remove std:: from std::min|max in comments; performance improvements on the SAT, bug fixes
This commit is contained in:
Laurent Perron
123
examples/python/rostering_with_travel.py
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123
examples/python/rostering_with_travel.py
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from ortools.sat.python import cp_model
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def SolveRosteringWithTravel():
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model = cp_model.CpModel()
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# [duration, start, end, location]
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jobs = [[3, 0, 6, 1],
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[5, 0, 6, 0],
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[1, 3, 7, 1],
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[1, 3, 5, 0],
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[3, 0, 3, 0],
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[3, 0, 8, 0]]
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max_length = 20
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num_machines = 3
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all_machines = range(num_machines)
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horizon = 20
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travel_time = 1
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num_jobs = len(jobs)
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all_jobs = range(num_jobs)
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intervals = []
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optional_intervals = []
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performed = []
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starts = []
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ends = []
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travels = []
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for m in all_machines:
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optional_intervals.append([])
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for i in all_jobs:
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# Create main interval.
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start = model.NewIntVar(jobs[i][1], horizon, 'start_%i' % i)
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duration = jobs[i][0]
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end = model.NewIntVar(0, jobs[i][2], 'end_%i' % i)
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interval = model.NewIntervalVar(start, duration, end, 'interval_%i' % i)
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starts.append(start)
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intervals.append(interval)
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ends.append(end)
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job_performed = []
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job_travels = []
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for m in all_machines:
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performed_on_m = model.NewBoolVar('perform_%i_on_m%i' % (i, m))
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job_performed.append(performed_on_m)
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# Create an optional copy of interval to be executed on a machine
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location0 = model.NewOptionalIntVar(
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jobs[i][3], jobs[i][3], performed_on_m, 'location_%i_on_m%i' % (i, m))
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start0 = model.NewOptionalIntVar(jobs[i][1], horizon, performed_on_m,
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'start_%i_on_m%i' % (i, m))
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end0 = model.NewOptionalIntVar(0, jobs[i][2], performed_on_m,
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'end_%i_on_m%i' % (i, m))
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interval0 = model.NewOptionalIntervalVar(
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start0, duration, end0, performed_on_m, 'interval_%i_on_m%i' % (i, m))
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optional_intervals[m].append(interval0)
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# We only propagate the constraint if the tasks is performed on the machine.
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model.Add(start0 == start).OnlyEnforceIf(performed_on_m)
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# Adding travel constraint
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travel = model.NewBoolVar('is_travel_%i_on_m%i' % (i, m))
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startT = model.NewOptionalIntVar(0, horizon, travel,
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'start_%i_on_m%i' % (i, m))
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endT = model.NewOptionalIntVar(0, horizon, travel, 'end_%i_on_m%i' % (i,
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m))
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intervalT = model.NewOptionalIntervalVar(
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startT, travel_time, endT, travel, 'travel_interval_%i_on_m%i' % (i,
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m))
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optional_intervals[m].append(intervalT)
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job_travels.append(travel)
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model.Add(end0 == startT).OnlyEnforceIf(travel)
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performed.append(job_performed)
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travels.append(job_travels)
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model.Add(sum(job_performed) == 1)
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for m in all_machines:
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if m == 1:
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for i in all_jobs:
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if i == 2:
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for c in all_jobs:
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if (i != c) and (jobs[i][3] != jobs[c][3]):
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is_job_earlier = model.NewBoolVar('is_j%i_earlier_j%i' % (i, c))
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model.Add(starts[i] < starts[c]).OnlyEnforceIf(is_job_earlier)
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model.Add(starts[i] >= starts[c]).OnlyEnforceIf(is_job_earlier.Not())
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# Max Length constraint (modeled as a cumulative)
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# model.AddCumulative(intervals, demands, max_length)
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# Choose which machine to perform the jobs on.
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for m in all_machines:
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model.AddNoOverlap(optional_intervals[m])
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# Objective variable.
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total_cost = model.NewIntVar(0, 1000, 'cost')
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model.Add(total_cost == sum(
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performed[j][m] * (10 * (m + 1)) for j in all_jobs for m in all_machines))
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model.Minimize(total_cost)
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# Solve model.
