def otimizar(malha_producao, horizon):
print('Iniciando a otimização')
print(malha_producao)
print(horizon)
print('Criando do objeto de otimização')
Cervejaria = pyschedule.Scenario("Cervejaria", horizon=horizon)
print('Carregando atividades de produção')
atividade = _LerJson_Atividades()
recursos = Fabrica()
recursos.gerarRecursos(Cervejaria)
print('Gerando as atividades (tasks)')
gerarTodasAtividades(Cervejaria, atividade, malha_producao)
print('Gerandos as restrições (constraints)')
gerarRestricoes(Cervejaria, atividade, recursos)
print('Definindo o objetivo de otimização')
Cervejaria.use_flowtime_objective()
print('Executando a otimização')
pyschedule.solvers.mip.solve(Cervejaria, msg=1, kind='CBC', ratio_gap=.0)
print('Otimização concluída')
print('Salvando a otimização, caso queira ser explorada no script exploring_last_optimization')
import pickle
with open('last_optimization.pickle', 'wb') as handle:
pickle.dump(Cervejaria, handle, protocol=pickle.HIGHEST_PROTOCOL)
print('Processo de otimização finalizado')
print('Extraindo as tarefas e recursos da solução')
df_solution = plot_get_solution(Cervejaria, fig_size=(30, 10))
print('Obtendo estatísticas da solução')
stats = {
'makespan' : 0
}
print('Retornando resposta para o front end')
return df_solution, stats
# Taillards 20x5 flow-shop instance downloaded from
# http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/flowshop.dir/tai20_5.txt
# columns = jobs, rows = machines
proc ='\
54 83 15 71 77 36 53 38 27 87 76 91 14 29 12 77 32 87 68 94\n\
79 3 11 99 56 70 99 60 5 56 3 61 73 75 47 14 21 86 5 77\n\
16 89 49 15 89 45 60 23 57 64 7 1 63 41 63 47 26 75 77 40\n\
66 58 31 68 78 91 13 59 49 85 85 9 39 41 56 40 54 77 51 31\n\
58 56 20 85 53 35 53 41 69 13 86 72 8 49 47 87 58 18 68 28'
#proc_table = [ [ int(x) for x in row.replace(' ',' ').strip().split(' ') ] for row in proc.split('\n') ]
proc_table = [ [ int(math.ceil( int(x)/10.0 )) for x in row.replace(' ',' ').strip().split(' ') ] for row in proc.split('\n') ]
n = len(proc_table[0])
m = len(proc_table)
n = 6
S = pyschedule.Scenario('Taillards_Flow_Shop_20x5')
T = { (i,j) : S.Task('T_%i_%i'%(i,j),length=proc_table[j][i]) for i in range(n) for j in range(m) }
R = { j : S.Resource('R_%i'%j) for j in range(m) }
S += [ T[i,j] < T[i,j+1] for i in range(n) for j in range(m-1) ]
for i in range(n) :
for j in range(m) :
T[i,j] += R[j]
S.use_makespan_objective()
if pyschedule.solvers.mip.solve_bigm(S,time_limit=120,msg=1):
pyschedule.plotters.matplotlib.plot(S,resource_height=100.0,hide_tasks=[S._tasks['MakeSpan']])
else:
print('no solution found')
#! /usr/bin/python
import sys
sys.path.append('../src')
import pyschedule
# get input size
n = 15
S = pyschedule.Scenario('n_queens_type_scheduling',horizon=n)
R = { i : S.Resource('R_%i'%i) for i in range(n) } #resources
T = { (i,j) : S.Task('T_%i_%i'%(i,j)) for i in range(n) for j in range(n) } #tasks
# precedence constrains
S += [ T[i,j-1] < T[i,j] for i in range(n) for j in range(1,n) ]
# resource assignment modulo n
for j in range(n):
for i in range(n):
T[(i+j) % n,j] += R[i]
if pyschedule.solvers.mip.solve(S,msg=1,kind='CBC'):
pyschedule.plotters.matplotlib.plot(S,color_prec_groups=False)
else:
