Warehousing device, method for operating a warehousing device, and computer-implemented method for a warehouse management system

Abstract

The invention relates to a warehousing device, to a method for operating a warehousing device and to a computer-implemented method for a warehouse management system. In view of the increasing requirements relating to efficiency and sustainability, there is a great need to reduce the energy consumption of warehousing devices. The problem addressed by the present invention is therefore that of proposing a warehousing device and a method for operating a warehousing device, by means of which the energy consumption of warehousing processes can be reduced and the efficiency can be optimised. The invention further relates to a computer-implemented warehouse managing system which enables an optimisation of the energy consumption, including with the use of order data. This is achieved by the present invention in particular by a warehousing device which in particular comprises the following: - a device for detecting process variables for storing load carriers, - a device for calculating an energy consumption factor for a load carrier which results from the process variables for storing the load carrier, and - a control device for positioning the load carrier in the warehousing device according to the energy consumption factor.

Description

[0001] Kardex Production Germany GmbH

[0002] 247P 2626

[0003] Storage device, method for operating a storage device and computer-implemented method for a warehouse management system

[0004] The invention relates to a storage device and a method for operating a storage device, as well as a computer-implemented method for a

[0005] Warehouse Management System.

[0006] Automated storage and retrieval systems (ASRS) are systems for automating intralogistics, for example, in high-bay warehouses. In these systems, goods-carrying load carriers are stored and retrieved at defined storage locations and transported to and from defined access points under computer control. A warehouse management system (WMS) can serve as the software-based management tool for warehouses, mapping, controlling, and planning the flow of materials. While such systems offer significant advantages in terms of efficient use of storage space, processing speed, personnel costs, and safety, their energy consumption also increases. Given the rising demands for efficiency and sustainability, there is considerable potential for reducing energy consumption in this area.

[0007] It is therefore an object of the present invention to propose a storage device and a method for operating a storage device with which the energy consumption of storage processes can be reduced and efficiency optimized. Furthermore, a computer-implemented warehouse management system is to be proposed that enables the optimization of energy consumption, also taking order data into account.

[0008] These tasks are solved by a storage device according to claim 1, a method for operating a storage device according to claim 6 and a computer-implemented method for a warehouse management system according to claim 11.

[0009] A storage device according to the invention comprises in particular a device for recording process variables for the storage of load carriers, a device for calculating an energy consumption factor for a load carrier, which results from the process variables for the storage of the load carrier, and a control device for positioning the load carrier in the storage device according to the energy consumption factor.

[0010] Here, the term "storage facility" refers specifically to an automated storage and retrieval system (ASRS), but also more generally to a general cargo warehouse, such as a high-bay warehouse, flat goods warehouse, block storage system, cold storage warehouse, storage lifts, or similar. The storage facility can include conveying equipment such as stacker cranes, roller conveyors, floor conveyors, elevators, cranes, extractors, or rail conveyor systems, which encompass varying degrees of automation. Within the storage facility, a storage location is assigned to a load carrier that carries goods to be stored. A load carrier is, for example, a tray, a pallet, a container, a workpiece carrier, a basket, or a general or facility-specific receptacle. During storage, the load carrier is transported by a conveying device from an access point to its assigned storage location.When a stored item is accessed again to retrieve or change its position, the load carrier is transported from its storage location to the access point. A storage operation can encompass numerous such accesses and storages, as well as the time intervals between them. There can be multiple access points, for example, on several levels of a high-bay warehouse.

[0011] The storage device according to the invention includes a device for recording process variables, which records process variables for each storage operation and assigns them to the respective load carriers. This device can be a sensor system that detects the process variables on the load carrier, a device that detects the state of the storage device, a database containing process variables for specific storage operations, load carriers, or stored goods, or a combination of several of these devices. Any number of process variables describing the storage operation, the load carrier to be stored, and the stored goods can be recorded.

