### Task 1
### Implements a simple LP with the gurobi py interface
from gurobipy import *
import random
import copy

### Parameters definitions
product_type = 2  # {standard, premium}

price = [300, 450]
required_painting = [6, 9]
required_assembly = [14, 23]
total_hour_painting = 90
total_hour_assembly = 180

mod = Model("motorcycle")
### Variable definitions
z_p = {} #production of motorcycle of type p
for ii in range(product_type):
    # for binary: vtype=GRB.BINARY
    z_p[ii] = mod.addVar(lb=0.0, obj=-price[ii], name="x_p{0}".format(ii))
mod.update()
# Objective already set above
### Constraints
mod.addConstr(quicksum(z_p[ii]*required_painting[ii] for ii in range(product_type)) <= total_hour_painting, "painting hours")
mod.addConstr(quicksum(z_p[ii]*required_assembly[ii] for ii in range(product_type)) <= total_hour_assembly, "assembly hours")
mod.addConstr(z_p[0] <= 12, "c0")
mod.addConstr(z_p[1] >= 1, "c1")
mod.addConstr(z_p[0] <= 11, "c2")
mod.addConstr(z_p[1] <= 1, "c3")

# Non-negativity set in variable definition
mod.optimize()

print("Optimal objective: {0:.3f}".format(mod.objVal))
for var in mod.getVars():
    print("{0}: {1:.3f}".format(var.varName, var.x))