Pyomo can't find solver

Question

I am new to Pyomo and I have the following optimization problem (original transport problem by Dantzig):

# -*- coding: utf-8 -*-
"""
Transport problem in Pyomo
Created on Mon Feb 15 09:55:06 2021
"""
import pyomo.environ as pyo
#Define the model
model = pyo.ConcreteModel()
#Define the sets
model.set_plants = pyo.Set(initialize=['Seattle', 'San_Diego'])
model.set_markets = pyo.Set(initialize=['New_York', 'Chicago', 'Topeka'])
Parameters
valuesForCapacity = {'Seattle':350, 'San_Diego':600}
valuesForDemand = {'New_York': 325, 'Chicago': 300, 'Topeka': 275}
model.param_capacityOfPlants = pyo.Param(model.set_plants, initialize=valuesForCapacity)
model.param_demandAtMarkets = pyo.Param(model.set_markets, initialize=valuesForDemand)
model.param_capacityOfPlants.pprint()
model.param_demandAtMarkets.pprint()
#Parameter entry as table
valuesForDistance =  {('Seattle', 'New_York'): 2.5, ('Seattle', 'Chicago'): 1.7, ('Seattle', 'Topeka'): 1.8,
('San_Diego', 'New_York'): 2.5, ('San_Diego', 'Chicago'): 1.8, ('San_Diego', 'Topeka'): 1.4}
model.param_distances = pyo.Param(model.set_plants, model.set_markets, initialize=valuesForDistance)
model.param_distances.pprint()
#Scalar
freightCostsPerUnitPerThousandMiles = 90
#Variables
model.variable_x = pyo.Var(model.set_plants,model.set_markets, within=pyo.NonNegativeReals)
model.variable_totalCosts = pyo.Var()
model.variable_x.pprint()
#Constraints
def supplyConstraintRule(model,i):
    return sum(model.variable_x[i,j] for j in model.set_markets)<=model.param_capacityOfPlants[i]
model.constraint_supply = pyo.Constraint (model.set_plants, rule=supplyConstraintRule)
def demandConstraintRule (model, j):
    return sum(model.variable_x [i,j] for i in model.set_plants)>=model.param_demandAtMarkets[j]
model.constraint_demand = pyo.Constraint (model.set_markets, rule=demandConstraintRule)
model.constraint_demand.pprint()
#Objective
def ObjectiveRule (model):
    return sum( sum(model.variable_x[i,j]* model.param_distances[i,j]*freightCostsPerUnitPerThousandMiles  for i in model.set_plants) for j in model.set_markets)
model.objective = pyo.Objective(rule=ObjectiveRule, sense =pyo.minimize)
model.objective.pprint()
opt = pyo.SolverFactory('glpk')
#opt = SolverFactory("gurobi", solver_io="python")
opt.solve(model) 

I tried to solve it with the statement opt = pyo.SolverFactory('glpk') as recommended here https://pyomo.readthedocs.io/en/stable/solving_pyomo_models.html but I get the error message "ApplicationError: No executable found for solver 'glpk'". I also tried to use the statement opt = SolverFactory("gurobi", solver_io="python")but here I get the error "NameError: name 'SolverFactory' is not defined"

Do you know what I have to do in order to solve the model? I'd appreciate every comment.

Answer 1

If you add pyo. to the beginning of your solver definition line, it works and gives the solution as follow:

param_capacityOfPlants : Size=2, Index=set_plants, Domain=Any, Default=None, Mutable=False
    Key       : Value
    San_Diego :   600
      Seattle :   350
param_demandAtMarkets : Size=3, Index=set_markets, Domain=Any, Default=None, Mutable=False
    Key      : Value
     Chicago :   300
    New_York :   325
      Topeka :   275
param_distances : Size=6, Index=param_distances_index, Domain=Any, Default=None, Mutable=False
    Key                       : Value
     ('San_Diego', 'Chicago') :   1.8
    ('San_Diego', 'New_York') :   2.5
      ('San_Diego', 'Topeka') :   1.4
       ('Seattle', 'Chicago') :   1.7
      ('Seattle', 'New_York') :   2.5
        ('Seattle', 'Topeka') :   1.8
variable_x : Size=6, Index=variable_x_index
    Key                       : Lower : Value : Upper : Fixed : Stale : Domain
     ('San_Diego', 'Chicago') :     0 :  None :  None : False :  True : NonNegativeReals
    ('San_Diego', 'New_York') :     0 :  None :  None : False :  True : NonNegativeReals
      ('San_Diego', 'Topeka') :     0 :  None :  None : False :  True : NonNegativeReals
       ('Seattle', 'Chicago') :     0 :  None :  None : False :  True : NonNegativeReals
      ('Seattle', 'New_York') :     0 :  None :  None : False :  True : NonNegativeReals
        ('Seattle', 'Topeka') :     0 :  None :  None : False :  True : NonNegativeReals
constraint_demand : Size=3, Index=set_markets, Active=True
    Key      : Lower : Body                                                          : Upper : Active
     Chicago : 300.0 :   variable_x[Seattle,Chicago] + variable_x[San_Diego,Chicago] :  +Inf :   True
    New_York : 325.0 : variable_x[Seattle,New_York] + variable_x[San_Diego,New_York] :  +Inf :   True
      Topeka : 275.0 :     variable_x[Seattle,Topeka] + variable_x[San_Diego,Topeka] :  +Inf :   True
objective : Size=1, Index=None, Active=True
    Key  : Active : Sense    : Expression
    None :   True : minimize : 225.0*variable_x[Seattle,New_York] + 225.0*variable_x[San_Diego,New_York] + 153.0*variable_x[Seattle,Chicago] + 162.0*variable_x[San_Diego,Chicago] + 162.0*variable_x[Seattle,Topeka] + 125.99999999999999*variable_x[San_Diego,Topeka]
153675.0

for that you need to add the following lines to your code:

opt = pyo.SolverFactory('gurobi', solver_io="python")
results = opt.solve(model)
print(pyo.value(model.objective))

As you import Pyomo.environ as pyo, for all the defined words in pyomo you need to add pyo. to the beginning. For instance, to get the value of the objective function:

my_obj = pyo.value(model.objective)

To be able to use glpk you need to install it by using:

conda install glpk

See also this if you cannot find the exe path. The following lines worked for me:

opt = pyo.SolverFactory('glpk')
results = opt.solve(model)
print(pyo.value(model.objective))

And I got the same solution: 153675.0

Edit2: To see all the variables' value:

for v in model.component_objects(pyo.Var,active=True):
    for index in v:
        print(index, ': ', pyo.value(v[index]))