Maintenance Task Grouping for Multi-Machine Scheduling
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Solution Overview
Problem
In multi-machine data processing environments, maintaining interrelated machines without disrupting business services is challenging due to dynamic dependencies and limited maintenance windows, as existing methods fail to account for these complexities, leading to incomplete or postponed maintenance tasks.
Innovation Solution
A method and system that construct a maintenance plan by grouping tasks, allowing for parallel execution and re-execution of prerequisite and post-requisite tasks within allocated time windows, ensuring that maintenance tasks are completed efficiently without exceeding available time, even in dynamic configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance tasks are performed on interrelated machines in dynamic multi-machine configurations, then system reliability is improved, but the complexity of coordination and scheduling increases significantly
Solution Approach 1:
The patent segments maintenance tasks into discrete units that can be independently scheduled and executed. Each task is broken down into specific actions that can be assigned to individual machines or groups of machines, allowing the complex maintenance operation to be divided into manageable segments that can be coordinated systematically rather than as a monolithic complex operation
Solution Approach 2:
The patent performs preliminary actions by identifying and establishing maintenance dependencies before the actual maintenance execution. The system pre-analyzes the multi-machine configuration to determine task relationships, prerequisites, and constraints, creating a structured maintenance plan that guides subsequent execution. This preliminary planning phase reduces coordination complexity during actual maintenance by having all relationships predetermined
2Productivity
If all maintenance tasks are scheduled within a single maintenance window, then maintenance productivity is improved, but the risk of service disruption increases when tasks exceed available time
Solution Approach 1:
The patent implements dynamic scheduling that adapts to actual maintenance progress and time consumption. Rather than rigidly adhering to a fixed single-window schedule, the system monitors task execution and dynamically adjusts the maintenance plan, allowing tasks to be postponed or rescheduled based on real-time conditions. This dynamic approach maintains productivity by maximizing task completion while preserving service continuity through flexible adaptation
Solution Approach 2:
The patent applies partial action by completing only those maintenance tasks that can be finished within the allocated maintenance window, rather than attempting to complete all tasks regardless of time constraints. High-priority tasks are completed while lower-priority tasks are postponed to subsequent windows, ensuring service continuity is maintained while still achieving productive maintenance outcomes
3Manufacturing precision
If maintenance tasks are executed in strict sequence to maintain proper dependencies, then task correctness is ensured, but the total maintenance time increases
Solution Approach 1:
The patent transitions from one-dimensional sequential execution to multi-dimensional parallel execution by organizing tasks into a structured dependency graph. Tasks that have no dependencies on each other are executed in parallel across different dimensions (concurrently), while maintaining proper ordering for tasks that have dependency relationships. This dimensional change allows correct task execution to occur faster by utilizing parallel processing where possible
4Reliability
If the maintenance plan includes all prerequisite and post-requisite tasks, then task completeness is improved, but the complexity of plan construction and execution increases
Solution Approach 1:
The patent implements feedback mechanisms that automatically track the completion status of maintenance tasks and use this information to determine when prerequisite and post-requisite tasks should be executed. The system continuously monitors task progress and uses this feedback to dynamically activate dependent tasks, ensuring completeness without requiring manual planning of all task relationships in advance. The feedback loop automatically manages the complexity of coordinating multiple dependent tasks
Data Source
AI summary
A plan including several groups of tasks is constructed for performing maintenance on a plurality of interrelated machines. A maintenance task in a first group is caused to execute within a window of time allocated for the maintenance. A determination is made that an estimated amount of time needed to execute a second group of tasks from the several groups is more than the remaining time in the window. In response to such a determination, the execution of the second group of tasks is omitted. The execution of a post-requisite task of the first group is completed. A maintenance task in the second group is executed during a second window of time allocated for the maintenance.