MATH-615 Introduction to Operations Research
Spring for 2017-2018
Math 615 Introduction to Operations Research
This is a course covering the foundations of operations research with emphasis on mathematical modeling and linear optimization. Linear optimization (or linear programming, LP) is a fundamental branch of optimization, with applications to several areas such as physical sciences, health care, manufacturing, logistics, computer science, and finance. This course will provide an integrated view of the theory, solution techniques and applications of LP.
There are many classic examples of optimization via LP. The diet problem takes a set of foods that have varying nutrient levels and costs. The constraint would be in the form of a recommended daily minimum nutritional intake, and the problem is to satisfy the nutritional goals at minimum cost. There is a suite of utilization of resources problems wherein, for example, land can be used to grow various grain types at assorted costs and for maximum profit; i.e., some species are cheaper to grow but less profitable, and the question is how the land should be apportioned.
A background in linear algebra and multivariate calculus is assumed. Topics covered include mathematical modeling, LP formulation, geometry of linear programming, the simplex method, duality theory and sensitivity analysis, and network flows. This course also introduces students to more advanced mathematical programming approaches such as non-linear and integer programming.
Mathematical modeling software will be used throughout the course for solving real-life examples.
No text required. Reserved library reading materials will be announced. References are Operations Research: Applications and Algorithms, by W L Winston (July, 2003) and Operations Research: An Introduction, (September, 2010)by H A Taha.
Must be enrolled in one of the following Levels:
MN or MC Graduate
Must be enrolled in one of the following Majors:
Mathematics and Statistics
Prerequisites: Math 137, Math 150
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