Storage and order picking system with optimized material flow

Abstract

Intralogistics system (10) which features: a transport network (14) comprising a plurality of transport units (15; 16, 18) and designed to implement a material flow within the intralogistics system (10) caused by transport orders (22), wherein each of the transport facilities (15) is operated with at least one preset, variable operating parameter (36); a large number of sensors (28) that cyclically detect current operating states (34); and a control system (26) which includes a material flow computer (30) that initially plans and generates the transport orders (22), and which cyclically coordinates the implementation of the transport orders (22) based on the current operating states (34); wherein the control (26) further comprises a digital material flow twin (32) which includes a material flow simulation model (40), an operating parameter optimization device (42) and an analysis device (44) and is set up: to cyclically simulate the material flow based on the respective current operating states (34) with and without varying operating parameters (36) of the transport equipment (15), to analyze the simulated material flows with regard to throughput improvement, and in the event that a throughput improvement is analyzed, to transmit the correspondingly varied operating parameters (36) to the corresponding transport facilities (15), which are then operated based on the varied operating parameters (36).

Description

[0001] The present disclosure relates generally to the field of intralogistics, and in particular to the optimized control of a material flow in an intralogistics system, such as a warehouse and / or order picking system. The material flow is coordinated in a quasi-continuous, throughput-optimized manner by cyclically changing the operating parameters of transport equipment, while initially generated transport orders remain unchanged and are continuously executed.

[0002] Generally, the term "material flow" (MF) refers to a sequence of storage, transport, and processing operations. According to VDI 2689, this term encompasses all processes and their interconnections involved in the extraction, processing, and distribution of MF objects (handling units, such as storage units, workpieces, conveyed goods, etc.) within defined functional areas. These areas can include various stations between a (goods) input (which may also be implemented by a production facility) and a (goods) output. MF includes all forms of the passage of MF objects through an MF system, which corresponds to a transport network within the area between the input and output.

[0003] Fig. Figure 8 schematically illustrates a conventional material flow, visualized by arrows, from an input (receiving) to an output (shipping) of an intralogistics system. Figure 10 shows various areas and stations (receiving, production, storage, order picking, consolidation, and packing station) connected via conveyor systems. Existing conveyor connections are shown with solid arrows. Further, alternative, and / or additional connections are shown with dashed arrows. The entirety of all connections defines a transport network. It is understood that the areas and stations can be connected to each other in a variety of ways via conveyor systems. A system planner determines this as needed.

[0004] Material flow (MF) processes are planned (in advance) in a conventional material flow computer (MFC) in the form of corresponding transport orders, and their execution is coordinated and monitored (in real time). The MFC plans and coordinates the corresponding source-destination relationships. The material flow control effected by the MFC is often described as a (picking) order management system assigned to the transport network. However, this general definition of material flow control does not do justice to the actual task when the intralogistics system has a complex, heterogeneous structure of high-performance transport facilities that must be precisely coordinated to enable optimal operational results. Material flow control has a central function. Despite a multitude of different transport facilities and expansion stages, it must ensure, for example, that the material flow control is always efficient and consistent.to ensure maximum throughput, the fastest possible deployment and / or the lowest energy consumption.

[0005] The primary task of material flow control is to perfectly coordinate the transport orders of the connected areas and stations. Available transport equipment must be assigned these orders and utilized as fully as possible without blocking the transport network. At all times, the operational status (e.g., utilization rate) of the network and the occupancy status of routes, waypoints, intersections, junctions, and transport equipment must be considered. The number of material flow objects and their transport speeds are interdependent.

[0006] However, the classic MFR (Transport Request Framework) is only partially capable of solving MF (Transport Request) problems that occur randomly in reality. The MFR cannot anticipate these problems when initially generating transport requests, but only reacts to problem messages that have already been received.

[0007] For example, a classic material flow control (MFC) system cannot predict a real and random slippage of a conveyed item during transport, e.g., on a belt conveyor, and the resulting reduction in throughput or other technical problems. The slippage leads to a delayed arrival time at the destination predetermined by the MFC. This delayed arrival can, in turn, result in the destination being occupied by another item that arrived in the meantime, or in a pre-planned sequence not being executed. In this case, the classic MFC system can only resolve this material delivery problem situationally, for example, by instructing the delivering conveyor belt to wait before delivering the item, contrary to the original plan.

[0008] Another example is driverless transport vehicles (AGVs). The classic multi-function controller (MFC) determines (collision-free) transport or driving orders for, for example, two AGVs in advance. However, during actual operation, the AGVs unexpectedly encounter each other in such a way that a collision risk exists (e.g., due to delayed departures caused by a delayed release of the AGV by its sensors, possible intermediate stops, or reduced driving speed due to glare or other external influences on the surrounding camera technology, etc.). The AGVs "see" each other via their integrated distance sensors and stop for safety reasons. The MFC is informed of both stopped states and decides, for example, based on the transport priorities linked to the respective transported goods, which AGV may proceed first.The MFR (Mobile Freight Forwarder) could not foresee, during its initial planning of transport orders, that, for example, the motor of one of the two AGVs would be nearing the end of its service life, meaning that the AGV could only operate at, say, 80% of its rated speed. In this case, the conventional MFR can only react to the problem situationally, and in the worst-case scenario, this can even lead to an (unexpected) system shutdown that can only be resolved by manual intervention from an external maintenance technician. This significantly reduces throughput.

