Picking system order batch optimization method and device, electronic equipment and product

Abstract

The invention discloses a picking system order batch optimization method and device, electronic equipment and a product, and relates to the technical field of intelligent warehouse logistics. The method comprises the steps of obtaining order information of a new order; adding a new order to the order reserve queue; judging whether an order re-planning condition is triggered at present or not; if the order re-planning condition is triggered at present, performing batch optimization on all orders which are not selected at present through an adaptive large neighborhood search algorithm, and determining a plurality of batches of orders which are in one-to-one correspondence with the plurality of partitions and autonomous mobile robots and sorters corresponding to the partitions; optimizing the order sorting sequence of the batch orders corresponding to each partition through an NEH algorithm to obtain the optimized batch orders corresponding to each partition; and allocating orders to each subarea based on the optimized batch orders, and allocating an autonomous mobile robot and a picker to each subarea. According to the invention, dynamic adjustment of orders in each partition can be realized, and the overall resource utilization rate of the system is improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent warehousing and logistics technology, specifically relating to a method, apparatus, electronic equipment, and product for optimizing order batches in a picking system. Background Technology

[0002] In the field of smart warehousing and logistics, the efficiency of picking operations directly affects the overall throughput of the warehouse and the timeliness of order fulfillment. With the rapid development of e-commerce, new retail and other business models, warehouse orders are characterized by multiple categories, small batches and high frequency.

[0003] Current mainstream order batch optimization technologies are mostly based on the "static planning" approach, such as traditional path planning strategies like S-shape and Largest-gap. These strategies primarily perform one-time batch planning when all orders are known. However, in real-world warehousing scenarios, orders typically flow in continuously in real-time. Traditional one-time planning methods cannot dynamically adjust the batch structure based on the latest order data, leading to uneven load distribution, a coexistence of idle and congested equipment, and excessively low order picking efficiency, making them unable to cope with dynamic environments.

[0004] Therefore, how to provide an effective solution to adapt to scenarios where orders are updated in real time and improve order picking efficiency has become an urgent problem to be solved in existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, electronic device, and product for optimizing order batches in a picking system, in order to solve the aforementioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing order batches in a picking system, comprising: Obtain the order information of newly arrived orders, including the order arrival time and the order deadline for picking; Add the new orders to the order backlog queue; Determine whether the order replanning condition has been triggered. If the order replanning condition is triggered, based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, the adaptive large neighborhood search algorithm is used to perform batch optimization on all currently unpicked orders, and determine multiple batch orders that correspond one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition. All currently unpicked orders include orders that have been assigned to each partition but have not been picked, as well as all newly arrived orders in the order reserve queue. The system status of the picking system includes the position of each autonomous mobile robot, the status of pickers in each partition, and the workload of each partition. With batch order completion time and picker movement distance as optimization objectives, the NEH algorithm is used to optimize the order picking order of batch orders corresponding to each partition, resulting in optimized batch orders for each partition. Orders are assigned to each partition based on the optimized batch orders corresponding to each partition, and autonomous mobile robots and pickers are assigned to each partition based on the autonomous mobile robots and pickers corresponding to each partition.

[0007] Based on the above-disclosed content, this invention obtains the order information of newly arrived orders, including the order arrival time and the order deadline for picking; adds the newly arrived orders to the order reserve queue; determines whether the order replanning condition has been triggered; if the order replanning condition has been triggered, then based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, batch optimization is performed on all currently unpicked orders using an adaptive large neighborhood search algorithm, determining multiple batch orders corresponding one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition; with the batch order completion time and picker movement distance as optimization objectives, the order picking order order of the corresponding batch orders in each partition is optimized using the NEH algorithm, obtaining the optimized batch orders corresponding to each partition; orders are allocated to each partition based on the optimized batch orders corresponding to each partition, and autonomous mobile robots and pickers are allocated to each partition based on the autonomous mobile robots and pickers corresponding to each partition. In this way, as orders continue to pour in, order replanning can be automatically triggered and batch orders can be allocated and optimized for each zone. Then, orders, autonomous mobile robots and pickers can be allocated to each zone, thereby realizing dynamic adjustment of orders in each zone, making the workload of each zone more balanced, improving the overall resource utilization of the system, improving order picking efficiency, better meeting the timeliness requirements of orders, and adapting to scenarios where orders are updated in real time.