[0012] A device for calculating an energy consumption factor calculates an energy consumption factor for the respective charge carrier based on the recorded process variables. The algorithm for calculating the energy consumption factor is determined by the selection of the process variables. The energy consumption factor indicates, in an abstract form, how much energy is required for a storage process of the charge carrier.

[0013] Based on the energy consumption factor, a control device determines the positioning of the load carrier within the storage system. For example, a load carrier with a high energy consumption factor can be positioned close to an access point to reduce energy consumption per storage and retrieval operation. The process variables can also be adjusted by positioning the load carriers. Furthermore, the control device can, of course, also take into account additional factors that must be considered when planning storage facilities, such as available storage space, load carrier size, or specific storage conditions, when positioning the load carriers. Thus, a storage system according to the invention can reduce the energy consumption of storage operations.By taking the energy consumption factor into account when positioning the load carriers, the storage device can be operated efficiently and sustainably.

[0014] The process variables recorded for the load carriers can include, in particular, weight, dimensions, handling frequency, kinematics, travel distance, retrieval times, storage efficiency, storage duration, and order data. The weight of a load carrier, including the stored goods, is a significant factor in the energy consumed during each storage operation. This weight can be measured, for example, by sensors on the load carriers, access points, or the conveyors of the storage system. Specifically, the weight of a load carrier can be derived from the instantaneous current flowing through the conveyor motors. Similarly, the dimensions of a load carrier can also be recorded and used as an indicator of estimated energy consumption and as a parameter for the storage process.The turnover rate describes how often a load carrier is requested at an access point within a given time period, i.e., how frequently energy must be expended to transport the load carrier. Other important process variables are the kinematics and travel distances required for positioning the load carriers within the storage system. For example, the energy consumption of storage operations can be reduced by moving the load carrier at a lower speed or by adjusting its travel distance to utilize gravity and momentum. Similarly, retrieval times—for instance, whether a load carrier is frequently requested at specific times of day—can be recorded to adjust the positioning of the corresponding load carriers throughout the day.The efficiency of warehouse utilization and the storage duration of goods are standard parameters in many warehouse systems, but these can also influence energy consumption, for example, in the form of electricity costs for lighting or cooling storage units. Additionally, order data, in particular, can be incorporated into the calculation of the energy consumption factor to make predictions about the future development of process variables, such as the turnover rate of certain goods.

[0015] The device for calculating the energy consumption factor can be configured to calculate the energy consumption factor at defined intervals, and the control device for positioning the load carriers can be set up to rearrange the load carriers accordingly at defined intervals. In this way, a dynamic adjustment of the load carrier positioning can be achieved in response to changing process variables. Such a determination of the energy consumption factor and rearrangement of the load carriers to optimize efficiency could, for example, occur once a day. It is also conceivable that the energy consumption factor of a load carrier could be updated with each storage and retrieval operation, for example, to react to a change in weight due to a larger removal of goods.

[0016] In particular, the control device can be configured to reorder load carriers within the storage system according to their energy consumption factors when the storage system's utilization is low, for example, during off-peak hours, so as not to disrupt normal operation. Similarly, reordering can be performed during periods of lower energy costs, such as at night. For this purpose, the control device can include an interface for determining energy tariffs or a database of known energy supplier schedules.

[0017] Furthermore, the control device can be configured to rearrange the load carriers in the storage system according to their energy consumption factors when this is manually specified, for example, by an operator. Such rearrangement can also occur automatically when a manually specified threshold is reached, exceeded, or fallen below.

[0018] The device for calculating the energy consumption factor can be configured to use a machine learning algorithm to make predictions based on historical process variables. In this way, the storage system can make predictive adjustments based on empirical data to optimize energy consumption. Specifically, the storage system can, based on a threshold determined by empirical data, rearrange the load carriers within the system according to their energy consumption factors when the defined threshold is reached, exceeded, or fallen below. Factors that can be considered in this way include historical order data, seasonal variations, weather, and other trends.

