Method for operating a laboratory sample distribution system, laboratory sample distribution system, and laboratory automation system

Abstract

The present disclosure relates to a method of operating a laboratory sample distribution system, the laboratory sample distribution system comprising a plurality of carriers (4) configured to transport one or more sample containers containing samples to be analyzed by a laboratory apparatus (3), a transfer plane (1) assigned to the laboratory apparatus (3) and supporting the plurality of carriers (4), and a drive device (13) configured to move the plurality of carriers (4) between planar positions (5) provided on the transfer plane (1) in response to drive control signals. The method comprises, before moving the carriers (4) on the transfer plane (1), pre-determining, by one or more processors of a data processing device, an offline route (6) on the transfer plane (1), determining a model representing the transfer plane (1) with planar locations (5') and the movement between locations between the planar locations (5') associated with the plurality of carriers (4), calculating, using the model, an optimized set of offline routes between pairs of planar locations from the plurality of planar locations (5'), the calculating comprising solving an optimization problem in which the routes between pairs of planar locations are optimized simultaneously, providing the optimized set of offline routes as the offline route (6) on the transfer plane (1), and controlling the drive device (13) such that the carriers (4) are moved along the pre-determined offline route (6) on the transfer plane (1). Further, a laboratory sample distribution system and a laboratory automation system are provided.

Description

Technical Field

[0001] The present disclosure relates to a method of operating a laboratory sample distribution system. Further, the present disclosure relates to a laboratory sample distribution system. Also, the laboratory sample distribution system is also referred to as a laboratory automation system.

Background Art

[0002] Laboratory automation systems are applied, in particular, for essentially automatically determining samples, such as samples of body fluids. Samples are typically received in sample vessels or containers that are processed via the laboratory automation system.

[0003] Such laboratory automation systems can comprise a plurality of units. A laboratory automation system typically comprises a plurality of laboratory stations or devices, such as pre-analysis, analysis and / or post-analysis stations or devices for example. Typically, containers are transferred between different stations of the system via a sample distribution or transfer system. Sample carriers with or without samples may be moved along a line for processing the samples, and the sample carriers are moved by a transfer device having one or more actuators and an actuator driver or drive for driving the carrier. For example, a sample vessel may be moved or repositioned from a first work station provided on a processing line within the system to a second work station. A work station or device may sometimes also be referred to as a work place.

[0004] Currently, only the transfer systems within the inspection room are fully automated. However, the designs of these transfer systems are mostly quite simple, equipped with conveyor systems, and the samples and sample containers / carriers are each moved along a predetermined route. Usually, the set of routes along different analyzers / inspection room stations is defined by hardware design (rail / track arrangement) and / or electronics (dip switch configuration, pre-programmed turntable logic, etc.) (e.g., belt-driven transfer system). Thus, a specific sample / container / carrier can be assigned to a specific predetermined route. The specific sample / container / carrier can be transferred along this specific predetermined route, "step off" from the route close to the specific analyzer to be processed by the analyzer, and "step on" to the route again to be further transferred along the specific predetermined route until reaching the end of the route. Then, the sample can be, for example, stored or discarded. Typically, these routes are designed in a manual process by "brain power", i.e., with respect to a specific order situation. For example, the inspection room designer defines the routes so that a standard set of orders is processed efficiently and predetermined requirements are met.

[0005] However, in the case of complex transfer systems, such a design may reach its limits.

[0006] WO 2016 / 188752 discloses a laboratory automation system comprising several laboratory stations and several sample container carriers (e.g., from 10 to 10,000), where the sample container carriers are adapted to transport one or more sample containers. The sample containers contain samples to be processed by the laboratory stations. A transfer plane is provided, which is adapted to support the sample container carriers. The transfer plane comprises several transfer areas, and one of the several transfer areas is assigned to a corresponding laboratory station of the several laboratory stations. By moving the corresponding sample container carrier to the assigned transfer area, the sample is transferred to the laboratory station. Driving means are provided, which are adapted to move the sample container carriers on the transfer plane simultaneously and independently of each other along individual transfer paths.

[0007] More complex transfer systems may require more sophisticated operating methods, especially in order to better utilize their capabilities.

[0008] The document of European Patent Application Publication No. 3095739 discloses a method of operating a laboratory sample distribution system comprising several sample container carriers adapted to transport one or more sample containers, the sample containers comprising samples to be analyzed by several laboratory stations. A transfer plane is provided, which is adapted to support the sample container carriers. The transfer plane comprises several transfer locations assigned to corresponding laboratory stations. Driving means are provided for moving the sample container carriers on the transfer plane. The method includes a step of pre - calculating a route according to the transfer locations during the initialization of the laboratory sample distribution system, and a step of controlling the driving means so that the sample container carriers move along the pre - calculated route after the initialization of the laboratory sample distribution system. During the initialization (start - up) of the laboratory sample distribution system, a predetermined route extending across the transfer plane is pre - calculated according to different transfer locations (between different transfer locations). The pre - calculated route is given on the transfer plane between the transfer locations. The transfer locations represent initial nodes or target nodes in the sense of graph theory. The route is calculated using an informed search algorithm, namely the A* algorithm or the D* algorithm. The A* algorithm is an algorithm used in path finding and graph traversal to efficiently calculate traversable paths between different nodes, for example in the form of transfer locations.

[0009] The document of European Patent Application Publication No. 3410123 refers to a method of operating a laboratory sample distribution system. The laboratory sample distribution system includes several sample container carriers, each of which is provided with at least one magnetically active device and is adapted to carry at least one sample container, and several interconnected transfer plane modules, each of which is adapted to support several of the aforementioned sample container carriers. Several electromagnetic actuators are provided, and several of the electromagnetic actuators are arranged in rows and columns and stationary below each transfer plane module. The electromagnetic actuators are adapted to move one of the sample container carriers on the transfer plane module along one of the rows or one of the columns by applying a magnetic moving force to the sample container carrier. The method includes: a) a step of assigning at least one of the transfer plane modules to a route category, wherein at least two traffic lanes are formed on the route-categorized transfer plane module, the sample container carriers are moved in a given transfer direction within each traffic lane, the transfer directions of the at least two traffic lanes are opposite to each other, and a change from one transfer direction to the opposite transfer direction is impossible for the sample container carriers moving on the route-categorized transfer plane module; and b) a step of assigning at least one other transfer plane module to an intermediate point category, wherein a change from one transfer direction to the opposite transfer direction is enabled for the sample container carriers moving on the intermediate-point-categorized transfer plane module.

[0010] The document of U.S. Patent No. 9,835,637 generally relates to an automation system for use in a laboratory environment, and more particularly, to a system and method for scheduling samples within an automation system by providing queuing logic.

[0011] An analysis system for analyzing biological samples is disclosed in the document of U.S. Patent Application Publication No. 2019 / 0120866.

[0012] The document of U.S. Patent No. 10,668,622 generally refers to an automation system for use in a laboratory environment, and more particularly, refers to a system and method for use in a clinical analyzer.

[0013] The document of U.S. Patent No. 9,315,334 discloses an automation system for an in vitro diagnostic environment. The system includes a plurality of intelligent carriers including on-board processing and navigation functions. The carriers control local movement, navigate decision points such as branch points within a track, and independently reach appropriate test stations.

[0014] In a complex transfer system, route calculation for carriers also becomes more complex. The size of the set of possible routes increases exponentially with both the number of carriers in the transfer plane / transfer plane module and the number of transfer fields. Even for a single carrier, the route connecting its current position to the destination is huge. However, in a typical use case (involving a large number of transfer fields and carriers), optimal routing needs to consider the routes of hundreds of carriers simultaneously.

Summary of the Invention

[0015] It is an object to provide an improved method for operating a laboratory sample distribution system and a laboratory sample distribution system. In particular, it is an object to provide a technique for improved determination of routes for carriers in a laboratory sample distribution system.

[0016] To solve this problem, a method of operating the laboratory sample distribution system according to independent claim 1 is provided. Further, a laboratory sample distribution system according to independent claim 16 is provided. Further, a laboratory automation system according to claim 17 is provided. Further embodiments are disclosed in the dependent claims.

[0017] According to one aspect, a method of operating a laboratory sample distribution system is provided. The laboratory sample distribution system comprises a plurality of carriers configured to transport one or more sample containers containing samples to be analyzed by laboratory equipment, a transfer plane assigned to the laboratory equipment and providing support for the plurality of carriers, and a drive device configured to move the plurality of carriers between planar positions provided on the transfer plane in response to a drive control signal. The method includes, prior to moving the carriers on the transfer plane, determining in advance, by one or more processors of a data processing device, a plurality of offline routes on the transfer plane (depending on transfer locations). The advance determination includes determining a model representing a plurality of inter-location movements between a transfer plane having a plurality of planar locations and a plurality of planar locations associated with the plurality of carriers, and using the model to calculate an optimized set of (a plurality of) offline routes between a plurality of pairs of planar locations from the plurality of planar locations, the calculating step including solving an optimization problem in which a plurality of routes between a plurality of pairs of planar locations are optimized simultaneously, and providing the optimized set of offline routes as offline routes on the transfer plane, for example, to the drive device. The drive device is controlled such that the carriers are moved along a pre-determined set of offline routes on the transfer plane.

