def construct_model():
try:
model.build_model(data=Data.INPUT_DATA, parameters=Data.PARAMETERS)
mc.mcprint(text="Model has been constructed successfully", color=mc.Color.GREEN)
mc.mcprint(text="\nThe instance is ready to be optimized", color=mc.Color.GREEN)
except Exception as e:
mc.register_error(error_string="There was an error generating the model")
mc.register_error(error_string=e)
def initialize_directories():
current_directories = os.listdir()
for directory_name in ConfigFiles.DIRECTORIES:
if directory_name not in current_directories:
directory_path = os.path.join(os.getcwd(), directory_name)
mc.mcprint(text="The directory '{}' doesn't exists.".format(directory_path),
color=mc.Color.ORANGE)
os.mkdir(directory_name)
mc.mcprint(text="Created '{}' successfully".format(directory_path),
color=mc.Color.GREEN)
def check_argument():
if len(sys.argv) == 2:
arg = sys.argv[-1]
arg = os.path.join(os.getcwd(), arg)
if os.path.isdir(arg):
mc.mcprint(text="Input Data Directory: ({})".format(arg),
color=mc.Color.GREEN)
ConfigFiles.DIRECTORIES["input_data"] = arg
else:
mc.register_error(error_string="The path '{}' doesn't exist".format(arg))
mc.mcprint(text="Using default directory ({})".format(ConfigFiles.DIRECTORIES["input_data"]),
color=mc.Color.YELLOW)
elif len(sys.argv) > 2:
mc.register_error(error_string="Solver doesn't accept more than 1 argument")
mc.exit_application(enter_quit=True)
def import_input_data(select_new_folder=False):
try:
select_input_data_folder() if select_new_folder else None
mc.mcprint(text="Importing raw data", color=mc.Color.ORANGE)
import_data()
mc.mcprint(text="The data has been imported successfully", color=mc.Color.GREEN)
mc.mcprint(text="Generating parameters from input data", color=mc.Color.ORANGE)
create_parameters()
mc.mcprint(text="Parameters has been generated successfully", color=mc.Color.GREEN)
mc.mcprint(text="Constructing Model", color=mc.Color.ORANGE)
construct_model()
except Exception as e:
clear_all_data()
mc.register_error(error_string="The input directory doesn't contain a valid structure")
mc.register_error(error_string=e)
return
def select_input_data_folder():
list_dir = os.listdir(os.getcwd())
excluded_dirs = ['output',
'__pycache__',
'mcutils']
menu_list = []
for directory in list_dir:
if not os.path.isfile(directory) and directory not in excluded_dirs:
menu_list.append(directory)
mc_import_data = mc.Menu(title="Import Input Data", subtitle="Select the directory to import",
options=menu_list, back=False)
selected_index = int(mc_import_data.show())
selected_folder = menu_list[selected_index - 1]
ConfigFiles.DIRECTORIES["input_data"] = os.path.join(os.getcwd(), selected_folder)
mc.mcprint(ConfigFiles.DIRECTORIES["input_data"], color=mc.Color.GREEN)
return selected_folder
def import_data():
mc.mcprint(text="Importing from data input directory", color=mc.Color.ORANGE)
mc.mcprint(text="The current directory is ({})".format(ConfigFiles.DIRECTORIES["input_data"]),
color=mc.Color.ORANGE)
path_dirs = {}
input_data_path = ConfigFiles.DIRECTORIES["input_data"]
for directory in os.listdir(input_data_path):
path_dirs[directory] = os.path.join(input_data_path, directory)
exclude = ['corridor.csv', 'surcharge.csv', 'route_supplier.csv']
for file_name in path_dirs:
path = path_dirs[file_name]
with open(path) as file:
object_data = {}
first_line = True
headers = []
for line in file:
if line.strip() == "":