[0019] The invention further relates to a method for operating a storage device. A method according to the invention for operating a storage device comprises, in particular, the following steps:

[0020] Acquisition of process variables for the storage of load carriers by a device for acquiring process variables,

[0021] Calculation of an energy consumption factor for a charge carrier, wherein the energy consumption factor results from the process variables for the storage of the charge carrier, by a device for calculating an energy consumption factor,

[0022] Positioning of the load carrier in the storage device according to the energy consumption factor by a control device.

[0023] The inventive method for operating a storage device provides for the acquisition of process variables. Any number of process variables can be acquired that describe the storage process, the load carrier to be stored, and the stored goods.

[0024] Based on the recorded process variables, an energy consumption factor is calculated for the respective load carrier. The algorithm for calculating the energy consumption factor is determined by the selection of the process variables. The energy consumption factor indicates, in an abstract form, how much energy is required for a storage process of the load carrier.

[0025] The positioning of load carriers within the storage system is determined by their energy consumption factor. For example, a load carrier with a high energy consumption factor can be positioned close to an access point to reduce energy consumption per storage and retrieval operation. Process variables can also be adjusted by positioning the load carriers. Furthermore, additional factors to consider when planning storage facilities, such as available storage space, load carrier size, or specific storage conditions, can also be taken into account when positioning the load carriers.

[0026] The energy consumption of storage processes can be reduced by a method according to the invention. By taking the energy consumption factor into account when positioning the load carriers, the storage device can be operated efficiently and sustainably.

[0027] The recorded process variables of the load carriers can include, in particular, weight, dimensions, handling frequency, kinematics, travel distance, retrieval times, efficiency of storage utilization, storage duration and order data.

[0028] The energy consumption factor can be calculated at fixed time intervals, and the positioning of the load carriers can be carried out accordingly at fixed time intervals.

[0029] In particular, the sorting of load carriers within the storage system according to their energy consumption factors can be carried out whenever the storage system's utilization is low, for example, during off-peak hours, so that normal operation of the storage system is not disrupted. Similarly, such sorting can be performed during periods of lower energy costs, for example, at night.

[0030] The calculation of the energy consumption factor can utilize a machine learning algorithm to make predictions based on historical process variables. In this way, predictive adjustments can be made based on "empirical values" to optimize energy consumption. Influences that can be considered in this way include, for example, historical order data, seasonal variations, weather, and other trends. Furthermore, the invention relates to a computer-implemented method for a warehouse management system. A computer-implemented method for a warehouse management system that controls at least one storage device comprises, in particular,

[0031] Receiving real-time order data for current orders,

[0032] Access to a database, comprehensive historical order data and associated process variables,

[0033] Recording of process variables for the storage of load carriers according to current orders,

[0034] Calculation of an energy consumption factor for load carriers for current orders, taking into account process variables and historical order data as well as real-time order data.

[0035] Optimization of the execution sequence of current orders taking into account energy consumption factors, dynamic adjustment of the execution sequence taking into account real-time order data,

[0036] Positioning of the load carrier in the storage device in the execution sequence according to the energy consumption factor.

[0037] A computer-implemented warehouse management system (WMS) is used to control at least one storage device, to track the flow of goods into, out of, and within that device, and to ensure a complete inventory. The WMS is implemented as software on a computer and causes the computer to perform the following steps, or to send corresponding commands to a storage device connected to the computer.

[0038] For this purpose, the warehouse management system receives real-time order data from current orders. This real-time order data contains information about the goods required for the current orders, which must be provided by a storage facility or stored within it.

[0039] At the same time, the procedure for a Warehouse Management System has access to a database of historical order data and associated process variables that were recorded during the execution of the historical orders.

[0040] The process variables for storing the load carriers required for the current orders are recorded by the storage device or taken from the real-time order data.

[0041] Based on the recorded process variables, historical process variables, and real-time order data, an energy consumption factor is calculated for the load carriers of the current orders. This factor indicates the expected energy consumption during the execution of the respective current orders. The algorithm for calculating the energy consumption factor is determined by the selection of process variables.

