Spaces:
Sleeping
Sleeping
File size: 19,468 Bytes
210d88d 17dea99 210d88d 17dea99 210d88d bac9b0d 210d88d bac9b0d 210d88d 17dea99 210d88d 863e9ab dfc2413 863e9ab 210d88d 17dea99 210d88d 6dfe51d 210d88d 6dfe51d 81bc3c3 275ea78 210d88d 17dea99 210d88d 17dea99 210d88d 17dea99 210d88d 17dea99 6dfe51d 210d88d 275ea78 210d88d 6dfe51d 210d88d 6dfe51d 210d88d 275ea78 210d88d d30d73f 17dea99 6dfe51d 17dea99 6dfe51d 17dea99 6dfe51d 17dea99 7b77d8a 81bc3c3 275ea78 81bc3c3 275ea78 81bc3c3 17dea99 81bc3c3 17dea99 81bc3c3 17dea99 81bc3c3 17dea99 dfc2413 81bc3c3 17dea99 81bc3c3 17dea99 81bc3c3 17dea99 81bc3c3 210d88d 17dea99 210d88d 6dfe51d 17dea99 6dfe51d 17dea99 6dfe51d 17dea99 6dfe51d 17dea99 6dfe51d 275ea78 6dfe51d 275ea78 210d88d 6dfe51d 210d88d 6dfe51d 210d88d 275ea78 6dfe51d 17dea99 275ea78 6dfe51d 275ea78 17dea99 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 | # objective_engine.py
from collections import defaultdict
from typing import List, Dict, Any
from ortools.sat.python import cp_model
from models import Task, Faculty, Section, SubjectType
from constraint_engine import ConstraintEngine
import constants as const
class ObjectiveEngine:
def __init__(
self,
model: cp_model.CpModel,
ce: ConstraintEngine,
tasks: List[Task],
faculties: List[Faculty],
sections: List[Section],
weights: Dict[str, int] = None
):
self.model = model
self.ce = ce
self.tasks = tasks
self.faculties = faculties
self.sections = sections
self.weights = weights or {
"subject_repetition": 100,
"morning_core": 50,
"late_heavy": 50,
"faculty_gaps": 5000,
"student_gaps": 10000,
"isolated_afternoon": 1000,
"campus_movement": 30,
"faculty_load_balance": 10,
"no_first_hour_free": 5000,
"pack_morning": 200,
"avoid_late_afternoon": 200
}
self.penalties: List[cp_model.IntVar] = []
# Precompute common variables for extreme efficiency
self.t_day = {}
self.t_dstart = {}
self.t_dend = {}
for task in self.tasks:
start_var = self.ce.task_vars[task.task_id][0]
day = self.model.NewIntVar(0, const.NUM_WORKING_DAYS - 1, f"t_{task.task_id}_day")
self.model.AddDivisionEquality(day, start_var, const.NUM_TEACHING_SLOTS_PER_DAY)
self.t_day[task.task_id] = day
d_start = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY - 1, f"t_{task.task_id}_dstart")
self.model.AddModuloEquality(d_start, start_var, const.NUM_TEACHING_SLOTS_PER_DAY)
self.t_dstart[task.task_id] = d_start
# Note: End slot could reach NUM_TEACHING_SLOTS_PER_DAY (which means it ends at the very end of the day)
d_end = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"t_{task.task_id}_dend")
self.model.Add(d_end == d_start + task.duration)
self.t_dend[task.task_id] = d_end
def build_objective(self):
"""
Applies all configured soft constraints and sets the minimization objective.
"""
print("Building Objective Function...")
