Storage and order picking system with optimized material flow

Abstract

The invention discloses an internal logistics system (10) comprising: a transport network (14) comprising a plurality of transport units (15; a plurality of transport devices (15) arranged in the internal logistics system (10) and configured to execute a flow of material caused by a transport order (22) in the internal logistics system (10), each transport device (15) being operated with at least one preset variable operating parameter (36); a plurality of sensors (28) which cyclically detect a current operating state (34); and a controller (26) including a material flow computer (30) that initially plans and generates the transport orders (22) and cyclically coordinates execution of the transport orders (22) according to the current operating state (34); wherein the controller (26) further comprises a digital material flow twinning (32), which comprises a material flow simulation model (40), an operating parameter optimization device (42) and an analysis device (44) and is configured to cyclically simulate the material flow, depending on the respective current operating state (34), with and without changing the operating parameters (36) of the transport means (15), the simulated material flow is analyzed in order to improve the throughput, and in the event of an analysis of the throughput improvement, the relevant varied operating parameters (36) are transmitted to the relevant transport means (15), and then the relevant transport means (15) are operated on the basis of the varied operating parameters (36). Further, a method for performing a flow of material is disclosed.

Description

[0001] The present disclosure relates generally to the field of intralogistics, and in particular to the optimized control of material flows in intralogistics systems (e.g., storage and / or order picking systems). By cyclically varying the operating parameters of transport devices, material flows are coordinated quasi-continuously in a throughput-optimized manner, while initially generated transport orders remain unchanged and are continuously executed.

[0002] In general, the term "material flow" (MF) refers to a series of storage, transport, and processing operations. According to VDI 2689, this term is understood to encompass all processes involved in the extraction, processing, and distribution of MF objects (e.g., handling units of storage units, workpieces, conveyor items, etc.) within defined functional areas, and their interconnection. These areas can include individual stations between (goods) receipt (which can also be handled by production facilities) and (goods) shipment. MF encompasses all forms of MF objects that pass through the MF system, which corresponds to the transport network in the area between the entry and exit points.

[0003] Figure 8 schematically illustrates the typical material flow from the inlet (receiving) to the outlet (delivering) of internal logistics system 10, represented by arrows. The various areas and stations (receiving, production, storage, picking, assembly, and / or packaging stations) are interconnected via conveyor systems. Existing conveyor connections are indicated by solid arrows. Further alternative and / or additional connections are indicated by dashed arrows. The totality of all these connections defines the transport network. It will be appreciated that the areas and stations can be connected to each other in a variety of ways, including the use of conveyors. This is determined by the system designer as needed.

[0004] The MF processes are planned (in advance) in a conventional material flow computer (MFC) according to the relevant transport orders, and their execution is coordinated and monitored (in real time). The MFC plans and coordinates the relevant source-destination relationships. Material flow control due to the MFC is often also described as the management of (picking) orders assigned to the transport network. However, this general definition of material flow control is not adequate for the actual tasks if there is an evolving, heterogeneous structure of efficient transport units in the intralogistics system, which require precise coordination for optimal operating results. Material flow control has a central function. Despite the presence of multiple different transport units and expansion stages, it should always ensure, for example, the maximum throughput, the fastest supply and / or the lowest energy consumption.

[0005] The primary task of material flow control is to perfectly coordinate transport orders for the connected areas and stations. Available transport units must be assigned to transport orders and, if possible, used without blocking the transport network. At any point in time, the operational status of the network (e.g., utilization) as well as the occupancy status of routes, waypoints, intersections, junctions, and transport units must be taken into account. The number of MF objects and their transport speeds interact with each other.

[0006] However, traditional MFC can only solve the random MF problems that occur in reality to a limited extent. When the transportation order is initially generated, MFC cannot predict these actual problems and can only react to the news of the problem that has already occurred.

[0007] For example, conventional MFCs cannot predict the real and random slippage of conveyed items (e.g., on a belt conveyor) during their transport, and the resulting throughput degradation or other technical problems. Slippage results in a delay in arrival at the destination, which was set in advance by the MFC. This delayed arrival may cause the destination to be occupied by another conveyed item arriving at the same time, or result in the pre-planned sequencing not occurring. In this case, conventional MFCs can only situationally resolve the delivery problem of the conveyed item by instructing the delivery belt conveyor, for example, to wait for delivery at a time different from the original plan.

[0008] Furthermore, driverless transport vehicles (DTVs) can serve as another example. For example, a conventional MFC determines a (collision-free) transport or driving order for two DTVs in advance. However, during the actual execution, the two DTVs unexpectedly meet, creating a risk of collision (e.g., due to a delayed departure, a possible stoppage due to a too-late authorization by the DTV's sensors, or a slowdown in driving due to glare or other external influences on the camera technology monitoring the environment). The two DTVs "see" each other via their integrated distance sensors and stop for safety reasons. The MFC learns of both stopping conditions and decides which DTV can drive first, for example, depending on the transport priority associated with the transported items being transported. During the initial planning of its transport order, the MFC cannot foresee that, for example, the engine of one of the two DTVs is about to reach the end of its service life, and therefore the DTV in question can only travel, for example, at 80% of its rated speed. In this case, conventional MFCs can only react to problems as they arise, and in the worst case, there can even be (unexpected) system downtime, which can only be resolved by manual external intervention by maintenance technicians. This significantly reduces throughput.