continue
if first_line:
if file_name in exclude:
headers = line.strip().split(";")
else:
headers = line.strip().split(";")[1:]
first_line = False
continue
if file_name not in exclude:
split = line.strip().split(";")
object_data[split[0]] = {}
for index, attribute in enumerate(split[1:]):
object_data[split[0]][headers[index]] = attribute
else:
split = line.strip().split(";")
object_data["_".join([file_name.split(".")[0]] + split[:2])] = {}
for index, attribute in enumerate(split):
object_data["_".join([file_name.split(".")[0]] + split[:2])][headers[index]] = attribute
Data.INPUT_DATA[file_name.split(".")[0]] = object_data
if len(Data.INPUT_DATA) == 0:
raise Exception("No compatible data has been found. "
"Please please insert valid data or change the input data directory")
def display_optimal_information():
model = Model.model
mc.mcprint(text=model.getStatus(), color=mc.Color.YELLOW)
if model.getStatus() != "infeasible":
for var in model.getVars():
value = int(model.getVal(var))
print("{}:\t{}".format(value, var)) if value != 0 else None
if model.getStatus() == "optimal":
mc.mcprint(text="Found the optimal solution successfully",
color=mc.Color.GREEN)
else:
mc.mcprint(text="The instance is INFEASIBLE", color=mc.Color.RED)
def initialize():
mc.mcprint(text="Initializing Solver...")
check_argument()
initialize_directories()
import_input_data()
def clear_all_data():
mc.mcprint(text="Applying Rollback...", color=mc.Color.ORANGE)
Data.INPUT_DATA = {}
Data.PARAMETERS = {}
model.reset_model()
def create_parameters():
data = Data.INPUT_DATA
if len(data.keys()) == 0:
raise Exception("There is no data")
# Generating: Plants
mc.mcprint(text="Generating Plants")
ra_a = {}
for plant in data['plants']:
ra_a[plant] = data['plants'][plant]['RegionID']
# Generating: Suppliers
mc.mcprint(text="Generating Suppliers")
rs_s = {}
for supplier in data['suppliers']:
rs_s[supplier] = data['suppliers'][supplier]['RegionID']
# Generating: Region of Reception (assuming region of reception == region of closest plant)
mc.mcprint(text="Generating Region of Reception")
rr_r = {}
pl_ra = {}
for reception in data['receptions']:
closest_plant = data['receptions'][reception]['ClosestAssemblyPlant']
for plant in data['plants']:
pl_ra[(reception, plant)] = 0
if plant == closest_plant:
rr_r[reception] = data['plants'][plant]['RegionID']
pl_ra[(reception, plant)] = 1
# Generating: Supplier Stock
mc.mcprint(text="Generating Supplier Stock")
s_s = {}
for supplier in data['suppliers']:
s_s[supplier] = int(data['suppliers'][supplier]['ItemAvailability'])
# Generating: Demand
mc.mcprint(text="Generating Demand")
m_ap = {}
for order_id in data['orders']:
order = data['orders'][order_id]
item_id = order['ItemID']
if item_id in data['items'].keys():
m_ap[(order['PlantID'], item_id)] = int(order['NumItemsOrdered'])
else:
error_message = "{} includes {} but this item has not been declared in the input file".format(order_id,
item_id)
raise Exception(error_message)
# Generating: Tax Supplier -> Reception (changing region)
mc.mcprint(text="Generating Tax Supplier -> Reception (changing region)")
t_sd = {}
for supplier in rs_s:
for reception in rr_r:
coordinate = [rr_r[reception], rs_s[supplier]]
id_ = "_".join(['surcharge'] + coordinate)
if id_ in data['surcharge'].keys():
t_sd[(supplier, reception)] = float(data['surcharge'][id_]['TaxPerContainer'].replace(",", "."))
