An Introduction to Management Science: Quantitative Approaches to Decision Making 15th edition

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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
  • 1: Case Problems
  • 1: Test Bank
  
  
• 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
  • 2: Case Problems
  • 2: Test Bank
  
  
• 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
  • 3: Case Problems
  • 3: Test Bank
  
  
• 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
  • 4: Case Problems
  • 4: Test Bank
  
  
• 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
  • 5: Test Bank
  
  
• 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
  • 6: Case Problems
  • 6: Test Bank
  
  
• 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
  • 7: Case Problems
  • 7: Test Bank
  
  
• 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
  • 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: Case Problems
  • 9: Test Bank
  
  
• 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 15: Case Problems
  
  
• Chapter 16: Markov Processes
  • 16.1: Market Share Analysis
  • 16.2: Accounts Receivable Analysis
  • 16: Exercises
  • 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
  
  
• Chapter 18: Simplex-Based Sensitivity Analysis with Duality
  • 18.1: Sensitivity Analysis with the Simplex Tableau
  • 18.2: Duality
  • 18: Exercises
  
  
• 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
  
  
• Chapter 20: Minimal Spanning Tree
  • 20.1: A Minimal Spanning Tree Algorithm
  • 20: Exercises
  • 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
  • 21: Case Problems