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cp.py
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import module
from module import *
from docplex.cp.model import *
import docplex.cp.utils_visu as visu
import matplotlib.pyplot as plt
from pylab import rcParams
from ortools.sat.python import cp_model
import numpy as np
import copy
def cp_scheduling(_prob: Instance, time_limit=300, init_sol: Schedule = None):
#TODO Subproblem에 이니셜 셋업 고려가 안되어 있음
prob = copy.deepcopy(_prob)
nbrOfJobs = prob.numJob
jobs = [*range(0, nbrOfJobs)]
nbrOfMachines = prob.numMch
machines = [*range(0, nbrOfMachines)]
processingTimes = prob.ptime
setup_matrix = prob.setup
mdl = CpoModel(name='cp_model')
prob.job_list = sorted((job for job in prob.job_list), key=lambda j: j.ID)
prob.machine_list = sorted((mch for mch in prob.machine_list), key=lambda m: m.ID)
processing_itv_vars = [[mdl.interval_var(start=(prob.machine_list[m].available, INTERVAL_MAX), optional=True,
size=processingTimes[m][j], name="interval_job{}_machine{}".format(j, m))
for m in machines] for j in jobs]
for j in jobs:
mdl.add(mdl.sum([mdl.presence_of(processing_itv_vars[j][m]) for m in machines]) == 1)
sequence_vars = [mdl.sequence_var([processing_itv_vars[j][m] for j in jobs], types=[j for j in jobs],
name="sequences_machine{}".format(m)) for m in machines]
for m in machines:
mdl.add(mdl.no_overlap(sequence_vars[m], setup_matrix[m], True))
if module.OBJECTIVE_FUNCTION == 'T':
objective = mdl.sum(
[max(mdl.end_of(processing_itv_vars[j][m]) - prob.job_list[j].due, 0) for j in jobs for m in machines])
elif module.OBJECTIVE_FUNCTION == 'C':
objective = mdl.sum([mdl.end_of(processing_itv_vars[j][m]) for j in jobs for m in machines])
else:
objective = max([mdl.end_of(processing_itv_vars[j][m]) for j in jobs for m in machines])
mdl.add(mdl.minimize(objective))
if init_sol is not None:
stp = mdl.create_empty_solution() # add_interval_var_solution(var, presence=None, start=None, end=None, size=None, length=None)
for bar in init_sol.bars:
stp.add_interval_var_solution(processing_itv_vars[bar.job.ID][bar.machine], presence=True, start=bar.start, end= bar.end)
mdl.set_starting_point(stp)
msol = mdl.solve(TimeLimit=time_limit) # log_output=True
print("Solution: ")
msol.print_solution()
MA = {i: [] for i in machines}
for i in jobs:
for k in machines:
if msol.get_var_solution(processing_itv_vars[i][k]).end != None:
print('Job {0} on Machine {1} completed at {2}'.format(i, k, msol.get_var_solution(
processing_itv_vars[i][k]).end))
job_i = prob.findJob(i)
job_i.end = msol.get_var_solution(processing_itv_vars[i][k]).end
MA[k].append(job_i)
for k in machines:
MA[k] = sorted((job for job in MA[k]), key=lambda m: m.end)
machine = prob.findMch(k)
for job in MA[k]:
machine.process(job)
obj = get_obj(prob)
if msol.solution.objective_values[0] != obj:
raise ValueError('Check Solution Result!')
result = Schedule('CP_CPLEX', prob, obj=obj)
result.print_schedule()
result.comp_time = msol.process_infos['TotalSolveTime']
result.status = msol.solve_status
return result
def cp_scheduling_subprob(_prob: Instance, time_limit=300):
#TODO Subproblem에 이니셜 셋업 고려가 안되어 있음
prob = copy.deepcopy(_prob)
nbrOfJobs = prob.numJob
jobs = [*range(0, nbrOfJobs)]
nbrOfMachines = prob.numMch
machines = [*range(0, nbrOfMachines)]
processingTimes = prob.ptime
setup_matrix = prob.setup
mdl = CpoModel(name='cp_model')
prob.job_list = sorted((job for job in prob.job_list), key=lambda j: j.ID)
prob.machine_list = sorted((mch for mch in prob.machine_list), key=lambda m: m.ID)
processing_itv_vars = [[mdl.interval_var(optional=True, size=processingTimes[m][j], name="interval_job{}_machine{}".format(j, m)) for m in machines] for j in jobs]
a = interval_var(length=10, start=5)
a.size = 10
a.start = 10
for j in jobs:
mdl.add(mdl.sum([mdl.presence_of(processing_itv_vars[j][m]) for m in machines]) == 1)
sequence_vars = [mdl.sequence_var([processing_itv_vars[j][m] for j in jobs], types=[j for j in jobs],
name="sequences_machine{}".format(m)) for m in machines]
for m in machines:
mdl.add(mdl.no_overlap(sequence_vars[m], setup_matrix[m], 1))
for mch in prob.machine_list:
if len(mch.assigned) != 0:
for job in mch.assigned:
mdl.add(mdl.presence_of(processing_itv_vars[job.ID][mch.ID]) == 1)
idx = mch.assigned.index(job)
if idx == 0:
