Operations Research | An Introduction to Research | By Pearson

Operations Research provides a broad focus on algorithmic and practical implementation of Operations Research (OR) techniques, using theory, applications and computations to teach students OR basics. The book can be used conveniently in a survey course that encompasses all the major tools of operations research or in two separate courses on deterministic and probabilistic decision-making.
With the Tenth Edition, the author preserves classical algorithms by providing essential hand computational algorithms as an important part of OR history. Based on input and submissions from OR students, professors and practitioners, the author also includes scenarios that show how classical algorithms can be beneficial in practice. These entries are included as Aha! Moments with each dealing with stories, anecdotes and issues in OR theory, applications, computations and teaching methodology that can advance the understanding of fundamental or concepts.
Features
Added text mini-updates appear throughout the book.
Computational issues in the revised simplex method appear in Chapter 7, including a comparison between product form and the LU decomposition used with the revised simplex method.
Using a brief introduction, inventory modeling is presented within the more encompassing context of supply chains.
This edition adds two new case analyses, resulting in a total 17 fully-developed real-life applications. All the cases appear in chapter 26 on the website and are cross-referenced throughout the book using abstracts at the start of their most applicable chapters.
All problems now appear at end of their respective chapters and are cross-referenced by text section to facilitate making problem assignments.
New problems have been added.
TORA software has been updated.
Table of Content:
1. What Is Operations Research?
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. Goal Programming
9. Integer Linear Programming
10. Heuristic and Constraint Programming
11. Traveling Salesperson Problem (TSP)
12. Deterministic Dynamic Programming
13. Inventory Modeling (with Introduction to Supply Chains)
14. Review of Basic Probability
15. Decision Analysis and Games
16. Probabilistic Inventory Models
17. Markov Chains
18. Queuing Systems
19. Simulation Modeling
20. Classical Optimization Theory
21. Nonlinear Programming Algorithms
Appendix A: Statistical Tables
Appendix B: Partial Answers to Selected Problems.