Policy-Driven Optimization Framework Decoupling Rules and Data
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Solution Overview
Problem
Traditional optimization applications face challenges such as duplicated efforts, inconsistency, redundancy, and operational overhead due to intertwined rules and data, leading to custom solutions and delayed market availability, as well as inability to customize optimization services per client and inefficient reuse of common functionalities across systems.
Innovation Solution
A model and policy-driven optimization framework that decouples optimization rules and data, allowing for policy-driven behavior changes without significant implementation effort, using a framework architecture with libraries and toolkits for scalability, resiliency, and data processing, enabling the creation of reusable optimization applications that can be exposed as a service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional optimization applications use intertwined rules and data, then custom solutions can be created, but development time and operational overhead increase significantly
Solution Approach 1:
The patent segments the optimization application into distinct modular components: policy engines that handle rules, data processing modules that manage data, and optimization solvers that compute solutions. This segmentation allows independent development and reuse of each component, reducing overall development time while maintaining customization capability through selective composition of modules.
Solution Approach 2:
The patent creates universal data processing modules and policy engines that can serve multiple optimization scenarios across different clients. These reusable components implement common functionality once and can be configured for different purposes, eliminating the need to rebuild custom solutions from scratch for each client while maintaining adaptability.
2Device complexity
If traditional optimization applications intertwine rules and data, then system simplicity is maintained, but scalability and reuse across systems are limited
Solution Approach 1:
The patent divides the system into separate policy engines, data processing modules, and optimization solvers. Each segment has a specific responsibility and can be independently developed, tested, and reused across different systems. This segmentation increases initial structural complexity but enables extensive reusability and scalability.
Solution Approach 2:
The patent introduces standardized interfaces and data models as intermediaries between the policy engines, data processing modules, and optimization solvers. These intermediaries enable loose coupling between components, allowing them to be reused across different systems without direct integration, thus improving scalability while managing complexity through standardization.
3Adaptability or versatility
If traditional optimization applications are built from scratch for each client, then specific client requirements are met, but common functionalities cannot be efficiently reused
Solution Approach 1:
The patent develops universal data processing modules and policy engines that can be configured to meet different client requirements. These modules implement common optimization functionalities once and can be reused across multiple client-specific applications, significantly improving development efficiency while maintaining the ability to customize for each client's specific needs.
Solution Approach 2:
The patent implements configurable policy engines and data processing modules that can dynamically adapt to different client requirements through configuration rather than code changes. This dynamic adaptability allows the same core functionality to serve multiple clients with different needs, improving productivity while maintaining customization.
4Ease of manufacture
If optimization rules and data are coupled, then implementation is straightforward, but policy-driven behavior changes require significant implementation effort
Solution Approach 1:
The patent separates policy engines that handle rules from data processing modules that manage data. This segmentation allows policy-driven behavior changes to be implemented by modifying only the policy engine configuration without affecting the data processing logic, providing flexibility while maintaining implementation simplicity through clear separation of concerns.
Solution Approach 2:
The patent introduces standardized interfaces as intermediaries between the policy engines and data processing modules. These interfaces define clear contracts that allow policy changes to be made independently of data processing implementation details, enabling flexible policy-driven behavior while maintaining straightforward implementation through standardized communication protocols.
Data Source
AI summary
In one embodiment, a method includes receiving, by one or more interfaces, an optimization application, a request for an optimization, one or more policies required to implement the optimization, and data required to implement the optimization. The method also includes converting, by one or more processors and the optimization application, the one or more policies into optimization constraints and objective functions. The method further includes determining, by one or more processors and the optimization application, a solution to the optimization based on the optimization constraints, the objective functions, and the data.