Query on the use of addMVar

Hello I am solving an optimization problem, I have finished my code but it turns out that when I execute all my response values ​​are zero. I'm not sure if I'm doing my code wrong or what's wrong. I attach my code, in addition to the restrictions with which I am working.

# COLOCACION Y CONFIGURACION OPTIMA DE UNIDADES DE CARRETERA EN REDES VEHICULARES.

# Definicion de librerias
from gurobipy import *
import numpy as np
import matplotlib.pyplot as plt
import random

# Extraccion de los datos del archivo de texto
f = open("datos_RSU.txt", "r")
contents = []  # creacion de los vectores necesarios
fin_contents = []
for _ in range(11):  # defino el numero de lineas del file
    str = f.readline()  # lectura del file
    content = str.split()  # divido el vector
    contents.append(content)  # forma una lista de listas
for i in range(10):
    fin_cont = [int(cont) for cont in contents[i]]
    # lo convierto a numeros enteros
    fin_contents.append(fin_cont)  # lista de listas

N = fin_contents[0][0]  # linea 1 identificada cantidad de RSU propuestas.
calles = fin_contents[1][0]  # linea 2 identificada conjunto de calles.
X = np.asarray(fin_contents[2])  # linea 3 identificada ubicaciones en eje X de RSU.
Y = np.asarray(fin_contents[3])  # linea 4 identificada ubicaciones en eje Y de RSU.
P = np.asarray(fin_contents[4])  # linea 5 niveles de potencia de antenas.
A = np.asarray(fin_contents[5])  # linea 6 tipos de antenas.
R = np.asarray(fin_contents[6])  # linea 7 nivel de trafico.
Naccident = fin_contents[7][0]  # linea 8 identifica puntos de alta insidencia.
Xca = np.asarray(fin_contents[8])  # linea 9 ubicacion x de alta insidencia.
Yca = np.asarray(fin_contents[9])  # linea 10 ubicacion y de alta insidencia.

# Definicion de parametros usados en el modelo
S = [i for i in range(calles) if i != 0]
rsu = [i for i in range(N) if i != 0]
Caccident = [i for i in range(Naccident) if i!=0]
nhops = 2
max_distancia = 200
Costo_LAN = 1
Costo_wifi = 1000
delta = 1
C = [(i, j) for i in X for j in Y]
acc_xy = (Xca, Yca)

# Figura 1
for i in X:
    for j in Y:
        circle1 = plt.Circle((i, j), 80, color='yellow')
        plt.gca().add_patch(circle1)

for i in Xca:
    for j in Yca:
        circle1 = plt.Circle((i, j), 60, color='magenta')
        plt.gca().add_patch(circle1)

plt.scatter(X, Y, color="red")
plt.scatter(Xca, Yca, color="black")
plt.xlim(-200, 1000)
plt.ylim(-200, 1100)

lx = []
ly = []
r = random.choice(R)
for i in range(r):
    X_e = (random.randrange(-10, 400, 10))
    Y_e = (random.randrange(0, 1100, 10))
    V_r = (X_e, Y_e)
    plt.scatter(X_e, Y_e, color="Blue")
    lx.append(X_e)
    ly.append(Y_e)
print(r)

coches = [i for i in range(r) if i != 0]
arc_coches = [0] + coches

for i in rsu:
    plt.annotate('$%d$' % (i), (X[i], Y[i] + 25), color='red')
plt.xlabel("Distancia X")
plt.ylabel("Distancia Y")
plt.title("Coches(Azul), RSU(Rojo) y Zonas de alta incidencia (Negro)")
plt.show()

# Funciones para comunicacion coche-rsu
ss = np.zeros((len(coches), len(rsu)))
for i in range(len(coches)):
    for j in range(len(rsu)):
        if np.hypot(lx[i] - X[j + 1], ly[i] - Y[j + 1]) < max_distancia:
            ss[i][j] = 1

print(ss)
distancia_a_la_rsu = []
for i in range(len(coches)):
    for j in range(len(rsu)):
        distancia_a_la_rsu.append(round(np.hypot(lx[i] - X[j + 1], ly[i] - Y[j + 1])))
vector = np.array(distancia_a_la_rsu)
nuevo = np.reshape(vector, (r - 1, N - 1))
ahorasi = nuevo.min(1)

t = (r - 1, N - 1)
ceros = np.zeros(t)
otra = []
for i in range(len(nuevo - 1)):
    for j in range(N - 1):
        if nuevo[i, j] == ahorasi[i]:
            ceros[i, j] = 1


nodos = [0] + rsu
arcos_Dij = [(i, j) for i in coches for j in nodos] #arcos contiene la descripcion de Dij
arcos_Hij= [(i, j) for i in coches for j in coches if i!=j] #arcos contiene la descripcion de Hij
arcos_Kij= [(i, j) for i in nodos for j in Caccident] #arcos contiene la descripcion de Kij
arcos_Iijkmn= [(i, j, k, m, n) for i in S for j in nodos for k in A for m in P for n in R] #arcos contiene la descripcion de Iij
distancia = {(i, j): round(np.hypot(X[i] - lx[j], Y[i] - ly[j]), 2) for i in nodos for j in arc_coches}
tiempo = {(i, j): round(np.hypot(X[i] - lx[j], Y[i] - ly[j]), 2) for i in nodos for j in arc_coches}
arco_var = [(i, j, k) for i in nodos for j in nodos for k in coches if i != j]

