Basic Modeling for Discrete Optimization

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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.

7 hours to complete
MiniZinc introduction
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.
20 videos (Total 219 min) 7 readings 1 quiz

5 hours to complete
Modeling with Sets
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.
6 videos (Total 64 min) 1 reading 1 quiz

8 hours to complete
Modeling with Functions
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.
7 videos (Total 86 min) 1 reading 1 quiz

8 hours to complete
Multiple Modeling
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.
6 videos (Total 67 min) 3 readings 1 quiz

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Cung cấp bởi: Coursera /  The University of Melbourne

Thời lượng: 28 giờ
Ngôn ngữ giảng dạy: Tiếng Anh
Chi phí: Miễn phí / 0
Đối tượng: Intermediate

Thông tin về nhà cung cấp

Coursera (/ kərˈsɛrə /) là một nền tảng học tập trực tuyến toàn cầu được thành lập vào năm 2012 bởi 2 giáo sư khoa học máy tính của đại học Stanford là Andrew NgDaphne Koller, nền tảng này cung cấp các khóa học trực tuyến (MOOC) cho cộng đồng người học online.

Coursera hợp tác với các trường đại học danh tiếng tại Bắc Mỹ và trên khắp thế giới, cùng với nhiều tổ chức khác để cung cấp các khóa học trực tuyến chất lượng, theo chuyên ngành và được cấp chứng chỉ trong nhiều lĩnh vực như kỹ thuật, khoa học dữ liệu, học máy, toán học, kinh doanh, khoa học máy tính, tiếp thị kỹ thuật số, nhân văn, y học, sinh học, khoa học xã hội , và nhiều ngành khác.

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