[0009] Classical MFR simulations are distinct from classical MF simulations, which are used in the (project planning) preliminary stages of planning an intralogistics system and are developed on the basis of historical data without a connection to a real system.

[0010] In general, every simulation is based on a (static) simulation model, where the model is fed with a set of parameters and the result is calculated or simulated over time. According to VDI 3633, "simulation" refers to the reproduction of a system with its dynamic processes in an experimental model in order to gain insights that are transferable to reality. In particular, the processes are developed over time. In a broader sense, simulation encompasses the preparation, execution, and evaluation of targeted experiments using a simulation model. However, every model has limitations (e.g., limited resources, energy, time, or money). Therefore, minimally influencing factors are often not considered with sufficient accuracy in the model, so that it often represents only a rough simplification of reality. These simplifications negatively affect the accuracy of the simulation results.Intralogistics areas and the transport network are combined and simulated in a highly simplified manner. The material handling (MF) simulation is fed with real order data from the past to depict the movement of MF objects through a respective system variant (offline) as realistically as possible. Simultaneously, the capacity, performance, and control of the respective system variant (virtually) are examined for optimization opportunities. The insights gained are then incorporated into the selected system variant, which is subsequently implemented.

[0011] A key task of (computer-implemented) material handling (MF) simulations is to test different (layout) variants of a planned MF system and their corresponding fundamental strategies in advance, without actually implementing each of them. For example, it can be tested whether, at a specific point in the MF or transport network, the use of a single transport device of type A (system variant 1) or of two parallel transport devices of type B (system variant 2) would be better with regard to a desired throughput.

[0012] Classical machine learning simulations are therefore regularly used during the (early) planning phase of the intralogistics system to analyze and compare the performance of different system variants. The best variant can then be selected and implemented. In some cases, the previously created simulation model is further used by copying data from the system once during planning validation (i.e., when checking whether the orders can be executed in the planned form and sequence) and providing it to the simulation model. The simulation model then continues to calculate feasibility based on the assumptions and abstractions made. However, continuous monitoring of the simulation run against the actual order execution process is not included.

[0013] The aforementioned, rapidly occurring MF problems (collisions, jams, wear, slippage, etc.) that arise randomly cannot be solved by either the MFR or an MF simulation.

[0014] The planning and coordination of the material flow (MF) becomes increasingly complex for the classic material flow computer the more the following aspects must be additionally considered: sequencing; large product ranges; decentralized control approaches in departure from the classic central material flow computer; wear effects; and / or errors in identifying the 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 method for controlling orders of a manufacturing device.

[0017] Therefore, one objective of this disclosure is to provide a high-performance, continuous, and resilient material flow in an intralogistics system. A high-performance material flow is throughput-optimized, where throughput can refer, for example, to the number of completed transport orders per unit of time and / or to the execution time of a transport order. In a continuous material flow, the material flow objects move continuously without unexpected stoppages, waiting times, blockages, or congestion. A resilient material flow reacts flexibly to randomly occurring problems.

[0018] This task is solved by an intralogistics system comprising: a transport network with a multitude of transport units configured to implement a material flow within the intralogistics system triggered by transport orders, each transport unit being operated with at least one preset, variable operating parameter, preferably storable and modifiable in the transport unit; a multitude of sensors that cyclically record current operating states, preferably of the transport units and / or the material flow; and a control system comprising a material flow computer that initially plans and generates the transport orders, preferably once, and that cyclically coordinates the execution of the transport orders, preferably by the material flow computer, based on the current operating states;wherein the control system further comprises a digital material flow twin, which includes a material flow simulation model, an operating parameter optimization unit, and an analysis unit, and which is configured to: cyclically simulate the material flow based on the respective current operating states, both without and with a variety of varying operating parameters of the transport equipment; analyze the simulated material flows with regard to throughput improvement; and, in the event that a throughput improvement is analyzed, transmit the correspondingly varied operating parameters to the relevant transport equipment, which are then operated with the varied operating parameters.

[0019] Unlike the traditional approach, unexpected material flow problems are not solved only when they actually occur. Nor are they solved with a fixed, predefined set of solutions. Instead, the problems are anticipated early on through material flow simulation, without the need to precisely define the problems themselves. Simply detecting a decrease in throughput is enough to trigger action. The solution lies in modifying the operating parameters of the transport equipment. The material flow is simulated almost any number of times during a simulation and optimization cycle for a very large number of different parameter settings, starting from the continuously observed actual situation in the warehouse. This allows for a comparison of the corresponding simulation results, including a simulation of the material flow without any parameter changes.Such a simulated material flow is superior if it results in a higher throughput than the material flow simulated without any parameter changes, or if, upon reaching predefined throughput values, sufficient capacity buffers remain to handle potential unexpected events with minimal impact (resilience). The simulation preferably runs until all transport orders are completed, i.e., until all handling units have been moved from their starting point to their destination.