[0042] Based on the calculated energy consumption factors of the current orders, the order of execution of the orders is optimized in order to save as much energy as possible.

[0043] However, the execution order is also dynamically adjusted based on real-time order data. For example, a new high-priority order can take precedence over orders that have already been queued, regardless of its energy consumption factor.

[0044] The load carriers are positioned within the storage device according to the specified execution sequence, applying the rules described above. In particular, process variables such as travel distances and kinematics can be adjusted to execute orders according to their priority and thus reduce the energy consumption of lower-priority orders.

[0045] The recorded process variables can include, for example, weight, dimensions, turnover rate, kinematics, travel distance, retrieval times, warehouse utilization efficiency, storage duration, energy consumption per order, and order execution time. In addition to the process variables already mentioned for individual load carriers, energy consumption per order and order execution time should also be noted, as these are based on the fact that an order can require multiple load carriers. The warehouse management system allows these storage processes to be combined for a single order and controlled as a single operation.

[0046] The dynamic adjustment of the execution order can be achieved, in particular, by taking user input into account. This allows a user to interrupt or override the execution sequence. Specifically, a warning system can also be implemented that transfers the decision-making to the user when complex or unusual scenarios arise that the algorithm cannot handle optimally.

[0047] The computer-implemented process for a warehouse management system can utilize a machine learning algorithm to calculate the energy consumption factor and optimize the execution sequence, making predictions based on historical order data. This allows for predictive adjustments based on empirical data to optimize energy consumption.

[0048] The invention also includes a computer program product that can be provided on a data carrier and includes instructions which, when loaded into the memory of a computer, cause it to execute the previously described method for a warehouse management system.

[0049] The computer-implemented process for a Warehouse Management System allows one or more storage facilities to be operated efficiently, while the inclusion of order data ensures efficient processes.

[0050] The described embodiments of the storage device, the method, and the computer-implemented method for a warehouse management system can be used individually or in combination to achieve reduced energy consumption and optimized efficiency in storage operations. The aforementioned and further aspects of the invention will become apparent from the detailed description of the exemplary embodiments, which is given with the aid of the following figures, of which:

[0051] Fig. 1 schematically depicts a storage device,

[0052] Fig. 2 shows a flowchart of a method for operating a storage device; and

[0053] Fig. 3 shows a flowchart of a computer-implemented procedure for a Warehouse Management System.

[0054] The following section will explain the device and the procedures in more detail based on the accompanying drawings. Reference numerals refer to the same elements.

[0055] Figure 1 schematically illustrates a storage device 1 in a top view, using a high-bay warehouse as an example. A load carrier 3 is stored in a storage unit of a high-bay warehouse 6 from an access point 8 by a conveying device 8, here represented as the mast of a storage and retrieval machine, or removed from it and transported to an access point 7. Hatched storage units represent already occupied units of the high-bay warehouse 6. The dashed double arrows represent the possible travel paths of the load carriers 3. A computer 10 serves to monitor the storage device 1 and includes the device for recording process variables 2, the device for calculating an energy consumption factor 4, and the control device for positioning the load carrier 5.It should be noted that the storage device 1 is shown schematically in a top view; in reality, the high-bay racking system 6 has several levels, and the access points 7 can also be arranged on different levels, and there can be multiple access points 7 on one storage device. The load carriers 3 can be trays, pallets, containers, or other receptacles designed for storing goods.

[0056] Process variables describing the storage process of the load carriers 3 are recorded by a process variable acquisition device 2. For this purpose, the process variable acquisition device 2 is connected via data transmission links 11 to a sensor 9 and the conveying device 8. The sensor 9, which in this illustration is attached to the access points 7, can be, for example, scales, optical sensors, or RFID receivers. This allows, among other things, the recording of process parameters such as weight, dimensions, and handling frequency. The conveying device 8 can, for example, determine the weight of the load carrier 3 from the instantaneous current, as well as travel distances and kinematics.Further process variables can be recorded via additional sensors not shown, or obtained from databases of the Warehouse Management System, for example the current utilization of the storage space, the turnover rate, retrieval times and the storage duration of the respective load carriers 3, as well as order data.

