anchor.diff:  
   == 5. Account for overconstrained planning
   
  -It can happen that there aren't enough resources to solve a planning problem without breaking hard constraints.
  +It can happen that there aren't enough resources to solve a planning problem without breaking a hard constraint.
   In that case, it's often better to leave some entities unassigned (as a little as possible of course).
   
   For example in employee rostering, when we need to assign 4 late shifts on the same day and we only have 3 employees,
   it's better to leave 1 shift unassigned than to assign an employee to 2 shifts at the same time.
   
   Going one step further, we can add virtual resources to indicate how many extra resources to buy/hire.
  -For example in the same employee rostering case, we could add 2 virtual employees. It will use only one of these
  -and tell us that we can make the schedule feasible again by hiring 1 extra employee.
  +For example in the same employee rostering case, we could add 2 virtual employees.
  +After solving, it will use one of these
  +which tell us that we can make the schedule feasible again by hiring 1 extra employee.
   
   Technically, we need to treat unassigned (or virtual assigned) entities differently in the constraints
   and add a medium score level (between hard and soft) to penalize the number of unassigned (or virtual assigned) entities.
  
positive.code: == 6. Be fair (load balancing)
  
  When distributing work across humans (or machines), don't use averages.
  The worst off human (or machine) counts the most.
  
  For example in employee rostering we want to distribute the shifts evenly,
  but we can't make it perfectly fair due to skill and other hard constraints.
  
negative.code: == 2. Implement all hard constraints
  
  *An optimal solution that takes only 99% of the hard constraints into account is 100% useless.*
  So implement all hard constraints.
  
  For example in Vehicle Routing, let's suppose we need to pickup nuclear cargo too,
  but forget to add a hard constraint to pick those up with a special vehicle:
  