self._minimize_subject_repetition()
self._prioritize_morning_core_subjects()
self._avoid_late_heavy_subjects()
self._minimize_faculty_gaps()
self._minimize_student_gaps()
self._penalize_isolated_afternoon_classes()
self._minimize_campus_movement()
self._penalize_first_hour_free()
self._penalize_empty_morning_slots()
self._penalize_late_afternoon_slots()
# Summation of all penalties
if self.penalties:
total_cost = sum(self.penalties)
self.model.Minimize(total_cost)
else:
self.model.Minimize(0)
def _minimize_subject_repetition(self):
weight = self.weights.get("subject_repetition", 0)
if weight == 0: return
tasks_by_sec_sub = {}
for task in self.tasks:
if task.subject.subject_type == SubjectType.THEORY:
key = (task.section.section_id, task.subject.subject_code)
if key not in tasks_by_sec_sub:
tasks_by_sec_sub[key] = []
tasks_by_sec_sub[key].append(task)
for (sec_id, sub_code), subject_tasks in tasks_by_sec_sub.items():
if len(subject_tasks) < 2: continue
for i in range(len(subject_tasks)):
for j in range(i + 1, len(subject_tasks)):
t1 = subject_tasks[i]
t2 = subject_tasks[j]
day_1 = self.t_day[t1.task_id]
day_2 = self.t_day[t2.task_id]
same_day = self.model.NewBoolVar(f"same_day_{t1.task_id}_{t2.task_id}")
self.model.Add(day_1 == day_2).OnlyEnforceIf(same_day)
self.model.Add(day_1 != day_2).OnlyEnforceIf(same_day.Not())
self.penalties.append(same_day * weight)
def _prioritize_morning_core_subjects(self):
weight = self.weights.get("morning_core", 0)
if weight == 0: return
afternoon_start_index = 4
for task in self.tasks:
if task.subject.is_core and task.subject.subject_type == SubjectType.THEORY:
daily_slot = self.t_dstart[task.task_id]
is_afternoon = self.model.NewBoolVar(f"is_afternoon_{task.task_id}")
self.model.Add(daily_slot >= afternoon_start_index).OnlyEnforceIf(is_afternoon)
self.model.Add(daily_slot < afternoon_start_index).OnlyEnforceIf(is_afternoon.Not())
self.penalties.append(is_afternoon * weight)
def _avoid_late_heavy_subjects(self):
weight = self.weights.get("late_heavy", 0)
if weight == 0: return
last_slot_index = const.NUM_TEACHING_SLOTS_PER_DAY - 1
for task in self.tasks:
if task.subject.is_heavy:
daily_slot = self.t_dstart[task.task_id]
is_last_slot = self.model.NewBoolVar(f"is_last_slot_{task.task_id}")
self.model.Add(daily_slot == last_slot_index).OnlyEnforceIf(is_last_slot)
self.model.Add(daily_slot != last_slot_index).OnlyEnforceIf(is_last_slot.Not())
self.penalties.append(is_last_slot * weight)
def _minimize_faculty_gaps(self):
weight = self.weights.get("faculty_gaps", 30)
if weight == 0: return
tasks_by_faculty = {f.id: [] for f in self.faculties}
faculty_ids_set = {f.id for f in self.faculties}
for task in self.tasks:
parts = task.faculty.id.split('_')
fids = parts if len(parts) > 1 and all(p in faculty_ids_set for p in parts) else [task.faculty.id]
for fid in fids:
if fid in tasks_by_faculty:
tasks_by_faculty[fid].append(task)
for faculty_id, f_tasks in tasks_by_faculty.items():
if not f_tasks: continue
for day in range(const.NUM_WORKING_DAYS):
min_start_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"fac_{faculty_id}_min_s_d{day}")
max_end_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"fac_{faculty_id}_max_e_d{day}")
start_vars = [const.NUM_TEACHING_SLOTS_PER_DAY]
end_vars = [0]
total_duration = 0
for task in f_tasks:
is_on_day = self.model.NewBoolVar(f"fac_{faculty_id}_on_d{day}_t{task.task_id}")
t_day = self.t_day[task.task_id]
self.model.Add(t_day == day).OnlyEnforceIf(is_on_day)
self.model.Add(t_day != day).OnlyEnforceIf(is_on_day.Not())
start_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, "")
self.model.Add(start_on_day == self.t_dstart[task.task_id]).OnlyEnforceIf(is_on_day)
self.model.Add(start_on_day == const.NUM_TEACHING_SLOTS_PER_DAY).OnlyEnforceIf(is_on_day.Not())
start_vars.append(start_on_day)
end_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, "")
self.model.Add(end_on_day == self.t_dend[task.task_id]).OnlyEnforceIf(is_on_day)
self.model.Add(end_on_day == 0).OnlyEnforceIf(is_on_day.Not())
end_vars.append(end_on_day)
total_duration += task.duration * is_on_day