[0020] In other words, this means that the control system of the present disclosure can intuitively identify and solve material flow problems without predefining the problem and / or the solution. This is a process of continuous improvement.

[0021] The material flow of the entire system is continuously and repeatedly analyzed and optimized, i.e., cyclically, based on current operating conditions. This optimization is dynamic, unlike static (initial) material flow planning.

[0022] Depending on the available computing power, the simulation and optimization cycles can be very short and the number of variable operating parameters can be very large. Ideally, with this disclosure, no material flow problems (congestion, delays, collisions, etc.) will occur.

[0023] The resulting material flow is efficient, constant, energy-efficient and / or resilient.

[0024] Preferably, the material flow computer is configured to: initially plan, generate, and transfer transport orders to the appropriate transport equipment based on picking orders, transport requests, and / or transfer orders, which can (also) be entered by a warehouse management system; preferably to continuously coordinate the material flow based on the current operating states by implementing a problem solution based on fixed, predefined solution rules in the event of a material flow problem; and to receive the operating states from the sensors.

[0025] The material flow computer used here is therefore no different from conventional material flow computers. The present disclosure can thus be applied to existing systems that already have a conventional material flow computer. In this sense, however, the present disclosure represents an extension of existing intralogistics systems. For this purpose, it may be sufficient to extend the existing system with appropriate control software. Typically, however, this software extension will also be accompanied by a parallel hardware (computer) upgrade. Then the existing system is able to react dynamically to material flow problems.

[0026] In particular, the transport network comprises a multitude of transport sources and a multitude of transport destinations, interconnected via a multitude of transport routes, with each transport order defining a handling unit-specific transport route from one of the sources to one of the destinations. The transport orders preferably do not include the operating parameters.

[0027] In traditional material flow planning, the operating parameters are therefore usually not variable values. Traditional planning is based on fixed, preset parameter values, which, however, may include tolerance ranges that are in turn based on empirical data.

[0028] Preferably, the throughput improvement results in a higher number of completed transport orders per unit of time compared to the material flow, which is simulated based on the respective current operating conditions without varying operating parameters.

[0029] The throughput improvement does indeed occur, even if the actual throughput achieved is lower than the originally planned throughput. This actual throughput is still better than the throughput that would be achieved if the (unexpectedly arising) material flow problems were addressed solely with the fixed solution tool.

[0030] Furthermore, it is advantageous if the appropriately varied operating parameters, which are to be transmitted to the corresponding transport facilities, leave the transport orders unchanged.

[0031] The present disclosure does not redesign the material flow (as such), but rather modifies the initially planned material flow repeatedly through minor, potentially barely perceptible, changes, ultimately resulting in a significantly improved throughput. The conventional material flow computer remains unaware that the material flow is constantly and positively influenced from the outside, namely by the digital twin of the material flow, through changes to the operating parameters of the transport equipment.

[0032] Preferably, at least some, preferably all, of the transport devices each include at least one of the sensors.

[0033] For the feedback loop regarding current operating states to function, the transport units, which in turn implement the material flow and whose operating parameters can be influenced, must report their operating states back to the higher-level control system with sufficient frequency. Therefore, it is advantageous if the corresponding sensors are integrated directly into the transport units.

[0034] Of course, it is also possible to use sensors that detect the operating states of the material flow and / or the transport equipment from the outside or indirectly. However, assigning a detected change in state to the transport equipment is more complex in this case.

[0035] In particular, the transport units include discontinuous conveyors, such as driverless transport systems, RGBs, switches, lifts, transfer units and the like, and / or continuous conveyors, such as roller conveyors, belt conveyors, chain conveyors and / or overhead conveyors.

[0036] The present disclosure is therefore applicable to both types of conventional conveying systems. The present disclosure is applicable to any conceivable material flow problem. Thus, the present disclosure is also applicable to any inventory system that, as is customary, consists of discontinuous and / or continuous conveyors for implementing material flow.

[0037] Preferably, the intralogistics system comprises a storage and order picking system, which furthermore includes at least one of the following functional areas: a warehouse; a goods receipt; a goods issue; a workstation; and / or a production area.

[0038] The present disclosure is therefore applicable to all common intralogistics applications. It can be used in the field of production intralogistics as well as in classic order picking environments (e.g., distribution centers).

