Rules Optimizer for Freight Facility Scheduling
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Solution Overview
Problem
Conventional approaches for facility logistics result in inefficiencies such as vehicle idle time, detention time, and dwell time, leading to increased costs and reduced efficiency for both carriers and shippers.
Innovation Solution
A rules optimizer system that utilizes historical data, current data, and future anticipated data to optimize scheduling decisions for facility operations, including freight loading and unloading, by generating a set of potential scheduling decisions and selecting the highest scoring option.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual check-in processes are used at facility gates, then access control can be maintained, but vehicle idle time increases and efficiency decreases
Solution Approach 1:
The system enables self-service through automated gate operators that can independently verify vehicle authorization and control access without requiring manual intervention from employees, thereby maintaining access control reliability while eliminating the time loss associated with manual check-in processes
Solution Approach 2:
The patent replaces manual mechanical gate opening processes with an automated electronic control system that uses computer processors, memory devices, and communication interfaces to automatically authorize and control gate access, substituting human labor with an automated information-processing system
2Productivity
If manual paper records are used for tracking vehicle arrivals and departures, then facility operations can be monitored, but record accuracy decreases due to loss or tampering
Solution Approach 1:
The system creates and maintains digital copies of vehicle arrival and departure records in memory devices, replacing physical paper records with electronic data storage that cannot be lost or tampered with, while preserving the full monitoring functionality for tracking vehicle movements
Solution Approach 2:
The patent substitutes manual paper record-keeping with an automated electronic recording system that uses processors, memory devices, and communication interfaces to digitally track and store vehicle arrival and departure information, eliminating the reliability issues associated with paper records
3Quantity of substance
If paper bills of lading are used for tracking freight contents, then freight information can be recorded, but reconciliation delays and difficulties increase
Solution Approach 1:
The system replaces physical paper bills of lading with digital electronic copies stored in memory devices, enabling automated freight content tracking and significantly reducing the time required for reconciliation by eliminating manual processing steps
Solution Approach 2:
The patent substitutes manual paper-based freight tracking with an automated electronic system that uses processors, communication interfaces, and memory devices to digitally record, transmit, and reconcile freight information, thereby reducing reconciliation time and improving tracking efficiency
4Productivity
If automated scheduling decisions are implemented using historical data and machine learning, then facility efficiency improves, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical facility data, vehicle information, and scheduling patterns in memory devices before actual scheduling occurs, enabling the machine learning model to quickly generate optimized scheduling decisions without requiring complex real-time calculations
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw historical data and scheduling decisions, automatically processing and analyzing historical information to generate optimized schedules, thereby improving facility efficiency while managing system complexity through a dedicated intermediary layer
Data Source
AI summary
A rules optimizer for optimizing one or more operations at a facility, the rules optimizer configured to: receive an event update associated with an upcoming operation scheduled at the facility; generate a set of potential scheduling decisions associated with the upcoming operation, wherein generating the set of potential scheduling decisions is based at least in part on historical data stored at the non-transitory computer-readable storage medium, and wherein generating the set of potential scheduling decisions is based at least in part on the received event update; select a scheduling decision from the set of potential scheduling decisions; and update a schedule associated with the facility in view of the selected scheduling decision; and transmit a notification to a vehicle providing information associated with the updated schedule, the vehicle adjusting an action in response to the updated schedule.