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Introduction to Management Science, 13th edition

  • Bernard W. Taylor

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Overview

Introduction to Management Science is built to provide you with a logical, step-by-step approach to complex problem-solving. You'll learn how to make decisions and solve complex problems successfully using both quantitative methods and software tools. To support your learning, the text provides extensive examples, problem sets, and relatable cases.

The 13th Edition incorporates Excel™ and other software resources to help you leverage the technology that you'll encounter throughout your career. You'll get hands-on as you practice modeling techniques to develop a useful framework for problem-solving. With this text, you'll acquire practical skills to be a successful problem-solver in the workplace.

Published by Pearson (January 31st 2022) - Copyright © 2019

ISBN-13: 9780137503933

Subject: Operations Management

Category: Management Science

Table of contents

Brief Contents

  1. Management Science
  2. Linear Programming: Model Formulation and Graphical Solution
  3. Linear Programming: Computer Solution and Sensitivity Analysis
  4. Linear Programming: Modeling Examples
  5. Integer Programming
  6. Transportation, Transshipment, and Assignment Problems
  7. Network Flow Models
  8. Project Management
  9. Multicriteria Decision Making
  10. Nonlinear Programming
  11. Probability and Statistics
  12. Decision Analysis
  13. Queuing Analysis
  14. Simulation
  15. Forecasting
  16. Inventory Management

Appendix A: Normal and Chi-Square Tables

Appendix B: Setting Up and Editing a Spreadsheet

Appendix C: The Poisson and Exponential Distributions

Solutions to Selected Odd-Numbered Problems

The following items can be found on the companion website that accompanies this text:

  • Module A: The Simplex Solution Method
  • Module B: Transportation and Assignment Solution Methods
  • Module C: Integer Programming: The Branch and Bound Method
  • Module D: Nonlinear Programming Solution Techniques
  • Module E: Game Theory
  • Module F: Markov Analysis

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