Basic Modeling for Discrete Optimization
Learn discrete optimization modeling with MiniZinc. Master problem-solving techniques for real-world applications.
Learn discrete optimization modeling with MiniZinc. Master problem-solving techniques for real-world applications.
Dive into the world of discrete optimization with this intermediate-level course. Learn to model complex decision-making problems using MiniZinc, a high-level modeling language. Master techniques for solving knapsack problems, graph coloring, production planning, and cryptarithm puzzles. Explore set selection, assignment problems, and multiple modeling viewpoints. Gain practical skills in using industrial-grade solving technologies to tackle challenging optimization scenarios in various fields, from logistics to resource management. This course equips you with powerful tools to address real-world optimization challenges efficiently.
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What you'll learn
Understand the basics of MiniZinc and its application in discrete optimization
Model and solve knapsack problems, graph coloring, and production planning challenges
Master techniques for set selection, including fixed and bounded cardinality sets
Develop skills in modeling pure assignment problems and partition problems
Learn to use global constraints for efficient problem-solving
Explore multiple modeling viewpoints for complex optimization scenarios
Skills you'll gain
This course includes:
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There are 4 modules in this course
This course provides a comprehensive introduction to discrete optimization using MiniZinc, a high-level modeling language. Students will learn to tackle a wide range of optimization problems, from simple knapsack problems to complex production planning and resource allocation challenges. The curriculum covers key concepts such as set selection, assignment problems, partitioning, and multiple modeling viewpoints. By combining MiniZinc's simplicity with powerful open-source industrial solving technologies, learners will develop skills to model and solve real-world optimization problems efficiently. The course emphasizes practical application, enabling students to unlock the potential of advanced optimization techniques in various fields, including logistics, scheduling, and decision support systems.
MiniZinc introduction
Module 1 · 7 Hours to complete
Modeling with Sets
Module 2 · 4 Hours to complete
Modeling with Functions
Module 3 · 8 Hours to complete
Multiple Modeling
Module 4 · 7 Hours to complete
Fee Structure
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Instructors
Innovative Educator and Researcher in Constraint Satisfaction
Professor Jimmy Ho Man Lee is a Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, where he specializes in the theory and application of constraint satisfaction and optimization. His research encompasses critical areas such as combinatorial optimization, scheduling, and resource allocation. In addition to his scholarly contributions, which include serving on the editorial boards of several prestigious journals, he was the Program Chair for the 17th International Conference on Principles and Practice of Constraint Programming in Perugia, Italy, in 2011. As an educator, Professor Lee is passionate about transforming the learning experience for his students; he actively engages in pedagogical research focused on folklore-based and game-based learning methodologies. His dedication to teaching excellence has been recognized with the CUHK Vice-Chancellor’s Exemplary Teaching Award, which he received in both 2005 and 2016. Through his research and commitment to innovative teaching practices, Professor Lee is making significant contributions to the fields of computer science and education.
Professor at The University of Melbourne and Pioneer in Constraint Programming
Peter J. Stuckey is a Professor in the Department of Computing and Information Systems at The University of Melbourne. He is a pioneer in the field of constraint programming and has made significant contributions, including co-authoring one of the first constraint logic programming systems, CLP(R), and writing the first textbook on constraint programming. His accolades include the 2010 Google Australia Eureka Prize for Innovation in Computer Science and the 2010 University of Melbourne Woodward Medal for Science and Technology. His current research focuses on developing solver-independent modeling languages for complex optimization problems and advancing solving technologies.
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4.8 course rating
444 ratings
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