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Introduction to Optimization Methods
Fall Term 2007
Course Description
Optimization is finding the best decision when there are many feasible options. Students will learn to formulate and solve optimization problems. The modeling and solution tools are useful in a variety of industries and business/government functions. Topics covered include: linear programming, integer programming, nonlinear programming, sensitivity analysis, combinatorial optimization, and practical lessons on deployment. Spreadsheet models will be the primary vehicles for building and solving optimization models.
Class Meetings
Tuesdays and Thursdays 10:10 - noon
X-period: Wednesdays 3:00 - 4:05pm
Classroom: 200 Cummings
Instructor
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Office Hours
By appointment; often available Tues/Thurs afternoons during the Fall term; drop-ins welcome
Principal Learning Objectives
- Translate a verbal or graphical description of a decision problem into a valid optimization model, by identifying variables, constraints, and an objective function.
- Express a given optimization model in an Excel spreadsheet, structured for use with Excel's Solver.
- Find solutions to optimization problems using appropriate algorithm and settings in Excel's Solver.
- Perform sensitivity analysis by tracing the effects of varying a parameter on the optimal decision variables and the objective function.
- Be aware of elements which facilitate successful deployment of optimization methods
Reading
The course text is Optimization Modeling with Spreadsheets, by K. R. Baker, Duxbury Press, 2006.
Supplementary readings will be provided through Blackboard