Systems and methods for dynamically generating optimal routes for vehicle operation management
A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.
1 . A vehicle routing and analytics (VRA) computing device for generating a respective optimal route for each vehicle of a plurality of vehicles, the VRA computing device communicatively coupled to the plurality of vehicles, wherein the VRA computing device comprises at least one processor in communication with at least one memory, wherein the at least one processor is programmed to:
retrieve a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with a respective vehicle of the plurality of vehicles;
retrieve a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and priority criteria;
execute at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
receive, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
generate the respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of each task of the subset of tasks; and
control at least one vehicle of the plurality of vehicles to travel along the respective optimal route.
2 . The VRA computing device of claim 1 , wherein each optimal route further includes navigation instructions instructing a respective driver of the respective vehicle to operate the respective vehicle according to the navigation instructions in the absence of a control signal for controlling the respective vehicle.
3 . The VRA computing device of claim 1 , wherein each task of the plurality of tasks is assigned to one vehicle of the plurality of vehicles.
4 . The VRA computing device of claim 1 , wherein the priority criteria include one or more of: an identification of the cargo as at least one of a person or an object, a prioritized vehicle characteristic, an availability limitation, or a vehicle characteristic restriction.
5 . The VRA computing device of claim 1 , wherein the at least one processor is further programmed to generate each optimal route by scheduling tasks with more restrictive priority criteria before scheduling tasks with less restrictive priority criteria.
6 . The VRA computing device of claim 1 , wherein one optimal route includes instructions regarding introduction of the cargo into the respective vehicle based at least in part upon the priority criteria of a corresponding task of the subset of tasks, the instructions to be provided to a human operator associated with the cargo.
7 . The VRA computing device of claim 1 , wherein each vehicle of the plurality of vehicles has a plurality of sensors disposed thereon and configured to collect sensor data during operation of the respective vehicle, the respective plurality of sensors disposed on each vehicle including at least one sensor configured to identify check-in and check-out of cargo to and from the respective vehicle, and wherein the at least one processor is further programmed to:
receive, from the plurality of vehicles, sensor data during operation of the plurality of vehicles according to the generated respective optimal routes; and
monitor the received sensor data for adherence to the generated optimal routes.
8 . A computer-implemented method for generating a respective optimal route for each vehicle of a plurality of vehicles, the method implemented using a vehicle routing analytics (VRA) computing device communicatively coupled to the plurality of vehicles, wherein the VRA computing device includes at least one processor in communication with at least one memory, wherein the method comprises:
retrieving a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with a respective vehicle of the plurality of vehicles;
retrieving a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and priority criteria;
executing at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
receiving, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
generating the respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of each task of the subset of tasks; and
controlling at least one vehicle of the fleet of vehicles to travel along the optimal route.
9 . The computer-implemented method of claim 8 , wherein generating each optimal route comprises generating each optimal route to include navigation instructions instructing a respective driver of the respective vehicle to operate the respective vehicle according to the navigation instructions in the absence of a control signal for controlling the respective vehicle.
10 . The computer-implemented method of claim 8 , wherein generating each optimal route comprises scheduling tasks with more restrictive priority criteria before scheduling tasks with less restrictive priority criteria.
11 . The computer-implemented method of claim 8 , wherein generating each optimal route comprises generating one optimal route to include instructions regarding introduction of the cargo into the respective vehicle based at least in part upon the priority criteria of a corresponding task of the subset of tasks, the instructions to be provided to a human operator associated with the cargo.
12 . The computer-implemented method of claim 11 , further comprising causing the instructions to be displayed to the human operator associated with the cargo at the initiation of the corresponding task.
13 . The computer-implemented method of claim 8 , wherein each vehicle of the plurality of vehicles has a plurality of sensors disposed thereon and configured to collect sensor data during operation of the respective vehicle, the respective plurality of sensors disposed on each vehicle including at least one sensor configured to identify check-in and check-out of cargo to and from the respective vehicle, the method further comprising:
receiving, from the plurality of vehicles, sensor data during operation of the plurality of vehicles according to the generated optimal routes; and
monitoring the received sensor data for adherence to the generated optimal routes.
14 . At least one non-transitory computer-readable storage medium having stored thereon computer-executable instructions that, when executed by at least one processor of a vehicle routing and analytics (VRA) computing device communicatively coupled to a plurality of vehicles, cause the at least one processor to:
retrieve a vehicle definition for each vehicle of the plurality of vehicles, each vehicle definition including availability parameters and delivery preferences associated with a respective vehicle of the plurality of vehicles;
retrieve a plurality of task definitions defining a respective plurality of tasks, each task including a respective cargo to be delivered, pick-up time, delivery time, pick-up location, delivery location, and priority criteria;
execute at least one of artificial intelligence and deep learning functionality using the vehicle definition and the plurality of task definitions as inputs;
receive, as output from the executed at least one of artificial intelligence and deep learning functionality, a respective subset of tasks for each vehicle of the plurality of vehicles;
generate a respective optimal route for each vehicle of the plurality of vehicles, each optimal route including a scheduled list of the subset of tasks for the vehicle to perform, wherein each optimal route prioritizes the subset of tasks based at least in part upon the priority criteria of each task of the subset of tasks; and
control at least one vehicle of the plurality of vehicles to travel along the respective optimal route.
15 . The at least one non-transitory computer-readable storage medium of claim 14 , wherein each optimal route further includes navigation instructions instructing a respective driver of the respective vehicle to operate the respective vehicle according to the navigation instructions in the absence of a control signal for controlling the respective vehicle.
16 . The at least one non-transitory computer-readable storage medium of claim 14 , wherein each task of the plurality of tasks is assigned to one vehicle of the plurality of vehicles.
17 . The at least one non-transitory computer-readable storage medium of claim 14 , wherein the priority criteria include one or more of: an identification of the cargo as at least one of a person or an object, a prioritized vehicle characteristic, an availability limitation, or a vehicle characteristic restriction.
18 . The at least one non-transitory computer-readable storage medium of claim 14 , wherein the computer-executable instructions further cause the at least one processor to generate each optimal route by scheduling tasks with more restrictive priority criteria before scheduling tasks with less restrictive priority criteria.
19 . The at least one non-transitory computer-readable storage medium of claim 14 , wherein one optimal route includes instructions regarding introduction of the cargo into the respective vehicle based at least in part upon the priority criteria of a corresponding task of the subset of tasks, the instructions to be provided to a human operator associated with the cargo.
20 . The at least one non-transitory computer-readable storage medium of claim 14 , wherein each vehicle of the plurality of vehicles has a plurality of sensors disposed thereon and configured to collect sensor data during operation of the respective vehicle, the respective plurality of sensors disposed on each vehicle including at least one sensor configured to identify check-in and check-out of cargo to and from the respective vehicle, and wherein the computer-executable instructions further cause the at least one processor to:
receive, from the plurality of vehicles, sensor data during operation of the plurality of vehicles according to the generated optimal routes; and
monitor the received sensor data for adherence to the generated optimal routes.