The book, Operations Research, covers several key aspects such as theory, computations, and applications based on operations research. The utilization of computation and application-driven information are some of the main changes made to this book, in comparison with previous editions.
The topics covered in this book include Advanced Linear Programming, Probabilistic Dynamic Programming, Case Analysis, Simulation Modeling, Markov Chains, Decision Analysis And Games, Review Of Basic Probability, Network Models, Integer Linear Programming, and Transportation Model And Its Variants. Some other concepts covered The Simplex Method and Sensitivity Analysis, Modeling with Linear Programming, Classical Optimization Theory, Deterministic Dynamic Programming, Deterministic Inventory Models, Goal Programming, and Forecasting Models.
Through the course of Operations Research, the author has elaborated greatly on the concept of AMPL, which is a dynamic language incorporated in modeling. This concept has been explained by incorporating several examples, on linear, nonlinear, network, and integer programming. In total, there are around 50 questions, which can be classified as mini cases that deal with real life situations. At the starting of each chapter, the readers are provided with a study guide that enables them to grasp the concepts, and the software utilized. The readers are also guided through utilizing Excel Solver in the context of linear, non-linear, network, and integer programming. The book also contains thoroughly analyzed practical events, and every chapter ends with summary applications, which may have been a part of published case studies. The author also explains a number of difficult mathematical concepts by means of numerical examples, other than the traditional proofs.
Preface
About the Author
Trademarks
Chapter 1 : What is Operations Research?
Chapter 2 : Modeling with Linear Programming
Chapter 3 : The Simplex Method and Sensitivity Analysis
Chapter 4 : Duality and Post-Optimal Analysis
Chapter 5 : Transportation Model and Its Variants
Chapter 6 : Network Models
Chapter 7 : Advanced Linear Programming
Chapter 8 : Goal Programming
Chapter 9 : Integer Linear Programming
Chapter 10 : Deterministic Dynamic Programming
Chapter 11 : Deterministic Inventory Models
Chapter 12 : Review of Basic Probability
Chapter 13 : Decision Analysis and Games
Chapter 14 : Probabilistic Inventory Models
Chapter 15 : Queuing Systems
Chapter 16 : Simulation Modeling
Chapter 17 : Markov Chains
Chapter 18 : Classical Optimization Theory
Chapter 19 : Nonlinear Programming Algorithms
Appendix A : AMPL Modeling Language
Appendix B : Statistical Tables
Appendix C : Partial Answers to Selected Problems
Index
On the CD-ROM
Chapter 20 : Additional Network and LP Algorithms
Chapter 21 : Forecasting Models
Chapter 22 : Probabilistic Dynamic Programming
Chapter 23 : Markovian Decision Process
Chapter 24 : Case Analysis
Appendix D : Review of Vectors and Matrices
Appendix E : Case Studies
