anchor.diff:  
   [WARNING]
   ====
  -Custom `MoveListFactory` and `MoveIteratorFactory` implementations must make sure that they don't move immovable entities.
  +Custom `MoveListFactory` and `MoveIteratorFactory` implementations must ensure that they do not move immovable entities.
   ====
   
   
   [[nonvolatileReplanning]]
  -=== Nonvolatile Replanning to minimize disruption (Semi-movable Planning Entities)
  -
  -Replanning an existing plan can be very disruptive on the plan.
  -If the plan affects humans (such as employees, drivers, ...), very disruptive changes are often undesirable.
  -In such cases, nonvolatile replanning helps by restricting planning freedom: the gain of changing a plan must be higher than the disruption it causes.
  -This is usually implemented by taxing all planning entities that change.
  -
  -image::RepeatedPlanning/nonDisruptiveReplanning.png[align="center"]
  -
  -For example, in the Machine Reassignment example, the entity has both the planning variable `machine` and its original value ``originalMachine``:
  +=== Nonvolatile Replanning to Minimize Disruption (Semi-movable Planning Entities)
  +
  +Replanning an existing plan can be very disruptive. If the plan affects humans (such as employees, drivers, ...), very disruptive changes are often undesirable. In such cases, nonvolatile replanning helps by restricting planning freedom: the gain of changing a plan must be higher than the disruption it causes. This is usually implemented by taxing all planning entities that change.
  +
  +image::nonDisruptiveReplanning.png[align="center"]
  +
  +In the machine reassignment example, the entity has both the planning variable `machine` and its original value ``originalMachine``:
   
   [source,java,options="nowrap"]
   ----
  
positive.code: [[realTimePlanning]]
  == Real-time Planning
  
  To do real-time planning, first combine <<backupPlanning,backup planning>> and <<continuousPlanning,continuous planning>> with short planning windows to lower the burden of real-time planning.
  As time passes, the problem itself changes:
  
  image::RepeatedPlanning/realTimePlanningVehicleRouting.png[align="center"]
  
  In the example above, three customers are added at different times (``07:56``, `08:02` and ``08:45``), after the original customer set finished solving at `07:55` and in some cases after the vehicles already left.
  Planner can handle such scenario's with `ProblemFactChange` (in combination with <<immovablePlanningEntities,immovable planning entities>>).
  
  
  [[problemFactChange]]
  === `ProblemFactChange`
  
  While the `Solver` is solving, an outside event might want to change one of the problem facts, for example an airplane is delayed and needs the runway at a later time.
  Do not change the problem fact instances used by the `Solver` while it is solving (from another thread or even in the same thread), as that will corrupt it.
  Instead, add a `ProblemFactChange` to the `Solver` which it will execute in the solver thread as soon as possible.
  
  [source,java,options="nowrap"]
  ----
  
negative.code: 
  To implement this:
  
  . Add an additional score level (usually a medium level between the hard and soft level) by switching <<scoreType,`Score` type>>.
  . Make the planning variable <<nullablePlanningVariable,nullable>>.
  . Add a score constraint on the new level (usually a medium constraint) to penalize the number of unassigned entities (or a weighted sum of them).
  
  
  [[overconstrainedPlanningWithVirutalValues]]
  === Overconstrained Planning with Virtual Values
  
  In overconstrained planning it is often useful to know which resources are lacking.
  In overconstrained planning with virtual values, the solution indicates which resources to buy.
  
  To implement this:
  
  . Add an additional score level (usually a medium level between the hard and soft level) by switching <<scoreType,`Score` type>>.
  . Add a number of virtual values. It can be difficult to determine a good formula to calculate that number:
  ** Do not add too many, as that will decrease solver efficiency.
  ** Importantly, do not add too few as that will lead to an infeasible solution.
  . Add a score constraint on the new level (usually a medium constraint) to penalize the number of virtual assigned entities (or a weighted sum of them).
  . Optionally, change all soft constraints to ignore virtual assigned entities.
  
  [[continuousPlanning]]
  == Continuous Planning (Windowed Planning)
  
  Continuous planning is the technique of planning one or more upcoming planning periods at the same time and repeating that process monthly, weekly, daily, hourly, or even more frequently.
  However, as time is infinite, planning all future time periods is impossible.
  
  A planning window allows you to have a fixed number of upcoming planning time periods.
  
  The planning window can be split up in several parts:
  
  * _History_ 
  +
  Immutable past time periods. It contains only immovable entities.
  +
  ** Historic entities can also affect score constraints that apply to movable entities. For example, in nurse rostering, a nurse that has worked the last five historic days in a row should not be assigned on the first tentative day of the planning window, they will have worked too many consecutive days.
  ** Do not load all historic entities in memory: even though immovable entities do not affect solving performance, they can cause out of memory problems when the data grows to years. Only load those that might still affect the current constraints with a good safety margin, for example load the past year.
  * _Tentative_ 
  +
  The first few time periods that are being planned freely for the last time. After this planning, their planning entities become <<immovablePlanningEntities,immovable>> or <<nonvolatileReplanning,semi-immovable>>.
  +
  ** The result of the tentative planning is usually shared with the business. For example, in nurse rostering, the nurses will use this schedule to plan their personal lives. Changing that schedule later becomes disruptive, however, were exceptions force us to do so, disruption can be minimized (see <<nonvolatileReplanning,Nonvolatile Replanning>>).
  * _Draft_ 
  +
  The latter time periods that are being planned freely, but not for the last time. They are likely to change again in the next planning effort.
  +
  ** The draft part is needed to assure that the tentatively planned entities will allow room of a good, feasible planning afterwards. 
  +
  ** That draft part is usually not shared with the business yet, because it is too volatile. However, it is stored in the database and used a starting point for the next plan.
  
  * _Future_ (out of scope)
  + 
  Planning entities that are not in the current planning window.
  +
  ** If <<assigningTimeToPlanningEntities,time is a planning variable>>, there is no future part. Instead, if the planning window is too small to plan all entities, see  <<overconstrainedPlanning,overconstrained planning>>.
  
  image::RepeatedPlanning/continuousPlanningEmployeeRostering.png[align="center"]
  
  In the employee rostering example above, we re-plan every four days.
  Each time, we actually plan a window of 12 days, but we only share the tentative roster of the next four days with the employees.
  
  [NOTE]
  