[0039] Furthermore, the aforementioned task is solved by a method for the improved implementation of an initially planned material flow in an intralogistics system, wherein the intralogistics system comprises: a transport network comprising a plurality of transport devices and configured to implement a material flow within the intralogistics system triggered by transport orders, each of which is operated with at least one preset, variable operating parameter, preferably storable in the transport device; a plurality of sensors; and a controller comprising a material flow computer; wherein the method comprises the steps: cyclic acquisition, by the sensors, of current operating states; and initial planning and generation of the transport orders, as well as cyclic coordination of the generated transport orders by the controller;wherein the control system further comprises a digital material flow twin, which includes a material flow simulation model, an operating parameter optimization unit, and an analysis unit, and which performs the following cyclical steps: simulating the material flow based on the respective current operating states with unchanged operating parameters as well as with a variety of modified operating parameters of the transport equipment; analyzing the simulated material flows with regard to throughput improvement; in the event that a throughput improvement is analyzed, transferring the correspondingly varied operating parameters to the corresponding transport equipment; and operating the corresponding transport equipment with the varied operating parameters, in particular monitoring the process with the equivalent process of the decision-dominating simulation run.

[0040] In this way, the advantages that were already discussed above in connection with the intralogistics system become apparent.

[0041] Preferably, the transport equipment is operated with the varied operating parameters without changing the initially planned and generated transport orders themselves.

[0042] The method of the present invention can therefore also be used in existing systems.

[0043] It is understood that the features mentioned above and those to be explained below can be used not only in the combinations specified, but also in other combinations or on their own, without leaving the scope of this disclosure.

[0044] Examples of implementation are shown in the drawings and are explained in more detail in the following description. Fig. Figure 1 shows a block diagram of an intralogistics system, implemented as an example of a storage and order picking system. Fig. Figure 2 shows a block diagram of possible transport facilities. Fig. Figure 3 shows a schematically illustrated transport network. Fig. Figure 4 shows a variety of exemplary transport orders in tabular form. Fig. Figure 5 illustrates how a digital twin works. Fig. Figure 6 shows a first architectural variant ( Fig. 6A) and a second architectural variant ( Fig. 6B) of a digital material flow twin. Fig. Figure 7 shows a flowchart for implementing a material flow using a classic material flow calculator. Fig. Figure 8 illustrates an example of a previously known material flow.

[0045] In the following, the term "material flow" (MF) is understood as the general term defined in the introduction, but is essentially limited to the entirety of all time-dependent spatial changes (i.e., the transport movements) of the MF objects (handling units, such as storage units, workpieces, conveyed goods, etc.) caused by transport orders 22. Changes to the MF objects themselves with regard to quantity, quality, and / or composition are not considered in detail below for the sake of simplicity, although they are possible. Each MF object moves from a source to a destination according to its transport order 22, for which a transport route 24 is usually selected from a multitude of different transport routes 24 by a material flow computer (MFC) 30, as described below with reference to the Fig. Points 1 to 4 will be explained in more detail.

[0046] To move objects of the MF, i.e. handling units (storage units, conveyed goods, workpieces, piece goods, etc.), through an intralogistics system 10, such as a storage and picking system 12, a transport network 14 consisting of several transport devices 15 is used, cf. Fig. 1. The transport network 14 is essentially formed from the transport devices 15. There can be transport devices 15 with variable operating parameters 36 and without variable operating parameters 36. The effect of the present disclosure is achieved with the transport devices 15 whose operating parameters 36 are variable.

[0047] The transport facilities 15 are interconnected to form a (transport) network 14, see also Fig. 3. The transport equipment 15 comprises one or more (modular) continuous conveyors 16 and / or one or more discontinuous conveyors 18, see Fig. 2.

[0048] Continuous conveyors 16 operate continuously and are mostly installed in a fixed location. They have a high conveying capacity, which is measured, for example, in the number of handling units transported per unit of time, and produce a continuous or quasi-continuous conveying flow. Their continuous operation and simple function allow for good automation and control of the conveying flow within the transport network 14. The corresponding conveying modules can be formed by roller conveyors, belt conveyors, chain conveyors, overhead conveyors, and / or similar devices.

[0049] Discontinuous conveyors 18 are mobile conveying units, such as automated guided vehicles (AGVs) 20, that transport handling units from a source to a destination. They can move to any point along a line, in an area, or in space. AGVs 20 are suitable for serving multiple sources and destinations, transporting heavy handling units, and bridging long distances. Depending on the configuration of the transport network 14, the control effort and automation requirements increase with the operational flexibility. Discontinuous conveyors 18 can also include automated guided vehicles (AGVs), autonomous moving robots (AMRs), conventional storage and retrieval machines (SRMs), and similar devices.

[0050] In the Fig. 3 is one possible configuration of the transport network 14 of the Fig. Figure 1 illustrates this schematically. A multitude of points AG and a multitude of transport routes #1 to #12 connecting points AG are shown. Points AG can be sources and / or destinations of the material handling system (MF), defining the start and end points of the aforementioned transport routes 24, which consist of one or more routes. Points AG can also represent branching and intersection points of the MF. In the digital twin, there are no inherently preferred transport routes with regard to MF optimization. The selection of the situationally optimal transport route, in addition to adjusting operating parameters, represents a further optimization possibility in the MF.

