AI Planning SOP Generation for Adaptive Scheduling Constraints
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Solution Overview
Problem
Traditional operational planning and scheduling systems lack adaptability to dynamic real-world complexities, fail to support multimodal inputs, and lack human-in-the-loop adaptability and explainability, requiring intensive manual efforts and domain expertise.
Innovation Solution
An AI-based system using domain-specific generative AI agents processes multimodal data, including videos and audio, to generate optimized operation planning and scheduling outputs, allowing non-technical users to teach and modify planning logic through natural language interfaces, with continuous learning and recursive root-cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rule-based scheduling tools are used, then system complexity is reduced, but adaptability to dynamic real-world complexities deteriorates
Solution Approach 1:
The patent replaces traditional mechanical rule-based scheduling systems with an AI-based system that uses machine learning models, neural networks, and computational algorithms to automatically adapt to dynamic real-world complexities without requiring manual rule configuration
Solution Approach 2:
The system dynamically adjusts scheduling parameters and optimization objectives based on real-time data inputs, changing problem formulations and constraint weights to adapt to varying operational conditions without manual reconfiguration
2Adaptability or versatility
If traditional systems with pre-configured rule sets are used, then ease of operation is improved, but adaptability to domain-specific variations deteriorates
Solution Approach 1:
The AI system performs self-configuration by automatically learning domain-specific planning logic from provided examples and data, eliminating the need for manual programming of domain rules while maintaining ease of use through automated adaptation
Solution Approach 2:
The system transitions from static pre-configured rules to dynamic learning models that continuously adapt to domain-specific variations through exposure to new data and examples, enabling flexibility without sacrificing operational simplicity
3Loss of time
If manual programming and rule maintenance are required, then measurement precision of planning logic is improved, but loss of time in configuration deteriorates
Solution Approach 1:
The system performs preliminary learning from examples during setup, automatically internalizing domain-specific planning logic before actual use, thereby eliminating time-consuming manual programming and rule maintenance during operational configuration
Solution Approach 2:
Manual programming and rule maintenance processes are replaced with automated machine learning techniques that learn planning logic from data examples, significantly reducing configuration time while maintaining or improving planning precision
4Loss of information
If traditional systems are used, then device complexity is reduced, but loss of information from unstructured data sources deteriorates
Solution Approach 1:
The patent introduces AI-based intermediary components including natural language processing modules, computer vision systems, and data cleansing algorithms that act as mediators between unstructured data sources and the scheduling system, enabling information extraction and integration without requiring manual data preparation
Solution Approach 2:
The system changes data processing parameters by automatically detecting, normalizing, and transforming unstructured data formats into structured representations suitable for scheduling algorithms, preserving information from diverse data sources while maintaining system functionality
5Productivity
If intensive manual efforts and domain expertise are required, then manufacturing precision of planning logic is improved, but productivity deteriorates
Solution Approach 1:
The AI system performs self-configuration and self-optimization by automatically learning from provided examples and data, eliminating the need for intensive manual efforts and domain expertise while maintaining high planning quality and increasing productivity
Solution Approach 2:
Manual planning configuration processes are replaced with automated machine learning systems that learn domain-specific logic from data examples, significantly improving productivity by eliminating time-consuming manual programming while maintaining or enhancing planning precision
Data Source
AI summary
The present invention discloses an artificial intelligence-based (AI-based) system and method for generating optimised operation planning and scheduling output. The AI-based system obtains at least one of: one or more data explanation videos, one or more process understanding videos, and unconstrained operational planning data, along with one or more prompts as an input. The AI-based system extracts one or more informative image frames and audio data, to train the one or more AI models and generate a planning standard operating procedure (SOP). The AI-based system processes the planning SOP, the constrained operational planning data, and the one or more prompts to generate the optimised operation planning and scheduling output based on an optimised function with a continuous feedback loop in response to at least one of: the one or more prompts, updated planning SOP, and real-time changes in the constrained operational planning data.