Description
Operations Research: A Practical Introduction is just that: a hands-on approach to the field of operations research (OR) and a useful guide for using OR techniques in scientific decision making, design, analysis and management. The text accomplishes two goals. First, it provides readers with an introduction to standard mathematical models and algorithms. Second, it is a thorough examination of practical issues relevant to the development and use of computational methods for problem solving.
Highlights:
- All chapters contain up-to-date topics and summaries
- A succinct presentation to fit a one-term course
- Each chapter has references, readings, and list of key terms
- Includes illustrative and current applications
- New exercises are added throughout the text
- Software tools have been updated with the newest and most popular software
Many students of various disciplines such as mathematics, economics, industrial engineering and computer science often take one course in operations research. This book is written to provide a succinct and efficient introduction to the subject for these students, while offering a sound and fundamental preparation for more advanced courses in linear and nonlinear optimization, and many stochastic models and analyses.
It provides relevant analytical tools for this varied audience and will also serve professionals, corporate managers, and technical consultants.
Table of Contents
Introduction to Operations Research. The Origins and Applications of Operations Research. System Modeling Principles. Algorithm Efficiency and Problem Complexity. Optimality and Practicality. Software for Operations Research. Illustrative Applications. Linear Programming. The Linear Programming Model. The Art and Skill of Problem Formulation. Preparation for the Simplex Method. The Simplex Method. Initial Solutions for General Constraints. Information on the Tableau. Duality and Sensitivity Analysis. Revised Simplex and Computational Efficiency. Software for Linear Programming. Illustrative Applications. Network Analysis. Graphs and Networks: Preliminary Definitions. Maximum Flow in Networks. Minimum Cost Network Flow Problems. Network Connectivity. Shortest Path Problems. Dynamic Programming. Project Management. Software for Network Analysis. Illustrative Applications. Integer Programming. Fundamental Concepts. Typical Integer Programming Problems. Zero-One Model Formulations. Branch-and-Bound. Cutting Planes and Facets. Cover Inequalities. Lagrangian Relaxation. Column Generation. Software for Integer Programming. Illustrative Applications. Nonlinear Optimization. Preliminary Notation and Concepts. Unconstrained Optimization. Constrained Optimization. Software for Nonlinear Optimization. Illustrative Applications. Markov Processes. State Transitions. State Probabilities. First Passage Probabilities. Properties of the States in a Markov Process. Steady-State Analysis. Expected First Passage Times. Absorbing Chains. Software for Markov Processes. Illustrative Applications. Queueing Models. Basic Elements of Queueing Systems. Arrival and Service Patterns. Software for Queueing Models. Illustrative Applications. Simulation. Simulation: Purposes and Applications. Discrete Simulation Models. Observations of Simulations. Software for Simulation. Illustrative Applications. Decision Analysis. The Decision-Making Process. An Introduction to Game Theory. Decision Trees. Utility Theory. The Psychology of Decision-Making. Software for Decision Analysis. Illustrative Applications. Heuristic and Metaheuristic Techniques for Optimization. Greedy Heuristics. Local Improvement Heuristics. Simulated Annealing. Parallel Annealing. Genetic Algorithms. Tabu Search. Constraint Programming and Local Search. Other Metaheuristics. Software for Metaheuristics. Illustrative Applications