[0051] In Fig. 4 are exemplary transport orders 22-1 to 22-3 for network 14 of the Fig. 2 illustrated in tabular form. Each of the transport orders 22 defines a start point, a destination point, and a route 24 between these points. Furthermore, each of the orders 22 defines a start time and a (calculated, predicted) end time. Each of the orders 22 is assigned to one (or more) specific handling units, which is also specified in the respective order 22. It is understood that the transport orders 22, in addition to those in Fig. The 4 properties shown may include one or more of the following information (not shown), such as: the start time, the end time, a handling unit ID, a prioritization level and similar information, which may be useful for further specifying a transport request 22.

[0052] Transport order 22-1 of the Fig. Figure 4 represents an exemplary transport of a handling unit (not illustrated) from point A (source) to point C (destination) via routes #2 and #6, where this transport is to be carried out by the transport device 15-1, e.g. by an AGV 20 (see Figure 4). Fig. 2) If one of the routes #1 to #12 is implemented by a continuous conveyor 16, the corresponding specification of the transport device 15 is not required. In this case, however, transfer times can be determined and defined. The transport orders 22-2 and 22-3, shown in line form, Fig. 4 define two transport paths 24-2 and 24-3, both starting at point B and ending at point E, but traversing network 14 differently. Fig. 3 extend.

[0053] The intralogistics system 10 of Fig. In addition to the transport network 14, the system comprises a controller 26 and sensors 28. The controller 26 includes a real (classic) material flow computer (MFC) 30 (hardware and software) and a digital material flow twin, i.e., a digital twin of the material flow (DZ-MF) 32 (software). The controller 26 is implemented by one or more computers and one or more control programs (software). The MF control operations, i.e., in particular the coordination of the transport orders 22, can be carried out centrally (by the MFC 30) or decentrally (MFC 30 in combination with, for example, a subordinate transport equipment controller), based on (current) operating states 34, which are reported back to the controller 26 by the sensors 28 to cyclically verify the execution of the orders 22. The states 34 can be communicated to the MFR 30 and / or the DZ-MF 32, and the states 34 can be exchanged with each other.

[0054] The MFR 30 is configured to initially plan and generate the transport orders 22 and then continuously coordinate them, as mentioned earlier. The transport orders 22 are triggered, for example, by picking orders (not shown) to retrieve storage containers (handling units) from their respective storage locations (starting point) and transport them to a workstation (destination point). There, a person or a robot removes stored items from the storage container(s) and places them on an order container (another handling unit), which is then also transported to the workstation according to another transport order 22 (synchronized in time and space). The MFR 30 communicates the initially generated transport orders 22 to the participating transport equipment 15 (and its controllers, if present), which then execute these orders 22 accordingly, possibly with additional coordination by the MFR 30.

[0055] The sensors 28 record, as sensor data, operating states 34 of the MF, the transport network 14, and the transport equipment 15. Some of the sensors 28 can be provided separately from the transport equipment 15, such as centrally positioned cameras in system 10 that provide 2D images of entire areas (e.g., of the warehouse), from which information (occupancy status, traffic density, etc.) about the MF on one or more routes can be extracted using image processing. Other sensors 28 are integrated directly into the transport equipment 15, such as speed, position, and distance sensors in the AGVs 20 or light barriers, weight sensors, and scanners at the inlet / outlet of the continuous conveyors 16.

[0056] The recorded operating states 34 are transmitted by the sensors 28 to the controller 26 (wired and / or wirelessly) via appropriately configured interfaces (including protocols, not shown). The sensor data constitute input data for the MFR 30 and the DZ-MF 32. The sensor data is used to synchronize the real MF with a simulated material flow, which is cyclically generated by the DZ-MF 32.

[0057] Exemplary operating states 34 are: occupancy states of the transport equipment 15; transport speeds of the transport equipment 15; (current) positions of the (movable) transport equipment 15; current motor currents or voltages; current charge levels of energy storage devices, and similar parameters. The operating states 34 change, so they are monitored by the control unit 26 in order to be able to react situationally in the event of (unexpected) changes.

[0058] The DZ-MF 32 is set up to virtually or digitally replicate the real MF within the transport network 14 by using a material flow simulation model (MF model) 40, as will be explained in more detail below.

[0059] Generally, digital twins (DCs) are understood to be virtual representations of tangible and / or intangible objects from the real world. In this case, one such object is the material flow. The virtual representations incorporate (functional) models, simulations, and / or algorithms that reproduce the properties and behaviors of the real objects as accurately as possible in the virtual world. The interactions of objects in reality are becoming increasingly complex. Relationships and dependencies between objects, as well as the effects of their changes, are becoming ever more difficult to predict (in reality). This is why the DC is so important. The DC makes it possible to create a virtual representation of reality. Changes to parameters can be tested in advance on this virtual representation through simulation.

