Operations Research: An Introduction, 11th edition

Published by Pearson (July 6, 2022) © 2023

  • Hamdy A. Taha University of Arkansas
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Instant access

In this eTextbook — More ways to learn

  • More flexible. Start learning right away, on any device.
  • More supportive. Get AI explanations and practice questions (select titles).
  • More interactive. Bring learning to life with audio, videos, and diagrams.
  • More memorable. Make concepts stick with highlights, search, notes, and flashcards.
  • More understandable. Translate text into 100+ languages with one tap.

Title overview

Operations Research uses a balanced combination of theory, applications and computations to help you learn the basics of operating research (OR). It focuses on algorithmic and practical implementation of OR techniques. Easy-to-understand numerical examples explain often difficult math concepts, helping you grasp the foundational idea without getting stuck on complex theorems or notations. Full case studies and math-free anecdotes show how algorithms are used in real-life applications.

The 11th Edition introduces analytics, artificial intelligence, and machine learning topics that strengthen and streamline the decision-making processes involved in OR. New stories, 3 new chapters, new case studies and sections provide an up-to-date introduction to the field of OR.

Table of contents

     
  1. Overview of Operations Research, Analytics, and AI in Decision Making
  2. Modeling with Linear Programming
  3. The Simplex Method and Sensitivity Analysis
  4. Duality and Post-Optimal Analysis
  5. Transportation Model and Its Variants
  6. Network Models
  7. Advanced Linear Programming
  8. Stochastic Linear Programming
  9. Integer Linear Programming
  10. Heuristic and Constraint Programming
  11. Traveling Salesperson Problem (TSP)
  12. Dynamic Programming (DP)
  13. Inventory Modeling
  14. Yield Management (YM)
  15. Decision Analysis and Games
  16. Markov Chains
  17. Markovian Decision Process
  18. Queuing Systems
  19. Discrete Event and Monte Carlo Simulations
  20. Classical Optimization Theory
  21. Nonlinear Programming Algorithms
  22. Case Analysis

Appendices

  1. Statistical Tables
  2. Partial Answers to Selected Problems
  3. AMPL Modeling Language
  4. Review of Vectors and Matrices
  5. Review of Basic Probability
  6. Forecasting Models

Author bios

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