: Scoring Models 14.4: Analytic Hierarchy Process 14.5: Establishing Priorities Using AHP 14.6: Using AHP to Develop an Overall Priority Ranking 14: Exercises (17) 14: Case Problems Chapter 15: Time Series Analysis and Forecasting 15.1: Time Series Patterns 15.2: Forecast Accuracy 15.3: Moving Averages and Exponential Smoothing 15.4: Linear Trend Projection 15.5: Seasonality 15: Exercises (35) 15: Case Problems (2) Chapter 16: Markov Processes 16.1: Market Share Analysis 16.2: Accounts Receivable Analysis 16: Exercises (8) 16: Case Problems Chapter 17: Linear Programming: Simplex Method 17.1: An Algebraic Overview of the Simplex Method 17.2: Tableau Form 17.3: Setting up the Initial Simplex Tableau 17.4: Improving the Solution 17.5: Calculating the Next Tableau 17.6: Tableau Form: The General Case 17.7: Solving a Minimization Problem 17.8: Special Cases 17: Exercises (18) Chapter 18: Simplex-Based Sensitivity Analysis with Duality 18.1: Sensitivity Analysis with the Simplex Tableau 18.2: Duality 18: Exercises (14) Chapter 19: Solution Procedures for Transportation and Assignment Problems 19.1: Transportation Simplex Method: A Special-Purpose Solution Procedure 19.2: Assignment Problem: A Special-Purpose Solution Procedure 19: Exercises (5) Chapter 20: Minimal Spanning Tree 20.1: A Minimal Spanning Tree Algorithm 20: Exercises (2) 20: Case Problems Chapter 21: Dynamic Programming 21.1: A Shortest-Route Problem 21.2: Dynamic Programming Notation 21.3: The Knapsack Problem 21.4: A Production and Inventory Control Problem 21: Exercises (6) 21: Case Problems


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