self.model.AddMinEquality(min_start_on_day, start_vars)
self.model.AddMaxEquality(max_end_on_day, end_vars)
gap = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"fac_{faculty_id}_gap_d{day}")
self.model.AddMaxEquality(gap, [0, max_end_on_day - min_start_on_day - total_duration])
self.penalties.append(gap * weight)
def _minimize_student_gaps(self):
weight = self.weights.get("student_gaps", 100)
if weight == 0: return
tasks_by_section = defaultdict(list)
for task in self.tasks:
tasks_by_section[task.section.section_id].append(task)
for sec_id, s_tasks in tasks_by_section.items():
if not s_tasks: continue
for day in range(const.NUM_WORKING_DAYS):
min_start_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"sec_{sec_id}_min_s_d{day}")
max_end_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"sec_{sec_id}_max_e_d{day}")
start_vars = [const.NUM_TEACHING_SLOTS_PER_DAY]
end_vars = [0]
total_duration = 0
for task in s_tasks:
is_on_day = self.model.NewBoolVar(f"sec_{sec_id}_on_d{day}_t{task.task_id}")
t_day = self.t_day[task.task_id]
self.model.Add(t_day == day).OnlyEnforceIf(is_on_day)
self.model.Add(t_day != day).OnlyEnforceIf(is_on_day.Not())
start_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, "")
self.model.Add(start_on_day == self.t_dstart[task.task_id]).OnlyEnforceIf(is_on_day)
self.model.Add(start_on_day == const.NUM_TEACHING_SLOTS_PER_DAY).OnlyEnforceIf(is_on_day.Not())
start_vars.append(start_on_day)
end_on_day = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, "")
self.model.Add(end_on_day == self.t_dend[task.task_id]).OnlyEnforceIf(is_on_day)
self.model.Add(end_on_day == 0).OnlyEnforceIf(is_on_day.Not())
end_vars.append(end_on_day)
total_duration += task.duration * is_on_day
self.model.AddMinEquality(min_start_on_day, start_vars)
self.model.AddMaxEquality(max_end_on_day, end_vars)
late_start_weight = self.weights.get("no_first_hour_free", 5000)
if weight > 0:
gap = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"sec_{sec_id}_gap_d{day}")
self.model.AddMaxEquality(gap, [0, max_end_on_day - min_start_on_day - total_duration])
self.penalties.append(gap * weight)
if late_start_weight > 0:
is_free_day = self.model.NewBoolVar(f"sec_{sec_id}_free_d{day}")
self.model.Add(total_duration == 0).OnlyEnforceIf(is_free_day)
self.model.Add(total_duration > 0).OnlyEnforceIf(is_free_day.Not())
late_start_penalty = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"sec_{sec_id}_late_d{day}")
self.model.Add(late_start_penalty == 0).OnlyEnforceIf(is_free_day)
self.model.Add(late_start_penalty == min_start_on_day).OnlyEnforceIf(is_free_day.Not())
self.penalties.append(late_start_penalty * late_start_weight)
def _penalize_isolated_afternoon_classes(self):
weight = self.weights.get("isolated_afternoon", 10)
if weight == 0: return
afternoon_start_index = 4
tasks_by_section = defaultdict(list)
for task in self.tasks:
tasks_by_section[task.section.section_id].append(task)
for sec_id, s_tasks in tasks_by_section.items():
for day in range(const.NUM_WORKING_DAYS):
afternoon_duration_sum = []
for task in s_tasks:
daily_slot = self.t_dstart[task.task_id]
t_day = self.t_day[task.task_id]
is_on_day = self.model.NewBoolVar(f"is_on_day_{task.task_id}_{day}_aft")
self.model.Add(t_day == day).OnlyEnforceIf(is_on_day)
self.model.Add(t_day != day).OnlyEnforceIf(is_on_day.Not())
is_afternoon = self.model.NewBoolVar(f"is_afternoon_{task.task_id}_{day}_aft")
self.model.Add(daily_slot >= afternoon_start_index).OnlyEnforceIf(is_afternoon)
self.model.Add(daily_slot < afternoon_start_index).OnlyEnforceIf(is_afternoon.Not())
is_on_day_and_afternoon = self.model.NewBoolVar(f"is_on_day_and_afternoon_{task.task_id}_{day}")
self.model.AddBoolAnd([is_on_day, is_afternoon]).OnlyEnforceIf(is_on_day_and_afternoon)
dur_term = self.model.NewIntVar(0, task.duration, f"aft_dur_{task.task_id}_{day}")
self.model.Add(dur_term == task.duration).OnlyEnforceIf(is_on_day_and_afternoon)
self.model.Add(dur_term == 0).OnlyEnforceIf(is_on_day_and_afternoon.Not())
afternoon_duration_sum.append(dur_term)
total_aft_duration = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY, f"total_aft_dur_sec{sec_id}_d{day}")