[0009] Traditional MFC should be distinguished from traditional MF simulation, which is used in the early (project planning) stage of internal logistics system design and is developed based on historical data without any connection to the real system.

[0010] Generally speaking, every simulation is based on a (static) simulation model, in which a set of parameters is fed into the model, and the results are calculated or simulated over time. According to VDI 3633, the term "simulation" refers to the reproduction of a system and its dynamic processes in an experimental model in order to gain insights that can be transferred to reality. In particular, these processes evolve over time. In a broad sense, simulation is understood to mean the preparation, execution, and evaluation of a target experiment using a simulation model. However, every model has its limitations (e.g., limited resources, energy, time, and funding). Consequently, the least influential factors in the model are often not accurately accounted for, often representing only a crude simplification of reality. These simplifications negatively impact the accuracy of the simulation results. Intralogistics areas and transport networks are combined and simulated in a highly simplified manner. The MF simulation is fed with real order data from the past to simulate the movement of MF objects through the corresponding system variants as realistically as possible (offline). Simultaneously, the capacity, performance, and control of the corresponding system variants are examined for (virtual) optimization potential. The acquired knowledge is then incorporated into the selected system variant, which is then implemented.

[0011] One of the main tasks of (computer-implemented) MF simulations is to test various (layout) variations of a planned MF system and a suitable basic strategy in advance, without having to implement every one of them. For example, it is possible to test whether, at a specific location in the MF or transport network, using a single A-type transport (system variant 1) or two parallel B-type transports (system variant 2) is more optimal with respect to the required throughput.

[0012] Therefore, during the (early) planning phase of an intralogistics system, traditional MF simulations are often used to analyze and compare the performance of system variants. The optimal variant can then be selected and implemented. In some cases, a simulation model created in advance is used continuously. During plan verification (i.e., to check whether the orders can be carried out in the planned manner and sequence), the data from the system is copied only once and fed into the simulation model. The simulation model then continuously calculates feasibility based on the assumptions and abstract concepts made. However, continuous "monitoring" of the simulation based on the actual process of order execution is not included.

[0013] The above-mentioned timely MF problems (collision, crowding, wear, sliding, etc.) appear randomly and therefore cannot be solved by MFC or MF simulation.

[0014] As the planning and coordination of MF becomes more complex for conventional material flow computers, additional considerations need to be taken into account: sequencing; sorting of large items; decentralized control methods as opposed to conventional central material flow computers; wear effects; and / or error recognition of material flow objects at decision points.

[0015] DE 10 2020 202 945 A1 relates to a storage system and a method for operating such a storage system.

[0016] DE 103 05 344 A1 relates to a system and a method for controlling orders of a production plant.

[0017] Therefore, the present disclosure aims to provide an efficient, continuous, and flexible material flow within an intralogistics system. Efficient material flow is throughput-optimized, where throughput can refer, for example, to the number of transport orders completed per unit time and / or the execution time of transport orders. In a continuous material flow, material flow objects move continuously without unexpected stops, waiting times, blockages, or congestion. Flexible material flow allows for flexible responses to randomly occurring problems.

[0018] This object is achieved by an internal logistics system, which comprises: a transport network, which includes a plurality of transport units and is configured to execute material flows caused by transport orders in the internal logistics system, wherein each transport device operates with at least one preset variable operating parameter (which can preferably be stored in the transport device and is variable); a plurality of sensors, which cyclically detect the current operating status (preferably of the transport unit and / or the material flow); and a controller, which includes a material flow computer, which initially plans and generates (preferably once) transport orders and cyclically coordinates the execution of transport orders based on the current operating status (preferably through the material flow computer); the controller further includes a digital material flow twin, which includes a material flow simulation model, an operating parameter optimization device and an analysis device and is configured to: cyclically simulate material flows with both unchanged and changed operating parameters of the transport devices based on the corresponding current operating status; analyze the simulated material flow to improve throughput; and, in case of analysis of throughput improvement, transmit the relevant changed operating parameters to the relevant transport devices, and then operate the relevant transport devices based on the changed operating parameters.

[0019] Contrary to traditional approaches, unexpected material flow problems aren't solved only when they actually occur. Nor can they be solved with a set of predefined rules. By simulating material flows, these problems can be predicted at an early stage, without requiring a precise definition of the problem itself. Simply identifying a throughput degradation is sufficient to initiate an active process. The solution lies in varying the operating parameters of the transport equipment. Typically, during a simulation and optimization cycle for a large number of different parameter settings, random simulations of the material flow are performed based on the "actual" conditions continuously observed in the warehouse. The relevant simulation results, including simulations without any parameter modifications, are then compared. A relevant simulated material flow is considered optimal if it results in a higher throughput than a simulated material flow without parameter modifications, or if, even after reaching the predefined throughput value, capacity buffers remain to handle possible unexpected events with minimal impact (resilience). Simulations are preferably run until all transport orders are completed, meaning all handling units have been moved from their origin to their destination.