[0057] The device for calculating an energy consumption factor 4 calculates an energy consumption factor for the charge carrier from the process variables, which indicates the energy consumption for a storage process of the charge carrier 3. Possible algorithms for this calculation, in particular using machine learning, are described in more detail below with reference to Fig. 2.

[0058] The control device for positioning the load carrier 5 then determines a storage position for the load carrier 3 based on its energy consumption factor, thereby reducing the energy consumption of the storage process, and instructs the conveying device 8 via the data transmission link 11 to position the load carrier in the corresponding storage unit. For example, load carriers 3 with a high energy consumption factor can be positioned as close as possible to an access point 7 where they are most likely to be requested.

[0059] The process variables can be recorded during the initial storage of a load carrier 3. Likewise, the process variables can be recorded again with each storage and retrieval operation, and the energy consumption factor can be recalculated to optimize the positioning of the load carriers 3 with regard to the energy consumption of the storage processes. Additionally, the load carriers 3 can be reordered at defined intervals, for example, daily. This preferably takes place during off-peak hours when the storage system 1 is only lightly utilized. Furthermore, the storage system 1 can be configured to perform such reordering when energy costs are low, for example, at night.

[0060] In Fig. 1, the storage device 1 is shown as a high-bay warehouse, or ASRS. However, it is evident to a person skilled in the art that the same principles can also be applied to other types of storage systems.

[0061] Fig. 2 shows a flowchart of a method for operating a storage device 1. The method comprises the following steps:

[0062] Sl: Acquisition of process variables. A device for acquiring process variables 2, connected to a sensor 9 and a conveying system 8, acquires process variables for the storage of a load carrier 3. Process variables can include, among other things, the weight or dimensions of the load carrier 3, its handling frequency, kinematics and travel paths, as well as retrieval times, efficiency of storage utilization, storage duration and order data.

[0063] S2: Calculation of an energy consumption factor for the load carrier 3. The device for calculating an energy consumption factor 4 calculates an energy consumption factor that describes the energy consumption for the storage process of the load carrier 3. The algorithm used depends on the selection and weighting of the specific process variables. Machine learning algorithms S21 can also be used to make predictions about changes in the process variables.

[0064] S3: Positioning of the load carrier 3 in the storage device 1. According to the energy consumption factor, the load carriers 3 are prioritized and positioned in the storage device 1 so that their storage process requires as little energy as possible.

[0065] Steps S2 and S3 can be repeated at defined time intervals to achieve dynamic adjustment. A simplified example of the procedure for operating a storage device 1 is described below. Light bulbs, books, and dumbbells are to be stored in a storage device 1. The weight of the respective load carriers 3 is determined from the instantaneous current of the motor of the conveying device 8, and the turnover rate is determined by statistically recording requests at access point 7 (Sl). The load carrier 3 containing the light bulbs weighs 1 kg and is requested 100 times a day at access point 7. The load carrier 3 containing the books weighs 3 kg and is requested 50 times a day at access point 7. The load carrier 3 containing the dumbbells weighs 10 kg and is requested 5 times a day at access point 7.The energy consumption factor (S2) is calculated using the formula "weight x turnover rate". This results in an energy consumption factor of 100 for the light bulbs, 150 for the books, and 50 for the dumbbells. Therefore, the books would be positioned closest to access point 7 (S3). However, it is also possible for a user to assign a higher priority to the light bulbs, in which case the light bulbs would be positioned closest to access point 7, followed by the books and then the dumbbells.

[0066] However, if the book turnover rate increases from 50 times a day to 80 times a day, the energy consumption factor rises to 240. During the next reordering, a dynamic adjustment is made, and load carrier 3 containing the books is repositioned closer to access point 7. Similarly, a change in the weight of load carrier 3 due to the removal of goods is conceivable, which would also require a dynamic adjustment.

