Load Recommendation System Using Profile Filtering
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Solution Overview
Problem
Freight suppliers and carriers face challenges in matching specific loads with appropriate carriers due to preferences, capacity, licenses, locations, and other factors, leading to inefficiencies and costs such as deadhead losses.
Innovation Solution
A system and method that filter and optimize load recommendations using location coordinates, carrier profiles, and shipper profiles, employing collaborative filtering and multi-object optimization algorithms to match loads with suitable carriers, considering preferences and capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual load matching is performed by suppliers and carriers, then flexibility in considering preferences and capabilities is maintained, but time consumption and inefficiency increase
Solution Approach 1:
The patent replaces manual mechanical matching processes with an automated computer-based system that uses algorithms to match loads with carriers. The system automatically processes carrier profiles, load requirements, and preference data without human intervention, thereby reducing time loss while maintaining matching flexibility through programmable criteria.
Solution Approach 2:
The system enables carriers to self-match with loads by automatically processing their profile data against available loads. The automated matching system serves carriers independently without requiring manual supplier intervention, allowing carriers to receive load recommendations autonomously based on their stored preferences and capabilities.
2Measurement precision
If comprehensive filtering based on multiple criteria is applied, then matching accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the matching process into distinct filtering stages: initial location-based filtering, then capability and license filtering, followed by preference-based filtering. This segmentation breaks down the complex multi-criteria matching into manageable sequential steps, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary filtering actions by pre-processing carrier profiles and storing key attributes (licenses, capabilities, preferences) before the actual matching occurs. This preliminary organization of data simplifies the subsequent matching process, allowing accurate multi-criteria filtering without complex real-time processing.
Data Source
AI summary
Systems, methods, and computer-readable storage media for recommending loads for transport. A system can receive location coordinates for a transport vehicle, and further receive data regarding available loads which can be transported by the transport vehicle. The system can then filter the available loads based at least in part on the location coordinates. The system can also receive at least one carrier profile and at least one shipper profile. Finally, the system can execute a load recommendation algorithm using the preference filtered loads, the at least one carrier profile, and the at least one shipper profile as inputs, resulting in at least one load recommendation score for a load within the preference filtered loads.