Maintenance Range Optimization via Machine Learning
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Solution Overview
Problem
Current maintenance optimization systems for geographically dispersed objects, such as roads, do not effectively optimize the range of maintenance, leading to increased costs due to inefficient prioritization and planning.
Innovation Solution
A maintenance range optimization apparatus and method that utilizes machine learning, specifically Q-learning, to construct a model from past maintenance data, including pre-maintenance states and costs, to determine the optimal range of maintenance while minimizing overall costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maintenance priority is set for each route based on safety and traffic importance, then maintenance urgency is improved, but overall maintenance cost increases due to inefficient movement between dispersed locations
Solution Approach 1:
The patent combines multiple maintenance routes into integrated maintenance ranges by grouping routes that are geographically close to each other. The maintenance management server determines optimal maintenance ranges that encompass multiple routes, allowing maintenance vehicles to service multiple locations within a single dispatched range, thereby reducing movement costs while maintaining priority-based urgency.
Solution Approach 2:
The patent transitions from one-dimensional route-based prioritization to two-dimensional range optimization by considering both the priority level of individual routes and their spatial relationships. The system creates maintenance ranges that optimize the balance between maintaining high-priority routes and minimizing travel distances, adding a spatial dimension to the traditional priority-based approach.
2Reliability
If maintenance is performed on all damaged routes, then road safety is improved, but movement cost increases due to geographic dispersion of maintenance places
Solution Approach 1:
The patent merges multiple geographically dispersed maintenance locations into consolidated maintenance ranges. By grouping routes that are close to each other spatially, the system enables maintenance vehicles to service multiple locations within a single optimized range, reducing total movement cost while ensuring all necessary maintenance is performed.
Solution Approach 2:
The patent applies local quality by creating maintenance ranges with different characteristics based on local conditions. Each maintenance range is optimized according to the specific geographic distribution and priority levels of routes within that local area, allowing tailored maintenance planning that addresses both safety requirements and cost efficiency for each region.
3Productivity
If maintenance ranges are expanded to include multiple routes, then movement efficiency is improved, but maintenance range determination complexity increases
Solution Approach 1:
The patent introduces a maintenance management server as an intermediary that automatically performs the complex calculations and optimizations required for determining maintenance ranges. The server receives route information and priority levels, then computes optimal maintenance ranges using algorithms that balance multiple factors, relieving the complexity from manual planning and providing automated, data-driven decisions.
Solution Approach 2:
The system implements feedback mechanisms where the maintenance management server continuously receives information about route conditions, priority levels, and maintenance outcomes. This feedback is used to refine and adjust maintenance range determinations over time, improving movement efficiency while managing complexity through iterative optimization based on actual performance data.
Data Source
AI summary
A maintenance range optimization apparatus 10 optimizes a range of maintenance on an object that requires maintenance at a plurality of places. The maintenance range optimization apparatus 10 includes a learning processing unit 20 that executes machine learning, using, as learning data, information from when maintenance was previously executed, including a pre-maintenance state, a maintenance cost and a movement cost of a place subjected to maintenance, and constructs a model indicating a relationship between the range of maintenance and an overall cost incurred in maintenance, and a maintenance range setting unit 30 that sets the range of maintenance using the model.