[0020] In other words, the controller of the present disclosure can intuitively identify and resolve material flow problems without predefining the problem and / or solution. This is a process of continuous improvement.

[0021] The material flow of the entire system is analyzed and optimized in a recurring (i.e. cyclic) manner based on the current operating status. Compared to static (initial) material flow planning, this optimization is dynamic.

[0022] Depending on the available computing power, the simulation and optimization cycles can be very short and the number of selectable operating parameters that can be varied can be very large. Ideally, the present disclosure will no longer cause any material flow problems (congestion, delays, collisions, etc.).

[0023] The resulting material flow is at least one of efficient, continuous, energy-efficient and flexible.

[0024] Preferably, the material flow computer is configured to: initially plan, generate transport orders based on picking orders, transport requirements and / or stock transfer orders, which can (also) be input by a warehouse management system, and transmit them to the relevant transport devices; preferably, in the event of material flow problems, continuously coordinate the material flow according to the current operating status by executing problem solutions based on determined predefined solution rules; and receive operating status from sensors.

[0025] Therefore, the material flow computer used here differs from conventional material flow computers. Therefore, the present disclosure can also be applied to existing systems already equipped with conventional material flow computers. However, in this sense, the present disclosure is an expansion of existing intralogistics systems. To achieve this, it is sufficient to expand the existing system with the relevant control software. Typically, this software expansion is accompanied by a parallel expansion of the hardware (computer). This allows the existing system to respond dynamically to material flow issues.

[0026] In particular, the transport network comprises a plurality of transport sources and a plurality of transport destinations connected to one another via a plurality of transport paths, wherein each transport order defines a processing unit-specific transport path from one of the sources to one of the destinations. Preferably, the transport order does not contain operational parameters.

[0027] Therefore, in traditional material flow planning, operating parameters are usually not variable values. Traditional planning is based on fixed preset parameter values. However, these parameter values ​​may contain tolerance ranges, which are based on empirical values.

[0028] Preferably, the throughput improvement results in a greater number of completed transport orders per unit of time compared to a material flow simulated according to the respective current operating state and without changing the operating parameters.

[0029] Even if the actual throughput achieved is lower than originally planned, an improvement in throughput has actually occurred. But the actual throughput is still better than the throughput that would have been achieved by simply using fixed solution tools to deal with the (unexpected) material flow problem.

[0030] Furthermore, it is advantageous if the relevant changed operating parameters are communicated to the relevant transport means so that the transport order remains unchanged.

[0031] Therefore, the present disclosure does not involve re-planning the material flow, but rather repeatedly changing the originally planned material flow to a clearly identifiable, better throughput through small, sometimes barely noticeable changes. Conventional material flow computers are completely unaware that the material flow is constantly being positively influenced from the outside by changes in the operating parameters of the transport device, that is, by the digital twin of the material flow.

[0032] Preferably, at least some (preferably each) of the transport devices respectively comprise at least one of the sensors.

[0033] In order for the feedback loop to work with the current operating status, the transport unit must report its operating status back to the higher-level controller frequently enough. The transport unit then implements the material flow accordingly and can be influenced by its operating parameters. Therefore, it is advantageous if the relevant sensors are directly integrated into the transport unit.

[0034] Of course, sensors can also be used to detect the operating state of the material flow and / or the transport device from the outside or indirectly. However, in this case, it is more complicated to assign the detected state change to the transport device.

[0035] In particular, the transport unit comprises a discontinuous conveyor, such as an unmanned guided vehicle, an SRD, a converter, an elevator, a transfer device, etc., and / or a continuous conveyor, such as a roller conveyor, a belt conveyor, a chain conveyor, and / or an overhead conveyor.

[0036] Thus, the present disclosure is applicable to both types of conventional conveying devices. The present disclosure is applicable to any conceivable material flow problem. Thus, the present disclosure is also applicable to any existing system, which typically consists of discontinuous and / or continuous conveyors to perform material flow.

[0037] Preferably, the intralogistics system comprises a storage and order picking system, which further comprises at least one of the following functional areas: warehouse; goods receiving; goods shipping; workstations; and / or production.

[0038] The present disclosure is also applicable to all common intralogistics applications. It can be used in intralogistics areas for production as well as in traditional picking environments (e.g. distribution centers).

