Laboratory Sample Carrier Routing with Offline Route Optimization
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Solution Overview
Problem
Existing laboratory sample distribution systems face challenges in efficiently managing complex transport routes for carriers due to exponential growth in possible route sets, especially with a large number of carriers and transportation fields, requiring more sophisticated operation methods to harness their potential.
Innovation Solution
A method and system for pre-determining optimized off-line routes on a transport plane by solving an optimization problem using a directed graph model, which involves calculating an optimized set of routes between pairs of plane locations prior to carrier movement, utilizing mixed-integer optimization and graph theory to manage carrier traffic efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of carriers and transportation fields are used to increase system capacity, then the productivity and throughput of the laboratory sample distribution system are improved, but the complexity of managing possible route sets increases exponentially
Solution Approach 1:
The patent segments the complex route management problem into two distinct phases: offline route determination (planning phase) and online route execution (operation phase). The offline phase pre-calculates optimal routes using graph theory and optimization algorithms, while the online phase simply executes these predetermined routes. This segmentation reduces the computational complexity from exponential to polynomial time during operation, enabling the system to handle large numbers of carriers and transportation fields efficiently.
Solution Approach 2:
The patent applies preliminary action by determining routes offline before the actual carrier movement occurs. The system pre-calculates optimal routes using mixed-integer optimization and graph theory algorithms during the offline phase, storing these routes for later execution. This preliminary route determination eliminates the need for complex real-time calculations during carrier movement, allowing the system to scale to large numbers of carriers without proportionally increasing operational complexity.
2Ease of operation
If fixed routes are defined via hardware design and electronics to simplify route management, then the ease of operation is improved, but the adaptability to different order situations and complex transport requirements deteriorates
Solution Approach 1:
The patent transforms the static, fixed route definition into a dynamic system. Instead of hardcoding routes in hardware design, the system uses software-based graph theory models that can dynamically calculate and adjust routes based on current order situations, carrier positions, and system state. The offline route determination algorithm adapts routes to match actual operational requirements, providing both ease of operation (through automated calculation) and adaptability (through flexible reconfiguration).
Solution Approach 2:
The patent changes the parameters of route definition from fixed hardware configurations to flexible software-controlled parameters. The system represents the transport system as a graph with nodes and edges, where route parameters (such as optimal paths, transfer locations, and sequencing) can be recalculated based on varying order situations. This parameter-based approach allows the same physical infrastructure to support multiple different route configurations without hardware reconfiguration.
3Ease of manufacture
If manual route design by laboratory designers is used to meet predefined requirements, then the ease of manufacture is improved, but the productivity and efficiency for complex transport systems deteriorates
Solution Approach 1:
The patent replaces manual, mechanical route design processes with automated computational algorithms. Instead of relying on laboratory designers to manually plan routes based on predefined requirements, the system uses mixed-integer optimization algorithms and graph theory to automatically calculate optimal routes. This substitution of manual mechanical design with automated computational methods enables the system to efficiently handle complex transport scenarios that would be intractable for manual design while maintaining ease of initial system setup.
Data Source
AI summary
A laboratory sample distribution system includes carriers that carry sample containers containing a sample to be analyzed by laboratory devices; a transport plane assigned to the laboratory devices and providing support to the carriers; and a driving device configured to move the carriers between positions on the transport plane. Prior to moving the carriers, off-line routes on the transport plane are pre-determined by determining a model representing the transport plane with plane locations and location-to-location movements between plane locations associated to the carriers, using the model to calculate an optimized set of off-line routes between pairs of plane locations by solving an optimization problem in which routes between the pairs are simultaneously optimized, and providing the optimized set of off-line routes as off-line routes on the transport plane. The driving device is controlled such that the carriers are moved along the pre-determined off-line routes on the transport plane.