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.
Overview of Operations Research, Analytics, and AI in Decision Making
Modeling with Linear Programming
The Simplex Method and Sensitivity Analysis
Duality and Post-Optimal Analysis
Transportation Model and Its Variants
Network Models
Advanced Linear Programming
Stochastic Linear Programming
Integer Linear Programming
Heuristic and Constraint Programming
Traveling Salesperson Problem (TSP)
Dynamic Programming (DP)
Inventory Modeling
Yield Management (YM)
Decision Analysis and Games
Markov Chains
Markovian Decision Process
Queuing Systems
Discrete Event and Monte Carlo Simulations
Classical Optimization Theory
Nonlinear Programming Algorithms
Case Analysis
Appendices
Statistical Tables
Partial Answers to Selected Problems
AMPL Modeling Language
Review of Vectors and Matrices
Review of Basic Probability
Forecasting Models
