AI Recipe-Driven Manufacturing Scheduling Under Dynamic Constraints

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Solution Overview

Problem

Scheduling high complexity manufacturing processes involving recipe-driven devices is challenging due to their high capital and operational costs, complex constraints, and the need for dynamic adjustment to unexpected events, which current methods fail to optimize efficiently.

Innovation Solution

A system utilizing AI and machine learning algorithms, including reinforcement learning, generates and dynamically updates schedules in real-time to optimize the use of recipe-driven devices while adapting to constraints and events, ensuring efficient resource allocation and compliance with business goals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional scheduling methods are used for recipe-driven devices, then implementation simplicity is maintained, but manufacturing efficiency and device utilization remain suboptimal

Engineering Contradiction:

Improvemanufacturing efficiencyVSAvoidscheduling system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces traditional manual or rule-based scheduling mechanisms with an AI-driven system that uses machine learning algorithms to automatically generate and optimize schedules. The scheduling system incorporates natural language processing to interpret work order requirements and generates optimized schedules without manual intervention, thereby improving manufacturing efficiency while managing complexity through automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically adjusts scheduling parameters such as device allocation, time slots, and resource distribution based on real-time conditions and historical data. The AI model continuously learns from execution outcomes and modifies scheduling parameters to optimize device utilization and throughput, enabling adaptive improvement of manufacturing efficiency.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If schedules are statically planned without dynamic adjustment, then planning stability is maintained, but adaptability to unexpected events deteriorates

Engineering Contradiction:

Improveschedule adaptabilityVSAvoidschedule predictability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The scheduling system transitions from static to dynamic operation by continuously monitoring device status, work order progress, and unexpected events. The AI model generates real-time schedule adjustments and push notifications to stakeholders, enabling the system to adapt to changing conditions while maintaining reliable delivery through proactive rescheduling.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements closed-loop feedback by tracking schedule execution outcomes and using this information to improve future scheduling decisions. The AI model learns from actual device performance, event occurrence patterns, and schedule adherence data to enhance its predictive capabilities and generate more reliable schedules over time.

Inventive Principle:

Principle #23Feedback

3Reliability

If comprehensive constraints are enforced for device operations, then operational compliance is improved, but scheduling flexibility deteriorates

Engineering Contradiction:

Improveconstraint complianceVSAvoidscheduling flexibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system handles multiple constraints across different dimensions (device capacity, time windows, resource availability, geometric constraints) by processing them simultaneously in a multi-dimensional optimization framework. The AI model evaluates constraint satisfaction across all dimensions and generates schedules that meet comprehensive requirements while maintaining flexibility through intelligent trade-off analysis.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

4Speed

If manual schedule adjustment is performed for unexpected events, then responsiveness to events is reduced, but operational simplicity is maintained

Engineering Contradiction:

Improveschedule update speedVSAvoidautomation system complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The scheduling system performs self-service by automatically detecting unexpected events, generating reschedule options, and implementing optimized schedules without manual intervention. The AI model monitors device status and work order progress continuously, and when events occur, it autonomously adjusts schedules and notifies relevant stakeholders, enabling rapid response while reducing the need for manual scheduling operations.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250271841A1Scheduling of Recipe-Driven Manufacturing

Publication Date: 2025.08.28 PLATAINE

AI summary

A method, system and computer program product then method comprising: obtaining a work order where at least one part is to undergo a recipe-driven process to be executed by a device, according to a recipe; obtaining the recipe upon which the recipe-driven process is to be executed; obtaining a plurality of constraints for the recipe-driven process associated with the work order, the plurality of constraints relating at least to an area or volume of the device and area or volume of the at least one part; generating a schedule for processing the at least one part by the recipe-driven process in accordance with the plurality of constraints; prior to execution of the recipe-driven process, receiving through an interface an automated notification of an event prohibiting execution of the recipe-driven process; and regenerating an updated schedule for preparing the work order, including the recipe-driven process.