Infeasible model
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Hi,
I'm trying to solve an MIP model to optimality using Gurobi. The thing is that once I increase the power of one of my decision variables, for instance to 4, the model gets infeasible. I did not change the constraints, how can a model become infeasible by just making a change to its objective function? Here's my model, x, y, z, and y2, are the variables and the issue is with y2[j,k]:
Main = Model(name='main')
Main.Params.LogToConsole = 1
x = Main.addMVar((m, n), vtype = GRB.BINARY)
y = Main.addMVar((n, p), vtype = GRB.CONTINUOUS)
y2 = Main.addMVar((n, p), vtype = GRB.CONTINUOUS)
z = Main.addMVar((n), vtype = GRB.BINARY)
Main.setParam("NonConvex", 2)
obj = (quicksum(a[j]*z[j] for j in range (n))
+ quicksum(c[j,k]*(y2[j,k]**2) for j in range (n) for k in range (p))
+ quicksum(t[i,j]*x[i,j]* (D2[i]**4) for i in range (m) for j in range (n)))
Main.setObjective(obj, GRB.MINIMIZE)
for j in range(n):
for k in range(p):
Main.addGenConstrPow(y[j, k], y2[j, k], 2)
Main.addConstrs(quicksum(x[i, j] for j in range (n)) == 1 for i in range (m))
Main.addConstrs(quicksum(D[i]*x[i, j] for i in range (m)) ==
quicksum(y[j,k] for k in range (p)) for j in range (n))
Main.addConstrs(y[j,k] >= 0 for j in range (n) for k in range (p))
Main.addConstrs(y2[j,k] >= 0 for j in range (n) for k in range (p))
Main.addConstrs(quicksum(y[j,k] for k in range (p)) <= v[j]*z[j] for j in range (n))
Main.addConstrs(quicksum(D[i]*x[i,j] for i in range (m)) <= v[j]*z[j] for j in range (n))
Main.optimize()
-
Hi Ensieh,
Can you extend your code so that it is a reproducible example? This will make it easier for someone from the community to lend help.
- Riley
0 -
Sure! Please see below.
n = 20
m = 20
p = 1
betha1 = 0.567 / 1000
a = np.full(n, 55)
scale = 4
v = np.ceil(np.array([1046., 1046., 627.6, 1046., 627.6, 1046., 1046., 627.6,
627.6, 627.6, 1046., 627.6, 627.6, 1046., 627.6, 627.6,
1046., 627.6, 1046., 1046.]))
df = pd.read_excel('distances.xlsx')
d_ij = df.values
d_ij = np.tile(d_ij, (scale, 1))
d_jk_l = [40, 440, 20, 0, 60, 170, 50, 30, 80, 340, 50, 390, 90, 90, 110, 70, 300, 50, 1390, 80]
d_jk = np.array(d_jk_l).reshape(-1, 1)
D = np.array([626, 241, 178, 151, 151, 78, 87, 62, 60, 48, 53, 39, 32, 35, 40, 20, 27, 30, 28, 37])
D = np.tile(D, scale)
D = D.astype('int64')c = np.round(np.multiply(betha1, d_jk), 5)
t = np.round(np.multiply(betha1, d_ij), 5)Main = Model(name='main')
Main.Params.LogToConsole = 0
Main.Params.MIPGap = 1e-6
Main.Params.OptimalityTol = 1e-6
Main.Params.IntFeasTol = 1e-6
Main.Params.Presolve = 2
Main.Params.ImproveStartGap = 1e-6
Main.Params.NumericFocus = 3
x = Main.addMVar((m, n), vtype = GRB.BINARY)
y = Main.addMVar((n, p), vtype = GRB.CONTINUOUS)
y2 = Main.addMVar((n, p), vtype = GRB.CONTINUOUS)
z = Main.addMVar((n), vtype = GRB.BINARY)
D2 = np.power(D, 4)
Main.setParam("NonConvex", 2)
obj = (quicksum(a[j]*z[j] for j in range (n))
+ quicksum(c[j,k]*(y2[j,k]) for j in range (n) for k in range (p))
+ quicksum(t[i,j]*x[i,j]* (D2[i]) for i in range (m) for j in range (n)))
Main.setObjective(obj, GRB.MINIMIZE)
for j in range(n):
for k in range(p):
Main.addGenConstrPow(y[j, k], y2[j, k], 4)
Main.addConstrs(quicksum(x[i, j] for j in range (n)) == 1 for i in range (m))
Main.addConstrs(quicksum(D[i]*x[i, j] for i in range (m)) ==
quicksum(y[j,k] for k in range (p)) for j in range (n))
Main.addConstrs(y[j,k] >= 0 for j in range (n) for k in range (p))
Main.addConstrs(y2[j,k] >= 0 for j in range (n) for k in range (p))
Main.addConstrs(quicksum(y[j,k] for k in range (p)) <= v[j]*z[j] for j in range (n))
Main.addConstrs(quicksum(D[i]*x[i,j] for i in range (m)) <= v[j]*z[j] for j in range (n))
Main.optimize()
print(Main.objval)0 -
Hi Ensieh,
df = pd.read_excel('distances.xlsx')
Your code is not reproducible.
- Riley
0
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