Iterative Trip Release for Transportation Planning
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Solution Overview
Problem
Current transportation management systems require manual intervention to review and modify entire transportation plans, which is time-consuming and inefficient, leading to delayed releases and potential missed opportunities for consolidation, especially when carrier capacity is tight.
Innovation Solution
Implementing a computer-programmed process that automatically releases selected portions of a transportation plan, allowing for iterative updates and partial releases based on predefined rules, enabling early notification of satisfactory trips and minimizing manual work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual review and modification of entire transportation plans is performed, then trip quality and optimization can be improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent divides the transportation plan into individual trips that can be independently evaluated and released. Instead of manually reviewing the entire plan, the system segments trips and applies automated rules to each segment, allowing partial releases without requiring complete manual review of all trips.
Solution Approach 2:
The system enables automated self-service through rule-based trip selection and release mechanisms. Trips that meet predefined criteria are automatically selected for release without requiring manual intervention, allowing the system to serve itself in identifying and releasing suitable trips while maintaining quality standards.
2Stability of the object's composition
If entire transportation plan is released at once, then execution consistency is improved, but flexibility and responsiveness to carrier capacity changes deteriorate
Solution Approach 1:
The patent implements a dynamic release process where trips are released iteratively based on carrier capacity availability and changing conditions. The system can adjust the release process in real-time, releasing some trips immediately while holding others for later release, creating a flexible yet consistent execution approach that adapts to carrier responses.
Solution Approach 2:
The system employs periodic action by releasing trips in multiple iterations rather than all at once. The release process can be repeated with different rule sets or modified criteria, allowing the system to maintain execution consistency through structured iterations while adapting to feedback from carrier capacity updates.
3Productivity
If automated rule-based release is implemented, then productivity and speed are improved, but control and oversight capabilities deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the results of automated rule-based releases are monitored and used to refine future release decisions. The system learns from carrier responses and capacity updates, adjusting rule parameters and selection criteria to improve both automation effectiveness and human oversight capability over time.
Solution Approach 2:
The rule-based release system serves multiple functions: it automatically selects trips for release, provides transparent criteria for selection, generates reports for human review, and adapts to different carrier and shipment scenarios. This multi-functionality maintains control capability while achieving high productivity through automation.
Data Source
AI summary
A computer, for planning moves of freight automatically receives a plan containing a number of trips to be performed to move freight using vehicles, and partially releases only a portion of the plan for execution instead of releasing the entire plan. The plan portion that is released includes a subset of trips that are selected by the computer from among all trips in the entire plan. Thereafter, the computer simply repeats the just-described acts. Iteratively releasing portions of a plan allows the computer to be instructed to release early certain trips that are satisfactory. Trips that are sub-optimal are re-generated in a next version of the plan, based on changes in orders in the interim. The computer may also be instructed to release a trip even if sub-optimal, if its time-to-departure becomes less than an advance notice needed by a truckload service that is to execute the trip.