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8385c96 872925c 8385c96 872925c 8385c96 872925c 8385c96 61ca3d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # solver.py
from typing import List, Dict, Optional, Any, Tuple
from ortools.sat.python import cp_model
from models import Task, Faculty, Section, Room
from constraint_engine import ConstraintEngine
class TimetableSolver:
def __init__(self, tasks, faculties, sections, rooms):
self.tasks = tasks
self.faculties = faculties
self.sections = sections
self.rooms = rooms
self.model = cp_model.CpModel()
self.solver = cp_model.CpSolver()
self.constraint_engine = None
self.objective_engine = None
def solve(
self,
time_limit_seconds: int = 60,
enable_soft_constraints: bool = True,
soft_constraint_weights: Dict[str, int] = None,
log_search_progress: bool = True,
num_workers: int = 8,
slm_constraints: List[Dict[str, Any]] = None,
scheduling_rules: List[Dict[str, Any]] = None,
) -> Tuple[str, Optional[Dict[str, Any]]]:
# 1. Hard constraints
print("Initializing Constraint Engine...")
self.constraint_engine = ConstraintEngine(
model=self.model, tasks=self.tasks, faculties=self.faculties,
sections=self.sections, rooms=self.rooms,
slm_constraints=slm_constraints, scheduling_rules=scheduling_rules
)
self.constraint_engine.apply_all_constraints()
# 2. Soft constraints
if enable_soft_constraints:
print("Initializing Objective Engine...")
from objective_engine import ObjectiveEngine
self.objective_engine = ObjectiveEngine(
model=self.model,
constraint_engine=self.constraint_engine,
tasks=self.tasks, faculties=self.faculties,
sections=self.sections, rooms=self.rooms,
weights=soft_constraint_weights
)
self.objective_engine.build_objective()
# 4. Configure solver
self.solver.parameters.max_time_in_seconds = time_limit_seconds
self.solver.parameters.log_search_progress = log_search_progress
self.solver.parameters.num_search_workers = num_workers
# 5. Solve
print(f"Starting solver (Limit: {time_limit_seconds}s)...")
status_val = self.solver.Solve(self.model)
status_map = {
cp_model.OPTIMAL: "OPTIMAL",
cp_model.FEASIBLE: "FEASIBLE",
cp_model.INFEASIBLE: "INFEASIBLE",
cp_model.MODEL_INVALID: "MODEL_INVALID",
cp_model.UNKNOWN: "UNKNOWN"
}
status_str = status_map.get(status_val, "UNKNOWN")
print(f"Solver finished: {status_str}")
solution = None
if status_val in (cp_model.OPTIMAL, cp_model.FEASIBLE):
solution = self._extract_solution()
print(f"Solution found! Objective: {self.solver.ObjectiveValue()}")
else:
print("No solution found.")
return status_str, solution
def _extract_solution(self):
import constants as const
schedule = {}
for task in self.tasks:
start_var, _, _, room_var = self.constraint_engine.task_vars[task.task_id]
start_slot = self.solver.Value(start_var)
room_index = self.solver.Value(room_var)
assigned_room = self.rooms[room_index]
day_index = start_slot // const.NUM_TEACHING_SLOTS_PER_DAY
period_index = start_slot % const.NUM_TEACHING_SLOTS_PER_DAY
schedule[task.task_id] = {
"task_obj": task,
"start_slot": start_slot,
"day_index": day_index,
"day_name": const.DAYS[day_index],
"period_index": period_index,
"room_id": assigned_room.room_id,
"room_name": f"{assigned_room.room_id} ({assigned_room.building})",
"faculty_name": task.faculty.name,
"subject_code": task.subject.subject_code,
"section_id": task.section.section_id,
"duration": task.duration
}
return schedule |