Cargo Route Network Graphs With Segmented Real-Time Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generating up-to-date network graphs for cargo routes in dynamic carrier networks is computationally intensive and time-consuming, leading to outdated graphs by the time they are recomputed, consuming significant processing power and memory.
Innovation Solution
A network graph framework that generates a default graph based on static network data and updates it with real-time network state data to create move and load graphs, using iterative optimization models to identify optimal routes efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network graphs are recomputed frequently to stay up-to-date in dynamic carrier networks, then the freshness and reliability of route information is improved, but the computational resources and time required for generation increase significantly
Solution Approach 1:
The patent segments the network graph generation into two distinct components: a default network graph generated from static network data that remains relatively stable, and dynamic updates applied using real-time network state data. This segmentation allows the system to maintain reliable, up-to-date route information without repeatedly computing the entire network graph, thereby reducing computational resource consumption while preserving data freshness.
2Measurement precision
If traditional optimization methods are used to solve combinatorial optimization problems for cargo routes, then solution accuracy is improved, but the computation time and processing power required increase exponentially
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing a default network graph based on static network data before real-time optimization is needed. This pre-computed structure serves as a foundation that significantly reduces the complexity of real-time combinatorial optimization problems, allowing the system to achieve near-optimal route solutions with much lower computation time and processing power requirements compared to solving the full optimization problem from scratch.
Data Source
AI summary
Some examples of the present disclosure include a system that can receive a predetermined set of routes for moving cargo through a network. The system can construct a model based on the predetermined set of routes, where the model defines a combinatorial optimization problem that is solvable to identify an optimal set of routes from the predetermined set of routes. The system can then execute an iterative process using the model to solve the combinatorial optimization problem and thereby determine the optimal set of routes through the network. The system can provide, to a client device, instructions generated based on the optimal set of routes for use in facilitating transport of the cargo through the network in accordance with the optimal set of routes.