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We continue with our series  and we create a function that takes care of reading the output of the algorithm, converting it to a readable form and plotting the routes on the map. def print_solution(data, manager, routing, assignment):       Route = []       for vehicle_id in range(data['num_vehicles']:                 index           = routing.Start(vehicle_id)                 idx               = manager.IndexToNode(index)                 plan_output = 'Route for vehicle {} with ID {} and capacity {}:\n'.format(vehicle_id, data['IDs'][idx], data['capacity'][vehicle_id])                 Route[Iter].append([data['locations'][idx], data['IDs'][idx]])                 prev_ind...

This is a continuation to the route optimization series . First we define the function that will be used by the optimizer to get the distances between points.   def distance_callback(from_index, to_index):                 from_node = manager.IndexToNode(from_index)                  to_node      = manager.IndexToNode(to_index)                          if from_node == to_node:                                return 0                          return data['distance_matrix'][from_node][to_node] IndexToNode is used to correlate between a location as a node index inside the being tested/optimized route by the solver and the index of the loc...

We have covered querying the server for data about events, offers and requests in the first tutorial . Now we start building the data to be fed into OR-tools. Offer_df      = False Request_df  = False while type(Offer_df)     == bool or           type(Request_df) == bool:                 df = read_events()                 Offer_df = read_offers(df['event_id'])                 if type(Offer_df) != bool:                        Request_df = read_requests(Offer_df['event_id']) The reason why we check for the type of Offer_df or Request_df  instead of checking if they are equal to False is that if Offer_df is a numpy array, then python will return an error as comparing a numpy array to a value is invalid and it will ask us to be more ...

This is a tutorial for an algorithm that has riped over the years of experience in doing algorithms and websites  for carpooling, Uber like service, Freight routing, and many other applications. I will be stating the current algorithm and i will discuss my decisions on certain code implementations as well as some of the previous decisions that i took and proved to be wrong, slow or error prone. This algorithm is capable of optimizing for thousands of locations, and made to be highly customizable. Features of the algorithm are: Reading data from and exporting data to an excel sheet or a Database. Multiple time windows per location. Multiple vehicle types, capacities, buffers, depots. Custom optimization parameters like distance, time, fuel consumption, etc. Pickup and dropoff. Custom loading and unloading time per stop. Custom service time at certain locations. Custom max idle time waiting for the next stop's time window. Custom distributed workload amongst ...