[0039] In addition, the above-mentioned purpose is solved by a method for improving the execution of an initially planned material flow in an internal logistics system, wherein the internal logistics system includes: a transport network, which includes a plurality of transport units and is configured to execute a material flow caused by a transport order in the internal logistics system, wherein each transport device operates with at least one preset variable operating parameter, which is preferably storable in the transport device; a plurality of sensors; and a controller, which includes a material flow computer; wherein the method includes the following steps: cyclically detecting the current operating status by means of sensors; and initially planning and generating transport orders by means of the controller and cyclically coordinating the generated transport orders; wherein the controller further includes a digital material flow twin, which includes a material flow simulation model, an operating parameter optimization device and an analysis device, and implements the following cyclic steps: simulating the material flow with unchanged operating parameters and multiple changed operating parameters of the transport device according to the corresponding current operating status; analyzing the simulated material flow to improve the throughput; in the case of analyzing the throughput improvement, transmitting the relevant changed operating parameters to the relevant transport device; and operating the relevant transport device with the changed operating parameters, in particular when monitoring the equivalent operation of the decision-driven simulation operation.

[0040] In this way, the benefits already discussed above in connection with the intralogistics system are achieved.

[0041] Preferably, the transport device is operated with the changed operating parameters without changing the originally planned and generated ongoing order itself.

[0042] The method of the present invention is also applicable to existing systems.

[0043] It is to be understood that the features mentioned above and yet to be explained below can be used not only in the respective combination but also in other combinations or alone, without departing from the scope of the present disclosure.

[0044] Examples are shown in the drawings and explained in more detail in the following description.

[0045] Figure 1 A block diagram of an intralogistics system, exemplarily implemented as a storage and order picking system, is shown.

[0046] Figure 2 A block diagram showing a possible transport arrangement.

[0047] Figure 3 A schematic diagram of the transport network is shown.

[0048] Figure 4 Several example shipping orders are shown in table format.

[0049] Figure 5 Demonstrated the operation of digital twins.

[0050] Figure 6 shows the first architectural variant of the digital material flow twin ( Figure 6A ) and a second architectural variant ( FIG6B ).

[0051] Figure 7 A flow chart showing the execution of material flow using a conventional material flow computer.

[0052] FIG8 shows an example of a known material flow.

[0053] In the following the term "material flow" (MF) is understood to mean a general term as defined in the introduction, which is however essentially restricted to the totality of all time-related local changes (i.e. transport movements) of MF objects (e.g. handling units of storage units, workpieces, conveyed items etc.) caused by transport orders 22. To simplify the presentation, changes of the MF objects themselves with respect to the quantity, quality and / or composition of the MF objects are not considered in detail below, although such changes are possible. Each MF object moves from a source to a destination in accordance with its transport order 22, for which purpose a transport path 24 is usually selected by a material flow computer (MFC) 30 from a plurality of different transport paths 24, to which reference will be made below. Figures 1 to 4 Explain in more detail.

[0054] In order to move the objects of the MF, i.e. the processing units (storage units, conveying items, workpieces, parts, etc.) through the intralogistics system 10 (e.g. the storage and order picking system 12), a transport network 14 of several transport devices 15 is used (see Figure 1 The transport network 14 is essentially composed of transport devices 15. There may be transport devices 15 with variable operating parameters 36 and transport devices 15 without variable operating parameters 36. The effects of the present disclosure are achieved by transport devices 15 whose operating parameters 36 are variable.

[0055] Transport means 15 are connected to one another to form a (transport) network 14 (see also Figure 3 ). The transport device 15 comprises one or more (modular) continuous conveyors 16, and / or one or more discontinuous conveyors 18 (see Figure 2 ).

[0056] Continuous conveyors 16 operate continuously and are typically stationary. They have a high conveying performance, measured, for example, in the number of process units transported per unit time, and produce a continuous or quasi-continuous conveying flow or MF. Their continuous operation and simple functionality allow for good automation and control of the MF in the transport network 14. The associated conveyor modules may consist of roller conveyors, belt conveyors, chain conveyors, overhead conveyors, and / or the like.

[0057] The discontinuous conveyor 18 is a movable conveying unit, such as an unmanned transport vehicle (DTV).

[0058] 20, which moves processing units from a source to a destination. They can travel to any point along a route, or within an area or space. DTV 20 is suitable for serving many sources and destinations, for transporting heavy processing units, and for bridging long distances. Depending on the design of the transportation network 14, the control workload and automation requirements will also increase with the flexibility of use. Discontinuous conveyors 18 can also include automatic (forced) guided vehicles (AGVs), autonomous mobile robots (AMRs), traditional storage and retrieval devices (SDRs), etc.

[0059] Figure 3 Schematically shows Figure 1A possible embodiment of a transport network 14 in the digital twin is shown. The figure shows a plurality of points AG and a plurality of transport routes #1 to #12 connecting the points AG. Points AG can be the source and / or destination of the MF and define the starting and ending points of the aforementioned transport path 24, which consists of one or more routes. Points AG can also represent branch points and intersections of the MF. In the digital twin, any transport route exists that is essentially preferred from the perspective of optimizing the MF. In the MF, in addition to adjusting operating parameters, selecting the optimal transport route according to the situation represents a further optimization possibility.

