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.
Basic Modeling for Discrete Optimization
Instructors: Prof. Peter James Stuckey
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There are 4 modules in this course
In this first module, you will learn the basics of MiniZinc, a high-level modeling language for discrete optimization problems. Combining the simplicity of MiniZinc with the power of open-source industrial solving technologies, you will learn how to solve applications such as knapsack problems, graph coloring, production planning and tricky Cryptarithm puzzles, with great ease.
What's included
19 videos7 readings1 programming assignment
In this module, you will learn how to model problems involving set selection. In particular, you will see different ways of representing set variables when the variable has no constraints on its cardinality, has fixed cardinality and bounded cardinality. You also have to ensure all model decisions are valid decisions, and each valid decision corresponds to exactly one model decision.
What's included
6 videos1 reading1 programming assignment
In this module, you will learn how to model pure assignment problems and partition problems, which are functions in disguise. These problems find applications in rostering and constrained clustering. In terms of modeling techniques, you will see the power of common subexpression elimination and intermediate variables, and encounter the global cardinality constraint for the first time. MiniZinc also provides constraints for removing value symmetries.
What's included
7 videos1 reading1 programming assignment
In the final module of this course you will see how discrete optimization problems can often be seen from multiple viewpoints, and modeled completely differently from each viewpoint. Each viewpoint may have strengths and weaknesses, and indeed the different models can be combined to help each other.
What's included
6 videos2 readings1 programming assignment
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The University of Melbourne
The University of Melbourne
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The University of Melbourne
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Reviewed on May 5, 2017
Very interesting, but I miss some theory explaining how the optimizations work behind.
Reviewed on Oct 3, 2017
very nice introduction to minizinc with great and easy assignments to get you started
Reviewed on Oct 14, 2020
Teaches the basics of modelling really well. I'm looking forward to the follow-up course.
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