Job Performance Plan Optimization via Clustered Data Segmentation
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Solution Overview
Problem
Current computer systems used by service organizations to schedule jobs in industrial environments face challenges in efficiently processing large amounts of data within a short computation time while maintaining accuracy, especially when dealing with changing service requests and workforce availability, leading to overnight calculations and potential delays in service delivery.
Innovation Solution
A computer-implemented method that preprocesses a data structure to identify routes between geographical locations, processes job data to provide an initial job performance plan, and updates the plan by selectively reassigning routes using a sub-set of the data structure, optimizing resource use and minimizing reassignments in response to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the computer processes all job data to calculate complete assignment plans, then the accuracy of the assignment is maintained, but the computation time becomes comparable to the time it takes to perform the service
Solution Approach 1:
The patent divides the complete job data into multiple clusters based on geographical locations and service types. Each cluster is processed independently by different processing units, allowing parallel computation. This segmentation reduces the overall computation time while maintaining assignment accuracy within each cluster, resolving the contradiction between comprehensive processing and time efficiency.
Solution Approach 2:
The system pre-processes and clusters job data before the actual assignment calculation. By organizing data into predefined geographical and service-type clusters in advance, the system reduces the complexity of real-time processing. This preliminary organization enables faster computation during service delivery while preserving the accuracy of assignments through structured data preparation.
2Adaptability or versatility
If the computer receives and processes continuously changing job data from multiple sources, then the assignment adapts to current conditions, but the computation resources and time required increase substantially
Solution Approach 1:
The system segments incoming job data streams from multiple sources into distinct clusters based on their origin, type, and geographical location. Each cluster is processed independently, allowing the system to handle changes in specific areas without reprocessing all data. This maintains adaptability to local changes while preserving overall computation efficiency.
Solution Approach 2:
Instead of processing all job data whenever changes occur, the system applies partial processing only to affected clusters. When job data changes in a specific geographical area or service type, only the relevant cluster is reprocessed. This partial action approach maintains assignment adaptability to current conditions while substantially reducing the computation resources and time required compared to full reprocessing.
3Reliability
If the computer calculates complete assignment plans day-ahead, then sufficient computation resources are available, but the service organization cannot respond quickly to changing requests
Solution Approach 1:
The system performs preliminary clustering and organization of job data in advance, creating a structured framework that can be quickly updated. This pre-processing establishes a reliable baseline assignment structure while enabling rapid adaptation when changes occur, as the clustered framework allows for efficient incremental updates rather than complete recalculation.
Solution Approach 2:
By segmenting the assignment plan into independent geographical and service-type clusters, the system maintains the stability of unchanged assignments while allowing flexible updates in affected areas. This segmentation enables the service organization to respond quickly to changing requests in specific locations without disrupting the overall reliable assignment structure established through day-ahead processing.
Data Source
AI summary
A computer-implemented method provides a job performance plan for a plurality of jobs to be performed at geographical locations. The computer receives job data that represent job requirements for specified locations and processes the job data with the data-structure to provide an initial job performance plan. The computer receives updated job data and identifies a sub-set of the data structure. The computer processes the updated job data with the sub-set to provide an updated job performance plan, wherein some of the routes are re-assigned to specified job performers. The sub-set is selected under consideration of location clusters. Step repetitions with different clusters are provided to increase accuracy.