Enhanced descriptions from Syndetics:

For junior/senior undergraduate and first-year graduate courses in Operations Research in departments of Industrial Engineering, Business Administration, Statistics, Computer Science, and Mathematics. Significantly revised, this text streamlines the coverage of the theory, applications, and computations of operations research. Numerical examples are effectively used to explain complex mathematical concepts. A separate chapter of fully analyzed applications aptly demonstrates the diverse use of OR. The popular commercial and tutorial software AMPL, Excel, Excel Solver, and Tora are used throughout the book to solve practical problems and to test theoretical concepts. New materials include Markov chains, TSP heuristics, new LP models, and a totally new simplex-based approach to LP sensitivity analysis.

Table of contents provided by Syndetics

• Chapter 1 What is Operations Research?
• 1.1 Operations Research Models
• 1.2 Solving the OR Model
• 1.3 Queueing and Simulation Models
• 1.4 Art of Modeling
• 1.5 More than Just Mathematics
• 1.6 Phases of an OR Study
• 1.7 About this Book
• Problems
• References
• Chapter 2 Modeling with Linear Programming
• 2.1 Two-Variable LP Model
• 2.2 Graphical LP Solution
• 2.3 Selected LP Applications
• 2.4 Computer Solution with Solver and AMPL
• Problems
• References
• Chapter 3 The Simplex Method and Sensitivity Analysis
• 3.1 LP Model in Equation Form
• 3.2 Transition from Graphical to Algebraic Solution
• 3.3 The Simplex Method
• 3.4 Artificial Starting Solution
• 3.5 Special Cases in the Simplex Method
• 3.6 Sensitivity Analysis
• Problems
• References
• Chapter 4 Duality and Post-Optimal Analysis
• 4.1 Definition of the Dual Problem
• 4.2 Primal-Dual Relationships
• 4.3 Economic Interpretation of Duality
• 4.4 Additional Simplex Algorithms
• 4.5 Post-Optimal Analysis
• Problems
• References
• Chapter 5 Transportation Model and its Variants
• 5.1 Definition of the Transportation Model
• 5.2 Nontraditional Transportation Models
• 5.3 The Transportation Algorithm
• 5.4 The Assignment Model
• 5.5 The Transshipment Model
• Problems
• References
• Chapter6 Network Models
• 6.1 Scope and Definition of Network Models
• 6.2 Minimal Spanning Tree Algorithm
• 6.3 Shortest-Route Problem
• 6.4 Maximal Flow Model
• 6.5 CPM and PERT
• Problems
• References
• Chapter 7 Advanced Linear Programming
• 7.1 Simplex Method Fundamentals
• 7.2 Revised Simplex Method
• 7.3 Bounded Variables Algorithm
• 7.4 Duality
• 7.5 Parametric Linear Programming
• Problems
• References
• Chapter 8 Goal Programming
• 8.1 A Goal Programming Formulation
• 8.2 Goal Programming Algorithms
• Problems
• References
• Chapter 9 Integer Linear Programming
• 9.1 Illustrative Applications
• 9.2 Integer Programming Algorithms
• 9.3 Traveling Salesperson (TSP) Problem
• Problems
• References
• Chapter 10 Deterministic Dynamic Programming
• 10.1 Recursive Nature of Computations in DP
• 10.2 Forward and Backward Recursion
• 10.3 Selected DP Applications
• 10.4 Problem of Dimensionality
• Problems
• References
• Chapter 11 Deterministic Inventory Models
• 11.1 General Inventory Model
• 11.2 Role of Demand in the Development of Inventory Models
• 11.3 Static Economic-Order-Quantity (EOQ) Models
• 11.4 Dynamic EOQ Models
• Problems
• References
• Chapter 12 Review of Basic Probability
• 12.1 Laws of Probability
• 12.2 Random Variables and Probability Distributions
• 12.3 Expectation of a Random Variable
• 12.4 Four Common Probability Distributions
• 12.5 Empirical Distributions
• Problems
• References
• Chapter 13 Decision Analysis and Games
• 13.1 Decision Making under Certainty-Analytic Hierarchy Process (AHP)
• 13.2 Decision Making under Risk
• 13.3 Decision under Uncertainty
• 13.4 Game Theory
• Problems
• References
• Chapter 14 Probabilistic Inventory Models
