Predictive Maintenance Task Allocation for High-Cost Fleet Operations
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Solution Overview
Problem
In multiple resource environments, such as fleets of ships or construction machinery, existing methods fail to effectively allocate tasks based on the likelihood of maintenance events, leading to potential disruptions and increased costs due to inadequate risk assessment and resource utilization.
Innovation Solution
A method and system that utilize predictive maintenance information and cost calculation to allocate tasks to resources, prioritizing tasks with high costs over resources with lower maintenance event likelihood, thereby optimizing task assignment and minimizing disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tasks are allocated without considering predictive maintenance information, then resource utilization is simplified and allocation is faster, but maintenance events cause disruptions and increased costs
Solution Approach 1:
The system performs preliminary maintenance risk assessment for each resource before task allocation. Predictive maintenance information is obtained in advance for all resources, and maintenance risk scores are calculated beforehand. This preliminary action allows the allocation system to consider maintenance risks without adding complexity during the actual allocation process, as the risk assessment is already completed.
Solution Approach 2:
The system introduces an intermediary maintenance risk score that mediates between task requirements and resource capabilities. This score serves as a bridge, translating complex predictive maintenance data into a simple metric that can be directly used in task allocation decisions, thereby reducing system complexity while improving reliability.
2Productivity
If high-cost tasks are assigned to resources with higher maintenance risk, then resource utilization is maximized, but maintenance disruptions increase and cost more
Solution Approach 1:
The system changes the allocation parameter from simple resource availability to a composite parameter that includes maintenance risk scores. By incorporating predictive maintenance information into the allocation criteria, the system optimizes task allocation to balance productivity and operational continuity, ensuring high-cost tasks are assigned to resources with lower maintenance risks.
3Reliability
If predictive maintenance information is integrated into task allocation, then maintenance-related disruptions are reduced, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential predictive maintenance information (maintenance risk scores) needed for task allocation, rather than processing all available maintenance data. This extraction approach reduces data processing overhead while maintaining the ability to make reliable allocation decisions based on maintenance risks.
Data Source
AI summary
A method of assigning a task to a resource in a multiple resource environment is performed by one or more processors or special-purpose computing hardware. The method includes receiving task information relating to at least one task to be performed by a resource in the multiple resource environment. The method also includes determining a cost value for each task, the cost value indicating a cost incurred if a maintenance event occurs during performance of the respective task. The method also includes receiving predictive maintenance information in relation to each of the multiple resources in the multiple resource environment, the predictive maintenance information indicating a likelihood of a maintenance event with respect to each of the multiple resources in the multiple resource environment. The method also includes allocating the at least one task to one of the resources in the multiple resource environment dependent on the predictive maintenance information of the multiple resources and the calculated cost score.