from docplex.mp.model import Model
from docplex.mp.environment import Environment


#################
# Variables
#################

# Max available power plant units per energy type
energy_units = {'nuclear' : 3, 'gas' : 5, 'biomass' : 26}

# Continuous power (MW) available per energy type for a single power plant
energy_power = {'nuclear' : 2500, 'gas' : 600, 'biomass' : 100}


env = Environment()
env.print_information()

model = Model(name="Energy_mix")

# Variable : nb of power plants
inside_vars = model.integer_var_dict(energy_units, name='inside')



#################
# Constraints
#################

# Resource capacity : nb of power plants must be smaller or equal to max available ones
model.add_constraints(( model.sum(inside_vars[p]) <= model.sum(energy_units[p]), 'ct_demand_%s' % p[0]) for p in energy_units )


# Demand satisfaction : power sum me be greater or equal than 10 000 MW
model.add_constraints(( model.sum(inside_vars[p]*energy_power[p] for p in energy_units) >= 10000, 'ct_demand_%s' % p[0]) for p in energy_units )

model.print_information()


#################
# Objective
#################

# Total power (sum over power plants and energy types)
total_prod = ( model.sum( inside_vars[p] * energy_power[p] for p in energy_units ) )

model.minimize( total_prod )


#################
# Solution
#################

model.solve()
obj = model.objective_value

print("* Production model solved with objective: {:g}".format(obj))
print("* Total inside cost=%g" % total_prod.solution_value)
for p in energy_units:
    print("Inside production of {product}: {ins_var}".format(product=p, ins_var=inside_vars[p].solution_value))