Master constraint programming techniques to solve complex optimization problems in scheduling, routing, and resource allocation.
Master constraint programming techniques to solve complex optimization problems in scheduling, routing, and resource allocation.
This advanced course provides comprehensive training in constraint programming, focusing on solving complex combinatorial optimization problems. Students will develop a custom constraint programming solver in Java, learning to implement various constraints and search strategies. The course covers essential topics from basic backtracking to advanced concepts like global constraints, scheduling, and vehicle routing problems. Through hands-on projects, participants will gain practical experience in designing efficient algorithms for resource optimization, making this course valuable for those seeking to tackle complex computational challenges.
Instructors:
English
English
What you'll learn
Understand the constraint programming paradigm and its applications
Design and implement a modern constraint programming library
Develop efficient modeling techniques using constraint programming
Implement new global constraints and extend solver capabilities
Create custom and black-box search strategies
Solve complex scheduling and vehicle routing problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 10 modules in this course
This comprehensive course delves into the principles and applications of constraint programming for solving complex optimization problems. The curriculum progressively builds from fundamental concepts to advanced techniques, teaching students to design and implement their own constraint programming library in Java. Through a project-based approach, participants learn about global constraints, search strategies, and practical applications in scheduling and vehicle routing. The course emphasizes both theoretical understanding and practical implementation, providing students with hands-on experience in developing efficient solutions for real-world optimization challenges.
From Backtracking Search to the Constraint Programming Paradigm
Module 1
Introduction to Mini-CP
Module 2
The sum and element global constraints
Module 3
The extensional table constraint
Module 4
The alldifferent constraint
Module 5
The Circuit constraint and Vehicle Routing Problems (VRP)
Module 6
Cumulative Scheduling Problems
Module 7
Disjunctive Scheduling Problems
Module 8
Blackbox Search
Module 9
Modeling
Module 10
Fee Structure
Instructors
1 Course
Distinguished Computer Science Expert and Optimization Specialist
Pierre Schaus serves as Professor of Computer Science at UCLouvain, where he specializes in artificial intelligence, optimization, constraint programming, and machine learning. His research focuses on developing efficient algorithms for scheduling, vehicle routing, and combinatorial optimization problems. As the instructor of UCLouvain's constraint programming course, he teaches advanced concepts in solver architecture, search techniques, and practical applications. His contributions include pioneering work in solving over-constrained problems through the Variable Objective Large Neighborhood Search (VO-LNS) framework. He maintains an active research program developing practical approaches to complex optimization challenges while leading the development of educational resources including the minicp.org platform. His expertise spans theoretical foundations and practical applications of constraint programming, particularly in areas of scheduling and resource allocation.
1 Course
Distinguished Computer Science Expert and Optimization Specialist
Laurent Michel serves as Professor of Computer Science at the University of Connecticut and co-Director of the Connecticut Advanced Computing Center. After completing his education at Notre-Dame de la Paix (Belgium) and Brown University, where he earned his PhD in 1999, he has established himself as a leading researcher in constraint programming and optimization. His research focuses on developing domain-specific languages for combinatorial optimization, with significant contributions to constraint-based local search and optimization modeling. His work includes pioneering publications in constraint programming, including "Constraint-based Local Search" and "Numerica: A Modeling Language for Global Optimization" published by MIT Press. As an educator and researcher since joining UConn in 2002, he has contributed to various fields including power systems optimization, warehouse location problems, and transparent parallelization of constraint programming. His research has garnered over 5,000 citations, demonstrating his significant impact on the field of computer science and optimization
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