Optimization Python - optimization

Question

I am trying to get the optimal solution
column heading: D_name , Vial_size1 ,Vial_size2 ,Vial_size3 , cost , units_needed
row 1: Act , 120 , 400 , 0 , $5 , 738
row 2: dug , 80 , 200 , 400 , $40 , 262
data in excel
column heading: Vials price size
Row 1: Vial size 1 5 120
Row 2: Vial size 2 5 400
prob=LpProblem("Dose_Vial",LpMinimize)
import pandas as pd
df = pd.read_excel (r'C:\Users\*****\Desktop\Vial.xls')
print (df)
# Create a list of the Vial_Size
Vial_Size = list(df['Vials'])
# Create a dictinary of units for all Vial_Size
size = dict(zip(Vial_Size,df['size']))
# Create a dictinary of price for all Vial_Size
Price = dict(zip(Vial_Size,df['Price']))
# print dictionaries
print(Vial_Size)
print(size)
print(Price)
vial_vars = LpVariable.dicts("Vials",size,lowBound=0,cat='Integer')
# start building the LP problem by adding the main objective function
prob += lpSum([Price[i]*vial_vars[i]*size[i] for i in size])
# adding constraints
prob += lpSum([size[f] * vial_vars[f] for f in size]) >= 738
# The status of the solution is printed to the screen
prob.solve()
print("Status:", LpStatus[prob.status])
# In case the problem is ill-formulated or there is not sufficient information,
# the solution may be infeasible or unbounded
for v in prob.variables():
if v.varValue>0:
print(v.name, "=", format(round(v.varValue)))
Vials_Vial_Size_1 = 3
Vials_Vial_Size_2 = 1
obj =round((value(prob.objective)))
print("The total cost of optimized vials: ${}".format(round(obj)))
The total cost of optimized vials: $3800
'
how to set it for 2 or more drugs and get the best optimal solution.

Answer 1

Here is an approach to solve the first part of the question, finding vial combinations that minimizes the waste (I'm not sure what role the price plays?):
from pulp import *
import pandas as pd
import csv
drugs_dict = {"D_name": ['Act', 'dug'],
"Vial_size1": [120, 80],
"Vial_size2": [400, 200],
"Vial_size3": [0, 400],
"cost": [5, 40],
"units_needed": [738, 262]}
df = pd.DataFrame(drugs_dict)
drugs = list(df['D_name'])
vial_1_size = dict(zip(drugs, drugs_dict["Vial_size1"]))
vial_2_size = dict(zip(drugs, drugs_dict["Vial_size2"]))
vial_3_size = dict(zip(drugs, drugs_dict["Vial_size3"]))
units_needed = dict(zip(drugs, drugs_dict["units_needed"]))
results = []
for drug in drugs:
print(f"drug = {drug}")
# setup minimum waste problem
prob = LpProblem("Minimum Waste Problem", LpMinimize)
# create decision variables
vial_1_var = LpVariable("Vial_1", lowBound=0, cat='Integer')
vial_2_var = LpVariable("Vial_2", lowBound=0, cat='Integer')
vial_3_var = LpVariable("Vial_3", lowBound=0, cat='Integer')
units = lpSum([vial_1_size[drug] * vial_1_var +
vial_2_size[drug] * vial_2_var +
vial_3_size[drug] * vial_3_var])
# objective function
prob += units
# constraints
prob += units >= units_needed[drug]
prob.solve()
print(f"units = {units.value()}")
for v in prob.variables():
if v.varValue > 0:
print(v.name, "=", v.varValue)
results.append([drug, units.value(), int(vial_1_var.value() or 0), int(vial_2_var.value() or 0), int(vial_3_var.value() or 0)])
with open('vial_results.csv', 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile)
csv_writer.writerow(['drug', 'units', 'vial_1', 'vial_2', 'vial_3'])
csv_writer.writerows(results)
Running gives:
drug = Act
units = 760.0
Vial_1 = 3.0
Vial_2 = 1.0
drug = dug
units = 280.0
Vial_1 = 1.0
Vial_2 = 1.0