anchor.diff:  
   == Define the constraints and calculate the score
   
  -A _score_ represents the quality of a given solution.
  +A _score_ represents the quality of a specific solution.
   The higher the better.
   OptaPlanner looks for the best solution, which is the solution with the highest score found in the available time.
  -It could be the _optimal_ solution.
  +It might be the _optimal_ solution.
   
   Because this use case has hard and soft constraints,
   use the `HardSoftScore` class to represent the score:
  
positive.code: 
  === The value range providers
  
  That `timeslotList` field is a value range provider.
  It holds the `Timeslot` instances which OptaPlanner can pick from to assign to the `timeslot` field of `Lesson` instances.
  The `timeslotList` field has an `@ValueRangeProvider` annotation to connect those two,
  by matching the `id` with the `valueRangeProviderRefs` of the `@PlanningVariable` in the `Lesson`.
  
  Following the same logic, the `roomList` field also has an `@ValueRangeProvider` annotation.
  
  
negative.code: :experimental:
  
  This guide walks you through the process of creating a Spring Boot
  application with OptaPlanner's constraint solving artificial intelligence (AI).
  
  == What you will build
  
  