[0018] According to another aspect, a laboratory sample distribution system is provided. The laboratory sample distribution system includes a plurality of carriers configured to transport one or more sample containers containing samples to be analyzed by a laboratory station, a transfer plane assigned to a laboratory device and providing support for the plurality of carriers, and a drive device configured to move the plurality of carriers between planar positions provided on the transfer plane in response to a drive control signal. The laboratory sample distribution system is configured to pre-determine, by one or more processors of a data processing device, a plurality of offline routes on the transfer plane (depending on the transfer locations) before moving the carriers on the transfer plane. Pre-determining includes determining a model representing a plurality of inter-location movements between a transfer plane having a plurality of planar locations and a plurality of planar locations associated with a plurality of carriers, and using the model to calculate an optimized set of (a plurality of) offline routes between a plurality of pairs of planar locations from the plurality of planar locations, including solving an optimization problem in which a plurality of routes between a plurality of pairs of planar locations are optimized simultaneously, and providing the optimized set of offline routes as the offline routes on the transfer plane. The laboratory sample distribution system is further configured to control the drive device such that the carriers are moved along the pre-determined optimized set of offline routes on the transfer plane.

[0019] According to yet another aspect, a laboratory automation system is provided, which system includes the laboratory sample distribution system according to the foregoing aspect and a plurality of laboratory devices.

[0020] A pre-determined offline route may correspond to at least one of a route that can be determined when the configuration (laboratory sample distribution system) is designed, a route that is determined when the system is turned on, a route that is determined manually, for example triggered by an operator, and a route that is triggered and determined by the system itself (for example, when the traffic / order / status of a part of the equipment or transfer plane changes). In particular, the pre-determined route may correspond to a route that is not calculated for each individual carrier but is fixed over a certain period of time.

[0021] The expression "offline" used refers to a route calculated offline. An offline route is not calculated in real time for each movement during the operation of the laboratory sample distribution system, but may be a route calculated, for example, during laboratory design or at the beginning of a working day before actually executing or operating (runtime) the laboratory sample distribution system.

[0022] Calculating an optimized set of offline routes between multiple pairs of planar locations from multiple planar locations may further include determining multiple offline routes between multiple pairs of planar locations from multiple planar locations (for example, in a directed graph model) and determining an optimized set of offline routes from the multiple routes, including solving an optimization problem (for example, a mixed integer optimization problem) in the multiple offline routes.

[0023] The model can be a directed graph model of the transfer plane, where planar locations are assigned to the nodes of the directed graph model, and (feasible) inter-location movements between two planar locations are assigned to the arcs connecting the nodes of the directed graph model. Alternatively, the model can be a model from graph theory, a numerical (statistical) model, and / or a model used in simulations. However, the model can also be another model, for example, a combination of the aforementioned models.

[0024] The planar locations can include one or more (groups of) planar positions. Each planar position (of the transfer plane) can correspond to a specific region / logical field on the transfer plane. The transfer plane can be segmented into several logical fields, for example, square logical fields of the same size and contour. The logical fields may form a regular grid, each cell of the grid may correspond to a logical field, each cell of the grid may be a square of the same size (identical in the X and Y directions), and / or the grid may (exactly) cover the transfer plane. Each transfer module (TM) may comprise one or more logical fields (for example, a subset of the logical fields forming the transfer plane). A set of transfer modules may form the transfer plane. The pitch between adjacent transfer modules (TMs) may form a pitch within a regular grid. For example, 10 to 10,000 planar positions may be provided. Each planar position / logical field can correspond to a transfer module, and multiple transfer modules together constitute the transfer plane. One or more (groups of) logical fields / planar positions may be assigned to the nodes of the directed graph model. A place movement between two groups of logical fields / planar positions may be assigned to the arcs connecting the nodes of the directed graph model, and each group may include one or more logical fields / planar positions.

[0025] The optimized set of offline routes provides a pre-determined offline route on the transfer plane. In one embodiment, only a place movement between two adjacent planar locations may be allowed. Only a place movement between two adjacent groups of logical fields / planar positions may be allowed. The arcs only need to connect adjacent nodes of the directed graph model.

[0026] Multiple offline routes between multiple pairs of planar locations in a directed graph model can be selected to form multiple possible routes connecting the multiple pairs of planar locations. The possible routes can be (directly) located on the transfer plane, preferably (directly) completely located on the transfer plane. Each pair of planar locations may indicate a pair of start location / node and destination location / node of one of the routes. The start location and destination location of a particular route may constitute the end location of this route. The multiple pairs of planar locations may correspond to multiple pairs of transfer locations.

[0027] The multiple pairs of planar locations may correspond to the most frequent start locations and destination locations (end locations) of the carrier.

[0028] The multiple pairs of transfer locations and / or the multiple pairs of planar locations may represent corresponding multiple pairs of start nodes and destination nodes of the routes in the sense of graph theory. The routes can be determined in advance between the transfer locations, that is, the end or terminal nodes of the routes are formed by the transfer locations. For example, when the first, second, and third (most frequent) end locations of the carrier are given, two routes (one-way and the other-way) between the first and second (most frequent) end locations, two routes (one-way and the other-way) between the first and third (most frequent) end locations, and two routes (one-way and the other-way) between the second and third (most frequent) end locations can be determined in advance.

[0029] For example, 10 to 10,000 sample container carriers may be provided. The laboratory sample distribution system may process 2,000 to 200,000 samples per day. For example, 2 to 50 laboratory stations may be provided. The transfer plane may be completely or at least partially flat.

[0030] First and second (sub) transfer planes may be provided, and the first and second transfer planes may be provided at different levels. The carrier may be transferred from the first transfer plane to the second transfer plane (and vice versa) via a lift (e.g., a paternoster lift) and / or a ramp. The transfer planes may comprise first and second (sub) transfer planes, and optionally further (sub) transfer planes. Accordingly, a method for determining an offline route may also include a route that extends across both first and second, and optionally further, transfer planes that are provided at different levels.

[0031] In this regard, a route connection segment of a route that extends across a plurality of transfer planes that are each provided at different levels may connect a route section on one of these planes, e.g., a route section on the first transfer plane, to a route section on another of these planes, e.g., a route section on the second transfer plane, and a route segment that connects route sections on different levels forms part of the route. This route connection segment may be constituted by a transfer system such as a ramp or a lift.

[0032] In the present application, generally, the term "transfer plane" refers to both two-dimensional and / or three-dimensional surfaces, i.e., surfaces that extend not only in two spatial dimensions but also in a third spatial dimension. Such a three-dimensional surface in the sense of this use may be, for example, a surface with one or more curvatures, ramps and / or lifts between (sub) planes.

[0033] (Sample Container) The carrier can be placed on the transfer plane. The carrier can be moved on and above the transfer plane. The transfer plane may include 2 to 100 transfer locations. The transfer locations may be assigned to corresponding laboratory stations or devices. Additionally or alternatively, the transfer locations may be assigned to areas managed by a separate routing system. For example, the waiting area may be controlled by a separate routing system. Also, there may be handovers, i.e., transfer mechanisms, between these areas for two or more separately controlled transfer systems. For example, each laboratory station or device may have a single corresponding transfer location configured to transfer the sample carrier and / or sample from the transfer plane to the laboratory device or vice versa. Alternatively, two or more transfer locations may be assigned to corresponding laboratory stations or devices, particularly output and input transfer locations. The transfer locations may be assigned statically or dynamically to the laboratory stations. In other words, the transfer locations may be changed as needed during operation / runtime.

[0034] Optimization problems that may be related to the model can be one of a multi-commodity flow problem, particularly a multi-commodity flow problem in a directed graph, a shortest path problem, and a minimum flow problem.

[0035] Calculating an optimized set of offline routes between multiple pairs of plane locations from multiple plane locations can include, for example, finding the optimal multi-commodity flow within a directed graph. The multi-commodity flow problem is a network flow problem regarding multiple commodities (flow requirements) between different source nodes and sink nodes. The end-point location / location pairs can define the source nodes and sink nodes.

[0036] The optimization problem may be solved, for example, by applying a MIP solver (Mixed-Integer-Programming-solver). A general-purpose solver for MIP problems can be used. The MIP solver can be one of the following, namely, Gurobi, IBM ILOG Cplex, and Coin-OR CBC, that is, the solution to the mixed integer optimization problem can be found with software products such as Gurobi, IBM ILOG Cplex, and Coin-OR CBC. Additional solvers that can solve this type of optimization problem are known. Generally, the mixed integer optimization problem deals with a mathematical optimization problem involving two types of variables, variables that take values in the integer domain and variables that take values in the continuous domain.

[0037] First, the optimization problem may be defined, for example, a multi-commodity flow problem, that is, the problem of interest is modeled. The problem of interest may be to find a set of routes on the transfer plane that is optimal with respect to one or more given criteria (constraints). Next, a solution method for the defined optimization problem, for example, a MIP solver, may be selected to solve the optimization problem.

[0038] Examples of possible solution approaches are presented in the paper P. FESTA, "Exact Approximation Algorithms for Solving Hard Combinatorial Optimization Problems and a Simple Introduction to Heuristic Algorithms", 16th International Conference on Transparent Optical Networks (ICTON), 2014, pages 1 to 20, doi:10.1109 / ICTON.2014.6876285. Optimization problems can be solved by exact optimization methods, approximation of the original problem by simpler ones, and solving simpler problems and / or by heuristics and / or meta-heuristics. Exact optimization methods can be (i) branch-and-bound methods, (ii) dynamic programming methods, or (iii) solvers (other exact optimization methods can also be used). Solvers may include multiple algorithms (not only exact algorithms). Exact optimization methods can also include combinations of the aforementioned methods. Solving the approximated simpler problem may include (i) one or more greedy algorithms, (ii) local search, (iii) one or more relaxation-based algorithms, or (iv) one or more random algorithms (other methods for solving the approximated simpler problem may be used). Also, solving the approximated simpler problem may include combinations of the aforementioned methods. Heuristics and / or meta-heuristics may be (i) simulated annealing, (ii) one or more evolutionary algorithms, (iii) tabu search, or (iv) one or more greedy randomized adaptive search procedures (GRASP). Also, heuristics and / or meta-heuristics may include combinations of the aforementioned methods.

