1st Edition
Operations Research A Practical Introduction
Students with diverse backgrounds will face a multitude of decisions in a variety of engineering, scientific, industrial, and financial settings. They will need to know how to identify problems that the methods of operations research (OR) can solve, how to structure the problems into standard mathematical models, and finally how to apply or develop computational tools to solve the problems.
Perfect for any one-semester course in OR, Operations Research: A Practical Introduction answers all of these needs. In addition to providing a practical introduction and guide to using OR techniques, it includes a timely examination of innovative methods and practical issues related to the development and use of computer implementations. It provides a sound introduction to the mathematical models relevant to OR and illustrates the effective use of OR techniques with examples drawn from industrial, computing, engineering, and business applications.
Many students will take only one course in the techniques of Operations Research. Operations Research: A Practical Introduction offers them the greatest benefit from that course through a broad survey of the techniques and tools available for quantitative decision making. It will also encourage other students to pursue more advanced studies and provides you a concise, well-structured, vehicle for delivering the best possible overview of the discipline.
The Origins and Applications of Operations Research
System Modeling Principles
Algorithm Efficiency and Problem Complexity
Optimality and Practicality
Guide to Software Tools
LINEAR PROGRAMMING
The Linear Programming Model
The Art of Problem Formulation
Graphical Solution of Linear Programming Problems
Preparation for the Simplex Method
The Simplex Method
Initial Solutions for General Constraints
Information in the Tableau
Duality and Sensitivity Analysis
Revised Simplex and Computational Efficiency
Guide to Software Tools
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
Guide to Software Tools
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
Guide to Software Tools
Illustrative Applications
NONLINEAR OPTIMIZATION
Preliminary Notation and Concepts
Unconstrained Optimization
Constrained Optimization
Guide to Software Tools
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
Guide to Software Tools
Illustrative Applications
QUEUING MODELS
Basic Elements of Queuing Systems
Arrival and Service Patterns
Analysis of Simple Queuing Systems
Guide to Software Tools
Illustrative Applications
SIMULATION
Simulation: Purposes and Applications
Discrete Simulation Models
Observations of Simulations
Guide to Software Tools
Illustrative Applications
DECISION ANALYSIS
The Decision Making Process
An Introduction to Game Theory
Decision Trees
Utility Theory
The Psychology of Decision Making
Guide to Software Tools
Illustrative Applications
HEURISTIC TECHNIQUES FOR OPTIMIZATION
Local Improvement Heuristics
Optimization by Simulated Annealing
Parallel Annealing
Genetic Algorithms
Neural Networks
Guide to Software Tools
Illustrative Applications
APPENDIX: Review of Essential Mathematics
Biography
Michael W. Carter and Camille C. Price
"The chapter on heuristics techniques is particularly welcome."
Short Book Reviews, Vol. 21, No. 2, August, 2001