else:
t_sd[(supplier, reception)] = 0
# Generating: Cost of Item (in supplier)
mc.mcprint(text="Generating Cost of Item (in supplier)")
cs_sp = {}
for supplier in data['suppliers']:
item = data['suppliers'][supplier]['ItemProduced']
cs_sp[(supplier, item)] = int(data['suppliers'][supplier]['UnitItemCost'])
# Generating: Cost from Reception -> Supplier (includes taxes)
mc.mcprint(text="Generating Cost from Reception -> Supplier (includes taxes)")
corridor_types = [
'Automatic',
'Manual',
'Subcontractor',
]
cor = corridor_types
k_daj = {}
for corridor in data['corridor']:
if not len(corridor.split("_")) > 1:
continue
reception, plant = corridor.split("_")[1:]
for corridor_type in corridor_types:
corridor_real_type = corridor_type
corridor_type = "TransportationCostPerContainer" + corridor_type
total_cost = int(data['corridor'][corridor][corridor_type])
if corridor_type == corridor_types[2]:
total_cost += float(data['corridor'][corridor]['TaxPerContainer'].replace(",", "."))
k_daj[(reception, plant, corridor_real_type)] = total_cost
# Generating: Handling Cost at Plant
mc.mcprint(text="Generating Handling Cost at Plant")
cd_aj = {}
for plant in data['plants']:
for corridor_type in corridor_types:
id_ = "PlantHandlingCostPerContainer" + corridor_type
cd_aj[(plant, corridor_type)] = 0
if id_ in data['plants'][plant].keys():
cd_aj[(plant, corridor_type)] = float(data['plants'][plant][id_].replace(",", "."))
# Generating: Handling Cost at Reception
mc.mcprint(text="Generating Handling Cost at Reception")
cd_dj = {}
for reception in data['receptions']:
for corridor_type in corridor_types:
cd_dj[(reception, corridor_type)] = 0
id_ = "ReceptionHandlingCostPerContainer" + corridor_type
if id_ in data['receptions'][reception].keys():
cd_dj[(reception, corridor_type)] = float(data['receptions'][reception][id_].replace(",", "."))
# Generating: Transportation Cost from Supplier -> Reception
mc.mcprint(text="Generating Transportation Cost from Supplier -> Reception")
ld_sd = {}
for supplier in data['suppliers']:
for reception in data['receptions']:
id_ = "_".join(["route_supplier"] + [supplier, reception])
if id_ not in data['route_supplier'].keys():
continue
ld_sd[(supplier, reception)] = int(data['route_supplier'][id_]['TransportationCostPerContainer'])
# Generating: Weight for Item
mc.mcprint(text="Generating Weight for Item")
f_p = {}
for item in data['items']:
f_p[item] = float(data['items'][item]['UnitWeight'])
# Generating: Max Number of Items per Container
mc.mcprint(text="Generating Max Number of Items per Container")
w_p = {}
for item in data['items']:
w_p[item] = int(data['items'][item]['MaximumNumPerContainer'])
# Generating: Max Number of Container from reception -> Pant (using automatic corridor)
mc.mcprint(text="Generating Max Number of Container from reception -> Pant (using automatic corridor)")
rca_d = {}
for reception in data['receptions']:
rca_d[reception] = int(data['receptions'][reception]['ReceptionMaxNumOfContainersAutomatic'])
# Generating: Max Weight from Reception -> Plant (using corridor automatic)
mc.mcprint(text="Generating Max Weight from Reception -> Plant (using corridor automatic)")
rwa_d = {}
for reception in data['receptions']:
rwa_d[reception] = float(data['receptions'][reception]['ReceptionMaxSumOfWeightsAutomatic'].replace(",", "."))
# Generating: Max Number of Container from Reception -> Plant (using corridor manual)
mc.mcprint(text="Generating Max Number of Container from Reception -> Plant (using corridor manual)")
rcm_d = {}
for reception in data['receptions']:
rcm_d[reception] = int(data['receptions'][reception]['ReceptionMaxNumOfContainersManual'])
# Generating: Max Number of Items from Reception -> Plant (using corridor manual)
mc.mcprint(text="Generating Max Number of Items from Reception -> Plant (using corridor manual)")
rim_d = {}
for reception in data['receptions']:
rim_d[reception] = int(data['receptions'][reception]['ReceptionMaxNumOfItemsManual'])
# Generating: Binary can send from Reception -> Plant (using corridor ?)
mc.mcprint(text="Generating Binary can send from Reception -> Plant (using corridor ?)")
