RIDES Loop Architecture for Distributed Schedule Optimization
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Solution Overview
Problem
Existing automated decision-making systems suffer from centralized control, high implementation costs, limited compatibility, and inability to dynamically value multiple factors, making them inflexible and difficult to adapt to complex enterprise tasks.
Innovation Solution
A distributed decision-making framework using the RIDES Loop Architecture, which breaks down complex tasks into independent loops that interact hierarchically, allowing for scalable, adaptive, and resilient decision-making through self-monitoring and standardized interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If centralized control is used for automated decision-making, then implementation is simpler, but system resilience and adaptability deteriorate
Solution Approach 1:
The patent divides the centralized decision-making system into multiple independent decision loops, each capable of autonomous operation. Each loop processes specific aspects of the scheduling problem independently, improving system resilience by eliminating single points of failure while maintaining coordinated decision-making through standardized interfaces.
Solution Approach 2:
The patent introduces a hierarchical dimension to the decision-making architecture, with multiple loops operating at different levels of abstraction. This allows simultaneous local autonomy at the loop level and global coordination at the system level, resolving the contradiction between simplicity and resilience.
2Ease of operation
If centralized control is used for automated decision-making, then coordination is easier, but system complexity and cost increase
Solution Approach 1:
The patent creates universal decision loops that can handle multiple types of scheduling decisions through standardized interfaces and common evaluation functions. This multi-functionality reduces overall system complexity by eliminating the need for specialized coordination mechanisms for each decision type.
Solution Approach 2:
Each decision loop independently evaluates tasks and makes decisions based on its own assessment of factors such as cost, time, and resource availability. This self-service capability eliminates the need for complex centralized coordination while maintaining effective decision-making through autonomous loops that naturally coordinate their decisions.
3Manufacturing precision
If custom-designed algorithms are used, then specific task performance improves, but compatibility with other systems deteriorates
Solution Approach 1:
The patent designs decision loops with standardized interfaces that can evaluate multiple task types and integrate with various scheduling systems. The universal evaluation framework maintains precise task execution through structured factor assessment while enabling compatibility across different applications and systems.
Solution Approach 2:
The patent uses configurable evaluation factors and weights that can be adjusted to optimize performance for specific task types while maintaining the same underlying decision-making structure. This parameter-based customization allows precise task execution without requiring custom algorithms, preserving compatibility through a unified framework.
4Manufacturing precision
If existing scheduling systems are adapted to specific applications, then application-specific performance improves, but implementation time and cost increase
Solution Approach 1:
The patent enables application-specific optimization through configurable evaluation factors, weights, and constraints that can be adjusted without modifying the underlying system architecture. This parameter-based customization achieves application-specific performance while significantly reducing implementation time compared to custom algorithm development.
Solution Approach 2:
The universal decision loop framework can be applied across multiple applications by configuring different evaluation criteria and constraints. This multi-functionality allows the same core system to serve specific application needs, eliminating the need for separate custom implementations for each application.
Data Source
AI summary
An embodiment of the disclosed invention is a computer-implemented method to build a schedule, which includes establishing a scheduling period and a number of tasks to be accomplished during the schedule, assigning an expected value to each of the tasks, and using an iterative RIDES loop to adjust the expected value for each task and rebuild the schedule until the expected value is optimized. Another embodiment is a computer-implemented method to optimize a schedule that includes using an iterative RIDES loop to determine whether to execute a first schedule allocation or continue to improve the schedule allocation. Another embodiment is a method for implementing a loop architecture to update an expected value for a task using data from the environment, wherein the expected task value is the maximum task value multiplied by a weighting factor.