The goal of the python model below is to choose the trucks with total minimum cost to fulfill all orders. I treat the truck type as 1 truck in the following code, but in fact, a truck type can have infinite number of truck, in that case, how should I modified the constraints and objective functions to fit that?


import pandas as pd
import docplex.mp
from docplex.mp.model import Model
df_order = pd.DataFrame([[1,5000],[2,1000],[3,2000]], columns =['OrderID','Weight'])
orders = df_order['OrderID'].values.tolist()
df_order.set_index('OrderID', inplace=True)

df_truck_types = pd.DataFrame([[1,'TYPE1',20000,'SP TRANSPORTS',40000],[2,'TYPE2',20000,'SP TRANSPORTS',40000],[3,'TYPE3',10000,'SP TRANSPORTS',30000]], columns =['TruckTypeID','VehicleType','Capacity','Transporter','TruckCost'])
truck_types = df_truck_types['TruckTypeID'].values.tolist()
df_truck_types.set_index('TruckTypeID', inplace=True)

def get_order(o):
return df_order.loc[o]

def get_truck_type(f):
return df_truck_types.loc[f]

# Decision Variable and Objective Function
mdl = Model(name='itc_load_planning')

Assignment = mdl.binary_var_matrix(orders, truck_types, name='Assignment')
#for objective 1
TruckCost=mdl.sum(Assignment[o,f] * get_truck_type(f).TruckCost for o in orders for f in truck_types)

mdl.minimize(TruckCost)

# C1
for o in orders:
mdl.add_constraint(mdl.sum(Assignment[o,f] for f in truck_types)==1, "C1")

# solve model
print("===solution===")
si = mdl.solve()
mdl.print_solution()

------------------------------
Jack Tan
------------------------------

#DecisionOptimization

Hi

I answered the same question at 

https://stackoverflow.com/questions/72802157/truck-type-infinite-number-of-them-vs-orders/72803527#72803527

regards

------------------------------
[Alex] [Fleischer]
[Data and AI Technical Sales]
[IBM]
------------------------------

Original Message:
Sent: Wed June 29, 2022 10:09 AM
From: Jack Tan
Subject: Truck Type (infinite number of them) vs Orders

The goal of the python model below is to choose the trucks with total minimum cost to fulfill all orders. I treat the truck type as 1 truck in the following code, but in fact, a truck type can have infinite number of truck, in that case, how should I modified the constraints and objective functions to fit that?


import pandas as pd
import docplex.mp
from docplex.mp.model import Model
df_order = pd.DataFrame([[1,5000],[2,1000],[3,2000]], columns =['OrderID','Weight'])
orders = df_order['OrderID'].values.tolist()
df_order.set_index('OrderID', inplace=True)

df_truck_types = pd.DataFrame([[1,'TYPE1',20000,'SP TRANSPORTS',40000],[2,'TYPE2',20000,'SP TRANSPORTS',40000],[3,'TYPE3',10000,'SP TRANSPORTS',30000]], columns =['TruckTypeID','VehicleType','Capacity','Transporter','TruckCost'])
truck_types = df_truck_types['TruckTypeID'].values.tolist()
df_truck_types.set_index('TruckTypeID', inplace=True)

def get_order(o):
return df_order.loc[o]

def get_truck_type(f):
return df_truck_types.loc[f]

# Decision Variable and Objective Function
mdl = Model(name='itc_load_planning')

Assignment = mdl.binary_var_matrix(orders, truck_types, name='Assignment')
#for objective 1
TruckCost=mdl.sum(Assignment[o,f] * get_truck_type(f).TruckCost for o in orders for f in truck_types)

mdl.minimize(TruckCost)

# C1
for o in orders:
mdl.add_constraint(mdl.sum(Assignment[o,f] for f in truck_types)==1, "C1")

# solve model
print("===solution===")
si = mdl.solve()
mdl.print_solution()

------------------------------
Jack Tan
------------------------------

#DecisionOptimization