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Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars. Similar problems also underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional undergraduate computer science methods. This course is intended for students who have completed Basic Modelling for Discrete Optimization. In this course you will learn much more about solving challenging discrete optimization problems by stating the problem in a state-of-the-art high level modeling language, and letting library constraint solving software do the rest. This course will focus on debugging and improving models, encapsulating parts of models in predicates, and tackling advanced scheduling and packing problems. As you master this advanced technology, you will be able to tackle problems that were inconceivable to solve previously. Watch the course promotional video here:

Principales reseñas


7 de nov. de 2020

Excellent course! I learned a lot. Although the assignments were sometimes hard to fathom, and its online nature mean getting hints was difficult, there's enough there to help get through.


27 de ago. de 2020

Great course! Engaging content and not-so-easy assignments to make you understand the subject better. Thanks Prof. Stuckey and Prof. Lee for making this course available on Coursera!

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