[0039] The method may further include determining a re-optimized set of offline routes from an optimized set of offline routes when initializing the laboratory sample distribution system. The determination of the re-optimized set of offline routes may depend on the expected carrier traffic on the transfer plane. For example, the optimized set of offline routes may include two sets of optimized routes, a first set for high traffic and a second set for low traffic. If low traffic is expected, the second set of optimized routes is selected when determining the re-optimized set of offline routes, and vice versa. Alternatively, the optimized set of offline routes may include several sets of optimized routes for several traffic scenarios. When determining the re-optimized set of offline routes, in this case, depending on the expected traffic, the corresponding set of optimized routes is selected from several sets of optimized routes.

[0040] (Expected) carrier traffic (carrier traffic intensity) may depend on several factors. Throughput may be higher on weekdays than on weekends and may be higher between 8:00 and 18:00 than at night. For example, if glucose screening is performed on a batch of samples or a clinical trial is conducted, the types of ordered tests may vary on a particular day or time. Therefore, the end point (starting point or destination point) assigned to the corresponding laboratory device may function as an end point more frequently. The corresponding route including this end point may have higher carrier traffic (carrier traffic intensity). Similarly, due to seasonal effects or pandemic situations, some tests are ordered much more frequently over a longer period.

[0041] The determined optimized set of offline routes can be used as a lookup table or represented by a lookup table. Whenever a carrier needs to travel from the starting field to the destination, the corresponding route can be selected from the lookup table.

[0042] The method may further include, in a data processing device, (i) providing first frequent end-point location data indicating a first selection of a flat location that most frequently provides an end point of a route of travel in a carrier; and (ii) determining a directed graph model of a transfer plane, wherein a first node of the directed graph model is assigned to a flat location from a first selection of flat locations, and a first arc starting and / or ending at the first node of the directed graph model is assigned to a location movement from and / or to a flat location from a first selection of flat locations.

[0043] Alternatively or additionally, the method may include, in a data processing device, providing first frequent end-point location data / data regarding a first pair of frequent end-point locations indicating a first selection of a flat location that most frequently provides an end point / a first selection of a pair of flat locations that most frequently provides a pair of (starting and destination) end points of a route of travel for a carrier. The first selection of the pair of flat locations may correspond to 5 to 10 pairs of end points, i.e., the first selection of the pair of flat locations may correspond to 5 to 10 routes of travel for a carrier.

[0044] Pairs of flat locations from a plurality of flat locations can include a first selection of flat locations and / or a first selection of pairs of flat locations. In particular, a first selection of flat locations and / or a first selection of pairs of flat locations can define pairs of flat locations from a plurality of flat locations. A first selection of flat locations and / or a first selection of pairs of flat locations can correspond to (but is not limited to) a transfer location and / or a pair of transfer locations. However, the first selection of pairs of flat locations can correspond to all pairs of end points (starting and destination) in a route of travel for a carrier.

[0045] Alternatively or additionally, the method may include providing, in a data processing apparatus, data regarding a first end point location and / or a first pair of end point locations indicative of a first selection of a planar location and / or a first selection of a pair of planar locations located in a first area corresponding to a first workflow and / or corresponding to a high-priority sample (time-critical test).

[0046] One transfer location may correspond to one or more laboratory devices, such as an analyzer. In the case of one or more laboratory devices, a plurality of planar locations (respectively) may define one transfer location. The first planar location among the plurality of planar locations may define one transfer location. Alternatively, the second planar location among the plurality of planar locations may define one transfer location. Either the first or second planar location defining one transfer location can be exchanged. In particular, whether the first or second planar location defines one transfer location can be exchanged during a prior determination of the offline route, in particular during the calculation of an optimized set of the offline route. The laboratory device may comprise an input (assigned to the first transfer location) for the carrier and an output (assigned to the second transfer location) for the carrier. In particular, the input and output can be exchanged during a prior determination of the offline route, in particular during the calculation of an optimized set of the offline route. In particular, the exchange can be performed in one or more of the optimization steps. The exchange can be performed if this has an advantage in terms of performance. A mere pair of input and output may correspond to a pair of planar locations. However, the input and output can correspond to the same planar location.

[0047] The method may further include, in a data processing apparatus, (i) providing second frequent end-point location data indicating a second selection of a flat location that provides an end point of a route for a carrier less frequently, wherein the second selection of the flat location is different from a first selection of the flat location; and (ii) determining a directed graph model of a transfer plane, wherein a second node of the directed graph model is assigned to a flat location from a second selection of the flat location, and a second arc that starts and / or ends (connects) the second node of the directed graph model is assigned to an inter-location movement from and / or to a flat location from a second selection of the flat location.

[0048] Alternatively or additionally, the method may include, in a data processing apparatus, providing second frequent end-point location data / data regarding a second frequently-occurring pair of end-point locations indicating a second selection of a flat location that provides an end point of a route for a carrier less frequently or a second selection of a pair of flat locations that provides a pair of end points (starting and destination points) of a route for a carrier less frequently. The second selection of the pair of flat locations may correspond to 5 to 10 pairs of end points, i.e., the second selection of the pair of flat locations may correspond to 5 to 10 routes for a carrier. The second selection of the flat location may be (completely / in pairs) different from the first selection of the flat location. The second selection of the pair of flat locations may be (completely / in pairs) different from the first selection of the pair of flat locations.

[0049] Pairs of flat locations from a plurality of flat locations may include a second selection of a flat location and / or a second selection of a pair of flat locations. In particular, the second selection, together with the first selection of the flat location, and / or the second selection, together with the first selection of the pair of flat locations, may define pairs of flat locations from a plurality of flat locations. The second selection of the flat location and / or the second selection of the pair of flat locations may correspond to non-transfer locations, i.e., the second selection of the flat location and / or the second selection of the pair of flat locations may not include (but is not limited to) transfer locations.

[0050] Alternatively or additionally, the method may further include providing, in a data processing apparatus, data regarding a second end location and / or a second pair of end locations indicative of a second selection of planar locations located in a second region corresponding to a second workflow and / or corresponding to samples with lower priority and / or a second selection of pairs of planar locations.

[0051] The first selection of plane locations / plane location pairs may define a first set of pairs of plane locations. The second (and first) selection of plane locations / plane location pairs may define a second set of pairs of plane locations. The first and second sets of pairs of plane locations may be included (defined) in the pairs of plane locations. The prior determination of the offline route is the first prior determination of the offline route, where the first set of a plurality of pairs of plane locations defines a plurality of pairs of plane locations for the first prior determination, and the second prior determination of the offline route, where the second set of a plurality of pairs of plane locations defines a plurality of pairs of plane locations for the second prior determination. The first and second prior determinations of the offline route may be made independently of each other. The first and second (optimized) offline routes may correspond to the first and second prior determinations of the offline route. The first and second prior determinations of the offline route may correspond to different or the same optimization problems. The first and second prior determinations of the offline route may correspond to different or the same methods for solving their respective optimization problems. The prior determination of the offline route may include a third prior determination of the offline route. The third prior determination of the offline route may correspond to a third (optimized) offline route. The third prior determination of the offline route may include calculating an optimized set of offline routes among the third selection of plane locations / plane location pairs. Similarly, fourth, fifth, and further optimized offline routes may be calculated in groups (5 to 10 routes and / or 5 to 10 pairs of plane locations). The second prior determination of the offline route may be made after the first prior determination of the offline route. The second prior determination of the offline route may depend on the first (optimized) offline route determined via the first prior determination of the offline route. The expected traffic load from the first set of routes may be considered during the second optimization. During the second prior determination of the offline route, the first (optimized) offline route may be fixed.The first and second pre-determinations of the offline route may correspond to determining a plurality of offline routes between a plurality of pairs of planar locations from a plurality of planar locations in a directed graph model.

[0052] Finding a solution may be made easier by optimizing the offline routes in some groups of routes. For example, in this case, the optimization problem can be simplified. Also, the time required to perform the optimization procedure can be reduced. The optimization process may focus on the routes that are most intensively used first while reducing the optimization requirements for routes that are not used very intensively.

[0053] It may also be envisioned to provide several routes (alternative routes) for the same start-destination combination. This provides the option to switch to an alternative route if there is a problem, such as traffic congestion (where the carrier may become immobile), on one of the alternative routes. Further, with multiple routes (ideally using different logical locations), the traffic load per route can be reduced to increase the total throughput (more total capacity between the corresponding start-destination combinations) or reduce the wear on the surface used by the route as the traffic is spread over more transfer surfaces.

[0054] Pairs of the first set of pairs of planar locations may comprise two planar locations from the first selection of planar locations. Pairs of the second set of pairs of planar locations may comprise two planar locations from the second selection of planar locations. However, additionally or alternatively, pairs of the second set of pairs of planar locations may comprise one planar location from the first selection of planar locations and one planar location from the second selection of planar locations.

