Basic Modeling for Discrete Optimization
- 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.
- 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.
- 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.
- 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.