This library implements a contraint solver via the iterative forward search algorithm. It also includes a helper module specifically for using the algorithm to timetable events.
To use the CSP solver first create a CSP value which describes your CSP, for example
csp :: CSP Solution
csp = MkCSP {
cspVariables = IS.fromList [1,2,3],
cspDomains = IM.fromList [(1, [1, 2, 3]), (2, [1, 2, 4]), (3, [4, 5, 6])],
cspConstraints = [ (IS.fromList [1, 2], \a -> IM.lookup 1 a != IM.lookup 2 a)
, (IS.fromList [2, 3], \a -> IM.lookup 2 a >= IM.lookup 3 a)
],
cspRandomCap = 30, -- 10 * (# of variables) is a reasonable default
cspTermination = defaultTermination
}This example represents a CSP with 3 variables, 1, 2 and 3, where variable 1 has domain [1, 2, 3], variable 2 has domain [1, 2, 4], and variable 3 has domain [4, 5, 6]. The contraints are that variable 1 is not equal to variable 2, and variable 2 is at least as big as variable 3. It uses the default termination condition, and performs 30 iterations before we select variables randomly.
You can then find a solution simply by evaluating ifs csp, which will perform iterations till the given termination function returns a Just value.
The toCSP function in Data.IFS.Timetable takes a mapping from slot IDs to intervals, a hashmap of event IDs to the person IDs involved, and a map of person IDs to the slots where they are unavailable and generates a CSP which can then be solved with ifs. For example:
slotMap :: IntMap (Interval UTCTime)
slotMap = IM.fromList [(1, eventTime1), (2, eventTime2), (3, eventTime3)]
events :: HashMap Int [person]
events = HM.fromList [(1, [user1, user2]), (2, [user1])]
unavailability :: HashMap person (Set Int)
unavailability = HM.fromList [(user1, S.empty), (user2, S.fromList [1,3])]
csp :: CSP r
csp = toCSP slotMap events unavailability defaultTerminationThis will generate a CSP that creates a mapping from the events 1 and 2 to the time slots 1, 2 and 3.
- Variables and values must be integers
- Only hard constraints are supported