[0055] The method may further include, in a data processing apparatus, (i) providing traffic data indicative of a predicted number of carriers traveling between a plurality of pairs of planar locations at a certain time interval, and (ii) calculating an optimized set of offline routes between a plurality of pairs of planar locations from a plurality of planar locations according to the predicted number of carriers traveling between the plurality of pairs of planar locations. Additionally or alternatively, the traffic data may indicate the actual / most recent number of carriers traveling between a plurality of pairs of planar locations within the time interval.

[0056] Providing the traffic data may include at least one of providing traffic data determined from a sample order list, providing traffic data determined from historical data indicative of the historical operation of a laboratory sample distribution system, providing traffic data determined from workflow data indicative of a workflow in one or more sample containers to be carried by a carrier, providing traffic data determined from the measured current and / or most recent number of carriers transferred, and providing traffic data determined from a simulation.

[0057] The traffic data may correspond to empty carriers and filled (routed) carriers.

[0058] The control of the drive device may further include (i) in the drive device, receiving a reservation request from a carrier traveling on an offline route selected from a plurality of pre-determined offline routes and located at a current route location along the selected offline route, the reservation request indicating a request to reserve the next route location (e.g., logical field / planar position) along the selected offline route, (ii) verifying by the drive device whether the next route location is available for travel, and (iii) moving the carrier from the current route location to the next route location along the selected offline route if it is confirmed by the drive device that the next route location is available for travel.

[0059] After confirming that the next route location is available for travel, before moving the carrier, the next route location can be reserved for this carrier, that is, the next route location can be blocked for other carriers. In particular, the next route location can be blocked with respect to other carriers until the target carrier reaches the next route location and then leaves the next route location. The above can similarly be applied to n next route locations. The n next route locations may correspond to a subset of locations (e.g., logical field / plane positions) along the selected offline route. In this case, the n next route locations can be successively unlocked for other carriers after the carrier has passed through them. Alternatively, the n next route locations may be unlocked at once for other carriers after the carrier has passed through all of them.

[0060] Calculating an optimized set of offline routes between pairs of planar locations from a plurality of planar locations by solving an optimization problem may further include applying at least one constraint selected from the following group, namely, (i) minimizing the route length of each of the offline routes from a plurality of offline routes, (ii) minimizing the weighted route length of each of the plurality of offline routes, (iii) minimizing the number of route curves in each of the offline routes from a plurality of offline routes, (iv) minimizing the number of offline routes that merge and / or cross another offline route, (v) evenly distributing the carrier traffic for each planar location, (vi) restricting the movement between locations between two planar locations to only the movement between adjacent planar locations, (vii) excluding planar locations reserved for carrier waiting, (viii) evenly distributing the predicted wear of the planar locations across the planar locations of the transfer plane, (ix) minimizing the energy consumption of the inspection room sample distribution system, and (x) minimizing / avoiding a region of 2×2 planar positions having four intersections. A region of 2×2 planar positions that enables a cyclic array of feasible field-to-field movements is minimized or avoided. A region of 2×2 planar positions having four intersections may indicate one potential deadlock situation. Similarly, additional potential deadlock situations may be defined. Additional or alternative constraints may be the minimization or avoidance of one or more additional potential deadlock situations.

[0061] Regarding minimizing the weighted route length, in the optimization procedure, weights may be assigned to a plurality of end point pairs, or the optimal route may be determined in a weighted graph.

[0062] Also, constraints can have weights. Routes with higher expected traffic may be given higher weights. More highly weighted routes may be preferred over less highly weighted routes when minimizing each route length, the number of route curves, and / or the number of intersections, and / or overlap with other routes (see the above constraints). The first and second pre - determinations of the offline routes may include applying different constraint conditions.

[0063] In a typical scenario, for example, 5 to 10 routes between pairs of planar locations may be optimized simultaneously with each other. Each route may refer to a pair of planar locations. The pairs of planar locations may be 5 to 10 pairs. The routes between 5 to 10 pairs of planar locations may be optimized simultaneously with each other.

[0064] Constraints can have weights. The weights can be set empirically. Constraints can be hard (e.g., can have "infinite weight") or soft (e.g., can have finite weight). Constraints regarding routes with high traffic can have a higher evaluation than those regarding routes with low traffic. According to an example referring to a route with low traffic intensity, the following weights are applied in optimization. (i) Route length: 5 penalty points per field used in the route (planar location or planar position); (ii) Route confluence: 50 penalty points for routes that merge with each other. According to another example referring to a route with higher traffic intensity, the following weights are applied in optimization. (i) Route length: 10 penalty points per field used in the route (planar location or planar position); (ii) Route confluence: 100 penalty points for routes that merge with each other.

[0065] The pre-determination of the offline route may further include, in the data processing device, receiving first route traffic information indicating high carrier traffic in the first offline route, and splitting at least one offline route into two or three or more different offline routes.

[0066] Determining a plurality of offline routes between a plurality of pairs of flat locations from a plurality of flat locations in a directed graph model may further include, in the data processing device, receiving first route traffic information indicating high carrier traffic in the first offline route, and splitting at least one offline route into two or three or more different offline routes.

[0067] Calculating an optimized set of offline routes between a plurality of pairs of flat locations from a plurality of flat locations may further include receiving first route traffic information indicating high carrier traffic in the first offline route, and splitting at least one offline route into two or more different offline routes.

[0068] The first route traffic information may be derived from traffic (intensity) data indicating the predicted number of carriers traveling between a plurality of pairs of flat locations within a certain time interval. A (traffic) threshold may be pre-determined. If the number of carriers traveling between the (corresponding) pair of flat locations within a certain time interval is greater than the threshold, the traffic intensity may be identified as high (high traffic intensity). If the number of carriers traveling between the (corresponding) pair of flat locations within a certain time interval is lower than the threshold, the traffic intensity may be identified as low (low traffic intensity).

[0069] Several (traffic) thresholds of different sizes can be defined. The more (traffic) thresholds are exceeded, the more frequently at least one offline route can be split. At least one offline route can be split into as many different offline routes as the amount by which the threshold is exceeded. The (traffic) threshold can indicate the maximum capacity of the route. In the case of several thresholds, the first / minimum threshold may indicate the maximum capacity of the route, and subsequent thresholds may indicate twice the maximum capacity of the route.

[0070] The different offline routes may comprise a first and a second offline route different from each other. The first and second offline routes may have an overlapping section and a separated section. For example, the separated section may comprise a parallel section. A carrier that first traversed at least one offline route before the splitting of this route may traverse one of the two or more different offline routes into which the at least one offline route was split. The carrier may be equally split among two or more different offline routes. Thus, in the case of two different offline routes, 50% of the carrier may traverse the first of the two different offline routes, and 50% of the carrier may traverse the second of the two different offline routes. Alternatively, the loads of the different offline routes may be different. For example, the shortest of the different offline routes may have the highest load. If one of the different offline routes is the best with respect to optimization (e.g., the shortest and / or has the fewest intersections with other routes), this route can have the highest load of the different offline routes. Alternatively, traffic can be taken in by one of the different offline routes until the (traffic) threshold is reached, and all traffic exceeding this threshold can be sent to a further route of the different offline routes.

[0071] A traffic intensity matrix or two-dimensional list can indicate the (expected) traffic intensity at each pair of planar locations (start point / end point combinations) (see Table 1). Each pair of planar locations can correspond to a specific (required) traffic intensity.

[0072] [Table 1]

[0073] Table 1: Example of a traffic intensity matrix with start points in the vertical columns and end points in the horizontal rows. Each table entry represents the expected traffic intensity at the corresponding pair of start and end points.

[0074] The method may further include providing, in a data processing device, traffic intensity data indicating the predicted number of carriers traveling between each of a plurality of pairs of planar locations within a time interval. The traffic intensity data may be included in traffic data. The traffic intensity may include a traffic intensity matrix or two-dimensional list.

[0075] Traffic data may be determined based on traffic intensity data and / or traffic intensity for each pair of planar locations. The traffic intensity data and / or traffic intensity for each pair of planar locations may be calculated based on the total cumulative traffic between each pair of planar locations, the peak traffic between each pair of planar locations, and / or the capacity of the inspection room apparatus assigned to each pair of planar locations. To determine the total cumulative traffic, for example, over a specific time interval, all movements between each pair of planar locations can be calculated, for example, from an ordered list. For example, the number of carriers moved / moved within 24 hours between each pair of planar locations can indicate the total cumulative traffic between each pair of planar locations. To determine the peak traffic, for example, a 15-minute or 30-minute time window (e.g., a movement time window) or a predetermined time interval over 24 hours may be defined, and the maximum traffic intensity can be identified for each respective pair of planar locations. The peak traffic can correspond to the global or local peak traffic. The global peak traffic corresponds to the traffic intensity at each pair of planar locations when the traffic between all pairs of planar locations is at its maximum. The local peak traffic corresponds to the maximum traffic intensity at each pair of planar locations. The maximum traffic intensities at different pairs of planar locations may exist at different times. The capacity of the inspection room apparatus can be considered to avoid waiting near the inspection room apparatus. The sum of the traffic (intensity) (for each respective route) can correspond to the transfer capacity of the system (for each respective route). The transfer capacity can be matched with the capacity of the inspection room apparatus (in each respective route).

[0076] The above (traffic) threshold may be compared with the traffic intensity data at each pair of planar locations to determine whether the traffic is high.

