Freight Routing Control for Less-Than-Truckload Deadhead Reduction
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Solution Overview
Problem
The traditional freight industry faces inefficiencies due to unpredictable truck availability, leading to 'deadhead' miles and increased costs, as well as challenges in just-in-time inventory management and shipping predictability, resulting in significant fees and time spent contacting brokers and carriers.
Innovation Solution
A computer-implemented method for coordinating less-than-truckload shipments by receiving data on pickup and delivery locations, times, and truck availability, and assigning shipments to trucks capable of executing them while returning to their starting location, with the option to control autonomous driving to optimize routes and reduce inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional broker-based freight coordination is used, then shippers can request shipments, but truck availability is unpredictable leading to deadhead miles and increased costs
Solution Approach 1:
The system changes the parameter of truck assignment from manual broker coordination to automated algorithmic assignment based on real-time location and availability data. This enables predictable truck availability by matching trucks to shipments based on their current position, destination, and scheduled return time, eliminating unpredictable deadhead miles.
Solution Approach 2:
The system performs preliminary action by pre-assigning trucks to shipments before they are needed. The computing system identifies trucks that will be available at the required time by calculating their scheduled return times from current assignments, allowing shippers to secure truck availability in advance without traditional broker delays.
2Productivity
If more trucks are assigned to handle shipments, then shipping capacity increases, but coordination complexity and costs increase
Solution Approach 1:
The computing system performs multiple functions within a single platform: receiving shipment requests, tracking truck locations, calculating availability, assigning trucks to shipments, and managing less-than-truckload consolidations. This universal system handles diverse shipping scenarios without requiring separate coordination mechanisms for each function.
Solution Approach 2:
The system enables self-service by allowing the computing system to automatically coordinate truck assignments without human broker intervention. The automated algorithms independently match trucks to shipments based on objective criteria, reducing coordination complexity while increasing shipping capacity through efficient resource utilization.
3Productivity
If autonomous driving control is implemented, then route optimization improves, but system complexity and regulatory requirements increase
Solution Approach 1:
The computing system acts as an intermediary between autonomous truck control systems and shipment coordination. It sends control signals to autonomous trucks to optimize routes and timing, while the trucks' autonomous systems handle the complex navigation and control tasks. This separation allows route optimization without requiring the central system to directly manage complex autonomous driving operations.
Data Source
AI summary
Data corresponding to a new less-than-truckload shipment request can be received from a first computing device. The data can include a pickup location, a delivery location, a pickup time, and a delivery time. One or more trucks that are assigned to a current less-than-truckload shipment and are capable of executing the new less-than-truckload shipment while also completing the respective current less-than-truckload shipment can be identified from accessing a database. A message corresponding to an invitation for executing the new less-than-truckload shipment can be transmitted to one or more operator computing devices of the identified one or more trucks capable.