you should try VANET algorithm to do this task

There are three parts to the problem ..

1) Have a method to solve the problem, an algorithm simply described in a plane language. Research "traveling salesman problem"

2) Convert the method into code that reads the specific case and solves it. Start simple with reading the problem description, reorder the data to suit the method from stage 1) and solve.

3) Test and refine the answer, write the documentation.

OR be very lazy and ask someone else to do that and learn little.

OR pay someone else to do that and learn little.

OR reframe the problem or a logic or general problem solver ( python may not be included).

First, you need to install the OR-Tools library if you haven't already

pip install ortools

from ortools.constraint_solver import routing_enums_pb2

from ortools.constraint_solver import pywrapcp

# Define the distance matrix (replace this with your actual distance matrix)

distance_matrix = [

[0, 20, 23, 19, 13, 16, 38, 38, 24, 12, 18, 13],

[20, 0, 5, 33.4, 31.6, 28.5, 37.8, 54.5, 47.5, 30, 36.2, 14.2],

[23, 5, 0, 40, 35, 32.6, 41.6, 62, 47.3, 40, 40.3, 18.6],

[19, 33.4, 40, 0, 13, 17.6, 15.5, 32, 37.5, 28, 37.8, 32.8],

[13, 31.6, 32.6, 13, 0, 43, 8, 22, 19.3, 23, 24, 20],

[16, 28.5, 35, 17.6, 13, 0, 10, 10, 15, 22, 20, 16],

[38, 37.8, 41.6, 15.5, 8, 10, 0, 17, 20, 23, 24, 26.2],

[38, 54.5, 62, 32, 22, 10, 17, 0, 14, 24, 26, 46],

[24, 47.5, 47.3, 37.5, 19.3, 15, 20, 14, 0, 10, 12, 31],

[12, 30, 40, 28, 23, 22, 23, 24, 10, 0, 12, 23],

[18, 36.2, 40.3, 37.8, 24, 24, 24, 26, 12, 12, 0, 20],

[13, 14.2, 18.6, 32.8, 20, 16, 26.2, 46, 31, 23, 20, 0]

]

# Create a routing model

def create_data_model():

data = {}

data['distance_matrix'] = distance_matrix

data['num_vehicles'] = 3 # Number of vehicles

data['depot'] = 0 # The depot is node 0

return data

def main():

# Instantiate the data problem

data = create_data_model()

# Create the routing index manager

manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),

data['num_vehicles'], data['depot'])

# Create Routing Model

routing = pywrapcp.RoutingModel(manager)

# Create and register a transit callback

def distance_callback(from_index, to_index):

from_node = manager.IndexToNode(from_index)

to_node = manager.IndexToNode(to_index)

return data['distance_matrix'][from_node][to_node]

transit_callback_index = routing.RegisterTransitCallback(distance_callback)

# Define cost of each arc

routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

# Set 10 seconds as the maximum time allowed per each vehicle

search_parameters = pywrapcp.DefaultRoutingSearchParameters()

search_parameters.time_limit.seconds = 10

# Solve the problem

solution = routing.SolveWithParameters(search_parameters)

# Print solution if found

if solution:

print_solution(manager, routing, solution)

def print_solution(manager, routing, solution):

print('Objective: {} units of milk traveled'.format(solution.ObjectiveValue()))

for vehicle_id in range(manager.GetNumberOfVehicles()):

index = routing.Start(vehicle_id)

plan_output = 'Route for vehicle {}:\n'.format(vehicle_id + 1)

while not routing.IsEnd(index):

plan_output += ' {} ->'.format(manager.IndexToNode(index))

index = solution.Value(routing.NextVar(index))

plan_output += ' {}\n'.format(manager.IndexToNode(index))

print(plan_output)

if __name__ == '__main__':

main()

This code sets up the VRP instance for your problem with three vehicles and multiple destinations, calculates the optimal routes, and prints the results. You can replace the distance_matrix with your actual distance matrix. Make sure that the depot is defined as node 0, and the number of vehicles (num_vehicles) is set to 3 as per your problem description.

thank you sir, i try

Vehicle routing problem (VRP) is a type of optimization problem that aims to find the optimal routes for a fleet of vehicles to visit a set of locations. VRP has many applications in logistics, transportation, delivery, and other fields. Solving VRP can be challenging, as it involves many constraints and variables, such as vehicle capacity, time windows, distance, and demand.

To solve VRP using Python, you need to use a suitable library or framework that can handle the complexity and computation of VRP. One of the most popular and powerful tools for VRP is OR-Tools, which is an open-source software suite developed by Google for operations research and combinatorial optimization. OR-Tools provides a rich set of algorithms and solvers for various types of VRP, such as capacitated VRP, VRP with time windows, VRP with pickups and deliveries, and more.