[0060] A general goal of using DZ is to simulate and optimize new solutions, planned changes, and new techniques in the virtual digital world before transferring them to the real world. Fig. Figure 5 illustrates how a classic double boiler works.

[0061] In the Fig. 5. First, data generated by a real object, such as an FTF 20 transporting a handling unit from A to B, is sensorily captured (S1), stored (S2) in the real world, and then transferred via a link (interface) to a digital twin, such as the DZ-MF 32. Fig. 1. transmitted (S3). In the virtual world, this sensor data can be analyzed and evaluated (S4), e.g., by controlling the material flow based on the initially defined transport orders and the current sensor data (operating states 34 in Fig. 1) is simulated again, whereby the simulation can be performed with the simulation model of the MFR 30 or with another simulation model. Then the (operating) parameters 36 are varied (S5) to simulate the functionality of the virtually represented object again with the respective parameter setting (for each changed parameter) (S6). Every possible parameter change, which can also include a set of changed parameters 36, can thus be simulated in order to subsequently evaluate the simulation results (with and without parameter changes) (S7). For this purpose, the simulation results are analyzed by comparing them with each other and weighting them according to one or more predefined criteria (e.g., increased throughput, shorter throughput time, shorter total processing time, reduced wear, reduced operating costs, more even utilization, lower personnel requirements, etc.).The parameters are evaluated to determine an optimal setting from the multitude of simulated parameter settings. These results, and in particular the optimal or optimized parameter setting, can be saved (S8), and the optimal operating parameter(s) 36 are transferred back to the real object (transport device 15) via the interface from the controller 26, and in particular from the DZ-MF 32 (S9), see also . Fig. 1. The real object adopts the optimized parameter setting (S10) and operates with this setting from then on (S11) until it receives a new parameter setting in a future cycle. Afterwards, the process described above can be repeated from step S1 to initiate and implement a process of continuous improvement. Fig. The visualized figure eight illustrates the cyclically optimizing influence of the DZ on the real world quite clearly. It goes without saying that the demands on the computing power of the DZ-MF 32 increase the shorter the cycle time is chosen and the more parameters 36 are varied per cycle.

[0062] Possible hierarchies of the DZ-MF 32 of the Fig. 1 are in the Fig. 6A and Fig. 6B is illustrated in more detail. Fig. 6A shows a first uniform variant and Fig. Figure 6B shows a second distributed variant of a DZ architecture.

[0063] In the case of the uniform architecture of the DZ-MF 32 in Fig. 6A The (simulation) models 38 for MF participants (i.e., transport equipment 15) are integrated into the MF (simulation) model 40. The participant models 38 comprise simulation models 38-1 for the real continuous conveyors 16 and / or simulation models 38-2 for the real discontinuous conveyors 18. The MF model 40 simulates the MF by virtually reproducing a sequence of transport operations based on the real transport orders 22 of the MFR 30, which are carried out by the transport equipment 15, additionally taking into account the operating states 34 of the sensors 28 in order to perform the parameter optimization process described above by means of a parameter optimization device 42, which is included in the DZ-MF 32. The DZ-MF 32 further includes an analysis device 44, which is configured to execute step S7.The analysis unit 44 compares the various material flows simulated on different parameters with the simulated material flow where parameters remain unchanged, and evaluates them from the perspective of improved throughput. Throughput can be expressed, for example, as: an increased number of completed transport orders 22 per unit of time; shorter lead times, i.e., shorter times to complete an order 22; a shorter overall processing time, i.e., a shorter time to complete all orders 22; reduced wear, e.g., of a drive motor that is subjected to less stress; reduced operating costs; more even utilization; lower personnel requirements, and the like.

[0064] The same applies to the distributed architecture of Fig. 6B. There, the participant models 38 are comprised of respective digital participant twins or digital transport equipment twins 46, which can be provided separately from the MF-DZ 32. The DZ-MF 32 and the DZ 46 of the transport equipment 15 are provided independently of each other and function independently of each other. The DZ-MF 32 simulates the MF based on the MF model 40, which can also include the MF participant models 38 (identical or in simplified form). In addition, the digital twins exist for at least some, and preferably all, of the transport equipment 15, i.e., DZ-TE 46.The DZ-TE 46 simulate the operating functions of their respective transport devices 15 and can - in addition to the throughput - effect additional performance improvements for the respective transport device 15 by repeatedly optimizing its operating parameters 36 in the simulation based on the real operating states 34 supplied by its sensors 28 and evaluating them from other perspectives in order to use the differently optimized parameters 36 in reality.

[0065] For example, it is possible to monitor the current and voltage behavior of an FTF 20 battery to enable the corresponding model 38 to anticipate above-average discharges due to aging or a wear-related battery failure. One or more operating parameters 36 of this FTF 20 could be modified so that the FTF 20 can be used for longer than predicted or can be serviced in time before the anticipated failure. The anticipated failure also represents an operating state 34 that can be communicated to the DZ-MF 32, which in turn can take it into account.