[0008] In one possible design, the method further includes: Based on the estimated time difference between the arrival of autonomous mobile robots in each zone at the rendezvous point and the arrival of pickers after completing picking, the rendezvous point location of each zone is determined, and a movement path from the current location to the corresponding zone is planned for each autonomous mobile robot. Send the location of the meeting point of each partition to the corresponding autonomous mobile robot.

[0009] In one possible design, determining whether the order replanning condition has been triggered includes: Determine whether the number of orders in the order reserve queue exceeds a preset quantity, whether the time interval since the last triggering of order replanning exceeds a preset time interval and there are new orders in the order reserve queue, whether there are urgent orders in the order reserve queue, whether the ratio of the total number of orders in the order reserve queue to the order capacity of the picking system exceeds a preset ratio, or whether at least one batch of allocated orders has been picked. If the order replanning condition is triggered, then based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, batch optimization is performed on all currently unpicked orders using an adaptive large neighborhood search algorithm, including: If the number of orders in the order reserve queue exceeds a preset quantity, the time interval since the last triggered order replanning exceeds a preset time interval, and there are new orders in the order reserve queue, there are urgent orders in the order reserve queue, the ratio of the total number of orders in the order reserve queue to the order capacity of the picking system exceeds a preset ratio, or at least one batch of assigned orders has been picked, then based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, batch optimization is performed on all currently unpicked orders using an adaptive large neighborhood search algorithm.

[0010] In one possible design, the order picking order for each partition is optimized using the batch order completion time and picker travel distance as optimization objectives. The NEH algorithm is then used to optimize the order picking order for each partition's corresponding batch orders, resulting in the optimized batch orders for each partition, including: The orders within each batch are initially sorted according to their order deadline for picking. Insert each order into the optimal position in the current sequence; Calculate the batch order completion time and picker movement distance after inserting the order into each position, and select the position that minimizes the evaluation index determined based on the batch order completion time and picker movement distance for insertion; The insertion priority is dynamically adjusted based on the urgency and batch size of the order. When all orders in each batch have been inserted into the sequence or the number of iterations has reached a preset number, the latest batch order in each partition is taken as the optimized batch order for that partition.

[0011] In one possible design, after obtaining the order information for the newly arrived order, the method further includes: Convert the order information of newly arrived orders into a standardized data format.

[0012] In one possible design, the order information may also include an order ID and product information.

[0013] In one possible design, the status of pickers in each zone includes the work status of pickers in each zone and the work efficiency parameters of pickers in each zone.

[0014] Secondly, the present invention provides an order batch optimization device for a picking system, comprising: The acquisition unit is used to acquire the order information of newly arrived orders, including the order arrival time and the order deadline for picking. Add a unit to add newly arrived orders to the order reserve queue; The judgment unit is used to determine whether the order replanning condition has been triggered. The first optimization unit is used to perform batch optimization on all currently unpicked orders based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, by using an adaptive large neighborhood search algorithm. This determines multiple batch orders that correspond one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition. All currently unpicked orders include orders that have been assigned to each partition but have not been picked, as well as all newly arrived orders in the order reserve queue. The system status of the picking system includes the position of each autonomous mobile robot, the status of pickers in each partition, and the workload of each partition. The second optimization unit uses the batch order completion time and picker movement distance as optimization objectives. It optimizes the order picking order of the batch orders corresponding to each partition through the NEH algorithm to obtain the optimized batch orders corresponding to each partition. The allocation unit is used to allocate orders to each partition based on the optimized batch orders corresponding to each partition, and to allocate autonomous mobile robots and pickers to each partition based on the autonomous mobile robots and pickers corresponding to each partition.