[0077] The first selection of planar locations may indicate a plurality of pairs (end points of carrier routes) with a high frequency of planar locations. The second selection of planar locations may indicate a plurality of pairs (end points of carrier routes) with a low frequency of planar locations. The first selection of planar locations may correspond to the higher entries of the traffic intensity matrix. The second selection of planar locations may correspond to the lower entries of the traffic intensity matrix.

[0078] The offline route pre-determination may include a first pre-determination of the offline route, where the first selection of planar locations defines a pair of planar locations, and a second pre-determination of the offline route, where the second selection of planar locations defines a pair of planar locations. The first pre-determination of the offline route may be prioritized. The first pre-determination of the offline route may be performed before the second pre-determination of the offline route. During the second pre-determination of the offline route, the first pre-determined offline route may be fixed / "frozen". The first and second pre-determinations of the offline route may be provided in the same way as the offline route pre-determination.

[0079] In optimization, traffic intensity can be used to weight the importance of each pair of planar locations. For example, in the case of a pair of planar locations with high traffic intensity, it is more important to reduce the number of bends or intersections of the corresponding route connecting the pair of planar locations than for a pair of planar locations with low traffic intensity.

[0080] The offline route pre-determination may further include, in a data processing device, receiving second route traffic information indicating high carrier traffic (intensity) in a second offline route, and preventing the second offline route from being route-adjusted while determining a plurality of offline routes and / or determining (calculating) an optimized set of offline routes. This step may alternatively or additionally be constituted by a step of calculating an optimized set of offline routes.

[0081] (For example, in a directed graph model) Calculating an optimized set of offline routes between pairs of planar locations from a plurality of planar locations using a model may involve, in a data processing apparatus, receiving first carrier traffic information indicating a first carrier traffic scenario in a plurality of offline routes, and (for example, in a directed graph model) determining a first plurality of offline routes between pairs of planar locations from a plurality of planar locations, and receiving second carrier traffic information indicating a second carrier traffic scenario in the plurality of offline routes, where the second carrier traffic scenario is different from the first carrier traffic scenario, and (for example, in a directed graph model) determining a second plurality of offline routes between pairs of planar locations from a plurality of planar locations. This step may alternatively or additionally be included in a pre-determination step.

[0082] The first plurality of offline routes between pairs of planar locations may comprise a first number of planar locations and / or planar positions (logical fields) that are configured / transversed by the first plurality of offline routes. The second plurality of offline routes between pairs of planar locations may comprise a second number of planar locations and / or planar positions (logical fields) that are configured / transversed by the second plurality of offline routes. The first and second numbers of planar locations and / or planar positions (logical fields) may be different. Each planar location and / or planar position (logical field) may correspond to a respective module of a transfer plane. Modules corresponding to / including only planar locations and / or planar positions (logical fields) that are not configured / transversed by the (first and / or second plurality of) offline routes may be switched off.

[0083] The control of the drive device may further include operating the laboratory sample distribution system at runtime and selecting an offline route from an optimized set of offline routes when it is determined that a runtime route for the carrier cannot be determined at runtime. The control of the drive device may further include operating the laboratory sample distribution system at runtime and selecting an offline route from a look-up table. In this case, the offline route may function as a fallback route.

[0084] At runtime, the route of the carrier may be selected (primarily) from a set of offline routes / look-up tables. However, one or more (single) routes may be re-optimized during runtime. The re-optimization of one or more (single) routes during runtime may be performed in response to user input, an error obtained for this route, or a change in the corresponding end point. The re-optimization of one or more (single) routes during runtime may depend on the current route, e.g., a (predetermined) set of offline routes, and / or available (un)occupied floor locations (blocked floor locations). One or more (single) routes may be determined during runtime. At runtime, offline and runtime-determined routes may be combined. Similarly, at runtime, a part of the route can be re-optimized. The re-optimization may be performed in response to changes in the system (new laboratory devices, repositioning of laboratory devices, reconstruction of the transfer plane, new workflows, new order profiles, the corresponding route crossing a defective module of the transfer plane, and / or new carriers).

[0085] Similar to the pre - determination of the offline route, the offline route can be determined in a post - determination step during the movement of the carrier. The post - determination may be performed offline separately from the sample distribution system. The post - determination may depend on the parameters provided by the sample distribution system and / or parameters provided by, for example, the user. The post - determination may be performed in response to changes in the system (new laboratory equipment, rearrangement of laboratory equipment, reconstruction of the transfer plane, new workflow, new order profile, and / or new carrier). The offline route determined offline through the post - determination may then be incorporated into the sample distribution system, that is, the offline route determined offline through the pre - determination may be replaced by the offline route determined offline through the post - determination.

[0086] In this method, the pre - determination of the offline route may further include determining a first optimized set of offline routes, assigning first application parameters to the first optimized set of offline routes, determining a second optimized set of offline routes that is different from the first optimized set of offline routes, and assigning second application parameters to the second optimized set of offline routes. Further, controlling the drive device may further include receiving application information indicating the current application parameters and selecting one of the first optimized set of offline routes and the second optimized set of offline routes to control the drive device when it is determined that the current application parameters match the first application parameters or the second application parameters. The first and second application parameters can indicate the first and second dates, periods, traffic situations (e.g., high traffic or low traffic), operating modes, and / or sequences.

[0087] Application parameters may correspond to the KPIs (Key Performance Indicators) of the laboratory automation system. Examples of application parameters may be traffic data, constraints, constraint weightings, etc., or combinations thereof.

[0088] Regarding the laboratory automation system, the plurality of laboratory devices (stations) may comprise one or more laboratory devices selected from a pre - analytical laboratory device, a laboratory device for sample analysis, and a post - analytical laboratory device. The pre - analytical station or device may be adapted to perform any kind of pretreatment of a sample, a sample container, and / or a sample container carrier. The analytical station may be adapted to generate a measurement signal using a sample or a part of the sample and a reagent, and the measurement signal indicates whether an analyte is present and, if so, its concentration. The post - analytical station may be adapted to perform any kind of post - treatment of a sample, a sample container, and / or a sample container carrier.

[0089] Deadlocks can be prevented. A deadlock is a situation where the set of carriers is prevented from making the next move because another carrier already occupies the field and cannot move. When movement between fields (logical fields / plane positions) is possible only in one X - direction and one Y - direction for each field, the minimum deadlock situation involves four carriers. In this case, the four carriers are located in a 2×2 field area. Each field is occupied by one carrier. In each field within the 2×2 field area, clockwise or counter - clockwise movement to the next field within the 2×2 field area is allowed and intended. However, due to the occupation, the possibility of movement of each carrier depends on the possibility of movement of other carriers, so such movement is impossible. Thus, there is a circular dependency of carriers and their next moves. Larger deadlock situations with more carriers may also exist, although caused by the same mechanism.

[0090] When field-to-field (logical field / planar position) movement is possible for each horizontal direction for each field, the minimum deadlock situation involves two carriers. In this case, similar to a 2x2 deadlock, the two carriers are positioned in a 2x1 (1x2) field area. Carriers involved in larger deadlocks can be placed at all transfer locations in a rectangular (n x m) area of the transfer plane. In addition to this situation, a deadlock may be associated with any closed arrangement of planar locations that includes carriers that, due to circular dependencies, cannot move. A deadlock may even involve different planes and planar locations of the ramps / lifts. The embodiments presented herein apply equally.

[0091] From each planar position / logical field, a carrier may generally be able to move in two X-directions and two Y-directions (left / right and up / down). Alternatively, a carrier may be restricted to moving in only one X-direction and one Y-direction from each planar position / logical field. However, the allowed directions of movement need not be the same for each planar position / logical field. For example, in a first planar position / logical field, a carrier may only be able to move diagonally up and to the right, and in a second planar position / logical field, a carrier may only be able to move diagonally down and to the left. The restrictions may be given to prevent a 2x1 (1x2) deadlock.

[0092] For example, to avoid a 2×2 deadlock, a specific pattern for movement between allowed locations on the transfer plane may be generated. At each plane position, the carrier may move only in one X direction and one Y direction. The plane may be rectangular (or may be composed of rectangular components). The plane may be divided into a square area of n×n plane positions (or a rectangular area of n×m plane positions). Each area of n×n plane positions may correspond to one transfer module. The carrier may be enabled to move in a first X direction in the upper part (half) of each square area, and may be enabled to move in a second X direction different from the first X direction in the lower part (half) of each square area of the transfer plane, i.e., the first and second X directions lead in opposite directions. The carrier may be enabled to move in a first Y direction in the left part (half) of each square area, and may be enabled to move in a second Y direction different from the first Y direction in the right part (half) of each square area of the transfer plane, i.e., the first and second Y directions lead in opposite directions. Each square area may include one area of 2×2 plane positions where circular movement is possible. Further, an area of 2×2 plane positions composed of four such square areas may allow circular movement. In these areas of 2×2 plane positions, four allowed movements may define an allowed circular movement (thereby forming a deadlock in each of these areas of 2×2 plane positions). One of the four allowed movements can be removed (to prevent a deadlock situation). Alternatively, two or more, preferably two, of the four allowed movements can be removed. Preferably, one or more of the removed allowed movements are movements that are not expected to be frequently used. In particular, one or more of the removed allowed movements are movements that are perpendicular to the main transfer flow.

