Building MIP Model GurobiPy become slow
AnsweredHi,
I'm trying to rerun the same model in Gurobipy in the same machine but it takes a lot of time to build it and after several minutes I got the error "out of memory". However, I used to run the same model in the same conditions without any problems. The configuration of the machine is 56 Threads and 500 GB. I'm working with the license 10.0.3
Thank you for help

Do you compare different Gurobi versions? Or something else?
In which setting is it possible to build and run the model, and in which setting do you get the outofmemory error?0 
Im' using the same version of Gurobi which 10.0.3.
I'm trying to reexecute the same program in machine with 56 Threads and 500 GB. Its staks in reading the license. Afther that, I got the message "out of memory" (the screenshot below). Is is related to the cache?
I suppose that each time I build a model, it is stored somewhere?Thank you.
0 
Can you share the code that shows how you reexecute? Do you use the same model object? Do you use the same data?
0 
I'm using the same model object and same data. Here is the model:
def CVRP(df,time_matrix_truck,customers, nodes, trucks, truck_capacity):
mdl = Model('CVRP3')
arc_k = {(i, j , k) for i in nodes for j in nodes for k in trucks if i != j}
xt = mdl.addVars(arc_k, vtype=GRB.BINARY, name='x')
mdl.modelSense = GRB.MINIMIZE
mdl.setObjective(grb.quicksum(time_matrix_truck[i][j] * xt[i, j, k] if i!=j else 0 for i in nodes
for j in nodes for k in trucks))
# Constraint 1 Customer is only served once by only one vehicle
for j in customers:
mdl.addConstr(grb.quicksum(xt[i, j, k] for i in nodes if i != j for k in trucks) == 1)
# Constraint 2 Vehicle must depart from and return to deport
for k in trucks:
mdl.addConstr(grb.quicksum(xt[0, j, k] for j in customers) == 1)
#mdl.addConstr(grb.quicksum(xt[i, 0, k] for i in customers) == 1)
# Constraint 3 Only one vehicle enters and leaves each customer location
for k in trucks:
for j in nodes:
mdl.addConstr(grb.quicksum(xt[i, j, k] if i != j else 0 for i in nodes)
 grb.quicksum(xt[j, i, k] if i != j else 0 for i in nodes) == 0)
#Constraint 4 Demand must not exceed vehicle cdrone_capacity
for k in trucks:
mdl.addConstr(grb.quicksum(df.demand[j] * xt[i, j, k] for i in nodes for j in customers if i != j) <= truck_capacity)
# Constraint 6: Subtour Elimination
subtours = []
for i in nodes[2:]:
subtours += itertools.combinations(customers, i)
# Create a list to hold the subtour elimination constraints
subtour_constraints = []
for s in subtours:
for k in trucks:
subtour_constraints.append(mdl.addConstr(grb.quicksum(xt[i, j, k] if i != j else 0 for i, j in itertools.permutations(s, 2)) <= len(s)  1))
return mdl, xt
And the parameters:def solving_model(mdl):
mdl.Params.Presolve = 0
mdl.Params.TimeLimit = 3600
mdl.Params.MIPGap = 0.1
mdl.Params.Threads = 32
mdl.Params.NodefileStart = 0.5
solution = mdl.optimize()
runTime = mdl.Runtime
status = mdl.status
# Check the status and print the results
if status == grb.GRB.OPTIMAL:
print('Total Time Travelled:', mdl.objVal)
print('Running Time:', runTime)
else:
print('No optimal solution found')
return status, runTime0 
Can you also share how you call CVRP and solving_model for the first and the second run?