[0015] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the order batch optimization method for a picking system as described in the first aspect or any possible design of the first aspect.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the order batch optimization method for the picking system described in the first aspect or any possible design of the first aspect.

[0017] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the order batch optimization method for a picking system as described in the first aspect or any possible design of the first aspect.

[0018] Beneficial effects: The picking system order batch optimization method, device, electronic equipment, and product provided by this invention can automatically trigger order replanning as orders continuously flow in, and allocate and optimize batch orders for each zone. Then, it allocates orders, autonomous mobile robots, and pickers to each zone, thereby realizing dynamic adjustment of orders in each zone, making the workload of each zone more balanced, improving the overall resource utilization of the system, improving order picking efficiency, better meeting the timeliness requirements of orders, adapting to scenarios where orders are updated in real time, and facilitating practical application and promotion. Attached Figure Description

[0019] Figure 1 A flowchart of an order batch optimization method for a picking system provided in this application embodiment; Figure 2 A block diagram of the order batch optimization device for the picking system provided in this application embodiment; Figure 3 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0021] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0022] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0023] To improve order picking efficiency, this application provides a method, apparatus, electronic device, and product for optimizing order batches in a picking system. This method, apparatus, electronic device, and product can dynamically adjust orders in each partition, making the workload of each partition more balanced, improving the overall resource utilization of the system, and adapting to scenarios where orders are updated in real time.

[0024] The order batch optimization method for picking systems provided in this application can be applied to picking systems used for order sorting management. It is understood that the execution entity described does not constitute a limitation on the embodiments of this application.

[0025] The following will provide a detailed description of the order batch optimization method for the picking system provided in the embodiments of this application.

[0026] like Figure 1 The diagram shown is a flowchart of a picking system order batch optimization method provided in the first aspect of the present application. The picking system order batch optimization method may include, but is not limited to, the following steps S101-S106.

[0027] Step S101. Obtain the order information for the newly arrived order.

[0028] The order information includes the order arrival time and the order deadline for picking, and may also include the order ID and product information.

[0029] In this embodiment, order information of newly arrived orders in the picking system can be obtained in real time, and the order information can be standardized to convert it into a standardized data format that can be used directly (such as converting the order arrival time and the order deadline for picking into a unified timestamp format). In addition, the urgency of each order can be calculated based on the current time and the order deadline for picking.

[0030] Step S102. Add the new order to the order reserve queue.

[0031] When a new order arrives, it is not immediately allocated. Instead, it is first added to the order reserve queue and then allocated when the order replanning conditions are met.

[0032] Step S103. Determine whether the order replanning condition has been triggered.

[0033] The order replanning conditions can be set according to actual conditions. For example, determining whether an order replanning condition has been triggered can be based on whether the number of orders in the order reserve queue exceeds a preset quantity, whether the time interval since the last trigger of order replanning exceeds a preset time interval and there are new orders in the order reserve queue, whether there are urgent orders in the order reserve queue, whether the ratio of the total number of orders in the order reserve queue to the order capacity of the picking system exceeds a preset ratio, or whether at least one batch of allocated orders has been picked. Meeting any one of these conditions is considered a triggering of the order replanning condition.

[0034] Step S104. If the order replanning condition is triggered, based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, the adaptive large neighborhood search algorithm is used to perform batch optimization on all currently unpicked orders, and to determine multiple batch orders that correspond one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition.

[0035] In this embodiment, the order picking area is divided into multiple partitions. If the number of orders in the order reserve queue exceeds a preset quantity, the time interval since the last order replanning was triggered exceeds a preset time interval, and there are new orders in the order reserve queue, there are urgent orders in the order reserve queue, the ratio of the total number of orders in the order reserve queue to the order capacity of the picking system exceeds a preset ratio, or at least one batch of assigned orders has been picked, then it can be determined that the order replanning condition has been triggered. At this time, based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, batch optimization can be performed on all currently unpicked orders using the Adaptive Large Neighborhood Search (ALNS) algorithm.