[0093] There may be two ways to obtain a route that avoids a 2×2 deadlock. (1) The allowed directions for determining the route are defined according to the above specific pattern and / or (2) when calculating an optimized set of offline routes, rules are added to avoid any subset of the field-to-field movements included in the route set defining a region of 2×2 planar locations where circular movement is allowed.

[0094] For all identified deadlocks, the size (the number of positions that need to be occupied to form the deadlock) may be calculated. Small deadlocks, such as 2×2 deadlocks, may occur more frequently than large deadlocks. Also, larger deadlocks may contain smaller deadlocks. Avoiding small and large deadlocks may involve reducing the solution space for finding an optimized route. It may be preferred to eliminate the opportunity to generate small deadlocks. A deadlock size threshold may be given. The calculation of an optimized set of offline routes may avoid creating routes that can result in deadlocks of a size below the deadlock size threshold.

[0095] Deadlocks can be prevented by moving the rules for the carriers. Each carrier may reserve the corresponding track segment. A first carrier may reserve a first track segment. The first track segment may include one or more subsequent planar positions on the route of the first carrier starting from the current planar position of the carrier. The position of the reserved first track segment may be blocked for other carriers. A flag may be set at the first planar position on the route of the first carrier following the reserved first track segment. The planar position with the flag set may not be blocked for other carriers. A second carrier may not reserve a second track segment that ends at the planar position with the flag set, i.e., the last planar position of the second track segment of the second carrier starting from the current position of the second carrier may not be the planar position with the flag set. A track segment may include a predetermined number, for example, one, two, or more planar positions. Alternatively, different carriers may reserve track segments with different numbers of planar positions.

[0096] The method may further include detecting a break (and / or unavailable) plane location including at least one break plane location, where at least one break plane location is constituted by at least one of the determined offline routes and divides at least one of the determined offline routes into a first sub-route and a second sub-route. In response to determining the break plane location, a region may be determined that includes at least one break plane location, at least one working plane location corresponding to the first sub-route, and at least one working plane location corresponding to the second sub-route. The method may further include calculating an optimized connection route between at least one working plane location corresponding to the first sub-route and at least one working plane location corresponding to the second sub-route, and the calculating includes solving an optimization problem in which the connection route between at least one working plane location corresponding to the first sub-route and at least one working plane location corresponding to the second sub-route is optimized by excluding at least one break plane location. The optimization may consider a provided (existing) set of offline routes. The first sub-route, the connection route, and the second sub-route may replace at least one of the determined offline routes. The method may further include controlling a driving device so that a carrier is moved along a pre-determined offline route on a transfer plane, and at least one of the determined offline routes is replaced by the first sub-route, the connection route, and the second sub-route. The calculation of the connection route can be performed semi-runtime. The system may be stopped when a break (or unavailable) plane location is detected. After the calculation of the connection route, the system can be restarted.

[0097] For each of the determined offline routes, a maximum expected load / maximum expected number of carriers transferred along (on) the route may be assigned. The method may include avoiding exceeding the maximum load / maximum number of carriers in one of the determined offline routes during operation (runtime). For example, the method may include stopping the supply of further carriers to a route that has reached its maximum load / its maximum number. When stopping the supply of further carriers to a route, further carriers (or respective samples / racks) that are to be transported along the route may be temporarily buffered on or near the corresponding laboratory device until the load / number of carriers on the route is sufficiently low, i.e., until the load / number of carriers on the route is below its maximum load / its maximum number of carriers or below a predetermined threshold assigned to the route. Alternatively or additionally, the method may include splitting a route that has reached its maximum load / its maximum number of carriers into two or more different offline routes in order to avoid exceeding the maximum load / maximum number of carriers in one of the determined offline routes.

[0098] Regarding a laboratory sample distribution system and / or a laboratory automation system, the embodiments described above in connection with a method of operating a laboratory sample distribution system may be provided as appropriate. Description of Further Embodiments

[0099] Hereinafter, embodiments will be described as an example with reference to the drawings. The drawings described below show the following.

Brief Description of the Drawings

[0100]

Figure 1

Figure 2

Figure 3A

Figure 3B

Figure 3C

Figure 3D

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Best Mode for Carrying Out the Invention

[0101] FIG. 1 shows a graph display of a laboratory sample distribution system. The laboratory sample distribution system includes a plurality of carriers 4 configured to transport one or more sample containers that house samples to be analyzed by a laboratory apparatus 3, a transfer plane 1 assigned to the laboratory apparatus 3 and providing support for the sample container carriers 4, and a drive device 13 configured to move the plurality of carriers 4 between planar positions 5 provided on the transfer plane 1 in response to a drive control signal.

[0102] The transfer plane 1 may include a plurality of transfer modules 12. The transfer plane 1 may include a plurality of plane position / logic fields 5. In the illustrated case, one plane position / logic field 5 defines one plane location 5'. Each transfer module 12 may be assigned to a respective plane position / logic field 5. Each inspection chamber device 3 may be assigned to one or more specific plane position / logic fields 5. FIG. 1 also shows a directed graph model 8 of the transfer plane 1. The directed graph model 8 includes a plurality of nodes 9 and a plurality of arcs 10 connecting the nodes 9. Each of the nodes 9 of the directed graph model 8 may correspond to a respective plane location 5'. Alternatively, with respect to a subset of the nodes 9, each node 9 may correspond to a respective plane location 5'. Each of the arcs 9 of the directed graph model 8 may correspond to a movement between two respective plane locations 5'. Alternatively, with respect to a subset of the arcs 9, each arc 9 may correspond to a movement between two respective plane locations 5'.

[0103] A specific laboratory device 3 can correspond to a specific planar position 5 / planar location 5'. One of these planar locations 5' can be a transfer location 16. Each laboratory device 3 may be assigned to one or two transfer locations 16. A carrier 4 located on a first transfer location 16 assigned to a specific laboratory device can be transferred from this transfer location 16 to the specific device. Alternatively, when a carrier 4 with a sample is located at the first transfer location 16 assigned to a specific laboratory device 3, the sample can be transferred from this transfer location 16 to the specific device 3. A carrier 4 located within a specific device 3 can be transferred to a second transfer location 16 assigned to the specific laboratory device 3. Alternatively, when a carrier 4 without a sample is located at the second transfer location 16 assigned to a specific laboratory device 3, the sample can be transferred from the specific device 3 to the carrier 4 on the second transfer location 16. The first and second transfer locations 16 may be assigned to the same specific laboratory device 3. The first and second transfer locations 16 can correspond to the input and output (input planar location / position and output planar location / position) of the laboratory device 3. The first and second transfer locations 16 can correspond to the same or different planar locations 5' / planar positions 5 assigned to a specific laboratory device 3. Note that only one carrier 4 can be provided at one planar position 5.

[0104] The carrier 4 may be moved between different planar locations 5' via the transfer plane 1. In particular, the carrier 4 may be moved between a plurality of pairs 11 of planar locations. The movement may correspond to a route 6 in the carrier 4. The first planar location 5' of this route may be the starting planar location, and the last planar location 5' of this route may be the destination planar location, and vice versa. The plurality of starting planar locations and the plurality of destination planar locations may correspond to the end point location and / or a plurality of pairs 11 of planar locations. For each pair 11 of planar locations, a different route 6 may be provided. Each pair 11 of planar locations may include two planar locations 5, for example a starting planar location 11' and a destination planar location 11", i.e., two end point locations. First frequent end point location data indicating a first selection of the planar location 14 that most frequently provides the end point of the running route 6 in the carrier 4 may be determined. This first selection of the planar location 14 may correspond to a first set of pairs 11 of planar locations. The first set of pairs 11 of planar locations may correspond to the transfer location 16, particularly the transfer location 16 that the carrier frequently visits. Second frequent end point location data indicating a second selection of the planar location 15 that provides the end point of the running route 6 in the carrier 4 less frequently may be determined. This second selection of the planar location 15 may correspond to a second set of pairs 11 of planar locations. The second selection 15 may not correspond to the transfer location 16. The second set of pairs 11 of planar locations may not correspond to the transfer location 16. The second set of pairs may include pairs 11 that do not correspond to the first selection 14 and / or pairs 11 in which one location of each pair 11 corresponds to the first selection 14 and the other location of each pair 11 corresponds to the second selection 15. The second set of pairs 11 of planar locations may correspond to the transfer location 16 that the carrier 4 does not visit very frequently. The alternating arrangement of the nodes 9 and the arcs 10 can form the route 6, with the first and last elements being the nodes 9, and these nodes 9 corresponding to the pairs 11 of planar locations.

[0105] FIG. 1 shows four endpoints. The four endpoints may correspond to the selection of the planar locations 14, 15. There are possible connection routes exceeding 12 between these four endpoints. However, FIG. 1 only shows two routes for simplicity. Each endpoint can form a pair 11 of planar locations with any of the other endpoints. Thus, these four endpoints can define 12 different pairs 11 (start point / end point pairs) of planar locations. Generally, m endpoints can define 2·b(m,2)=m·(m - 1) different pairs 11 of planar locations (where b() is the binomial coefficient). However, according to FIG. 1, two first endpoints 11', 11" correspond to the first selection of the planar location 14, and two other endpoints (second endpoints) correspond to the second selection of the planar location 15. The first endpoint may define the first pair of planar locations. The second endpoint may define the second pair of planar locations. The first traffic corresponding to the first pair of planar locations may be higher than the second traffic corresponding to the second pair of planar locations. Thus, the number of carriers 4 traveling between the first pair of planar locations may be more than the number of carriers 4 traveling between the second pair of planar locations, for example, within a given time interval.

