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.