[0036] The currently unpicked orders include all orders assigned to each zone but not yet picked, as well as all newly arrived orders in the order reserve queue. The system status of the picking system includes the location of each autonomous mobile robot, the status of pickers in each zone, and the workload of each zone. The picker status in each zone can include the picker's working status (such as idle or performing a picking task) and the picker's work efficiency parameters (such as a picking time of 5 seconds per item, used to predict completion time).

[0037] The execution process of the ALNS algorithm may include the following steps: 1. Initial solution generation: Sort all currently unpicked orders (including orders allocated to each partition but not yet picked, as well as all newly arrived orders in the order reserve queue) by deadline to generate the initial batch allocation.

[0038] 2. Iterative optimization process: Adaptively select destruction operators (random destruction / worst solution destruction / correlation destruction) to perform destruction operations and remove some order batch allocations; use repair operators (greedy repair / regret value repair) to redistribute the removed orders; simulate annealing mechanism to decide whether to accept the new solution; update operator weights.

[0039] 3. Output results: Determine which batch each order belongs to, and which AMR and partition each batch is assigned to.

[0040] Step S105. Using the batch order completion time and picker movement distance as optimization objectives, the NEH algorithm is used to optimize the order picking order of the batch orders corresponding to each partition, so as to obtain the optimized batch orders corresponding to each partition.

[0041] Specifically, the NEH algorithm is used to optimize the order picking order of batch orders corresponding to each partition, resulting in optimized batch orders for each partition. This may include, but is not limited to, the following steps: Step S1051. Perform initial sorting of orders within each batch of orders according to the order deadline for picking.

[0042] Step S1052. Insert each order into the optimal position in the current sequence.

[0043] Step S1053. Calculate the batch order completion time and picker movement distance after inserting the order into each position, and select the position that minimizes the evaluation index determined based on the batch order completion time and picker movement distance for insertion.

[0044] Step S1054. Dynamically adjust the insertion priority based on the urgency of the order and the batch size.

[0045] Step S1055. When all orders in each batch have been inserted into the sequence or the number of iterations has reached the preset number, the latest batch order in each partition is taken as the optimized batch order corresponding to each partition.

[0046] The NEH algorithm can be used to determine the picking order of each order within a batch order.

[0047] Step S106. Assign orders to each partition based on the optimized batch orders corresponding to each partition, and assign autonomous mobile robots and pickers to each partition based on the autonomous mobile robots and pickers corresponding to each partition.

[0048] In one or more embodiments, the location of the meeting point for each zone can be determined based on the estimated time difference between the arrival of the autonomous mobile robot at the meeting point and the arrival of the picker at the meeting point after completing the picking, to ensure that the time difference between the two is within a preset time interval (e.g., 5 seconds). At the same time, a movement path from the current position to the corresponding zone can be planned for each autonomous mobile robot, and then the location of the meeting point for each zone can be sent to the corresponding autonomous mobile robot.

[0049] The order batch optimization method for a picking system provided by this invention involves: acquiring order information of newly arrived orders, including order arrival time and order deadline for picking; adding the newly arrived orders to the order reserve queue; determining whether an order replanning condition has been triggered; if the order replanning condition has been triggered, optimizing all currently unpicked orders based on the order information of all unpicked orders and the system state of the picking system, with the optimization objective of minimizing the maximum completion time, and using an adaptive large neighborhood search algorithm to determine multiple batch orders corresponding to multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition; optimizing the order picking order ... In this way, as orders continue to pour in, order replanning can be automatically triggered, and batch orders can be allocated and optimized for each zone. Then, orders, autonomous mobile robots, and pickers can be allocated to each zone, thereby achieving dynamic adjustment of orders in each zone. This makes the workload of each zone more balanced, improves the overall resource utilization of the system, improves order picking efficiency, better meets the timeliness requirements of orders, adapts to scenarios where orders are updated in real time, and is easy to apply and promote.

