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:
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     Chapter 6:  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 17.4Steady-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