mdl.add(first(sequence_vars[mch.ID], processing_itv_vars[job.ID][mch.ID]))
if idx != (len(mch.assigned) - 1):
next = mch.assigned[idx+1]
mdl.add(previous(sequence_vars[mch.ID], processing_itv_vars[job.ID][mch.ID], processing_itv_vars[next.ID][mch.ID]))
# elif idx == (len(mch.assigned) - 1) and len(mch.assigned) != 1: # Last One
# last = mch.assigned[idx]
# for j in jobs:
# if job.ID != j and j not in (assigned.ID for assigned in mch.assigned):
# mdl.add(previous(sequence_vars[mch.ID], processing_itv_vars[job.ID][mch.ID], processing_itv_vars[j][mch.ID]))
if module.OBJECTIVE_FUNCTION == 'T':
objective = mdl.sum(
[max(mdl.end_of(processing_itv_vars[j][m]) - prob.job_list[j].due, 0) for j in jobs for m in machines])
elif module.OBJECTIVE_FUNCTION == 'C':
objective = mdl.sum([mdl.end_of(processing_itv_vars[j][m]) for j in jobs for m in machines])
else:
objective = max([mdl.end_of(processing_itv_vars[j][m]) for j in jobs for m in machines])
mdl.add(mdl.minimize(objective))
msol = mdl.solve(TimeLimit=time_limit) # log_output=True
print("Solution: ")
msol.print_solution()
if msol.solve_status != 'Optimal' and msol.solve_status != 'Feasible':
print('check')
MA = {i: [] for i in machines}
for i in jobs:
for k in machines:
if prob.findJob(i).complete is False and msol.get_var_solution(processing_itv_vars[i][k]).end != None:
print('Job {0} on Machine {1} completed at {2}'.format(i, k, msol.get_var_solution(
processing_itv_vars[i][k]).end))
job_i = prob.findJob(i)
job_i.end = msol.get_var_solution(processing_itv_vars[i][k]).end
MA[k].append(job_i)
for k in machines:
MA[k] = sorted((job for job in MA[k]), key=lambda m: m.end)
machine = prob.findMch(k)
for job in MA[k]:
if job not in machine.assigned:
machine.process(job)
obj = msol.solution.objective_values[0]
obj = get_obj(prob)
if msol.solution.objective_values[0] != obj:
raise ValueError('Check Solution Result!')
result = Schedule('CP_SUBPROB', prob, obj=obj)
result.print_schedule()
return result
def cp_scheduling_ortools(prob: Instance):
jobs = [*range(0, prob.numJob)]
machines = [*range(0, prob.numMch)]
setup_matrix = prob.setup
processingTimes = prob.ptime
H = 100000000000000
""" SJ = range(0, prob.numJob)
max_s = np.array(setup_matrix).max()
for i in SJ:
M = M + max([row[i] for row in processingTimes])
M = M + max_s
H = M + max_s"""
model = cp_model.CpModel()
presence_vars = [[model.NewBoolVar(name="presence_machine{}_job{}".format(m, j)) for j in jobs] for m in machines]
start_vars = [[model.NewIntVar(0, H, name="start_machine{}_job{}".format(m, j)) for j in jobs] for m in machines]
end_vars = [[model.NewIntVar(0, H, name="end_machine{}_job{}".format(m, j)) for j in jobs] for m in machines]
processing_itv_vars = [
[model.NewOptionalIntervalVar(start=start_vars[m][j], end=end_vars[m][j], size=processingTimes[m][j],
is_present=presence_vars[m][j], name="interval_machine{}_job{}".format(m, j))
for j in jobs] for m in machines]
for m in machines:
model.AddNoOverlap(processing_itv_vars[m])
presence_lit = [[[model.NewBoolVar('%i and %i in %i' % (j1, j2, m)) for j2 in jobs] for j1 in jobs] for m in
machines]
precedence = [[[model.NewBoolVar('%i -> %i in %i' % (j1, j2, m)) for j2 in jobs] for j1 in jobs] for m in machines]
for m in machines:
for j1 in jobs:
for j2 in jobs:
if j1 != j2:
lit12 = precedence[m][j1][j2]
lit21 = precedence[m][j2][j1]
model.Add(start_vars[m][j2] >= end_vars[m][j1] + setup_matrix[m][j1][j2]).OnlyEnforceIf(lit12,
presence_vars[
m][j1],
presence_vars[
m][j2])
model.Add(start_vars[m][j1] >= end_vars[m][j2] + setup_matrix[m][j2][j1]).OnlyEnforceIf(lit21,
presence_vars[
m][j1],
presence_vars[
m][j2])
model.AddBoolOr(lit12, lit21, presence_vars[m][j1].Not(), presence_vars[m][j2].Not())
for j in jobs:
alt_intvs = []
for m in machines:
alt_intvs.append(presence_vars[m][j])
model.Add(cp_model.LinearExpr.Sum(alt_intvs) == 1)
objective = cp_model.LinearExpr.Sum([end_vars[m][j] for j in jobs for m in machines])
model.Minimize(objective)
solver = cp_model.CpSolver()
solver.parameters.max_time_in_seconds = 300
# solver.parameters.enumerate_all_solutions = True
solver.parameters.log_search_progress = True
status = solver.Solve(model)
if status in [cp_model.OPTIMAL]:
return [solver, "OPTIMAL"]
elif status in [cp_model.FEASIBLE]:
return [solver, "FEASIBLE"]
else:
return [solver, "no"]