[0050] Please see Figure 2 The second aspect of this application provides an order batch optimization device for a picking system, comprising: The acquisition unit is used to acquire the order information of newly arrived orders, including the order arrival time and the order deadline for picking. Add a unit to add newly arrived orders to the order reserve queue; The judgment unit is used to determine whether the order replanning condition has been triggered. The first optimization unit is used to perform batch optimization on all currently unpicked orders based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, by using an adaptive large neighborhood search algorithm. This determines multiple batch orders that correspond one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition. All currently unpicked orders include orders that have been assigned to each partition but have not been picked, as well as all newly arrived orders in the order reserve queue. The system status of the picking system includes the position of each autonomous mobile robot, the status of pickers in each partition, and the workload of each partition. The second optimization unit uses the batch order completion time and picker movement distance as optimization objectives. It optimizes the order picking order of the batch orders corresponding to each partition through the NEH algorithm to obtain the optimized batch orders corresponding to each partition. The allocation unit is used to allocate orders to each partition based on the optimized batch orders corresponding to each partition, and to allocate autonomous mobile robots and pickers to each partition based on the autonomous mobile robots and pickers corresponding to each partition.

[0051] The working process, working details and technical effects of the order batch optimization device for the picking system provided in the second aspect of this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0052] like Figure 3 As shown, a third aspect of this application provides an electronic device, including a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the order batch optimization method of the picking system as described in the first aspect of the application.

[0053] Specifically, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or last-in-first-out (FILO) memory, etc.; the processor may not be limited to microprocessors of the STM32F105 series, ARM (Advanced RISC Machines), x86 architecture processors, or processors with integrated NPU (neural-network processing units); the transceiver may be, but is not limited to, WiFi (Wireless Fidelity) wireless transceivers, Bluetooth wireless transceivers, General Packet Radio Service (GPRS) wireless transceivers, ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard), 3G transceivers, 4G transceivers, and / or 5G transceivers, etc.

[0054] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions containing the order batch optimization method for a picking system as described in the first aspect of the embodiment. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the order batch optimization method for a picking system as described in the first aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0055] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the order batch optimization method for a picking system as described in the first aspect of this embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0056] It should be understood that specific details are provided in the following description to facilitate a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, the system may be shown in block diagrams to avoid obscuring the example with unnecessary details. In other instances, well-known processes, structures, and techniques may be shown without unnecessary details to avoid obscuring the exemplary embodiments.

[0057] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing order batches in a picking system, characterized in that, include: Obtain the order information of newly arrived orders, including the order arrival time and the order deadline for picking; Add the new orders to the order backlog queue; Determine whether the order replanning condition has been triggered. If the order replanning condition is triggered, based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, the adaptive large neighborhood search algorithm is used to perform batch optimization on all currently unpicked orders, and determine multiple batch orders that correspond one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition. All currently unpicked orders include orders that have been assigned to each partition but have not been picked, as well as all newly arrived orders in the order reserve queue. The system status of the picking system includes the position of each autonomous mobile robot, the status of pickers in each partition, and the workload of each partition. With batch order completion time and picker movement distance as optimization objectives, the NEH algorithm is used to optimize the order picking order of batch orders corresponding to each partition, resulting in optimized batch orders for each partition. Orders are assigned to each partition based on the optimized batch orders corresponding to each partition, and autonomous mobile robots and pickers are assigned to each partition based on the autonomous mobile robots and pickers corresponding to each partition.

2. The order batch optimization method for the picking system according to claim 1, characterized in that, The method further includes: Based on the estimated time difference between the arrival of autonomous mobile robots in each zone at the rendezvous point and the arrival of pickers after completing picking, the rendezvous point location of each zone is determined, and a movement path from the current location to the corresponding zone is planned for each autonomous mobile robot. Send the location of the meeting point of each partition to the corresponding autonomous mobile robot.

