def do_pyomo_work(profit_rate_windows):
Products = ['Doors', 'Windows']
ProfitRate = {'Doors': 300, 'Windows': profit_rate_windows}
Plants = ['Door Fab', 'Window Fab', 'Assembly']
HoursAvailable = {'Door Fab': 4, 'Window Fab': 12, 'Assembly': 18}
HoursPerUnit = {
('Doors', 'Door Fab'): 1,
('Windows', 'Window Fab'): 2,
('Doors', 'Assembly'): 3,
('Windows', 'Assembly'): 2,
('Windows', 'Door Fab'): 0,
('Doors', 'Window Fab'): 0
}
#Concrete Model
model = ConcreteModel()
#Decision Variables
model.WeeklyProd = Var(Products, within=NonNegativeReals)
#Objective
model.obj = Objective(expr=sum(ProfitRate[i] * model.WeeklyProd[i]
for i in Products),
sense=maximize)
def CapacityRule(model, p):
"""User Defined Capacity Rule
Accepts a pyomo Concrete Model as the first positional argument,
and a list of Plants as a second positional argument
"""
return sum(HoursPerUnit[i, p] * model.WeeklyProd[i]
for i in Products) <= HoursAvailable[p]
model.Capacity = Constraint(Plants, rule=CapacityRule)
opt = SolverFactory("glpk")
instance = model.create()
results = opt.solve(instance)
#results.write()
return results.Solution()
# Decision variables
model.WeeklyProd = Var(Products, within=NonNegativeReals)
# Objective
model.obj = Objective(expr = sum(ProfitRate[i] * model.WeeklyProd[i] for i in Products),
sense=maximize)
def CapacityRule(model, p):
"""User defined capacity rule -
Accepts a pyomo Concrete Model as the first positional argument,
and a plant index as a second positional argument
"""
return sum(HoursPerUnit[i,p] * model.WeeklyProd[i] for i in Products) <= HoursAvailable[p]
# Constraint
model.Capacity = Constraint(Plants, rule=CapacityRule)
#This is an optional code path that allows the script to be run outside of
#pyomo command-line. For example: python wyndor.py
if __name__ == '__main__':
#This replicates what the pyomo command-line tools does
from coopr.opt import SolverFactory
opt = SolverFactory("glpk")
instance = model.create()
results = opt.solve(instance)
#sends results to stdout
results.write()
#Objective
model.obj = Objective(expr=
sum(ProfitRate[i] * model.WeeklyProd[i] for i in Products),
sense = maximize)
for i in Products:
print model.WeeklyProd[i]
def CapacityRule(model, p):
"""User Defined Capacity Rule
Accepts a pyomo Concrete Model as the first positional argument,
and and a plant index as a second positional argument
"""
return sum(HoursPerUnit[i,p] * model.WeeklyProd[i] for i in Products) <= HoursAvailable[p]
#This statement is what Pyomo needs to generate one constraint for each plant
model.Capacity = Constraint(Plants, rule = CapacityRule)
#This is an optional code path that allows the script to be run outside of
#pyomo command-line. For example: python wyndor.py
if __name__ == '__main__':
#This replicates what the pyomo command-line tools does
from coopr.opt import SolverFactory
opt = SolverFactory("glpk")
instance = model.create()
results = opt.solve(instance)
#sends results to stdout
results.write()
sense=maximize)
# Constraint - uses a Capacity Rule and Land Area function
# meaning: for each plant, the resource used should not exceed the total resource available
def CapacityRule(model, p):
"""User defined capacity rule -
Accepts a pyomo ConcreteModel as the first positional argument,
and a plant index as a second positional argument"""
return sum(Resource_required[i,p] * model.SeasonProd[i] for i in Plants) <= Resource_available[p]
# meaning: sum of all planting area should not exceed the total area owned by farmer
def LandAreaRule(model):
"""User-defined maximum area"""
return sum(model.SeasonProd[i] for i in Plants) <= Area
model.Capacity = Constraint(Resource, rule=CapacityRule)
model.LandArea = Constraint(rule=LandAreaRule)
#This is an optional code path that allows the script to be run outside of
#pyomo command-line. For example: python farmer.py
if __name__ == '__main__':
#This replicates what the pyomo command-line tools does
from coopr.opt import SolverFactory
opt = SolverFactory("glpk")
instance = model.create()
results = opt.solve(instance)
#sends results to stdout
results.write()
