Freight Routing Control for Less-Than-Truckload Assignment
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Solution Overview
Problem
The traditional freight industry faces inefficiencies due to unpredictable truck availability, leading to 'deadhead' miles, waiting times, and increased costs, as well as challenges in just-in-time inventory management and shipping predictability.
Innovation Solution
A computer-implemented method for coordinating less-than-truckload shipments by identifying suitable trucks through a database, transmitting invitations for new shipments, and providing control signals for autonomous driving to optimize route planning and reduce downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional broker methods are used to contact carriers, then shipment requests can be processed, but truck availability is unpredictable leading to deadhead miles and waiting times
Solution Approach 1:
The system performs preliminary actions by proactively seeking out available trucks and offering them shipments before they are needed. The platform continuously monitors truck availability and pre-arranges shipments, rather than waiting for shipment requests to come in and then searching for trucks. This reverses the traditional approach and eliminates waiting time by having trucks ready in advance.
Solution Approach 2:
The system implements continuous feedback loops where trucks report their status (available, on shipment, maintenance) to the platform, and the platform responds by offering appropriate shipments. This real-time feedback mechanism allows the system to dynamically adjust shipment assignments based on actual truck availability, improving predictability and reducing deadhead miles.
2Productivity
If manual shipment assignment is used, then carriers can be contacted, but the process is inefficient and costs increase
Solution Approach 1:
The system replaces the mechanical manual process of brokers contacting carriers with an automated digital platform. The platform uses algorithms to automatically match trucks with shipments, transmit offers electronically, and coordinate logistics without human intervention. This substitution of manual mechanical processes with automated systems dramatically improves coordination efficiency and reduces operational costs.
Solution Approach 2:
The system enables self-service where trucks can independently view available shipments, accept offers, and coordinate their own schedules through the platform. Carriers and shippers can directly interact through the digital interface without requiring broker mediation, streamlining the assignment process and reducing costs associated with manual coordination.
3Loss of energy
If trucks are assigned without optimization, then shipments can be executed, but deadhead miles increase and costs rise
Solution Approach 1:
The system employs dynamic optimization algorithms that continuously adjust shipment assignments based on real-time truck locations, availability, and shipment requirements. Rather than static assignment, the system dynamically reroutes and reassigns trucks to minimize deadhead miles while responding to changing conditions. This dynamic approach efficiently reduces empty miles despite the increased computational complexity of the assignment system.
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.