Optimization-Based Load Planning for Laboratory Analyzer Scheduling
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Solution Overview
Problem
Large-scale diagnostic laboratories face inefficiencies in managing multiple laboratory analyzers, leading to challenges in turn-around-time, reagent usage, quality assurance costs, and system robustness due to the need for optimal assignment of tests across a large number of instruments.
Innovation Solution
An optimization-based load planning method using a mixed integer linear program (MILP) that schedules and directs tests across laboratory analyzers based on inventory, test types, and operational priorities, ensuring balanced load, efficient reagent usage, and robust system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling and monitoring is used for laboratory analyzers, then operational flexibility is maintained, but turn-around-time increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical scheduling operations with an automated computer-based optimization system. The load planning module automatically generates schedules by receiving analyzer inventory data, test requests, and preferences, then outputs optimized assignments without human intervention, thereby reducing turn-around-time and increasing throughput.
Solution Approach 2:
The system enables self-service scheduling where the computer server automatically performs load planning and test assignment without requiring manual operator input for each scheduling decision. The optimization module independently processes test requests and generates schedules based on predefined preferences and real-time analyzer status.
2Productivity
If more laboratory analyzers are added to increase capacity, then productivity improves, but device complexity and system robustness challenges increase
Solution Approach 1:
The load planning system serves multiple functions simultaneously: it schedules tests, monitors analyzer capacity, manages test assignments, and optimizes resource utilization across the entire analyzer network. This multi-functional approach consolidates what would otherwise require separate systems for each task, managing complexity while supporting expanded analyzer capacity.
Solution Approach 2:
The computer server acts as an intermediary between the expanded network of analyzers and the scheduling requirements. It receives test requests, processes them through optimization algorithms, and distributes assignments to appropriate analyzers, thereby managing the complexity introduced by having multiple analyzers without requiring direct complex interconnections between them.
3Loss of substance
If tests are assigned to minimize reagent usage, then loss of substance decreases, but manufacturing precision and quality assurance costs may worsen
Solution Approach 1:
The system changes the scheduling parameters by incorporating reagent consumption rates as a key optimization criterion. The load planning module receives reagent information and uses it to generate schedules that minimize reagent usage while maintaining test quality requirements, thereby reducing substance loss without compromising precision.
4Productivity
If automated load planning is implemented, then productivity and turn-around-time improve, but device complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a load planning module that receives input data and generates schedules, and a separate execution layer that implements the schedules on analyzers. This segmentation allows the complex optimization logic to be isolated in the software module while keeping the hardware analyzers relatively simple, thereby managing overall system complexity.
Data Source
AI summary
Systems and methods include an optimization-based load planning module for laboratory analyzers of bio-fluid samples. The optimization-based load planning module is executable on a computer server and is configured to optimize assay (lab test) assignments across a large number of laboratory analyzers based on one or more of the following user selected and weighted objectives: reduced turn-around-time, load balancing, efficient reagent usage, lower quality assurance costs, and/or improved system robustness. The optimization-based load planning module outputs a load plan comprising computer executable instructions configured to cause a system controller of a laboratory analyzer system to schedule and direct each requested test to be performed at one or more selected laboratory analyzers of the laboratory analyzer system in accordance with the user selected and weighted objectives. Other aspects are also described.