Maintenance Planning Optimization via Segmented Sub-Models
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Solution Overview
Problem
Current maintenance planning systems fail to effectively balance technical, environmental, and human-centric objectives, leading to increased complexity and inefficiency in machinery maintenance scheduling, particularly in industrial settings where multiple variables such as cost, emissions, worker skills, and training needs must be considered.
Innovation Solution
A method and system that decompose complex maintenance optimization problems into sub-models, allowing for multi-objective and multi-weighted decision-making, incorporating sustainability and human-centric factors, to optimize maintenance planning, task allocation, and resource utilization, while adjusting computational complexity for fast convergence and alignment with strategic goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If maintenance planning considers multiple objectives (cost, emissions, worker skills, training needs), then decision-making comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex maintenance planning problem into multiple independent sub-models, each handling a specific objective (cost optimization, emissions reduction, worker skill development, training allocation). This modular approach allows comprehensive multi-objective decision-making while managing system complexity through decomposition into manageable components that can be solved separately and integrated.
2Productivity
If optimization includes sustainability and human-centric factors, then environmental and social performance is improved, but computational complexity increases
Solution Approach 1:
The patent divides the optimization problem into distinct sub-models, with dedicated models for sustainability objectives (emissions reduction) and human-centric factors (worker skill development, training needs). This segmentation enables the system to address environmental and social performance requirements without overwhelming computational complexity, as each sub-model focuses on specific objectives with appropriate constraints and parameters.
3Measurement precision
If multiple sub-models are used for multi-objective optimization, then solution quality is improved, but computation time increases
Solution Approach 1:
By segmenting the optimization into independent sub-models that can be executed separately, the system achieves high solution quality through comprehensive multi-objective optimization while managing computation time through parallel processing potential and focused search spaces in each sub-model.
Solution Approach 2:
The patent implements a hierarchical approach where essential maintenance tasks are identified and optimized first through critical sub-models, then additional objectives are layered in. This partial action approach ensures core optimization needs are met with acceptable computation time, while allowing for enhanced solution quality when additional time is available for more comprehensive optimization.
Data Source
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AI summary
This invention refers to a method for maintaining machinery, comprising the steps of: a) determining, by a computer, when the machinery will need to be maintained, b) acquiring, by a computer, tasks to be executed by workers, c) generating, by a computer, a list of tasks for at least one of the workers, the list is generated including a task for maintaining the machinery as determined in step a) and including the tasks acquired in step b), d) maintaining the machinery based on the list of tasks for the one of the workers, wherein steps a) to c) are executed independently from each other.