0 
Yes, sure!
def main():
file_path = "/Exps/SA/expSetP/40P/Pn40k5.vrp"
dataI = load_data(file_path)
n = dataI[0]
nT = int(dataI[1])
truck_capacity = dataI[2]
data = dataI[3]
print(
"instance: ", n, " customers ", nT, " trucks", truck_capacity, " truck capacity"
)
print("DATA")
print(data)
distance_truck = distance_matrix_truck(data)
speedT = 11.176
time_truck = time_matrix(distance_truck, speedT)
# Define sets
# customers = [x for x in range(1, n)]
# print("Customers Set\n", customers)
# nodes = [0] + customers
print("Nodes Set\n", nodes)
trucks = [x for x in range(1, nT + 1)]
# Modelling
# dic_res = dict()
list_vars = []
resC = CVRP(data, time_truck, customers, nodes, trucks, truck_capacity)
mdlT = resC[0]
xt = resC[1]
dic_res[mdlT] = xt
numIter = 1
for mdl in dic_res:
print("===================model==========:", mdl)
name = mdl.ModelName
if name == "CVRP3":
xt = dic_res[mdl]
xd = None
solving_model(mdl)
plot_solution(nodes, mdl, data, xt, xd, nT, nD)
runTimeTot = 0
if __name__ == "__main__":
main()0 
I figure out that the license will finish by Oct 7, 2024. Is the issue related to the fact that the license will expire very soon?
Thank you
Assia
0 
The outofmemory error is not related to the fact that the license will expire in some days.
I do not see in the code snippet you sent that the loop \(\texttt{for mdl in dic_res}\) is executed more than once. Could you try to share a (minimal) reproducible example?
0 
I tested on otehr machine. I takes more time to display licence of Gurobi.
Here is the data:
NAME : Pn40k5
COMMENT : (Augerat et al, Min no of trucks: 5, Best value: 458)
TYPE : CVRP
DIMENSION : 40
EDGE_WEIGHT_TYPE : EUC_2D
CAPACITY : 140
NODE_COORD_SECTION
1 30 40
2 37 52
3 49 49
4 52 64
5 20 26
6 40 30
7 21 47
8 17 63
9 31 62
10 52 33
11 51 21
12 42 41
13 31 32
14 5 25
15 12 42
16 36 16
17 52 41
18 27 23
19 17 33
20 13 13
21 57 58
22 62 42
23 42 57
24 16 57
25 8 52
26 7 38
27 27 68
28 30 48
29 43 67
30 58 48
31 58 27
32 37 69
33 38 46
34 46 10
35 61 33
36 62 63
37 63 69
38 32 22
39 45 35
40 59 15
DEMAND_SECTION
1 0
2 7
3 30
4 16
5 9
6 21
7 15
8 19
9 23
10 11
11 5
12 19
13 29
14 23
15 21
16 10
17 15
18 3
19 41
20 9
21 28
22 8
23 8
24 16
25 10
26 28
27 7
28 15
29 14
30 6
31 19
32 11
33 12
34 23
35 26
36 17
37 6
38 9
39 15
40 14
DEPOT_SECTION
1
1
EOFHere is a simple main:
def main():
file_path = "Pn40k5.vrp"
dataI = load_data(file_path)
n = dataI[0]
nT = int(dataI[1])
truck_capacity = dataI[2]
data = dataI[3]
print("instance: ", n," customers ",nT," trucks", truck_capacity, " truck capacity")
print("DATA")
print(data)
distance_truck = distance_matrix_truck(data)
speedT = 11.176
time_truck = time_matrix(distance_truck, speedT)
customers = [x for x in range(1, n)]
print("customers ", customers)
nodes = [0] + customers
print("Nodes Set\n", nodes)
trucks = [x for x in range(1, nT+1)]
mdl = CVRP(data, time_truck, customers, nodes, trucks, truck_capacity)
print("===================model==========:", mdl)
solving_model(mdl)
if __name__ == "__main__":
main()0 
Now I understand: it is not the problem that you reexecute the same code twice in one run, and the second time, it gets stuck and crashes with an outofmemory error.
Instead, you want to run a code that you think worked before.
Please note that your number of subtourconstraints explodes with the number of nodes in your model.
For 21 nodes, the number of subtours is 2,097,130. For 40 nodes, it is already 1,099,511,627,735.
Hence, this formulation does not work for larger instances.
So, I assume when you ran the code successfully, you considered a smaller instance with fewer nodes.Note: Here are Jupyter notebooks that discuss different ways to model the Capacitated VRP and the VRP.
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