self.model.Add(total_aft_duration == sum(afternoon_duration_sum))
is_isolated = self.model.NewBoolVar(f"is_isolated_aft_sec{sec_id}_d{day}")
is_gt_0 = self.model.NewBoolVar(f"aft_gt_0_sec{sec_id}_d{day}")
self.model.Add(total_aft_duration > 0).OnlyEnforceIf(is_gt_0)
self.model.Add(total_aft_duration == 0).OnlyEnforceIf(is_gt_0.Not())
is_le_2 = self.model.NewBoolVar(f"aft_le_2_sec{sec_id}_d{day}")
self.model.Add(total_aft_duration <= 2).OnlyEnforceIf(is_le_2)
self.model.Add(total_aft_duration > 2).OnlyEnforceIf(is_le_2.Not())
self.model.AddBoolAnd([is_gt_0, is_le_2]).OnlyEnforceIf(is_isolated)
self.penalties.append(is_isolated * weight)
def _minimize_campus_movement(self):
weight = self.weights.get("campus_movement", 3)
if weight == 0: return
tasks_by_section = defaultdict(list)
for task in self.tasks:
tasks_by_section[task.section.section_id].append(task)
for sec_id, s_tasks in tasks_by_section.items():
if len(s_tasks) < 2: continue
for i in range(len(s_tasks)):
for j in range(i + 1, len(s_tasks)):
t1 = s_tasks[i]
t2 = s_tasks[j]
start1 = self.ce.task_vars[t1.task_id][0]
end1 = self.ce.task_vars[t1.task_id][1]
room1 = self.ce.task_vars[t1.task_id][3]
start2 = self.ce.task_vars[t2.task_id][0]
end2 = self.ce.task_vars[t2.task_id][1]
room2 = self.ce.task_vars[t2.task_id][3]
is_consecutive_1_2 = self.model.NewBoolVar(f"cons_{t1.task_id}_{t2.task_id}")
self.model.Add(end1 == start2).OnlyEnforceIf(is_consecutive_1_2)
self.model.Add(end1 != start2).OnlyEnforceIf(is_consecutive_1_2.Not())
is_consecutive_2_1 = self.model.NewBoolVar(f"cons_{t2.task_id}_{t1.task_id}")
self.model.Add(end2 == start1).OnlyEnforceIf(is_consecutive_2_1)
self.model.Add(end2 != start1).OnlyEnforceIf(is_consecutive_2_1.Not())
are_consecutive = self.model.NewBoolVar(f"are_cons_{t1.task_id}_{t2.task_id}")
self.model.AddBoolOr([is_consecutive_1_2, is_consecutive_2_1]).OnlyEnforceIf(are_consecutive)
self.model.AddBoolAnd([is_consecutive_1_2.Not(), is_consecutive_2_1.Not()]).OnlyEnforceIf(are_consecutive.Not())
same_room = self.model.NewBoolVar(f"same_room_{t1.task_id}_{t2.task_id}")
self.model.Add(room1 == room2).OnlyEnforceIf(same_room)
self.model.Add(room1 != room2).OnlyEnforceIf(same_room.Not())
move_penalty = self.model.NewBoolVar(f"move_{t1.task_id}_{t2.task_id}")
self.model.AddBoolAnd([are_consecutive, same_room.Not()]).OnlyEnforceIf(move_penalty)
self.penalties.append(move_penalty * weight)
def _penalize_first_hour_free(self):
pass
def _penalize_empty_morning_slots(self):
weight = self.weights.get("pack_morning", 50)
if weight == 0: return
tasks_by_section = defaultdict(list)
for task in self.tasks:
tasks_by_section[task.section.section_id].append(task)
for sec_id, s_tasks in tasks_by_section.items():
if not s_tasks: continue
morning_overlaps = []
for task in s_tasks:
d_start = self.t_dstart[task.task_id]
d_end = self.t_dend[task.task_id]
capped_start = self.model.NewIntVar(0, 4, "")
self.model.AddMinEquality(capped_start, [d_start, 4])
capped_end = self.model.NewIntVar(0, 4, "")
self.model.AddMinEquality(capped_end, [d_end, 4])
overlap = self.model.NewIntVar(0, 4, "")
self.model.Add(overlap == capped_end - capped_start)
morning_overlaps.append(overlap)
# Total morning slots are 20 (5 days * 4 slots)
total_slots = const.NUM_WORKING_DAYS * 4
empty_slots = self.model.NewIntVar(0, total_slots, f"sec_{sec_id}_empty_morning")
self.model.Add(empty_slots == total_slots - sum(morning_overlaps))
self.penalties.append(empty_slots * weight)
def _penalize_late_afternoon_slots(self):
weight = self.weights.get("avoid_late_afternoon", 40)
if weight == 0: return
for task in self.tasks:
d_start = self.t_dstart[task.task_id]
d_end = self.t_dend[task.task_id]
capped_start = self.model.NewIntVar(6, const.NUM_TEACHING_SLOTS_PER_DAY, "")
self.model.AddMaxEquality(capped_start, [d_start, 6])
capped_end = self.model.NewIntVar(6, const.NUM_TEACHING_SLOTS_PER_DAY, "")
self.model.AddMaxEquality(capped_end, [d_end, 6])
overlap = self.model.NewIntVar(0, const.NUM_TEACHING_SLOTS_PER_DAY - 6, "")
self.model.Add(overlap == capped_end - capped_start)
self.penalties.append(overlap * weight)
|