TSP 求解器问题。找不到正确的行程

问：

我想在给定距离矩阵的情况下求解 TSP。但是，我的代码只返回“array（[0， 0， 0， 0]）”，这意味着它只想留在当前位置而不离开。但我显然想以最低的成本游览所有地点。

这是我到目前为止拥有的代码：

import numpy as np
from ortools.linear_solver import pywraplp

class TSPSolver:
    def __init__(self, distance_matrix, method):
        self.distance_matrix = np.array(distance_matrix)
        self.method = method

    def solve_tsp(self):
        return self.method.solve()

class TSPMethod:
    def __init__(self, distance_matrix):
        self.distance_matrix = np.array(distance_matrix)

    def solve(self):
        raise NotImplementedError("This method should be overridden in subclasses")

class FlowBasedMethod(TSPMethod):
    def solve(self):
        self.distance_matrix = self.distance_matrix.astype(int) # integers required by SCIP
        n = self.distance_matrix.shape[0]

        # Create the linear solver
        solver = pywraplp.Solver.CreateSolver('SCIP')

        # Create variables
        x = {}
        for i in range(n):
            for j in range(n):
                x[i, j] = solver.IntVar(0, 1, f'x_{i}_{j}')

        u = [solver.IntVar(0, n, f'u_{i}') for i in range(n)]

        # Constraints
        for i in range(n):
            solver.Add(solver.Sum(x[i, j] for j in range(n)) == 1)  # each city must be departed from exactly once
            solver.Add(solver.Sum(x[j, i] for j in range(n)) == 1)  # each city must be visited exactly once

        for i in range(1, n):
            for j in range(1, n):
                if i != j:
                    solver.Add(u[i] - u[j] + n * x[i, j] <= n - 1)  # subtour elimination

        # Objective function: minimize the total distance
        solver.Minimize(solver.Sum(self.distance_matrix[i, j] * x[i, j] for i in range(n) for j in range(n)))

        # Solve the problem
        status = solver.Solve()
        # If a solution was found, extract the tour
        if status == pywraplp.Solver.OPTIMAL:
            # Start from the first city
            tour = [0]
            current_city = 0
            while len(tour) < n:
                # Go to the next city
                for j in range(n):
                    if x[current_city, j].solution_value() > 0.5:
                        tour.append(j)
                        current_city = j
                        break
            return np.array(tour)

        else:
            print('No solution found!')
            return None

distance_matrix = [
    [0, 10, 15, 20],
    [10, 0, 35, 25],
    [15, 35, 0, 30],
    [20, 25, 30, 0]
]

method = FlowBasedMethod(distance_matrix)
solver = TSPSolver(distance_matrix, method)
solver.solve_tsp()

预期输出：array（[0， 1， 3， 2]）