Drone-Truck Route Modeling for Range-Limited Last-Mile Delivery
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Solution Overview
Problem
The integration of drones and trucks for delivery presents a logistical challenge due to their distinct transportation modes, with drones being limited by payload capacity and range, and trucks constrained by infrastructure and accessibility, requiring improved routing algorithms for seamless and efficient combined delivery systems.
Innovation Solution
A computer system that processes input data on drones and ground assets to determine an optimized delivery route, incorporating drones into logistics operations to improve last-mile delivery and reduce costs, using a drone definition table, assignment table, and transit table to manage drone flight times and battery discharge, and employing heuristic and optimization algorithms to schedule sorties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If drones are used for delivery, then speed and direct access to remote areas are improved, but payload capacity and range are limited
Solution Approach 1:
The delivery system is segmented into multiple components: a truck for long-distance transportation of goods and a drone for final delivery. The truck carries the payload to a location near the destination, and the drone takes over for the final leg, thus combining the payload capacity of the truck with the speed and accessibility of the drone.
Solution Approach 2:
The drone acts as an intermediary between the truck and the final delivery location. It receives goods from the truck and delivers them to remote or hard-to-reach areas, bridging the gap between road infrastructure limitations and the need for direct access.
2Speed
If drones are used for delivery, then direct access to congested areas is improved, but range is limited
Solution Approach 1:
The total delivery route is segmented into two parts: a long-distance ground portion covered by the truck and a short-distance aerial portion covered by the drone. This segmentation allows the system to achieve both long-range capability and fast access to congested areas.
Solution Approach 2:
The truck performs preliminary action by transporting goods to a location near the destination before the drone takes over. This preliminary ground transportation extends the effective range of the drone system without requiring the drone to have long-range flight capability.
3Quantity of substance
If trucks are used for delivery, then payload capacity and range are improved, but road infrastructure dependency and traffic congestion are worsened
Solution Approach 1:
The delivery operation is segmented into a ground phase (truck) and an aerial phase (drone). The truck handles the phase requiring high payload capacity, while the drone handles the phase requiring ease of access to locations not easily reachable by road.
Solution Approach 2:
The system transitions from two-dimensional ground transportation to three-dimensional aerial transportation for the final delivery leg. This dimensional change allows bypassing road infrastructure limitations and traffic congestion while maintaining the payload capacity advantage of the truck for the initial transport.
4Productivity
If combined drone and truck delivery systems are implemented, then delivery efficiency is improved, but system complexity and infrastructure investment requirements are increased
Solution Approach 1:
The complex combined delivery system is segmented into distinct functional modules: a truck module for ground transport and a drone module for aerial delivery. Each module can be independently managed and optimized, reducing the overall system complexity while maintaining high delivery efficiency.
Data Source
AI summary
In an embodiment, a system disclosed herein receives input information relating to an unmanned aerial vehicle, ground delivery assets, and user preferences or configurations for delivery routes. The system processes the inputted information and determines an optimized delivery route that utilizes the unmanned aerial vehicle and the ground delivery assets. In an embodiment, a computer-implemented method, comprises accessing a drone definition table and a drone assignment table of a database system; accessing digitally stored delivery route data specifying a plurality of nodes connected by edges, the nodes being ordered from a starting point to a last node, wherein each of the nodes represents a delivery location, wherein each of the nodes comprises a service attribute specifying ground-only delivery or ground/drone delivery; selecting, from the delivery route data, a particular node having the service attribute specifying ground/drone delivery and that is next in order with respect to the starting point; removing the particular node from the delivery route data; determining a drone flight time associated with a distance, from a preceding node to a subsequent node, with respect to the particular node; based on the drone definition table and the drone assignment table, determining whether the drone flight time exceeds a maximum drone flight time of a particular drone; in response to determining that the drone flight time exceeds the maximum drone flight time, restoring the particular node in the delivery route data; and, otherwise, writing, to one or more sortie tables in the database, one or more sortie records corresponding to a sortie of the particular drone that can occur between the preceding node and the subsequent node; repeating the selecting, removing, determining, restoring, and writing steps for a plurality of other particular nodes in the delivery route data having the service attribute specifying ground/drone delivery.