[0060] Figure 4 Shown in tabular form Figure 2 1 to 22-3 of the network 14. Each transport order 22 defines a starting point, a destination point, and a route 24 between these points. In addition, each order 22 defines a start time and a (calculated, predicted) end time. Each order 22 is assigned to one (or more) specific processing units, which are also recorded in the corresponding order 22. It is understood that, in addition to Figure 4 In addition to the characteristics shown, the transport order 22 may include one or more of the following information (not shown), such as: start time, end time, ID of the processing unit, priority, etc., which may help further specify the transport order 22.

[0061] Figure 4 The transport order 22-1 of FIG. 1 represents an exemplary transport of a processing unit (not shown) from point A (source) to point C (destination) via routes #2 and #6, where the transport is provided by transport equipment 15-1 (e.g., by DTV 20, see FIG. Figure 2 ) is implemented. When one of the routes #1 to #12 is carried out by a continuous conveyor 16, no specification of the transport device 15 is required. However, in this case, for example, the transport time needs to be determined and set. Figure 4 The transport orders 22-2 and 22-3 define two transport routes 24-2 and 24-3, both of which start at point B and end at point E, but extend in different ways through Figure 3 The network 14 is shown.

[0062] In addition to transport network 14, Figure 1The internal logistics system 10 further comprises a controller 26 and sensors 28. The controller 26 comprises a real (conventional) material flow computer (MFC) 30 (hardware and software) and a digital material flow twin, i.e. a digital twin of the material flow (DT-MF) 32 (software). The controller 26 is executed by one or more computers and one or more control programs (software). The MF control processes, i.e. the coordination of transport orders 22 in particular, can be carried out centrally (via the MFC 30) or in a decentralized manner (e.g. the MFC 30 is combined with the lower-level transport device controllers), i.e. the execution of the order 22 is cyclically verified based on the (current) operating status 34 reported back to the controller 26 from the sensors 28. The status 34 can be passed to the MFC 30 and / or to the DT-MF 32, which exchange the status 34 with each other.

[0063] As described above, the MFC 30 is configured to initially plan and generate transport orders 22, and to continuously coordinate the transport orders 22. The transport orders 22 are triggered by, for example, picking orders (not shown) to retrieve, for example, storage containers (processing units) from their corresponding storage locations (origins) and transport them to workstations (destination points), where a person or robot removes the stored items from the storage containers and delivers them to order containers (further processing units), which are also transported to the workstations (synchronized in time and space) according to another transport order 22. The MFC 30 passes the initially generated transport orders 22 to the involved transport devices 15 (and their controllers, if any), which then execute these orders 22 accordingly, with additional coordination by the MFC 30 as necessary.

[0064] Sensors 28 detect the operational status 34 of the MF, the transport network 14, and the transport devices 15 as sensor data. Some of these sensors 28 may be provided separately to the transport devices 15, such as a centrally located camera in the system 10 that provides a 2D image of the entire area (e.g., a warehouse) from which information about the MF (occupancy status, traffic density, etc.) for one or more routes can be extracted through image processing. Other sensors 28 are directly integrated into the transport devices 15, such as speed, position, and distance sensors in the DTV 20, or light barriers, weight sensors, and scanners at the entrances and exits of the continuous conveyor 16.

[0065] The detected operating state 34 is transmitted by the sensor 28 to the (wired and / or wireless) controller 26 via a correspondingly configured interface (including a protocol, not shown). The sensor data represents the input data to the MFC 30 and the DT-MF 32. The sensor data is used to synchronize the real MF with the simulated material flow cyclically generated by the DT-MF 32.

[0066] Exemplary operating states 34 are: the occupancy state of the transport device 15; the transport speed of the transport device 15; the (current) position of the (movable) transport device 15; the current engine current and voltage; the current charge state of the energy storage device; etc. The operating states 34 change so that they can be observed by the controller 26 so that it can react accordingly when (unexpected) changes occur.

[0067] The DT-MF 32 is configured to virtually or digitally replicate the real MF in the transportation network 14 by using a material flow simulation model (MF model) 40 , as will be explained in more detail below.

[0068] Generally speaking, a digital twin (DT) is understood as a virtual representation of physical and / or immaterial objects in the real world. In this case, one of these objects is a material flow. This virtual representation includes (functional) models, simulations, and / or algorithms that reproduce the properties and behavior of real objects as accurately as possible in the virtual world. The interactions of objects in reality are becoming increasingly complex. The relationships and dependencies between objects, as well as the impact of their changes, are becoming increasingly difficult to assess (in reality). Therefore, DT is extremely important. DT allows the creation of a virtual representation of reality. Changes (to the parameters) of this virtual representation can be tested in advance through simulations.

[0069] The overall goal of using DT is to simulate optimized new solution approaches, planned changes, and new technologies first in the virtual digital world before transferring them to the real world. Figure 5 The operation of a conventional DT is shown.