[0106] Figure 2 shows a graphical representation of a flowchart of a method for operating an inspection laboratory sample distribution system. The method according to Figure 2 includes, before moving the carrier 4 on the transfer plane 1, pre-determining (20) an offline route on the transfer plane by one or more processors of the data processing device, for example according to the transfer location. Pre-determining 20 includes determining (21) a model representing a plurality of location-to-location movements between the transfer plane having a plurality of plane locations and the plurality of plane locations associated with the plurality of carriers. Further, pre-determining 20 includes calculating an optimized set of offline routes between a plurality of pairs of plane locations from the plurality of plane locations using the model, and the calculating includes solving an optimization problem in which a plurality of routes between a plurality of pairs of plane locations are optimized simultaneously with each other. Pre-determining 20 further includes providing (23) the optimized set of offline routes as an offline route on the transfer plane. The method further includes controlling (30) the drive device so that a plurality of carriers 4 are moved along the pre-determined offline route 6 on the transfer plane 1.

[0107] The plurality of pairs 11 of plane locations may comprise a first and a second set of the plurality of pairs 11 of plane locations. Pre-determining 20 can include two executions of the first and second pre-determining. In the first pre-determining, a plurality of routes 6 for the first set of the plurality of pairs 11 of plane locations may be determined, and in the second pre-determining, a plurality of routes 6 for the second set of the plurality of pairs 11 of plane locations may be determined. Alternatively, calculating (22) the optimized set of offline routes can include two executions, an execution in the first set of the plurality of pairs 11 of plane locations and an execution in the second set. In either case, the first execution may be prioritized. The first execution can be performed before the second execution. During the second execution, the routes determined via the first execution may be fixed. In Figure 1, the first route 6' may be calculated via the first execution, and the second route 6'' may be calculated via the second execution.

[0108] For example, calculating an optimized set of offline routes (22) can include a first execution to determine a first route 6, and independently thereof, calculating an optimized set of offline routes (22) can include a second execution to determine a second route 6. Thereafter, the first and second routes 6 can be re-optimized according to both the first and second routes 6 and / or according to all of a plurality of pairs 11 of planar locations. This subsequent step can correspond to determining an optimized set of offline routes from a plurality of routes (23).

[0109] FIG. 3A shows a graphical representation of an example of a minimum deadlock when movement between fields (logical fields / plane positions) is possible only in one X direction and one Y direction for each respective field. In this case, four plane positions 5 in the area of the 2×2 plane position 5 are occupied by four carriers 4. Thus, each of the four plane positions 5 is occupied by one carrier 4. For each respective plane position 5, a counterclockwise movement 31 to another plane position in the area of the 2×2 plane position 5 is allowed with respect to the 2×2 plane position 5 area. Also, for each of the four carriers 4, such movement is intended. However, since each of the four plane positions 5 is occupied, none of the four carriers 4 can reserve the next plane position 5 and / or execute the intended movement. This results in a deadlock. Similarly, although not shown, a deadlock can be constituted by more than four carriers 4 and plane positions 5. In each such case, the deadlock carriers 4 can form a closed circle. An adjacency matrix corresponding to the blocked carriers and the arcs corresponding to the next movement 4 may be given. By calculating the (finite) power of the adjacency matrix, it can be determined whether the blocked carriers 4 form a circle, i.e., whether the blocked carriers form a deadlock.

[0110] Figure 3B shows a graphical representation of an example of an allowable movement 32 of the carrier 4 at a given planar position 5. At a given planar position 5, the carrier 4 may be allowed to move only in one X direction and one Y direction. Figure 3B shows an embodiment in which the carrier 4 is allowed to move leftward in the X direction and upward in the Y direction. On the other hand, Figure 3C further shows a non-allowable movement 33 of the carrier at a given planar position 5.

[0111] Figure 3D shows a graphical representation of a specific pattern in the allowable movement 32 between locations on the transfer plane 1. In Figure 3D, the transfer plane 1 is divided into a square region of 6×6 planar positions 35. Each region of the 6×6 planar positions 35 corresponds to one transfer module 12. In the upper half of each square region 35, the carrier 4 is allowed to move leftward in the X direction, and in the lower half of each square region 35, the carrier 4 is allowed to move rightward (only) in the X direction. In the left half of each square region 35, the carrier 4 is allowed to move downward in the Y direction, and in the right half of each square region 35, the carrier 4 is allowed to move upward (only) in the Y direction. In this pattern, each square region 35 includes one region of 2×2 planar positions 34 at the center that can result in a deadlock. In this region of 2×2 planar positions 34, clockwise carrier movement is allowed. Further, the pattern includes additional such regions of 2×2 planar positions 34, and each planar position 5 of each additional such region of 2×2 planar positions 34 corresponds to a corner position 5 of the square region 35. In Figure 3D, each such region of 2×2 planar positions 34 (which potentially forms a deadlock) is marked with an exclamation point. The four planar positions included in this 2×2 planar position are derived from the four adjacent corners of the four regions. There is one fully displayed 6×6 region and eight parts of the 6×6 region (only partially shown in Figure 3D).

[0112] FIG. 4 shows a graphical representation of a reserved track segment 42 and subsequent flagged planar positions 43 in a first carrier 4'. FIG. 4 shows a method for preventing deadlocks. The first carrier 4' reserves a track segment 42 at planar position 5 along its route 41. The first planar position 5 of segment 42 corresponds to the planar position 5 where the first carrier 4' will next enter, starting from the current planar position 5. Starting from the first planar position 5, the track segment 42 includes one or more directly adjacent further planar positions 5 up to the last planar position 5 of the track segment 42. In FIG. 4, the track segment 42 includes two planar positions 5, i.e., the first and the last. The planar positions 5 of the track segment 42 of the first carrier 4' may be reserved for the first carrier 4', i.e., these planar positions 5 may be blocked for carriers 4 other than the first carrier 4'. The planar position 5 on the route 41 of the first carrier 4' that follows immediately after the last field of segment 42 of the first carrier 4' can be flagged with respect to the first carrier 4'. The flagged planar positions 43 of the first carrier 4' may not be reserved by other carriers 4, i.e., the flagged planar positions 43 may not be included by segments 42 of other carriers 4. Thus, in FIG. 4, in particular the fourth carrier 4'' is not allowed to reserve the flagged planar positions 43 and thus is not allowed to reserve the next two planar positions to the right of its current position.

[0113] Figure 5 shows a graph display of the split route 51. If the traffic of the determined route 51 has high traffic, for example, if the traffic exceeds a threshold, the route 51 may be split into several sub-routes 52, 53. In the case of Figure 5, the route 51 with high traffic is split into two sub-routes 52, 53 (in order to reduce the congestion potential at the intersection with other routes). The sub-routes 52, 53 include overlapping sections, separation / merging sections, and parallel sections. The traffic of the route 51 with high traffic may be divided such that 50% goes to the upper route 52 and 50% goes to the lower route 53. Alternatively, the traffic of the route 51 with high traffic may be distributed to the sub-routes 52, 53 with different weightings. For example, the shortest of the sub-routes 52, 53 of the split route 51 may be given the highest proportion of the traffic. To avoid collisions at the intersection 54 of the routes, the carrier 4 assigned to the split route 51 may alternatively follow one of the sub-routes 52, 53.

[0114] Figures 6 to 8 show three situations that can occur when solving the optimization problem. The three situations refer to three possibilities of providing two routes connecting two pairs of planar locations. According to the first situation of Figure 6, the first route 61 connects the first pair of planar locations, and the second route 62 connects the second pair of planar locations. According to the second situation of Figure 7, an additional first route 71 connects the first pair of planar locations, and an additional second route 72 connects the second pair of planar locations. According to the third situation of Figure 8, a further first route 81 connects the first pair of planar locations, and a further second route 82 connects the second pair of planar locations.

[0115] The first routes 61, 71, 81 may be capable of handling higher traffic than the second routes 62, 72, 82. The penalty points for each route may be proportional to the traffic intensity of each route. According to this example, during the process of solving the optimization problem, the following penalty strategies (constraints) may be applied. For the case of a route with low traffic intensity, the following are applicable. (i) Route length: 5 penalty points per field (plane location or plane position) used in the route; (ii) Route confluence: 50 penalty points for routes that merge with each other; (iii) Curve of the route: 2 penalty points for each curve in the route. For the case of a route with high traffic intensity, the following are applicable. (i) Route length: 10 penalty points per field (plane location or plane position) used in the route; (ii) Route confluence: 100 penalty points for routes that merge with each other; (iii) Curve of the route: 4 penalty points for each curve in the route. In the first situation, the route with higher traffic has 10 fields, 0 intersections, and 2 curves. The route with lower traffic has 8 fields, 0 intersections, and 5 curves. Therefore, according to the first situation, the route with higher traffic is associated with 108 penalty points, and the route with lower traffic is associated with 50 penalty points. Therefore, the first situation is associated with 158 penalty points. In the second situation, the route with higher traffic has 10 fields, 2 intersections, and 2 curves. The route with lower traffic has 8 fields, 2 intersections, and 1 curve. Therefore, according to the second situation, the route with higher traffic is associated with 308 penalty points, and the route with lower traffic is associated with 142 penalty points. Therefore, the second situation is associated with 450 penalty points. The second situation is associated with more penalty points than the first situation. In the third situation, the route with higher traffic has 10 fields, 0 intersections, and 1 curve.The route with lower traffic has 8 fields, 0 intersections, and 1 curve. Thus, according to the third situation, the route with higher traffic is associated with 104 penalty points, and the route with more traffic is associated with 42 penalty points. Thus, the third situation is associated with 148 penalty points. Thus, when solving the optimization problem, the first situation is more preferable than the second situation, and the third situation is more preferable than both the first and second situations.