3. The order batch optimization method for the picking system according to claim 1, characterized in that, Determine whether the order replanning condition has been triggered, including: Determine whether the number of orders in the order reserve queue exceeds a preset quantity, whether the time interval since the last triggering of order replanning exceeds a preset time interval and there are new orders in the order reserve queue, whether there are urgent orders in the order reserve queue, whether the ratio of the total number of orders in the order reserve queue to the order capacity of the picking system exceeds a preset ratio, or whether at least one batch of allocated orders has been picked. If the order replanning condition is triggered, then based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, batch optimization is performed on all currently unpicked orders using an adaptive large neighborhood search algorithm, including: If the number of orders in the order reserve queue exceeds a preset quantity, the time interval since the last triggered order replanning exceeds a preset time interval, and there are new orders in the order reserve queue, there are urgent orders in the order reserve queue, the ratio of the total number of orders in the order reserve queue to the order capacity of the picking system exceeds a preset ratio, or at least one batch of assigned orders has been picked, then based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, batch optimization is performed on all currently unpicked orders using an adaptive large neighborhood search algorithm.

4. The order batch optimization method for the picking system according to claim 1, characterized in that, Using batch order completion time and picker travel distance as optimization objectives, the NEH algorithm is used to optimize the order picking order of batch orders corresponding to each partition, resulting in optimized batch orders for each partition, including: The orders within each batch are initially sorted according to their order deadline for picking. Insert each order into the optimal position in the current sequence; Calculate the batch order completion time and picker movement distance after inserting the order into each position, and select the position that minimizes the evaluation index determined based on the batch order completion time and picker movement distance for insertion; The insertion priority is dynamically adjusted based on the urgency and batch size of the order. When all orders in each batch have been inserted into the sequence or the number of iterations has reached a preset number, the latest batch order in each partition is taken as the optimized batch order for that partition.

5. The order batch optimization method for the picking system according to claim 1, characterized in that, After obtaining the order information of the newly arrived order, the method further includes: Convert the order information of newly arrived orders into a standardized data format.

6. The order batch optimization method for a picking system according to claim 1, characterized in that, The order information also includes the order ID and product information.

7. The order batch optimization method for a picking system according to claim 1, characterized in that, The status of pickers in each zone includes their work status and work efficiency parameters.

8. A picking system order batch optimization device, characterized in that, include: The acquisition unit is used to acquire the order information of newly arrived orders, including the order arrival time and the order deadline for picking. Add a unit to add newly arrived orders to the order reserve queue; The judgment unit is used to determine whether the order replanning condition has been triggered. The first optimization unit is used to perform batch optimization on all currently unpicked orders based on the order information of all currently unpicked orders and the system status of the picking system, with the optimization objective of minimizing the maximum completion time, by using an adaptive large neighborhood search algorithm. This determines multiple batch orders that correspond one-to-one with multiple partitions, as well as the autonomous mobile robots and pickers corresponding to each partition. All currently unpicked orders include orders that have been assigned to each partition but have not been picked, as well as all newly arrived orders in the order reserve queue. The system status of the picking system includes the position of each autonomous mobile robot, the status of pickers in each partition, and the workload of each partition. The second optimization unit uses the batch order completion time and picker movement distance as optimization objectives. It optimizes the order picking order of the batch orders corresponding to each partition through the NEH algorithm to obtain the optimized batch orders corresponding to each partition. The allocation unit is used to allocate orders to each partition based on the optimized batch orders corresponding to each partition, and to allocate autonomous mobile robots and pickers to each partition based on the autonomous mobile robots and pickers corresponding to each partition.

9. An electronic device, characterized in that, The system includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the order batch optimization method for the picking system as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the order batch optimization method for the picking system as described in any one of claims 1 to 7.

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