[0070] exist Figure 5 In the ,data generated by a real object (e.g., DTV 20 transporting a processing unit from A to B) is detected by sensors in the real world (S1), stored (S2), and then transmitted (S3) to the digital twin (e.g., Figure 1 In the virtual world, these sensor data can be analyzed and evaluated (S4), for example, by using a transport order that has been initially set up and the current sensor data ( Figure 1The material flow is simulated again (in the operating state 34 in the MFC 30), wherein the simulation can be performed using a simulation model of the MFC 30 or using any other simulation model. The (operating) parameters 36 are then changed (S5) so that the operation of the virtually mapped object is simulated again (S6) with the corresponding parameter settings (for each modified parameter). Thus, all possible parameter modifications can be simulated, which may also include a set of modified parameters 36, so that the simulation results (with and without parameter changes) can be subsequently evaluated (S7). To this end, the simulation results are analyzed by comparing them with each other and weighting and evaluating them according to one or more given criteria (e.g., increased throughput, shorter throughput time, shorter total processing time, reduced wear, reduced operating costs, more uniform utilization, less personnel deployment, etc.) in order to determine the best parameter setting from a plurality of simulated parameter settings. These results and in particular the best and optimized parameter settings can be stored (S8) and the best operating parameters 36 are transmitted back (S9) by the controller 26 and in particular the DT-MF 32 via an interface to the real object (transport equipment 15) (see Figure 1 ). The real object takes over the optimized parameter settings (S10) and continues to work with these settings from now on (S11) until it receives new parameter settings in a future cycle. Subsequently, the above process can be repeated starting from step S1 in order to start and execute the process of continuous improvement. Figure 5 The flat figure 8 shown clearly illustrates the optimization impact of DT on a real-world cycle. It can be appreciated that the shorter the cycle time selected and the more parameters 36 are varied per cycle, the higher the demand on the DT-MF 32 computing power becomes.

[0071] Figure 6A and 6B show in more detail Figure 1 There are 32 possible hierarchies in DT-MF. Figure 6A A first unified variant of the DT architecture is shown, and FIG. 6B shows a second distributed variant of the DT architecture.

[0072] exist Figure 6A In the case of the unified architecture of the DT-MF 32 in FIG, the (simulation) model 38 of the MF participant (i.e. the transport device 15) is integrated into the MF (simulation) model 40. The participant model 38 includes a simulation model 38-1 for the real continuous conveyor 16 and / or a simulation model 38-2 for the real discontinuous conveyor 18.

[0073] 2. The MF model 40 simulates the MF by virtually replicating the sequence of transport processes for real transport orders 22 based on the MFC 30, as implemented by the transport devices 15. The operating status 34 of the sensors 28 is also taken into account, allowing for parameter optimization by the parameter optimization device 42 included in the DT-MF 32. The DT-MF 32 further includes an analysis device 44 configured to perform step S7. The analysis device 44 compares different material flows simulated with different parameters with simulated material flows with unchanged parameters and evaluates them from the perspective of improved throughput. Throughput can be expressed, for example, as: an increase in the number of transport orders 22 completed per unit time; shorter processing time, i.e., a shorter time to complete one order 22; shorter total processing time, i.e., a shorter time to complete all orders 22; reduced wear, such as reduced wear on the drive motor due to a lighter load; lower operating costs; more uniform utilization; and reduced personnel deployment, among other factors.

[0074] The same applies to the distributed architecture of Figure 6B. There, the participant models 38 are contained in corresponding digital participant twins or digital transport device twins 46, which are provided separately from the MF-DT 32. The DT-MF 32 and DT 46 of the transport device 15 are provided independently of each other and operate independently of each other. The DT-MF 32 simulates the MF based on the MF model 40, but this MF model 40 can even include the MF participant models 38 (identical or in a simplified form). In addition, at least some (preferably all) of the transport devices 15 have digital twins, namely DT-TD 46. The DT-TD 46 simulates the functions of their corresponding transport devices 15 and can bring additional performance improvements (in addition to throughput) to the corresponding transport devices 15 by repeatedly optimizing their operating parameters 36 in the simulation based on the real operating state 34 provided by their sensors 28 and evaluating them from different aspects so that the parameters 36 optimized in different ways can be used in reality.

[0075] In this way, for example, the current and voltage behavior of the DTV 20 battery pack can be monitored to allow the associated model 38 to predict higher-than-average discharge due to failures caused by battery pack aging or wear. One or more operating parameters 36 of the DTV 20 can be modified (i.e., changed) to extend the service life of the DTV 20 beyond expectations or to facilitate timely maintenance before a predicted failure occurs. The predicted failure also represents an operating condition 34 that can be communicated to the DT-MF 32 for subsequent consideration by the DT-MF 32.

[0076] In the following, some examples of unexpected problems with MF are described, which are difficult or impossible to solve with the conventional MFC 30 without the support of DT-MF 32. For this purpose, please refer to Figure 7 Flowchart of the process.

