Network Flow Optimization with Dynamic Load Constraints
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Solution Overview
Problem
Complexity in optimizing network flow due to varying constraints and metrics in transportation and supply chain networks, where improving one metric often negatively impacts others, making it challenging to balance efficiency across multiple time periods.
Innovation Solution
A computer-implemented method for managing network flow optimization by prioritizing items for transportation, considering network constraints, costs, and loading constraints, and adjusting the quantity of transportation units to optimize item delivery across origins and destinations while smoothing demand fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network flow optimization focuses on improving one metric (e.g., throughput), then that metric is improved, but other metrics (e.g., latency, bandwidth usage) are negatively impacted
Solution Approach 1:
The patent implements dynamic optimization that adjusts network flow parameters in real-time based on changing conditions. The system continuously monitors multiple metrics and dynamically modifies routing decisions, bandwidth allocation, and flow rates to balance throughput and latency according to current network state, rather than using static optimization settings
Solution Approach 2:
The patent introduces a multi-dimensional optimization approach that considers multiple metrics simultaneously across different dimensions (throughput, latency, bandwidth usage, cost). By optimizing across these multiple dimensions rather than single-parameter optimization, the system achieves balanced improvement across competing metrics through multi-objective optimization algorithms
2Adaptability or versatility
If supply chain networks adapt to changing external conditions (e.g., climatic changes, labor disruptions), then responsiveness to demand changes is improved, but optimization complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-planning multiple optimization scenarios and pre-positioning resources before disruptions occur. The system uses predictive analytics to anticipate potential disruptions (climatic changes, labor disruptions) and pre-adjusts network configurations, inventory positions, and transportation schedules, reducing the complexity of real-time decision-making when disruptions actually occur
Solution Approach 2:
The patent implements continuous feedback mechanisms that monitor network performance and external conditions, feeding this information back into the optimization system. This closed-loop control enables the system to automatically adapt to changing conditions while maintaining manageable complexity through automated feedback-driven adjustments rather than manual re-optimization
3Productivity
If transportation units are optimized for peak demand periods, then throughput during peak periods is improved, but efficiency during low-demand periods deteriorates
Solution Approach 1:
The patent merges transportation demands across different time periods and routes by implementing load consolidation and shared transportation resources. The system combines shipments from multiple origins/destinations into consolidated loads that efficiently utilize transportation units during both peak and low-demand periods, reducing waste while maintaining peak throughput capability
Solution Approach 2:
The patent dynamically changes operational parameters (transportation unit allocation, routing, load factors, speed profiles) based on real-time demand conditions. During peak periods, the system increases throughput by deploying additional units and optimizing routes; during low-demand periods, it reduces resource allocation and adjusts parameters to maintain efficiency, rather than using fixed parameter settings
Data Source
AI summary
A method, system and/or computer usable program product for managing optimization of a transportation network system including identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods, prioritizing each of the set of items, utilizing a set of network constraints and costs, utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit, optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit, and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs.