Time-Varying Control System for Dynamic Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the optimal allocation of resources in enterprises, particularly in production systems with time-varying conditions, face challenges in accurately modeling complex dynamical systems and achieving operational stability due to limitations in conventional control systems.
Innovation Solution
A time-varying control system is implemented using computing hardware with engines for generating control vectors, actual revenue and cost measurement, and dynamic matrix revisions to optimize resource allocation across multiple products, accounting for exogenous factors and avoiding dependency on difficult-to-model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional control systems are used for resource allocation in enterprises with time-varying conditions, then the system structure remains simple, but the modeling accuracy and ability to provide complete guidance deteriorate
Solution Approach 1:
The control system is divided into multiple specialized computing engines, each responsible for specific functions: main processing engine for control vector generation, actual revenue measurement engine for revenue data, actual cost measurement engine for cost data, retained earnings computation engine for earnings calculation, and matrix generator engine for dynamic matrix creation. This segmentation allows each engine to be optimized for its specific task while maintaining overall system manageability.
Solution Approach 2:
The system implements dynamic matrices that are continuously updated based on current enterprise state and market conditions. The control vector is regenerated in real-time as variables affecting resource allocation change over time, allowing the system to adapt to time-varying conditions while maintaining modeling accuracy.
2Adaptability or versatility
If known control theory methods are applied to determine optimal resource allocation, then the theoretical framework is established, but the system becomes unable to provide complete guidance when variables are functions of time
Solution Approach 1:
The system continuously measures actual revenue and costs, computes retained earnings, and uses these feedback values to update the dynamic matrices and regenerate the control vector. This closed-loop feedback mechanism ensures the system adapts to changing conditions while maintaining reliable guidance for resource allocation decisions.
Solution Approach 2:
The matrix generator engine pre-computes dynamic matrices based on current enterprise state before the main processing engine generates the control vector. This preliminary action allows the system to be prepared for time-varying conditions, ensuring complete guidance is available when allocation decisions must be made.
3Adaptability or versatility
If computational tools are applied to model complex dynamical systems in economics and biology, then the scope of application is expanded, but the modeling accuracy deteriorates due to lack of precision
Solution Approach 1:
The system uses dynamic matrices whose parameters are continuously updated based on actual enterprise performance data (revenue, costs, retained earnings). This allows the model to maintain high accuracy when applied to complex economic systems by adapting parameters to reflect current conditions rather than relying on static assumptions.
Data Source
AI summary
Automatic control of an enterprise. A control vector is generated corresponding to a target allocation of resources between the plurality of products to be produced by the enterprise. An observation vector represents actual retained earnings attributed to each of the plurality of products during a first time period, the actual retained earnings being based on data representing the actual revenue and on data representing the actual cost. A dynamic matrix, a control matrix, a cost matrix, and an observation matrix are produced. Each matrix represents a corresponding dynamic relationship between a sets of different control parameters or measured or predicted values, and is revised at each subsequent time period to produce predicted values for each corresponding matrix. According to various embodiments, the revision can have two components: (a) vectorization; and (b) forecasting.