e_da = {}
for reception in data['receptions']:
for plant in data['plants']:
e_da[(reception, plant)] = 0
for reception in data['receptions']:
plant = data['receptions'][reception]['ClosestAssemblyPlant'].replace("Assembly", "")
e_da[(reception, plant)] = 1
# Generating: Binary can Item use Corridor automatic?
mc.mcprint(text="Generating Binary can Item use Corridor automatic?")
i_i = {}
for item in data['items']:
is_automatic_compatible = int(data['items'][item]['IsAutomaticCompatible'])
i_i[item] = 1 if is_automatic_compatible > 0 else 0
Data.PARAMETERS = {
'RAa': ra_a, # Region of plant a
'RRr': rr_r, # Region of reception r
'RSs': rs_s, # Region of supplier s
'PLra': pl_ra,
'Ss': s_s,
'Map': m_ap,
'Tsd': t_sd,
'CSsp': cs_sp,
'Kdaj': k_daj,
'CDaj': cd_aj,
'CDdj': cd_dj,
'LDsd': ld_sd,
'Fp': f_p,
'Wp': w_p,
'RCAd': rca_d,
'RWAd': rwa_d,
'RCMd': rcm_d,
'RIMd': rim_d,
'Eda': e_da,
'COR': cor,
'Ii': i_i,
}
def build_model(data, parameters):
Model.model = pyscipopt.Model("Model_Team_8")
model = Model.model
# Aux
big_m = 9999999
pa = parameters
# Variable Declarations
x_srpic = {}
p_srpic = {}
mc.mcprint(text="Constructing Variable X & P")
for supplier in data['suppliers']:
for reception in data['receptions']:
for plant in data['plants']:
for item in data['items']:
for corridor in parameters['COR']:
variable_x_name = "X[{}][{}][{}][{}][{}]".format(
supplier, reception, item, plant, corridor)
variable_p_name = "P[{}][{}][{}][{}][{}]".format(
supplier, reception, item, plant, corridor)
if (supplier, reception) in pa['LDsd'].keys() \
and (supplier, item) in pa['CSsp'].keys():
x_srpic[supplier, reception, plant, item,
corridor] = model.addVar(
lb=0,
ub=None,
name=variable_x_name,
vtype="INTEGER")
p_srpic[supplier, reception, plant, item,
corridor] = model.addVar(
lb=0,
ub=None,
name=variable_p_name,
vtype="INTEGER")
mc.mcprint(text="Constructing Objective Function")
objective_function = model.addVar(name="objective function", lb=None)
model.addCons(objective_function == (
pyscipopt.quicksum(
(pa['Tsd'][s, r] * pa['PLra'][r, p] + pa['CDdj'][r, c] +
pa['LDsd'][s, r]) * x_srpic[s, r, p, i, c] +
pa['CSsp'][s, i] * p_srpic[s, r, p, i, c]
for s in data['suppliers'] for r in data['receptions']
for p in data['plants'] for i in data['items']
for c in parameters['COR'] if (s, r, p, i, c) in x_srpic.keys()) +
pyscipopt.quicksum(
(pa['Kdaj'][r, p, c] + pa['CDaj'][p, c]) * x_srpic[s, r, p, i, c]
for s in data['suppliers'] for r in data['receptions']
for p in data['plants'] for i in data['items']
for c in parameters['COR'] if (s, r, p, i, c) in p_srpic.keys())))
model.setObjective(objective_function, "minimize")
# Constraint: Demand
mc.mcprint(text="Cons: Demand")
for p in data['plants']:
for i in data['items']:
if (p, i) in parameters['Map'].keys():
# mc.mcprint(text=parameters['Map'][p,i], color=mc.Color.RED)
model.addCons(
pyscipopt.quicksum(
p_srpic[s, r, p, i, c] for s in data['suppliers']
for r in data['receptions'] for c in parameters['COR']
if (s, r, p, i,
c) in p_srpic.keys()) == (parameters['Map'][p, i]))
