Operations Research: An Introduction 9/e uses theory, applications, and computations to teach students the basics of OR:
●Numerical examples are effectively used to explain complex mathematical concepts, thus avoiding the use of complex notations and theorems.
●A separate chapter of fully analyzed applications aptly demonstrates the diverse use of OR.
●The popular commercial and tutorial software AMPL, Excel, Excel Solver, and Tora are used throughout the book to solve practical problems and to test theoretical concepts.
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.
Topics include Markov chains, TSP heuristics, new LP models, a simplex-based approach to LP sensitivity analysis, and more. The ninth edition also includes a dedicated chapter on the traveling salesperson, as well as, for the first time in any OR textbook, a (math-free) Section 3.7 explains how the different linear programming algorithms (simplex, dual simplex, revised simplex, and interior point) are “meshed” together to produce highly-successful commercial codes for solving LP in practice.
Ch 1 What Is Operations Research?
Ch 2 Modeling with Linear Programming
Ch 3 The Simplex Method and Sensitivity Analysis
Ch 4 Duality and Post-Optimal Analysis
Ch 5 Transportation Model and Its Variants
Ch 6 Network Models
Ch 7 Advanced Linear Programming
Ch 8 Goal Programming
Ch 9 Integer Linear Programming
Ch10 Heuristic and Constraint Programming
Ch11 Traveling Salesperson Problem (TSP)
Ch12 Deterministic Dynamic Programming
Ch13 Deterministic Inventory Models
Ch14 Review of Basic Probability
Ch15 Decision Analysis and Games
Ch16 Probabilistic Inventory Models
Ch17 Markov Chains
Ch18 Queuing Systems
Ch19 Simulation Modeling
Ch20 Classical Optimization Theory
Ch21 Nonlinear Programming Algorithms
On the Companion Website
http://www.pearsonhighered.com/tahainternational/
Ch22 Additional Network and LP algorithms
Ch23 Forecasting Models
Ch24 Probabilistic Dynamic Programming
Ch25 Markovian Decision Process
Ch26 Case Analysis