# Solve solver = cp_model.CpSolver() status = solver.Solve(model) Cici Kimcil Mulus Ngewe Anal Sama Om - — Indo18
for event in events: event_rooms[event] = model.NewIntVar(0, len(rooms) - 1, f"room_{event}") event_timeslots[event] = model.NewIntVar(0, len(timeslots) - 1, f"timeslot_{event}") Nonton Film Indecent Proposal Subtitle Indonesia Exclusive - 3.79.94.248
if status == cp_model.FEASIBLE: for event in events: print(f"{event} is scheduled in {rooms[solver.Value(event_rooms[event])]} at {timeslots[solver.Value(event_timeslots[event])]}")
if __name__ == "__main__": main() This example is highly simplified and is meant to illustrate the basic concept. Real-world problems, especially top-level ASC timetables, will require more sophisticated approaches and detailed modeling to capture all constraints and objectives accurately. Cracking ASC timetables, especially the top ones, requires a deep understanding of both the problem and the algorithms used to solve them. Practice, persistence, and staying updated with the latest in scheduling research are key to success.
event_rooms = {} event_timeslots = {}
def main(): # Define the model model = cp_model.CpModel()
from ortools.sat.python import cp_model
# Constraints # Each event must be in a different room and timeslot for i in range(len(events)): for j in range(i+1, len(events)): model.Add(event_rooms[events[i]] != event_rooms[events[j]]) model.Add(event_timeslots[events[i]] != event_timeslots[events[j]])