• 14.1 Continuous Review Models
• 14.2 Single-Period Models
• 14.3 Multiperiod Model
• Problems
• References
• Chapter 15 Queueing Systems
• 15.1 Why Study Queues?
• 15.2 Elements of a Queuing Model
• 15.3 Role of Exponential Distribution
• 15.4 Pure Birth and Death Models (Relationship between the Exponential and Poisson Distributions)
• 15.5 Generalized Poisson Queuing Model
• 15.6 Specialized Poisson Queues
• 15.7 (M/G/1):(GD/Inf/Inf)-Pollaczek-Khintchine (P-K) Formula
• 15.8 Other Queuing Models
• 15.9 Queueing Decision Models
• Problems
• References
• Chapter 16 Simulation Modeling
• 16.1 Monte Carlo Simulation
• 16.2 Types of Simulation
• 16.3 Elements of Discrete-Event Simulation
• 16.4 Generation of Random Numbers
• 16.5 Mechanics of Discrete Simulation
• 16.6 Methods for Gathering Statistical Observations
• 16.7 Simulation Languages
• Problems
• References
• Chapter 17 Markov Chains
• 17.1 Definition of a Markov Chain
• 17.2 Absolute and n-Step Transition Probabilities
• 17.3 Classification of the States in a Markov Chain
• 17.4 Steady-State Probabilities and Mean Return Times of Ergodic Chains
• 17.5 First Passage Time
• 17.6 Analysis of Absorbing States
• Problems
• References
• Chapter 18 Classical Optimization Theory
• 18.1 Unconstrained Problems
• 18.2 Constrained Problems
• Problems
• References
• Chapter 19 Nonlinear Programming Algorithms
• 19.1 Unconstrained Algorithms
• 19.2 Constrained Algorithms
• Problems
• References
• Appendix A AMPL Modeling Language
• A.1 Rudimentary AMPL Model
• A.2 Components of AMPL Model
• A.3 Mathematical Expressions and Computed Parameters
• A.4 Subsets and Indexed Sets
• A.5 Accessing External Files
• A.6 Interactive Commands
• A.7 Iterative and Conditional Execution of AMPL Commads
• A.8 Sensitivity Analysis Using AMPL
• Reference
• Appendix B Statistical Tables
• Appendix C Partial Answers to Selected Problems
• Index
• On the CD
• Chapter 20 Additional Network and LP Algorithms
• 20.1 Minimim-Cost Capacitated Flow Problem
• 20.2 Decomposition Alogrithm
• 20.3 Karmarkar Interior-Point Method
• Problems
• References
• Chapter 21 Forecasting Models
• 21.1 Moving Average Technique
• 21.2 Exponential Smoothing
• 21.3 Maximization of the Event of Achieving a Goal
• Problems
• References
• Chapter 22 Probabilistic Dynamic Programming
• 22.1 A Game of Chance
• 22.2 Investment Problem
• 22.3 Maximization of the Event of Achieving a Goal
• Problems
• References
• Chapter 23 Markovian Decision Process
• 23.1 Scope of the Markovian Decision Problem
• 23.2 Finite-Stage Dynamic Programming Model
• 23.3 Infinite-Stage Model
• 23.4 Linear Programming Solution
• Problems
• References
• Chapter 24 Case Analysis
• Case 1 Airline Fuel Allocation Using Optimum Tankering
• Case 2 Optimization of Heart Valves Production
• Case 3 Scheduling Appointments at Australian Tourist Commission Trade Events
• Case 4 Saving Federal Travel Dollars
• Case 5 Optimal Ship Routing and Personnel Assignments for Naval Recruitment in Thailand
• Case 6 Allocation of Operating Room Time in Mount Sinai Hospital
• Case 7 Optimizing Trailer Payloads at PFG Building Glass
• Case 8 Optimization of Crosscutting and Log Allocation at Weyerhaeuser
• Case 9 Layout Planning of a Computer Integrated Manufacturing (CIM) Facility
• Case 10 Booking Limits in Hotel Reservations
• Case 11 Casey's Problem: Interpreting and Evaluating a New Test
• Case 12 Ordering Golfers on the Final Day of Ryder Cup Matches
• Case 13 Inventory Decisions in Dell's Supply Chain
• Case 14 Analysis of an Internal Transport System in a Manufacturing Plant
• Case 15 Telephone Sales Manpower Planning at Qantas Airways
• Appendix D Review of Vectors and Matrices
• D.1 Vectors
• D.2 Matrices
• D.3 Quadratic Forms
• D.4 Convex and Concave Functions
• Problems
• References
• Appendix E Case Studies