[0067] Furthermore, it is possible for a machine learning algorithm to make predictions about the future and dynamically adjust the energy consumption factor. For example, the collected data includes order data from a past fitness event. For an upcoming fitness event, the algorithm predicts increased demand for dumbbells. To reduce energy consumption during the storage and retrieval of the load carrier 3 containing the dumbbells, it is positioned closer to the access point 7 based on this prediction. Figure 3 illustrates the process flow of a computer-implemented procedure for a warehouse management system. The warehouse management system controls at least one storage device 1 and processes its inventory and order data. The warehouse management system can be executed on a computer 10.

[0068] Three sources serve as input for the process for a Warehouse Management System:

[0069] CI: Receiving real-time order data. The system receives order data for current orders. This data contains information about required goods and corresponding process variables. Multiple load carriers (3) may be required for a single order.

[0070] C2: Access to a database. Historical order data and associated process variables are stored in a database.

[0071] C3: Acquisition of process variables. For the load carriers 3 required for current orders, further process variables are acquired by sensors 9 or conveying equipment 8.

[0072] Based on this data, an energy consumption factor is calculated and orders are prioritized.

[0073] C4: Calculation of energy consumption factors. For the load carriers 3 required for the current orders, an energy consumption factor is calculated from the recorded process variables and taking historical order data into account. In particular, when using a machine learning algorithm C41, conclusions can be drawn from this historical order data regarding current orders and future changes in the process variables.

[0074] C5: Optimization of the execution sequence of orders. The execution sequence of current orders is optimized taking into account the calculated energy consumption factors, thus reducing the energy consumption of storage device 1. The optimization of the execution sequence can also be performed using a machine learning algorithm, C51.

[0075] C6: Dynamic adjustment of the execution order. The execution order is dynamically adjusted when real-time order data indicates orders with higher priority or higher energy consumption factors. User input (C61) can also be considered, which can interrupt or override the execution order. If complex or unusual scenarios occur that cannot be optimally handled by algorithm C51, the decision can also be made by a user.

[0076] C7: Positioning the load carriers 3. The current orders are executed in sequence, and the load carriers 3 are positioned in the storage device 1 according to their energy consumption factor. Energy efficiency can be further increased by appropriately controlling the travel paths and kinematics of the load carriers 3 and the conveying device 8. For example, the travel speed can be reduced for orders with lower priority, or load carriers 3 with high energy consumption factors can be positioned so that inertial effects can be utilized during their transport by the conveying device 8.

[0077] The computer-implemented warehouse management system extends the method described in relation to Fig. 2 by incorporating real-time and historical order data to manage the inventory of one or more storage devices in an energy-efficient manner. Efficiency can be further improved over time through the use of C51 machine learning algorithms.

[0078] As a simple example, consider a warehouse for fitness equipment. Storage unit 1 contains load carriers 3 with yoga mats and dumbbells. The yoga mats weigh 2 kg and are requested 70 times a day, while the dumbbells weigh 10 kg and are requested 30 times a day. Twenty orders for dumbbells are received for today. At the beginning of the day, the warehouse management system prioritizes the dumbbells and positions them closer to access point 7 to improve energy efficiency during order fulfillment. All 20 dumbbell orders are processed sequentially. Afterward, load carrier 3 containing the remaining dumbbells is moved further away from access point 7. The freed-up storage space can then be used to accommodate load carrier 3 for the yoga mats, which have a higher turnover rate.If a large number of orders for yoga mats are received during the day, potentially disrupting the order fulfillment process and unpredictable for the C51 algorithm, the warehouse management system may request confirmation from a human user. At the end of the day, the system can store the day's orders as historical order data in the database for use in evaluating future orders.

[0079] As can be seen from the above explanations, the storage device according to the invention, the method for operating the storage device, and the computer-implemented method for warehouse management enable energy-efficient control of storage processes. Overall, this allows the energy consumption of various storage systems to be reduced while simultaneously increasing the efficiency of order processing.

