Optimization problems arise whenever one seeks to use activities in the best possible way, to maximize profits, to minimize costs, or more generally to find a "best" solution to a complex problem. Discrete Optimization models are those optimization models that involve a discrete structure, such as when activity levels are restricted to discrete values or when modeling complex logical relationships (Integer Programming), or when optimizing over a combinatorial structure, such as a graph or a network (Combinatorial Optimization). Discrete optimization applies to many functional fields of management, such as production and operations, supply chain, transportation and logistics, project planning, health care, marketing, as well as capital budgeting and investment planning involving discrete activities. It also applies to several disciplines in science, such as computer science, mathematics, physics and biology, and to many fields in engineering.
The course will present fundamental models and methods in discrete optimization. The emphasis will be placed on useful modeling methodologies and their application in some of the areas mentioned above. The course will present guidelines for choosing among alternate formulations, as well as among alternate solution approaches.
The course will consist of lectures, team homework assignments, and an individual Final Exam. The homework assignments will include several case studies presenting practical applications of discrete optimization models.
Instructor Biography – Maurice Queyranne
Course Outline – Class of 2018 (Updated August 3, 2017)
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