else:
model.addCons(
pyscipopt.quicksum(p_srpic[s, r, p, i, c]
for s in data['suppliers']
for r in data['receptions']
for c in parameters['COR']
if (s, r, p, i,
c) in p_srpic.keys()) == 0)
# Constraint: Divide Items in Containers
mc.mcprint(text="Cons: Distribute P in X")
for s in data['suppliers']:
for r in data['receptions']:
for p in data['plants']:
for i in data['items']:
for c in parameters['COR']:
if (s, r, p, i, c) in p_srpic.keys():
model.addCons(
p_srpic[s, r, p, i,
c] <= x_srpic[s, r, p, i, c] *
parameters['Wp'][i])
# Constraint: Max Container from R -> P (automatic)
mc.mcprint(text="Cons: Max Container from R -> P (automatic)")
c = "Automatic"
for r in data['receptions']:
for p in data['plants']:
model.addCons(
pyscipopt.quicksum(
x_srpic[s, r, p, i, c] for i in data['items']
for s in data['suppliers']
if (s, r, p, i,
c) in x_srpic.keys()) <= parameters['RCAd'][r])
# Constraint: Cons: Max Container from R -> P (manual)
mc.mcprint(text="Cons: Max Container from R -> P (manual)")
c = "Manual"
for r in data['receptions']:
for p in data['plants']:
model.addCons(
pyscipopt.quicksum(
x_srpic[s, r, p, i, c] for i in data['items']
for s in data['suppliers']
if (s, r, p, i,
c) in x_srpic.keys()) <= parameters['RCMd'][r])
# Constraint: Max Weight from R -> P (automatic)
mc.mcprint(text="Cons: Max Weight from R -> P (automatic)")
c = "Automatic"
for r in data['receptions']:
for p in data['plants']:
model.addCons(
pyscipopt.quicksum(
p_srpic[s, r, p, i, c] * parameters['Fp'][i]
for s in data['suppliers'] for i in data['items']
if (s, r, p, i,
c) in p_srpic.keys()) <= parameters['RWAd'][r])
# Constraint: Max Items from R-> P (manual)
mc.mcprint(text="Cons: Max Items from R -> P (manual)")
c = "Manual"
for r in data['receptions']:
model.addCons(
pyscipopt.quicksum(
p_srpic[s, r, p, i, c] for s in data['suppliers']
for p in data['plants'] for i in data['items']
if (s, r, p, i, c) in p_srpic.keys()) <= parameters['RIMd'][r])
# Constraint: Check if Item can go through corridor (automatic)
mc.mcprint(text="Cons: Check if Item can go through corridor (automatic)")
c = "Automatic"
for s in data['suppliers']:
for r in data['receptions']:
for p in data['plants']:
for i in data['items']:
if (s, r, p, i, c) in x_srpic.keys():
model.addCons(
x_srpic[s, r, p, i, c] <= big_m * pa['Ii'][i])
# Constraint: Don't surpass Supplier Stock
mc.mcprint(text="Cons: Don't surpass Supplier Stock")
for s in data['suppliers']:
model.addCons(
pyscipopt.quicksum(
p_srpic[s, r, p, i, c] for r in data['receptions']
for p in data['plants'] for i in data['items']
for c in parameters['COR']
if (s, r, p, i, c) in p_srpic.keys()) <= parameters['Ss'][s])
# Constraint: Manual Corridor can only send to closest plant
mc.mcprint(text="Cons: Manual Corridor can only send to closest plant")
c = "Manual"
for r in data['receptions']:
for p in data['plants']:
model.addCons(
pyscipopt.quicksum(x_srpic[s, r, p, i, c]
for i in data['items']
for s in data['suppliers']
if (s, r, p, i, c) in x_srpic.keys()) <=
parameters['Eda'][(r, p)] * big_m)