[0077] The MFC 30 receives external requirements (picking orders, stock transfer orders, transport requests, etc.), such as from a picking order management system (not shown) and / or a warehouse management system (not shown), see step S20. The MFC 30 then (if necessary, based on a material flow simulation) initially plans and generates the relevant transport orders 22, initially and once providing the MFC 30 with the relevant requirements. During planning, the MFC 30 may use a set of preset operating parameters 36 for the transporters 15. The MFC 30 determines the transport orders 22 in a throughput-optimized manner, for example, based on the currently executed logic. To this end, the MFC 30 may take into account delays learned from experience. The correspondingly generated initial transport orders 22 are then transferred to the corresponding transporters 15, see step S22. Optionally, in step S24, before actual execution begins, the controller 26 (particularly the DT-MF 32) may verify the feasibility and achievability of the correspondingly planned and generated transport orders 22 based on the actual status 34 (in advance), see step 23.

[0078] Then, as long as no unexpected operating states 34 occur in the material flow and / or the transport device 15, the transport device 15 starts executing the order 22 (step S24), thus generating the (originally planned) material flow. So far, the method disclosed herein does not differ from conventional methods.

[0079] According to conventional methods, if an unexpected problem occurs spontaneously (e.g., two DTVs 20 are about to collide because one of them is traveling slower than expected; or a receiving conveyor is unable to deliver its conveyed items because the receiving conveyor is occupied, etc.), the associated sensor 26 will directly provide this (unexpected) operating status 34 to the controller 26 or MFC 30, or to the involved (distributed) sub-control units (e.g., to the DTV controller or to the conveyor controller), see step 26. If the MFC 30 and / or sub-control units are able to resolve the problem based on a set of predefined possible solution rules (e.g., higher priority takes precedence; greater delay can be taken first, etc.), which are themselves unchangeable, as checked in step S28 by, for example, reporting the relevant status 34, then there is an actual reduction or degradation in throughput (step S30), but no serious system failure that can only be resolved by external intervention by maintenance technicians (step S32). External intervention represents the last resort solution option based on a set of predefined rules. MF continues until all orders 22 have been completed, i.e., ready. Meanwhile, if an unexpected operating state 34 occurs again, some steps in steps S26-S32 are generally performed.

[0080] However, the present disclosure precedes the conventional problem-solving approach with DT-MF 32, see Blocks A and Figure 5 .

[0081] Figure 7 Block A corresponds to Figure 5 An optimization method using DT-MF 32 is provided to cyclically search for operating parameters 36 for the transport device 15 involved in the execution of the MF, which parameters result in a better material flow than a material flow simulated according to the current operating state 34 without any parameter changes in the forecast.

[0082] For example, the digital material flow twin 32 predicts an impending collision between two DTVs 20 and a transport problem between adjacent continuous conveyors 16 long before these situations actually occur. In the best case, the DT-MF 32 prevents these situations from occurring in the future. The DT-MF 32 (intuitively) changes the operating parameters within the framework of its parameter optimization, for example, by changing the transport speed of one (or both) of the DTVs 20 or the infeed continuous conveyor 16, so that there are no collisions or delayed deliveries. In this case, the transport speed represents the variable operating parameter 36. However, the parameter 36 can also be an (alternative) route on the path 24, in which case the transport order 22 itself will be modified. There are no restrictions on the choice of possible transport devices and their parameter settings. The DT-MF excludes non-goal-oriented settings and finds the optimal settings in particular within the limits of the available computing time and the optimization algorithm used.

[0083] Thus, DT-MF 32 prevents problems from occurring not by applying a predefined fixed solution, but rather by using (at least) one of a number of parameter changes that have proven beneficial during the current simulation cycle. Ideally, unexpected operating states 34 are no longer reported back to controller 26 (step S26), so that transport orders 22 representing the material flow can be completed with a throughput based solely on the parameter changes (step S34). Even if the throughput actually achieved in this manner is lower than the throughput originally estimated during the initial planning, this throughput still represents a significant improvement compared to the execution of the initially planned transport orders 22.

[0084] Of course, despite Block A, there may still be interruptions in the material flow that can only be remedied by conventional means. However, the probability of such interruptions occurring is significantly lower than using conventional methods.

[0085] Furthermore, it will be appreciated that the aforementioned effects can be achieved even if MF optimizes not the operating parameters 36 of the entire transport network 14, but only a localized area of ​​the network (e.g., a pre-installed area of ​​a warehouse). In this case, the conveyor system in the pre-installed area represents a subsystem of the transport network 14, including the associated transport devices 15. If, among these transport devices 15, there are transport devices whose operating parameters 36 cannot be changed, only those transport devices whose parameters 36 are variable are optimized. Nevertheless, in this case, the improved material flow described above can also be achieved.

