Modular Divide-and-Conquer Optimization for Large Transportation Networks
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Solution Overview
Problem
Large-scale optimization problems, particularly in transportation networks, are challenging due to their NP-hard nature, making it difficult for conventional systems to scale effectively and efficiently solve complex optimization tasks.
Innovation Solution
A divide-and-conquer framework with a modularized algorithmic scheme is employed, utilizing a system of functional engines like coordinating, partition, routing, picking, and lane optimizer engines, along with data persistence, to break down optimization problems into subproblems that can be solved in parallel, leveraging cloud computing for scalability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional optimization methods are used to solve large-scale transportation network problems, then solution accuracy can be maintained, but computational time and resource requirements become prohibitively high
Solution Approach 1:
The patent divides the large-scale optimization problem into multiple smaller subproblems by partitioning the transportation network into clusters of facilities and locations. Each subproblem is solved independently and in parallel, significantly reducing computational time while maintaining solution quality through coordinated optimization across clusters.
2Productivity
If the problem size increases in transportation network optimization, then more comprehensive solutions can be achieved, but the NP-hard nature makes solving increasingly challenging
Solution Approach 1:
The system partitions the transportation network into multiple clusters, each representing a manageable subproblem. This segmentation allows the system to handle larger overall problem sizes by distributing computational load across multiple independent optimization tasks that can be processed in parallel.
Solution Approach 2:
The patent introduces a new dimension of parallel processing by solving multiple subproblems simultaneously across different computational units. This transforms a single complex sequential optimization problem into multiple simpler parallel problems, effectively reducing computational complexity.
3Reliability
If more computational resources are allocated to solve optimization problems, then better optimization results can be obtained, but system scalability and efficiency are reduced
Solution Approach 1:
By dividing the optimization problem into independent subproblems that can be solved in parallel, the system achieves better optimization results without requiring proportionally more resources. Each subproblem can be solved with moderate computational resources, improving overall scalability.
Solution Approach 2:
The patent creates a universal framework that can handle various transportation network optimization problems by using standardized clustering and subproblem formulation. This multi-functional approach maintains consistency and quality across different problem sizes and types while preserving system scalability.
Data Source
AI summary
A method including obtaining an optimization request at a coordinating engine. The method also can include triggering engines to process the optimization request. At least one of the engines divides the optimization request into subproblems. At least a portion of the engines solve the subproblems. Respective instances of at least one of the engines are triggered to handle respective ones of the subproblems. Each of the engines provides a dynamic algorithmic flow using modularized algorithmic solvers. The dynamic algorithmic flow is adjusted based on a respective input to each of the engines. The method additionally can include outputting, from the coordinating engine, one or more results in response to the optimization request, based on results for the subproblems generated by the engines. Other embodiments are described.