print('no solution found')
3 15 1 13 7 11 8 6 9 10 14 2 4 12 5\n\
6 9 11 3 4 7 10 1 14 5 2 12 13 8 15\n\
9 15 5 14 6 7 10 2 13 8 12 11 4 3 1\n\
11 9 13 7 5 2 14 15 12 1 8 4 3 10 6'
proc_table = [[int(x) for x in row.replace(' ', ' ').strip().split(' ')]
for row in proc.split('\n')]
proc_table = [[
int(math.ceil(int(x) / 10.0))
for x in row.replace(' ', ' ').strip().split(' ')
] for row in proc.split('\n')]
mach_table = [[int(x) for x in row.replace(' ', ' ').strip().split(' ')]
for row in mach.split('\n')]
n = 6 #len(proc_table)
S = pyschedule.Scenario('Taillards_Job_Shop_15x15')
T = {(i, j): S.Task('T_%i_%i' % (i, j), length=proc_table[i][j])
for i in range(n) for j in range(n)}
R = {j: S.Resource('R_%i' % j) for j in range(n)}
S += [T[i, j] < T[i, j + 1] for i in range(n) for j in range(n - 1)]
for i in range(n):
for j in range(n):
T[i, j] += R[mach_table[i][j] % n]
S.use_makespan_objective()
if pyschedule.solvers.mip_bigm.solve(S, kind='CBC', msg=1):
pyschedule.plotters.matplotlib.plot(S,
resource_height=100.0,
hide_tasks=[S._tasks['MakeSpan']])
else:
cities_table = [(city, float(lon), float(lat))
for city, lon, lat in cities_table]
n = 10 # use only few cities to test, more cities take a lone time #len(cities_table)
coords = {
cities_table[i][0]: (cities_table[i][2], cities_table[i][1])
for i in range(n)
}
cities = list(coords)
# add coordinates of vitual start and end at the first city in list
start_city = cities_table[0][0]
coords['start'] = coords[start_city]
coords['end'] = coords[start_city]
# scenario and city tasks
S = pyschedule.Scenario('TSP_Germany')
T = {city: S.Task(city) for city in coords}
Car = S.Resource('Car')
# the car has to pass every city
for city in coords:
T[city] += Car
# make sure that the tour start and ends at start_city
S += T['start'] < {T[city] for city in coords if city != 'start'}
S += T['end'] > {T[city] for city in coords if city != 'end'}
# add euclidean distances as conditional precedences
S += [ T[city] + int(eucl_dist(coords[city],coords[city_])) << T[city_] \
for city in coords for city_ in coords if city != city_ ]
# get cities table
cities_table = [ row.split(' ') for row in cities.split('\n') ]
cities_table = [ (city,float(lon),float(lat)) for city,lon,lat in cities_table ]
n = 10 # use only few cities to test, more cities take a lone time #len(cities_table)
capacity = int(n/2) # each vehicle can visit half of the cities
coords = { cities_table[i][0] : (cities_table[i][2],cities_table[i][1]) for i in range(n) }
cities = list(coords)
# add coordinates of vitual start and end at the first city in list
start_city = cities_table[0][0]
coords['start'] = coords[start_city]
coords['end'] = coords[start_city]
# create scenario, city visit tasks, and start and end tasks of blue and red vehicle
S = pyschedule.Scenario('VRP_Germany',horizon=30)
T = { city : S.Task(city) for city in cities }
# resources
# capacity + 2 to include start and end task
R_blue, R_red = S.Resource('blue'), S.Resource('red')
# tasks
T['start'] = S.Task('start')
T['end'] = S.Task('end')
# precedences
S += T['start'] < { T[city] for city in cities if city != 'start' }
S += T['end'] > { T[city] for city in cities if city != 'end' }
# resource assignments