Claims

1. A method of operating a laboratory sample distribution system, the laboratory sample distribution system comprising: a plurality of carriers (4) configured to transport one or more sample containers containing samples to be analyzed by a laboratory device (3); a transfer plane (1) assigned to the laboratory device (3) for supporting the plurality of carriers (4); a drive device (13) configured to move the plurality of carriers (4) between planar positions (5) provided on the transfer plane (1) in response to drive control signals; comprising; the method comprising: before moving the carrier (4) on the transfer plane (1), one or more processors of a data processing device pre-determine a plurality of offline routes (6) on the transfer plane (1), the pre-determining comprising: determining a model representing a plurality of inter-location movements between the transfer plane (1) having a plurality of planar locations (5') and a plurality of planar locations (5') associated with the plurality of carriers (4); using the model to calculate an optimized set of offline routes between a plurality of pairs of planar locations from the plurality of planar locations (5'), the calculating comprising solving an optimization problem in which a plurality of routes between the plurality of pairs of planar locations are optimized simultaneously; providing the optimized set of offline routes as offline routes (6) on the transfer plane (1); including, pre-determining; controlling the drive device (13) such that the carrier (4) is moved along the offline route (6) on the pre-determined transfer plane (1); A method including.

2. The model is a directed graph model (8) of the transfer plane (1), a plurality of nodes (9) of the directed graph model (8) are assigned to a plurality of planar locations (5'), and a plurality of arcs (10) connecting the nodes (9) of the directed graph model (8) are assigned to a plurality of inter-location movements between two planar locations (5'), the method according to claim 1.

3. The optimization problem is: a multi-commodity flow problem, particularly a multi-commodity flow problem in a directed graph, a shortest path problem, and a minimum flow problem, is one of the methods according to claim 1 or 2.

4. The method according to any one of claims 1 to 3, wherein the optimization problem is solved by applying an MIP solver.

5. In the data processing apparatus, providing first frequent end-point location data indicating a first selection (14) of a planar location that most frequently provides an end point of a route of movement for the carrier (4); determining the directed graph model (8) of the transfer plane (1), wherein a first node (9) of the directed graph model (8) is assigned to the planar location (5') from the first selection (14) of the planar location, and a first arc (10) starting and / or ending at the first node (9) of the directed graph model (8) is assigned to an inter-location movement from and / or to a planar location (5') from the first selection (14) of the planar location, determining the directed graph model (8); The method according to claim 2, further comprising:

6. In the data processing apparatus, providing second frequent end-point location data indicating a second selection (15) of a planar location where the frequency of providing an end point of a movement route (6) of the carrier (4) is lower, the second selection (15) of the planar location being different from the first selection (14) of the planar location; determining the directed graph model (8) of the transfer plane (1), wherein a second node (9) of the directed graph model (8) is assigned to the planar location (5') from the second selection (15) of the planar location, and a second arc (10) starting and / or ending at the second node (9) of the directed graph model (8) is assigned to an inter-location movement from and / or to a planar location (5') from the second selection (15) of the planar location, determining the directed graph model (8); The method according to claim 2 or 5, further comprising:

7. In the data processing apparatus, providing traffic data indicating a predicted number of carriers (4) moving between a plurality of pairs (11) of the planar locations within a time interval; calculating an optimized set of the offline routes between the plurality of pairs of the planar locations from the plurality of planar locations (5') according to the predicted number of carriers (4) moving between the plurality of pairs (11) of the planar locations within the time interval; The method according to any one of claims 1 to 6, further comprising

8. Said providing the traffic data comprises providing traffic data determined from a sample order list, providing traffic data determined from historical data indicative of the historical operation of a laboratory sample distribution system, providing traffic data determined from workflow data indicative of the workflow in said one or more sample containers to be transported by said carrier (4), providing traffic data determined from the current and / or most recently measured number of carriers (4) transferred, providing traffic data determined from a simulation, The method according to claim 7, further comprising at least one of

9. Said controlling the drive device (13) comprises in the drive device (13), receiving a reservation request from a carrier (4) moving on a selected offline route (6) from said plurality of pre-determined offline routes (6) and located at the current route location along said selected offline route (6), said reservation request indicating a request to reserve the next route location along said selected offline route (6), verifying whether the next route location is available for movement by said drive device (13), when it is confirmed by said drive device (13) that the next route location is available for movement, moving said carrier (4) from said current route location to said next route location along said selected offline route (6), The method according to any one of claims 1 to 8, further comprising

10. Said calculating, by solving said optimization problem, an optimized set of offline routes between a plurality of pairs of planar locations from said plurality of planar locations (5') comprises the following group minimizing the route length of each of said plurality of offline routes, minimizing the weighted route length of each of said plurality of offline routes, minimizing the number of route curves in each of said plurality of offline routes, minimizing the number of offline routes that merge and / or intersect with another offline route, uniformly distributing carrier traffic for each planar location (5'), Restricting movement between locations to only movement between adjacent planar locations (5') between two planar locations, Excluding planar locations (5') reserved for carrier waiting, Evenly distributing predicted wear of planar locations across the planar locations (5') of the transfer plane (1), Minimizing the energy consumption of the inspection room sample distribution system, and Minimizing / avoiding an area of a 2×2 planar position having four intersections, The method according to any one of claims 1 to 9, further comprising applying at least one constraint selected from the above.

11. Pre-determining the offline route (20) is performed in the data processing device by Receiving first route traffic information indicating high carrier traffic for a first offline route (51), Dividing the first offline route (51) into two or more different offline routes (52, 53), The method according to any one of claims 1 to 10, further comprising the above.

12. Pre-determining the offline route (20) is performed in the data processing device by Receiving second route traffic information indicating high carrier traffic for a second offline route, Preventing the second offline route from being route-adjusted while determining the plurality of offline routes and / or determining an optimized set of offline routes, The method according to any one of claims 1 to 11, further comprising the above.

13. Calculating an optimized set of the offline routes between a plurality of pairs of planar locations from the plurality of planar locations (5') using the model is performed in the data processing device by Receiving first carrier traffic information indicating a first carrier traffic scenario for the plurality of offline routes (6), Determining a first plurality of offline routes (6) between the plurality of pairs of planar locations (11) from the plurality of planar locations (5'), Receiving second carrier traffic information indicating a second carrier traffic scenario for the plurality of offline routes (6), wherein the second carrier traffic scenario is different from the first carrier traffic scenario. Determining a second plurality of offline routes (6) between the plurality of pairs of the planar locations (11) from the plurality of planar locations (5'); The method according to any one of claims 1 to 12, further comprising.

14. Controlling the drive device (13) comprises: Operating the laboratory sample distribution system at runtime; Selecting one offline route (6) from an optimized set of the offline routes (6) when it is determined that a runtime route cannot be determined for the carrier (4) at runtime; The method according to any one of claims 1 to 13, further comprising.

15. Determining the plurality of offline routes in advance comprises: Determining a first optimized set of offline routes (6); Assigning first application parameters to the first optimized set (6) of the offline routes; Determining a second optimized set of offline routes (6) that is different from the first optimized set of the offline routes (6); Assigning second application parameters to the second optimized set of the offline routes (6); Further comprising, Controlling the drive device comprises: Receiving application information indicating current application parameters; When it is determined that the current application parameters match the first application parameters or the second application parameters, selecting one of the first optimized set of the offline routes and the second optimized set of the offline routes to control the drive device (13); The method according to any one of claims 1 to 14, further comprising.

16. A laboratory sample distribution system, comprising: A plurality of carriers (4) configured to transport one or more sample containers containing samples to be analyzed by a laboratory device (3); A transfer plane (1) assigned to the laboratory device (3) and supporting the plurality of carriers (4); A drive device (13) configured to move the plurality of carriers (4) between planar positions (5) provided on the transfer plane (1) in response to a drive control signal; Comprising, The laboratory sample distribution system is Before moving the carrier on the transfer plane, one or more processors of the data processing device pre-determine a plurality of offline routes (6) on the transfer plane (1), and the pre-determining is determining a model representing a plurality of inter-location movements between the transfer plane (1) having a plurality of plane locations (5') and the plurality of plane locations (5') associated with the plurality of carriers (4), using the model to calculate an optimized set of offline routes between a plurality of pairs of plane locations from the plurality of plane locations (5'), and the calculating includes solving an optimization problem in which a plurality of routes between the plurality of pairs of plane locations are optimized simultaneously, and providing the optimized set of offline routes as the offline routes (6) on the transfer plane (1) including in the pre-determining, controlling the drive device (13) such that the carrier (4) is moved along the offline route (6) on the pre-determined transfer plane (1), and A laboratory sample distribution system configured to perform.

17. A laboratory automation system comprising the laboratory sample distribution system according to claim 16 and a plurality of laboratory devices (3).

18. The laboratory automation system according to claim 17, wherein the plurality of laboratory devices (3) comprise one or more laboratory devices selected from a pre-analysis laboratory device, a laboratory device for sample analysis, and a post-analysis laboratory device.

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