[0086] Reference numerals

[0087] 10 Internal logistics system

[0088] 12 Storage and order picking systems

[0089] 14 (Transportation) Network

[0090] 15 Transport device

[0091] 16 Continuous Conveyor

[0092] 18. Discontinuous conveyor

[0093] 20. Unmanned Transport Vehicles (DTV)

[0094] 22 Shipping Order

[0095] 24 transport routes

[0096] 26 Controller

[0097] 28 sensors

[0098] 30 Material Flow Computer (MFC)

[0099] 32 Digital Twin of Material Flow (DT-MF)

[0100] 34 (Running) Status

[0101] 36 (Run) Parameters

[0102] 38 (Simulation) Model of MF Participants

[0103] 38-1 Continuous Conveyor Model

[0104] 38-2 Discontinuous Conveyor Model

[0105] 40 MF (simulation) model

[0106] 42 parameter optimization device

[0107] 44 Analytical Device

[0108] 46 Digital Twin of Transport Device 15

Claims

1. An internal logistics system (10), comprising: A transport network (14) comprising a plurality of transport units (15; 16, 18) and is configured to execute a material flow caused by a transport order (22) in the internal logistics system (10), wherein each of the transport devices (15) is operated with at least one preset variable operating parameter (36); a plurality of sensors (28) that cyclically detect the current operating state (34); and a controller (26) comprising a material flow computer (30) that initially plans and generates the transport order (22) and cyclically coordinates the execution of the transport order (22) based on the current operational status (34); The controller (26) further comprises a digital material flow twin (32), wherein the digital material flow twin (32) comprises a material flow simulation model (40), an operating parameter optimization device (42) and an analysis device (44) and is configured to: cyclically simulating the material flow with varying and unchanged operating parameters (36) of the transport device (15) depending on the respective current operating state (34), analyzing said simulated material flow to improve throughput, And when the throughput improvement is analyzed, the relevant changed operating parameters (36) are transmitted to the relevant transport device (15), and then the relevant transport device (15) is operated based on the changed operating parameters (36).

2. The internal logistics system according to claim 1, wherein: The material flow computer (30) is configured to: Initially planning and generating the transport order (22) based on the picking order, transport requirements and / or stock transfer order, and transmitting it to the relevant transport device (15); In the event of a material flow problem, continuously coordinating the material flow according to the current operating state (34) by executing a problem solution based on determined predefined solution rules; and The operating status (34) is received from the sensor (34).

3. The internal logistics system according to claim 1 or 2, wherein: The transport network (14) includes a plurality of transport sources and a plurality of transport destinations connected to each other via a plurality of transport paths (24), and Each of the transport orders (22) defines a handling unit-specific transport path (24) from one of the sources to one of the destinations.

4. The internal logistics system according to any one of claims 1 to 3, wherein: The throughput improvement results in a greater number of transport orders (22) being completed per unit time compared to the material flow simulated according to the respective current operating state (34) and without changing the operating parameters (36).

5. The internal logistics system according to any one of claims 1 to 4, wherein: The relevant changed operating parameters to be transmitted to the relevant transport device keep the transport order unchanged.

6. The internal logistics system according to any one of claims 1 to 5, wherein: At least some of said transport devices (15), preferably each of said transport devices (15), respectively comprise at least one of said sensors (28).

7. The internal logistics system according to any one of claims 1 to 6, wherein: The simulation of the material flow (respectively) reaches a point in time at which all of the initially generated transport orders ( 22 ) have been completed.

8. The internal logistics system according to any one of claims 1 to 7, wherein: The transport unit (15) comprises: a discontinuous conveyor (18), in particular an unmanned transport vehicle (20), and / or Continuous conveyor (16), in particular a roller conveyor, a belt conveyor, a chain conveyor, and / or an overhead conveyor.

9. The internal logistics system according to any one of claims 1 to 8, wherein: The internal logistics system (10) is a storage and order picking system (12), which further includes at least one of the following functional areas: storehouse; Goods receipt; Goods shipment; workstation; and / or Production.

10. A method for improving the execution of an initial planned material flow in an internal logistics system (10), the internal logistics system (10) comprising: A transport network (14) comprising a plurality of transport units (15; 16, 18) and configured to execute a material flow caused by a transport order (22) in the internal logistics system (10), wherein each of the transport devices (15) operates with at least one preset variable operating parameter (36); a plurality of sensors (28); and a controller (26) comprising a material flow computer (30); wherein the method comprises the following steps: Cyclic detection of the current operating state (34) by the sensor (28); and Initially planning and generating the transport orders (22) by the controller (30) and cyclically coordinating the generated transport orders; The controller (26) further comprises a digital material flow twin (32), wherein the digital material flow twin (32) comprises a material flow simulation model (40), an operating parameter optimization device (42) and an analysis device (44), and implements the following cyclic steps: Simulating the material flow with a constant operating parameter (36) and a plurality of changed operating parameters (36) of the transport device (15) according to the corresponding current operating state (34); analyzing the simulated material flow to improve throughput; In case of analysis of throughput improvement, transmitting the relevant changed operating parameters (36) to the relevant transport means (15); and The associated transport device (15) is operated with the changed operating parameters (36).

11. The method according to claim 10, wherein: The transport device (15) is operated using the changed operating parameters (36) without changing the originally planned and generated in-progress order (22).

Images