Reusable Optimization Model Framework for NP-Hard Problems
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Solution Overview
Problem
Creating and maintaining optimization models for NP-hard problems is time-consuming and costly, especially for large-scale and complex scenarios, as existing solutions require re-implementation with changes, making it impractical to develop a universal model that can solve all cases efficiently.
Innovation Solution
A framework for creating and reusing optimization models that allows dynamic manipulation of entities such as objectives, variables, and constraints, enabling the transformation and extension of models to solve larger or more complex problems through methods like constraint leveling, attribute management, and variable freezing, allowing for multi-staged solving processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a dedicated optimization model is created for a specific use case, then the model can be solved efficiently by a solver, but creating and maintaining the model becomes time-consuming and expensive, especially for large-scale and complex problems
Solution Approach 1:
The patent segments the optimization model into distinct entities (variables, constraints, objectives) that can be independently manipulated and combined. This allows the model to be divided into reusable components that can be assembled for different use cases without recreating the entire model from scratch, thereby reducing development and maintenance time while preserving solving efficiency.
Solution Approach 2:
The patent creates a universal framework where a single optimization model structure can serve multiple use cases through parameterization and configuration. The model entities are designed to be reusable across different problems, allowing the same framework to handle various optimization scenarios without requiring separate dedicated models for each case, thus reducing time investment while maintaining problem-specific efficiency.
2Adaptability or versatility
If the optimization problem changes (e.g., adding new types of tasks), then the model can adapt to new requirements, but modifying the model requires further programming, testing, and redeployment
Solution Approach 1:
The patent implements dynamic model configuration where constraints and parameters can be modified without restructuring the entire model. The framework allows runtime adjustment of model entities through configuration files or programmatic interfaces, enabling adaptation to new requirements without complex reprogramming, testing, and redeployment cycles.
Solution Approach 2:
The patent enables adaptation to changing optimization problems through parameter modification rather than structural changes. By allowing users to adjust parameters, add or remove constraints, and modify objectives through configuration rather than code changes, the system achieves high adaptability while minimizing the complexity of modification processes.
3Productivity
If decomposition methods are applied to solve separate models for each case, then the problems can be managed at scale, but it becomes almost impossible to create an all-in-one universal model that solves all cases
Solution Approach 1:
The patent resolves this contradiction by creating a universal optimization framework that can handle both small and large-scale problems through configuration rather than requiring decomposition into separate models. The framework's parameterized entities and flexible constraint system allow a single model structure to adapt to various problem scales and types, eliminating the need for decomposition while maintaining the ability to solve large-scale problems efficiently.
Data Source
AI summary
A computer program product comprising a non-transitory computer readable storage medium retaining program instructions configured to cause a processor to perform actions, which program instructions implement: a framework for creating a model of an NP-hard problem, the model comprising at least one entity selected from the group comprising: an objective, a variable, an equation and a constraint, wherein the framework provides methods for automatically transforming the model, comprising: one or more methods for manipulating or changing a status of the entity of the model, the methods comprising a method for imposing or ignoring the constraint; and one or more methods related to operations to be applied to the entity of the model.