9.2.7 Sports Scheduling  Video 4: Logical Constraints  Summary and Q&A
TL;DR
Integer optimization allows for the addition of logical constraints using binary variables to restrict certain scenarios in a decisionmaking process.
Questions & Answers
Q: How can logical constraints be implemented in integer optimization?
Logical constraints can be implemented in integer optimization by using binary variables to represent different restrictions. These variables can be set to 0 or 1, and mathematical expressions can be used to enforce specific constraints.
Q: What is an example of a logical constraint using binary variables?
A example of a logical constraint is that teams A and B cannot play in both weeks 3 and 4. This can be represented by the constraint x_AB3 + x_AB4 <= 1, where x_AB3 and x_AB4 are binary variables representing whether teams A and B play in weeks 3 and 4.
Q: How can the constraint be enforced if teams A and B play in week 4, they must also play in week 2?
The constraint can be enforced by adding the constraint x_AB2 >= x_AB4, where x_AB2 and x_AB4 are binary variables representing whether teams A and B play in weeks 2 and 4. This ensures that if x_AB4 equals 1, x_AB2 must also be 1.
Q: How can the constraint be modeled when teams C and D must play in week 1 or 2 and not both?
The constraint can be modeled as x_CD1 + x_CD2 >= 1, where x_CD1 and x_CD2 are binary variables representing whether teams C and D play in weeks 1 and 2. This ensures that at least one of the variables is set to 1.
Summary & Key Takeaways

Integer optimization allows the addition of logical constraints using binary variables to restrict certain scenarios.

Constraints can be added to ensure that teams cannot play in both certain weeks, or that if they play in one week, they must play in another.

Adding logical constraints can lead to new solutions that may be more balanced but with a lower objective value.