I have some problems with my prcedence constraint code. Here an example:
I would like to implement the following predecessor constraint:
where:
i = tasks;
t = period;
j = model of product
x = binary variable which returns 1
if task i is done in period t for model j and 0 otherwise.
In order to satisfy the constraint, P_i represent a set with de predecessor tasks of i.
In order to standardize the code, I use a predecessor matrix to create sets depending on the task, saved in a dictionary. Here it is my code:
import pyomo.environ
from pyomo.core import *
from pyomo.opt import SolverFactory
M_predecessor = [[0,0,0,0,],[0,0,0,0],[1,1,0,0,],[0,0,1,0,]]
predecessor = dict()
for i in range(4):
b = i+1
predecessor[b] = []
for j in range(4):
if M_predecessor[i][j] == 1:
predecessor[b].append(j+1)
model = ConcreteModel()
model.TASKS = RangeSet(1,len(M_predecessor))
model.PERIODS = RangeSet(1,10)
model.MODELS = [1]
Here it is the constraint:
def rest1_rule(model, i, j):
return sum(t * model.x[i,t,j] for t in model.PERIODS) >= (
sum(t * model.x[p for p in predecessor[i],t,j] for t in model.PERIODS)) + model.tiempo[p for p in predecessor[i],j]
model.rest1 = Constraint(model.TASKS, model.MODELS, rule=rest1_rule)
I am not sure how to implement it in my constraint, please any idea? Is there another form to do it? Thanks in advance
@model.Constraint(model.TASKS, model.TASKS, model.MODELS)
def rest1(m, i, p, j):
if p in predecessor[i]:
return sum(t * m.x[i, t, j] for t in m.PERIODS) >= (
sum(t * m.x[p, t, j] for t in m.PERIODS)
+ model.timepo[p])
else:
return Constraint.NoConstraint