Embed code for Intro to Optimization

1. Intro to Optimization.py

#Import ortools solver
from ortools.linear_solver import pywraplp

#Creating a solver object 
solver_lin = pywraplp.Solver('Swiggy Food Solver',pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)

#Step 1 - Create Decision variables

# x[0] for number of biryanis 
# x[1] for number of Bowls
# x[2] for number of Wraps

#0 and 1000 define the upper and lower bound for the values x[0],x[1] and x[2] can take 

x = [solver_lin.NumVar(0, 1000,'x[%i]' % i) for i in range(3)]



#Step 2 - Add constraints
solver_lin.Add(2*x[0] + x[1] + x[2] <= 1500)                           
solver_lin.Add(x[0] + 3*x[1] + 2*x[2] <= 3000)                          
solver_lin.Add(x[0] + 2*x[1] + 3*x[2] <= 4000)


#Step 3 - Define objective function

#Total population variable
max_items_total = solver_lin.NumVar(0,3000,'max_items_total') 

#Since x[0], x[1] and x[2] already have constraints on them(1000 max), 
# whatever upperbound we define on max_items_total doesn't matter - it's just syntactical sugar

solver_lin.Add(max_items_total == x[0] + x[1] + x[2])


#Step 4 - Set objevtive function and solve
solver_lin.Maximize(max_items_total)                                
solver_lin.Solve()  #Should return 0 if run successfully

#Print results
print("Total number of items we can cook is", round(max_items_total.SolutionValue()), "\n")

print("No of Biryanis we can cook is", round(x[0].solution_value()))
print("No of Bowls we can cook is", round(x[1].solution_value()))
print("No of wraps we can cook is", round(x[2].solution_value()))