[0066] The following are some examples of unexpected problems encountered in the MFR that a classic MFR 30, i.e., without support from the DZ-MF 32, could only solve poorly or not at all. Reference is made to the flowchart of the Fig. 7 referenced.

[0067] The MFR 30 receives requests (picking orders, transfer orders, transport requests, etc.) from external sources, e.g., from a picking order management system (not shown) and / or a warehouse management system (not shown), see step S20. The MFR 30 then initially plans and generates the corresponding transport orders 22, possibly based on a material flow simulation that is initially and only once fed with the relevant requests. During planning, the MFR 30 can use a predefined set of operating parameters 36 of the transport equipment 15. The MFR 30 determines the transport orders 22, e.g., based on currently implemented logic, with optimized throughput, whereby the MFR 30 can already factor in delays derived from experience. The initially generated transport orders 22 are then communicated to the corresponding transport equipment 15, see step S22.Optionally, the transport orders 22 planned and generated in this way can be verified by the control system 26, in particular by the DZ-MF 32, based on actual states 34 (in advance), see step S23, before the actual implementation begins in step S24.

[0068] The transport equipment 15 then begins to execute the orders 22 (step S 24), which result in the (initially planned) material flow, provided that no unexpected operating condition 34 of the material flow and / or the transport equipment 15 occurs. Up to this point, the procedure of the present disclosure does not differ from the classical procedure.

[0069] In the classical approach, if an unexpected problem occurs spontaneously (e.g., a collision is imminent between two AGVs 20 because one of them is traveling slower than expected; or a conveyor cannot deliver its conveyed goods because the receiving conveyor is occupied, etc.), the associated sensors 26 deliver this (unexpected) operating state 34 either directly to the controller 26 or the MFR 30, or to a participating (decentralized) sub-control unit (e.g., the AGV controller or the conveyor controller), see step 26. If the MFR 30 and / or the sub-control unit are able to solve this problem based on a predefined set of rules of possible solutions (e.g., whoever has a higher priority has right of way; whoever is more delayed goes first, etc.), which are inherently unchangeable, and which is checked in step S28 by, for example, reporting back corresponding states 34, then there is indeed a reduction in throughput.-deterioration (step S30), but not leading to a more serious system shutdown that can only be resolved by an external intervention from a maintenance technician (step S32). External intervention represents the last resort within the previously established rule set. The maintenance process continues until all tasks 22 are completed. If unexpected operating conditions 34 occur again during this time, some steps S26-S32 are performed again using the standard procedure.

[0070] The present disclosure, however, precedes the classical problem-solving approach with DZ-MF 32, see Block A and Fig. 5.

[0071] Block A of the Fig. 7 corresponds to the optimization procedure of Fig.5, that uses the DZ-MF 32 to cyclically find operating parameters 36 for the transport facilities 15 involved in the implementation of the MF, which result in a better throughput than the material flow simulated on the current operating states 34 without parameter changes would be expected in the forecast.

[0072] The digital material flow twin 32 anticipates, for example, a future collision between the two AGVs 20 or a transfer problem between the adjacent continuous conveyors 16 long before these situations actually occur. Ideally, the DZ-MF 32 prevents these situations from arising in the future. The DZ-MF 32 (intuitively) modifies the operating parameters as part of its parameter optimization, for example, by changing the transport speed of one (or both) of the AGVs 20 or of the supplying continuous conveyor 16 so that a collision or delayed discharge is avoided altogether. In this case, the transport speed represents the variable operating parameter 36. However, parameter 36 could also be an (alternative) route within path 24, in which case the transport order 22 itself would be modified. There are no limits to the choice of possible transport devices and their parameter settings.The DZ-MF eliminates unproductive settings and finds an optimal setting, especially within the limits of the available computing time and the optimization algorithms used.

[0073] The DZ-MF 32 thus does not prevent the problem by applying predefined, fixed solutions, but rather by (at least) one of the many parameter variations that have proven advantageous during the current simulation cycle. Ideally, no unexpected operating states 34 are reported back to the controller 26 (step S26), so that the transport orders 22 representing the material flow are processed solely on the basis of the parameter changes with a throughput (step S34) that represents a significant improvement compared to implementing the initially planned transport orders 22, even if the throughput actually achieved in this way is lower than the throughput originally estimated during the initial planning.

[0074] Of course, despite Block A, material flow disruptions can still occur that can only be resolved using traditional methods. However, the probability of such disruptions is significantly lower than with the traditional approach.

