For courses in operations research.
Theory, applications, and computations of operations research
Operations Research uses a combination of theory, applications and computations to teach operating research (OR) basics. It focuses on algorithmic and practical implementation of OR techniques. Numerical examples explain often difficult math concepts, helping students grasp the idea without getting stuck on complex theorems. Full case studies and math-free anecdotes show how algorithms are used in real life.
The 11th Edition introduces analytics, artificial intelligence, and machine learning topics. New stories, 3 new chapters, new case studies and sections bring readers up to date on the field.
Hallmark features of this title
- All algorithmic details are explained using carefully-chosen numerical examples, rather than complex mathematical notations or theorems.
- The focal points that unify algorithms within an optimization area are stressed to provide insight about the functionality of each algorithm.
- Aha! Moments are math-free stories that show how classical algorithms are beneficial in practice.
- 18 fully-developed case studies demonstrate the diverse real-life applications of operations research (OR).
- Excellent support software for understanding the algorithmic details (interactive TORA and Excel spreadsheets) and for solving large practical OR problems (AMPL and Solver) is available on the text''s companion website at www.pearsonhighered.com/taha
New and updated features of this title
- NEW: Analytics, artificial intelligence, and machine learning topics are incorporated in a new Chapter 1 and a new case study.
- NEW: Chapters on stochastic linear programming (8) and yield management (14).
- NEW: Sections cover new two-phase method with no artificial variable (3.4.3); the 100% rule for LP sensitivity analysis (3.6.5); generalized simplex algorithm (4.4.2); concurrent changes in feasibility and optimality (4.5.4); transition from textbook to commercial software in post-optimal analysis (4.6); Benders'' decomposition algorithm (9.2.3); and Bayesian probability with ML applications (15.3).
- UPDATED: Chapter 19 on discrete event and Monte Carlo simulations.
- UPDATED: Sections discuss sensitivity analysis (Section 3.6); post-optimal analysis (4.5); reversal heuristic (11.4.2) recursive nature of dynamic programming computations (12.1); recursive equation and principle of optimality (12.1.1); ergodic (Regular) Markov chain (16.4); and direct search method (21.1.1).
- UPDATED: Topics from the 10th Edition companion website are now included in their respective chapters for easy reference.
