ortools scip can't give a solution

[Killajam]

My problem definition is that there are m delivery points and n demand points, and now three optimization goals need to be met: maximizing the traffic volume, minimizing the number of trips and minimizing the distance.
So I built a matrix of moment decision variables m x n, where xij represents the amount of freight transported from point I to point j.

# evaluate the obj value
def evaluate(solver):
    status = solver.Solve()
        if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:
            obj_value =solver.Objective().Value()
        else:
            obj_value = float('-inf')
     return obj_value
# Indicates that the variable function makes the decision variable greater than 0 equal to 1
def indicator(solver, decision, k):
    M = 10000
    y = solver.IntVar(0, 1, '')
    solver.Add((1.0 / M) * (decision - k) - 0.5 / M <= y)
    solver.Add((1.0 / M) * (decision - k) + (M - 1) / M >= y)
    return y
solver = pywraplp.Solver_CreateSolver('SCIP')
solver.EnableOutput()
# e stands for the point of supply and s for the point of receipt
decision_var = [[0 for j in range(Conf.s)] for i in range(Conf.e)]
for i in range(Conf.e):
  for j in range(Conf.s):
     # There are some points that cannot be transported directly, and the mixmat between them is 0
      if Conf.mixmat[i][j] == 1:
         # e_num_vector[i] stands for the maximum supply and s_num_vector[j] stands for the maximun quantity received
          decision_var[i][j] = solver.IntVar(0, min(Conf.e_num_vector[i], Conf.s_num_vector[j]),
                                             'x%s%s' % (i, j))
      else:
          decision_var[i][j] = solver.IntVar(0, 0, 'x%s%s' % (i, j))
        decision_var = np.array(decision_var)
# basic constraints
for i in range(Conf.e):
    solver.Add(sum(self.decision_var[i]) <= Conf.e_num_vector[i])
for j in range(Conf.s):
     solver.Add(sum(self.decision_var[:, j]) <= Conf.s_num_vector[j])

# Maximize the traffic volume
volume_obj = self.solver.Sum([self.solver.Sum(self.decision_var[i]) for i in range(len(Conf.e_ed))])
solver.Maximize(volume_obj)
v_obj_value = evaluate(solver)
solver.add(volume_obj >= v_obj_value)

# Minimize the number of trips 
trips_obj = solver.Sum([solver.Sum([indicator(solver, j, 0) for j in decision_var[i]]) for i in range(Conf.e)])
solver.Minimize(trips_obj )
t_obj_value = evaluate(solver)
solver.add(trips_obj <= t_obj_value)

# Minimize the distance. dismat stands for the dismatrix,where dismat[i][j] is the distance between i to j.
distance_obj = solver.Sum([np.array([indicator(solver, j, 0) for j in decision_var[i]]).dot(Conf.dismat[i]) for i in range(Conf.e)])
solver.Minimize(distance_obj )
distance_obj_value = evluate(distance_obj)

My decision variable matrix is usually 200 by 200 or more.It usually takes more than an hour to solve with a SCIP, and in some cases it can be impossible.
When I get rid of the second optimization objective, the solution time is much less.I was wondering if there were any tricks to change my model?

[Killajam]

I have also tried to use CP-SAT to solve, but the effect is not very good.
I found that the solving time of the model involving indicator function is very slow or even unable to find the solution. Why is this?

[lperron]

Nobody can run the problem. Can you post a full standalone code ?