Description

For first courses in operations research, operations management.
Covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. Emphasizes the importance of modeling and problem formulation, this text teaches students how to apply algorithms to real-world problems to arrive at optimal solutions.
Visit the author-maintained web site athttp://comp.uark.edu/~rrardin/oorbook

Features

• Integrates treatment of network flows, integer and combinatorial optimization, and nonlinear programming with coverage of standard linear programming topics:
  • Prepares students for the variety of model forms needed in practice and provides instructors with flexibility in depth of coverage (Chapter 2).
• Develops algorithms around a unified improving search theme, providing students with a paradigm that will extend into more advanced cases:
  • Allows instructors to efficiently treat both simplex and interior-point methods for linear programming (Chapter 3).
• Integrates modeling and formulation with coverage of optimization algorithms to develop an understanding of formulation skills, algorithms and principles simultaneously. Pg.___
• Highlights all main definitions, principles and algorithms in special-interest boxes. Pg.___
• Features one- to two-page Primers reviewing prerequisite material where necessary. Pg.___
• Sample exercises recap constructions and computations of longer developments. Pg.___
• Includes a wealth of charts, tables and figures. Pg.___
• Exercises at the ends of chapters provide practice with calculations, concepts, and formulation of small problems, in addition to developing modeling skills with more challenging problems drawn from O. R. practice. Pg.___
• Selected answers are provided at the back of the book and an Instructor's Manual is available with full solutions.

Table of Contents

 1. Problem Solving with Mathematical Models.


 2. Deterministic Optimization Models in Operations Research.


 3. Improving Search.


 4. Linear Programming Models.


 5. Simplex Search for Linear Programming.


 6. Interior Point Methods for Linear Programming.


 7. Duality and Sensitivity in Linear Programming.


 8. Multiobjective Optimization and Goal Programming.


 9. Shortest Path and Discrete Dynamic Programming.


10. Network Flows.


11. Discrete Optimization Models.


12. Discrete Optimization Methods.


13. Unconstrained Nonlinear Programming.


14. Constrained Nonlinear Programming.