Engineering

Basic text on deterministic optimization methods. Techniques of modeling real world decision making problems, modeling examples that illustrate the use of modeling techniques, and a variety of problem classes are presented. Various types of algorithms with explanations of how each algorithm works and what conclusion can be drawn from its output, and a review of Matrix Algebra and Geometry and a chapter on Heuristic Methods.

• organizes coverage to be adaptable to the extent of student's backgrounds and experiences.
• reveals that simple heuristics are not enough when solving complex optimization problems so students learn the importance of using techniques that are guaranteed to yield optimum solutions.
• models mathematically and categorizes the many different types of optimization problems covered, including:
• linear.
• discrete.
• 0-1.
• combinatorial.
• dynamic.
• nonlinear.
• offers over 250 exercises and 100 worked examples from a variety of application areas that cover each type of optimization problem.
• extends coverage past the solution to explain to students what useful information can be derived from the output of an optimization algorithm.
• adheres to the goal programming technique and heuristic methods to reach the perfect levels of diversity, complexity, and relevance to the real world.
• explains the importance of efficient strategies to compute good lower bounds for the minimum objective value in a variety of optimization models.
• reviews the essential concepts of matrix algebra—including linear independence, rank, bases, pivot steps, elimination steps, updating inverses, etc.—for students with limited backgrounds.
• constructs a strong pedagogical foundation, including:
• historical anecdotes on different types of of optimization problems.
• flow charts for each algorithm.
• numerous examples that illustrate every type of possibility that can occur.
• formulation exercises from different areas of application.
• a glossary where all technical terms and symbols are clearly defined.

1. Introduction.
2. Modeling Linear Programs.
3. Review of Matrix Algebra and Geometry.
4. Duality and Optimality Conditions in LP.
5. Hungarian Method: A Primal-Dual Method for the Assignment Problem.
6. Primal Algorithm for the Transportation Problem.
7. The Simplex Method for General LP.
8. Algorithms for Multiobjective Models.
9. Modeling Integer and Combinatorial Programs.
10. The Branch and Bound Approach.
11. Heuristic Methods for Combinatorial Optimization Problems.
12. Dynamic Programming.
13. Critical Path Methods in Project Management.
14. Nonlinear Programming.
Index.

Operations Research [CORE TEXTS] (Industrial Engineering)
Operations Research [CORE TEXTS] (Decision Science)
Mathematical Programming [CORE TEXTS] (Decision Science)

View a Sample Chapter PDF:

Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students, contact your Pearson Higher Education representative for pricing and ordering information.

Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students contact your Pearson Higher Education representative.