[0075] Furthermore, it follows that the effects described above can also be achieved if the material flow is not optimized across the entire transport network 14 via the operating parameters 36, but only in a sub-area of ​​the network, such as a storage pre-zone. In this case, the conveyor technology of the pre-zone constitutes a subsystem of the transport network 14 with its associated transport equipment 15. If, among these transport equipment 15, there are those whose operating parameters 36 are not variable, then only those whose parameters 36 are variable will be optimized. Nevertheless, the improved material flow described at the outset can still be achieved in this case. Reference symbol list 10 Intralogistics systems 12 Storage and order picking systems 14 (Transport) network 15 Transport equipment 16 continuous conveyors 18 discontinuous conveyors 20 automated guided vehicles (AGVs) 22 Transport order 24 Transport route 26 Control 28 sensors 30 Material Flow Calculators (MFR) 32 Digital Twin of the Material Flow (DZ-MF) 34 (Operating) Conditions 36 (operating) parameters 38 (Simulation) Model for MF Participants 38-1 Continuous conveyor model 38-2 Discontinuous conveyor model 40 MF (Simulation) Model 42 Parameter optimization device 44 Analysis unit 46 Digital twin of a transport facility 15

Claims

[1] Intralogistics system (10) which features: a transport network (14) comprising a plurality of transport units (15; 16, 18) and designed to implement a material flow within the intralogistics system (10) caused by transport orders (22), wherein each of the transport facilities (15) is operated with at least one preset, variable operating parameter (36); a large number of sensors (28) that cyclically detect current operating states (34); and a control system (26) which includes a material flow computer (30) that initially plans and generates the transport orders (22), and which cyclically coordinates the implementation of the transport orders (22) based on the current operating states (34); wherein the control (26) further comprises a digital material flow twin (32) which includes a material flow simulation model (40), an operating parameter optimization device (42) and an analysis device (44) and is set up: to cyclically simulate the material flow based on the respective current operating states (34) with and without varying operating parameters (36) of the transport equipment (15), to analyze the simulated material flows with regard to throughput improvement, and in the event that a throughput improvement is analyzed, to transmit the correspondingly varied operating parameters (36) to the corresponding transport facilities (15), which are then operated based on the varied operating parameters (36). [2] Intralogistics system according to claim 1, wherein the material flow computer (30) is set up: to initially plan, generate and transfer the transport orders (22) based on picking orders, transport requests and / or stock transfer orders to the appropriate transport facilities (15); to continuously coordinate the material flow based on the current operating states (34) by implementing a problem solution based on fixed, predefined solution rules in the event of a material flow problem; and to receive the operating states (34) from the sensors (34). [3] Intralogistics system according to claim 1 or 2, wherein the transport network (14) comprises a multitude of transport sources and a multitude of transport destinations, which are interconnected via a multitude of transport routes (24), and Each of the transport orders (22) defines a handling unit-specific transport route (24) from one of the sources to one of the destinations. [4] Intralogistics system according to one of claims 1 to 3, wherein the throughput improvement results in a higher number of completed transport orders (22) per unit of time compared to the material flow simulated based on the respective current operating states (34) without varying operating parameters (36). [5] Intralogistics system according to one of claims 1 to 4, wherein the correspondingly varied operating parameters, which are to be transmitted to the corresponding transport equipment, leave the transport orders unchanged. [6] Intralogistics system according to one of claims 1 to 5, wherein at least some, preferably all, of the transport devices (15) each comprise at least one of the sensors (28). [7] Intralogistics system according to one of claims 1 to 6, wherein the simulation of the material flow (each) extends to a time at which all of the initially generated transport orders (22) have been completed. [8] Intralogistics system according to any one of claims 1 to 7, wherein the transport units (15) comprise: Discontinuous conveyors (18), in particular driverless transport vehicles (20), and / or Continuous conveyors (16), in particular roller conveyors, belt conveyors, chain conveyors and / or overhead conveyors. [9] Intralogistics system according to one of claims 1 to 8, wherein the intralogistics system (10) is a storage and order picking system (12) which further comprises at least one of the following functional areas: a camp; a goods receipt; a goods issue; a workstation; and / or a production. [10] Method for improved implementation of an initially planned material flow in an intralogistics system (10) comprising: a transport network (14) comprising a plurality of transport devices (15; 16, 18) configured to implement a material flow within the intralogistics system (10) triggered by transport orders (22), each of the transport devices (15) being operated 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 steps: cyclical acquisition, by the sensors (28), of current operating states (34); and initial planning and creation of transport orders (22) as well as cyclical coordination of the created transport orders by the control system (30); wherein the controller (26) further comprises a digital material flow twin (32) which includes a material flow simulation model (40), an operating parameter optimization device (42) and an analysis device (44) and which performs the following cyclic steps: Simulating the material flow based on the respective current operating states (34) with unchanged operating parameters (36) as well as with a variety of changed operating parameters (36) of the transport equipment (15); Analyzing simulated material flows with regard to throughput improvement; In the event that a throughput improvement is analyzed, the correspondingly varied operating parameters (36) are transferred to the corresponding transport equipment (15); and Operating the corresponding transport equipment (15) with the varied operating parameters (36). [11] Method according to claim 10, wherein the transport equipment (15) is operated with the varied operating parameters (36) without changing the initially planned and generated transport orders (22).

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