The seventh edition of this highly acclaimed book continues to provide balanced coverage of the theory, applications and computations of operations research. It explains complex mathematical concepts effectively, by means of carefully designed numerical examples and through use of extensive software support. With its main thrust on the software components, this new edition emphasizes the role of modern computational tools in enhancing the effectiveness of operations research as a decision-making tool. Conceptually, the book is organized into three parts: deterministic models, probabilistic models, and nonlinear models. Starting with an introduction to the concept of operations research and linear programming, it delves deep into the topic as it explains the advanced programming concepts, the different models of operations research, the optimization theory, and finally ends with an analysis of nonlinear programming algorithm. For better understanding of the concepts, integrated into the text are: TORA software modules for matrix inversion, linear programming, transportation models, queuing models, project planning with CPM and PERT, and game theory Excel spreadsheet templates designed to solve general problems in dynamic programming, inventory models, and the analytical hierarchy process (AHP). The book also includes examples of the commercial packages LINGO and AMPL to show the reader how very large mathe-matical programming models are solved in practice. Table of Contents 1. What Is Operations Research?  2. Introduction To Linear Programming.  3. The Simplex Method.  4. Duality and Sensitivity Analysis.  5. Transportation Model and its Variants.  6. Network Models.  7. Advanced Linear Programming.  8. Goal Programming.  9. Integer Linear Programming. 10. Deterministic Dynamic Programming. 11. Deterministic Inventory Models. 12. Review of Basic Probability. 13. Forecasting Models. 14. Decision Analysis and Games. 15. Probabilistic Dynamic Programming. 16. Probabilistic Inventory Models. 17. Queueing Systems. 18. Simulation Modeling. 19. Markovian Decision Process. 20. Classical Optimization Theory. 21. Nonlinear Programming Algorithms. Appendix A. Review of Vectors and Matrices. Appendix B. TORA Primer. Appendix C. Statistical Tables. Appendix D. Partial Solution to Selected Problems. Index.