智慧物流革新：Deepseek优化运输管理与供应链调度，驱动未来物流产业变革

 from ortools.constraint_solver import routing_enums_pb2

    
 from ortools.constraint_solver import pywrapcp
    
  
    
 # 创建一个简单的距离矩阵
    
 distance_matrix = [
    
     [0, 2, 9, 10],
    
     [1, 0, 6, 4],
    
     [15, 7, 0, 8],
    
     [6, 3, 12, 0]
    
 ]
    
  
    
 # 创建Routing模型
    
 def create_data_model():
    
     data = {}
    
     data['distance_matrix'] = distance_matrix
    
     data['num_vehicles'] = 1
    
     data['depot'] = 0
    
     return data
    
  
    
 def main():
    
     # 创建数据模型
    
     data = create_data_model()
    
  
    
     # 创建RoutingIndexManager
    
     manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']), data['num_vehicles'], data['depot'])
    
  
    
     # 创建RoutingModel
    
     routing = pywrapcp.RoutingModel(manager)
    
  
    
     # 创建距离回调
    
     def distance_callback(from_index, to_index):
    
     from_node = manager.IndexToNode(from_index)
    
     to_node = manager.IndexToNode(to_index)
    
     return distance_matrix[from_node][to_node]
    
  
    
     transit_callback_index = routing.RegisterTransitCallback(distance_callback)
    
  
    
     # 设置成本函数
    
     routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
    
  
    
     # 设置搜索参数
    
     search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    
     search_parameters.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
    
  
    
     # 求解
    
     solution = routing.SolveWithParameters(search_parameters)
    
  
    
     # 输出结果
    
     if solution:
    
     print("Route:")
    
     route_distance = 0
    
     index = routing.Start(0)
    
     while not routing.IsEnd(index):
    
         print(f" {manager.IndexToNode(index)} -> ", end='')
    
         previous_index = index
    
         index = solution.Value(routing.NextVar(index))
    
         route_distance += routing.GetArcCostForVehicle(previous_index, index)
    
     print(f"{manager.IndexToNode(index)}")
    
     print(f"Route distance: {route_distance}")
    
     else:
    
     print("No solution found.")
    
  
    
 if __name__ == '__main__':
    
     main()
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/rub4U5oKJw2LWVn6aNSGytMATEcm.png)

 import numpy as np

    
 import pandas as pd
    
 from sklearn.linear_model import LinearRegression
    
 from sklearn.model_selection import train_test_split
    
  
    
 # 生成模拟数据
    
 data = {
    
     'date': pd.date_range(start='2023-01-01', periods=100, freq='D'),
    
     'demand': np.random.randint(100, 500, size=100)
    
 }
    
 df = pd.DataFrame(data)
    
  
    
 # 特征工程
    
 df['day_of_week'] = df['date'].dt.dayofweek
    
 df['month'] = df['date'].dt.month
    
 df['year'] = df['date'].dt.year
    
  
    
 # 定义X和y
    
 X = df[['day_of_week', 'month', 'year']]
    
 y = df['demand']
    
  
    
 # 分割数据集
    
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
  
    
 # 训练模型
    
 model = LinearRegression()
    
 model.fit(X_train, y_train)
    
  
    
 # 预测
    
 predictions = model.predict(X_test)
    
  
    
 # 输出预测结果
    
 print("预测需求：", predictions)
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/0QL6ToHx2tUAKksBYpP49burwjMa.png)