# Estructura del modelo

model = Model('RSU.log')
xijk = [(i, j, k) for i in nodos for j in A for k in P]
zik = [(i, k) for i in nodos for k in P]
yi = [(i) for i in nodos]
hij = [(i, j) for i in S for j in R]
ri = [(i) for i in R]
mx = [(i, j) for i in nodos for j in P]

d = [(round(np.hypot(X[i] - lx[j], Y[i] - ly[j]))) for i in nodos for j in arc_coches]
mj = []
mij = (d, P)
for i in d:
    for j in P:
        mi = i * j
        mj.append(round(mi))

result = []
for item in mj:
    if item not in result:
        result.append(item)

# Variables de decision
I = model.addVars(arcos_Iijkmn, vtype=GRB.BINARY, name="I")
D = model.addVars(arcos_Dij, vtype=GRB.BINARY, name="D")
H = model.addVars(arcos_Hij, vtype=GRB.CONTINUOUS, name="H")
K = model.addVars(arcos_Kij, vtype=GRB.CONTINUOUS, name="K")
x = model.addVars(xijk, vtype=GRB.BINARY, name="x")
y = model.addVars(yi, vtype=GRB.BINARY, name="y")
z = model.addVars(zik, vtype=GRB.BINARY, name="z")
h = model.addVars(hij, vtype=GRB.CONTINUOUS, name="h")
m = model.addVars(mx, vtype=GRB.BINARY, name="m")
r = model.addVars(ri, vtype=GRB.CONTINUOUS, name="r")

print("valor de x: ", x)
print("valor de y: ", y)
print("valor de z: ", z)
print("valor de h: ", h)
print("valor de r: ", r)
print("valor de m: ", m)


# Definicion del modelo
model.setObjective(quicksum(m[i, k] * z[i, k] for i, k in zik), GRB.MINIMIZE)
model.addConstrs(quicksum(x[i, j, k] for j in A for k in P) <= 1 for i in nodos)
model.addConstrs(quicksum(x[i, j, k] for j in A for k in P) == y[i] for i in nodos)
model.addConstrs(quicksum(x[i, j, k] for j in A) == z[i, k] for i in nodos for k in P)
model.addConstrs(quicksum(I[i, j, k, m, n] * x[j, k, m] for j, k, m in xijk) >= h[i, n] for i, n in hij)
model.addConstrs(quicksum(h[i, j] for i in S) >= r[j] for j in R)
model.addConstr(quicksum(K[i, j] * y[j] for i,j in arcos_Kij) >= 1)

model.update()
model.Params.LogFile = 'RSU.log'
model.optimize()

print("valor de x: ", x)
print("valor de y: ", y)
print("valor de z: ", z)
print("valor de h: ", h)
print("valor de r: ", r)
print("valor de m: ", m)

# Figura 2
plt.figure(2)
plt.scatter(lx, ly, color='blue')
plt.scatter(X[0:], Y[0:], color='red', marker="D")
plt.scatter(Xca[0:], Yca[0:], color='black', marker="X")
for i in rsu:
    plt.annotate('$%d$' % (i), (X[i], Y[i] + 25), color='red')
for i in arc_coches:
    plt.annotate('$%d$' % (i), (lx[i - 1] + 10, ly[i - 1] - 5.5))
col = ['magenta', 'green', 'blue', 'black', 'cyan', 'red', 'magenta', 'red', 'magenta', 'green', 'blue', 'black',
       'cyan', 'red', 'magenta', 'red']
for i in arc_coches:
    for j in rsu:
        if ss[i - 1][j - 1] == 1:
            plt.plot([lx[i - 1], X[j]], [ly[i - 1], Y[j]], '--', color=col[j], alpha=0.7)
plt.xlim(-100, 1100)
plt.ylim(-100, 1100)
plt.xlabel("Distancia X")
plt.ylabel("Distancia Y")
plt.title("coche-rsu Problem")
plt.show()

# Figura 3
plt.figure(3)
plt.scatter(lx, ly, color='blue')
plt.scatter(X[0:], Y[0:], color='red', marker="D")
plt.scatter(Xca[0:], Yca[0:], color='black', marker="X")
for i in rsu:
    plt.annotate('$%d$' % (i), (X[i], Y[i] + 25), color='red')
for i in arc_coches:
    plt.annotate('$%d$' % (i), (lx[i - 1] + 10, ly[i - 1] - 5.5))
col = ['magenta', 'green', 'blue', 'black', 'cyan', 'red', 'magenta', 'red', 'magenta', 'green', 'blue', 'black',
       'cyan', 'red', 'magenta', 'red']
for i in arc_coches:
    for j in rsu:
        if ceros[i - 1][j - 1] == 1:
            plt.plot([lx[i - 1], X[j]], [ly[i - 1], Y[j]], '--', color=col[j], alpha=0.7)
plt.xlim(-100, 1100)
plt.ylim(-100, 1100)
plt.xlabel("Distancia X")
plt.ylabel("Distancia Y")
plt.title("Antena mas cercana")
plt.show()

Data entered into the model:

10
5
10 10 10 10 10 200 200 200 200 200
10 200 350 700 1000 10 200 350 700 1000
10 8 6
3
30 20 10
3
10 10 10
250 523 900