[0080] The embodiments shown here are not limiting. In particular, the features of these embodiments can be combined to achieve additional effects. It is obvious to those skilled in the art that modifications can be made to these embodiments without departing from the fundamental principles of the invention, the scope of which is defined in the claims.

Claims

Kardex Production Germany GmbH 247P 2626 Draft patent claims 1. Storage device (1) comprising a device for recording process variables (2) for storing load carriers (3), a device for calculating an energy consumption factor (4) for a load carrier (3) resulting from the process variables for storing the load carrier (3), and a control device for positioning (5) the load carrier (3) in the storage device (1) according to the energy consumption factor.

2. Storage device according to claim 1, characterized in that the process variables include weight, dimensions, turnover rate, kinematics, travel distance, retrieval times, efficiency of storage utilization, storage duration and / or order data.

3. Storage device according to claim 1 or 2, characterized in that the device for calculating the energy consumption factor (3) calculates the energy consumption factor at defined time intervals and the control device for positioning (5) the load carriers (3) arranges the load carriers (3) accordingly.

4. Storage device according to one of the preceding claims, characterized in that the control device for positioning (5) is configured such that a reordering of the load carriers (3) according to the energy consumption factor is carried out during off-peak times with low utilization of the storage device (1) and / or with low electricity costs.

5. Storage device according to one of the preceding claims, characterized in that the device for calculating the energy consumption factor (4) uses a machine learning algorithm. uses it to make predictions based on historical process variables.

6. Method for operating a storage device, comprising the steps: Acquisition of process variables (Sl) for a storage of charge carriers (3) by a device for acquiring process variables (2), Calculation of an energy consumption factor (S2) for a charge carrier (3), wherein the energy consumption factor results from the process variables for the storage of the charge carrier (3), by a device for calculating an energy consumption factor (4), and Positioning of the load carrier (S3) in the storage device (1) according to the energy consumption factor by a positioning control device (5).

7. Method according to claim 6, characterized in that the process variables include weight, dimensioning, turnover rate, kinematics, travel distance, retrieval times, efficiency of warehouse utilization, storage duration and / or order data.

8. Method according to claim 6 or 7, characterized in that the calculation of the energy consumption factor (S2) is carried out at fixed time intervals and the positioning of the charge carriers (S3) is adjusted accordingly.

9. Method according to one of claims 6 to 8, characterized in that a reordering of the load carriers (3) according to the energy consumption factor is carried out during off-peak times with low utilization of the storage device (1) and / or with low electricity costs.

10. Method according to one of claims 6 to 9, characterized in that the calculation of the energy consumption factor (S2) is based on a machine learning algorithm (S21) which enables predictions based on historical process variables.

11. Computer-implemented method for a warehouse management system controlling at least one storage device (1), comprising Receiving (CI) real-time order data of current orders, accessing (C2) a database encompassing historical order data and associated process variables, Acquisition (C3) of process variables for the storage of load carriers (3) according to the current orders, Calculation (C4) of an energy consumption factor for load carriers (3) for current orders taking into account process variables and historical order data as well as real-time order data, optimization (C5) of an execution sequence of the current orders taking into account the energy consumption factors, dynamic adjustment (C6) of the execution sequence taking into account real-time order data, Positioning (C7) of the load carriers (3) in the storage device (1) in execution sequence according to the energy consumption factor.

12. Computer-implemented method according to claim 11, characterized in that the process variables include weight, dimensions, turnover rate, kinematics, travel distance, retrieval times, efficiency of storage utilization, storage duration, energy consumption per order and / or execution time of orders.

13. Computer-implemented method according to claim 11 or 12, characterized in that the dynamic adjustment of the execution sequence takes into account user input (C61).

14. Computer-implemented method according to claims 11 to 13, characterized in that the calculation (C4) of the energy consumption factor and the optimization (C5) of the execution sequence are carried out using a machine learning algorithm (C41, C51) that enables predictions based on historical order data.

15. Computer program product comprising instructions which, when executed by a computer (10), cause the computer (10) to execute the method according to any one of claims 11 to 14.

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