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solver = cp_model.CpSolver()
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result = solver.Solve(model)
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print()
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print(result)
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print('Statistics')
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print(' - conflicts : %i' % solver.NumConflicts())
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print(' - branches : %i' % solver.NumBranches())
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print(' - wall time : %f ms' % solver.WallTime())
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def main():
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SolveRosteringWithTravel()
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if __name__ == '__main__':
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main()
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176
examples/python/school_scheduling_sat.py
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176
examples/python/school_scheduling_sat.py
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@@ -0,0 +1,176 @@
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from ortools.sat.python import cp_model
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class SchoolSchedulingProblem(object):
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def __init__(self, subjects, teachers, curriculum, specialties, working_days,
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periods, levels, sections, teacher_work_hours):
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self.subjects = subjects
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self.teachers = teachers
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self.curriculum = curriculum
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self.specialties = specialties
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self.working_days = working_days
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self.periods = periods
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self.levels = levels
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self.sections = sections
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self.teacher_work_hours = teacher_work_hours
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class SchoolSchedulingSatSolver(object):
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def __init__(self, problem):
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# Problem
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self.problem = problem
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# Utilities
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self.timeslots = [
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'{0:10} {1:6}'.format(x, y)
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for x in problem.working_days
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for y in problem.periods
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]
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self.num_days = len(problem.working_days)
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self.num_periods = len(problem.periods)
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self.num_slots = len(self.timeslots)
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self.num_teachers = len(problem.teachers)
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self.num_subjects = len(problem.subjects)
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self.num_levels = len(problem.levels)
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self.num_sections = len(problem.sections)
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self.courses = [
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x * self.num_levels + y
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for x in problem.levels
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for y in problem.sections
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]
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self.num_courses = self.num_levels * self.num_sections
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all_courses = range(self.num_courses)
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all_teachers = range(self.num_teachers)
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all_slots = range(self.num_slots)
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all_sections = range(self.num_sections)
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all_subjects = range(self.num_subjects)
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all_levels = range(self.num_levels)
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self.model = cp_model.CpModel()
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self.assignment = {}
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for c in all_courses:
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for s in all_subjects:
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for t in all_teachers:
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for slot in all_slots:
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if t in self.problem.specialties[s]:
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name = 'C:{%i} S:{%i} T:{%i} Slot:{%i}' % (c, s, t, slot)
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self.assignment[c, s, t, slot] = self.model.NewBoolVar(name)
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else:
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name = 'NO DISP C:{%i} S:{%i} T:{%i} Slot:{%i}' % (c, s, t, slot)
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self.assignment[c, s, t, slot] = self.model.NewIntVar(0, 0, name)
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# Constraints
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# Each course must have the quantity of classes specified in the curriculum
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for level in all_levels:
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for section in all_sections:
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course = level * self.num_sections + section
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for subject in all_subjects:
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required_slots = self.problem.curriculum[self.problem.levels[
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level], self.problem.subjects[subject]]
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self.model.Add(
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sum(self.assignment[course, subject, teacher, slot]
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for slot in all_slots
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for teacher in all_teachers) == required_slots)
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# Teacher can do at most one class at a time
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for teacher in all_teachers:
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for slot in all_slots:
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self.model.Add(
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sum([
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self.assignment[c, s, teacher, slot]
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for c in all_courses
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for s in all_subjects
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]) <= 1)
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# Maximum work hours for each teacher
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for teacher in all_teachers:
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self.model.Add(
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sum([
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self.assignment[c, s, teacher, slot]
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for c in all_courses for s in all_subjects for slot in all_slots
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]) <= self.problem.teacher_work_hours[teacher])
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# Teacher makes all the classes of a subject's course
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teacher_courses = {}
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for level in all_levels:
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for section in all_sections:
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course = level * self.num_sections + section
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for subject in all_subjects:
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for t in all_teachers:
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name = 'C:{%i} S:{%i} T:{%i}' % (course, subject, teacher)
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teacher_courses[course, subject, t] = self.model.NewBoolVar(name)
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temp_array = [
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self.assignment[course, subject, t, slot] for slot in all_slots
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]
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self.model.AddMaxEquality(teacher_courses[course, subject, t],
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temp_array)
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self.model.Add(
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sum(teacher_courses[course, subject, t]
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for t in all_teachers) == 1)
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# Solution collector
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self.collector = None
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def solve(self):
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print('Solving')
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solver = cp_model.CpSolver()
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solution_printer = SchoolSchedulingSatSolutionPrinter()
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status = solver.SearchForAllSolutions(self.model, solution_printer)
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print()
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print('Branches', solver.NumBranches())
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print('Conflicts', solver.NumConflicts())
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print('WallTime', solver.WallTime())
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def print_status(self):
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pass
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class SchoolSchedulingSatSolutionPrinter(cp_model.CpSolverSolutionCallback):
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def NewSolution(self):
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print('Found Solution!')
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def main():
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# DATA
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subjects = ['English', 'Math', 'History']
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levels = ['1-', '2-', '3-']
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sections = ['A']
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teachers = ['Mario', 'Elvis', 'Donald', 'Ian']
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teachers_work_hours = [18, 12, 12, 18]
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working_days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
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periods = ['08:00-09:30', '09:45-11:15', '11:30-13:00']
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curriculum = {
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('1-', 'English'): 3,
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('1-', 'Math'): 3,
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('1-', 'History'): 2,
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('2-', 'English'): 4,
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('2-', 'Math'): 2,
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('2-', 'History'): 2,
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('3-', 'English'): 2,
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('3-', 'Math'): 4,
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('3-', 'History'): 2
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}
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# Subject -> List of teachers who can teach it
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specialties_idx_inverse = [
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[1, 3], # English -> Elvis & Ian
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[0, 3], # Math -> Mario & Ian
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[2, 3] # History -> Donald & Ian
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]
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problem = SchoolSchedulingProblem(
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subjects, teachers, curriculum, specialties_idx_inverse, working_days,
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periods, levels, sections, teachers_work_hours)
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solver = SchoolSchedulingSatSolver(problem)
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solver.solve()
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solver.print_status()
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if __name__ == '__main__':
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main()
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