Order allocation method and device, equipment, storage medium and program product

Abstract

The invention provides an order allocation method and device, equipment, a storage medium and a program product. The order allocation method comprises the following steps: dividing a storage area into a plurality of reference sub-areas; virtual inventory distribution of the target order is carried out on each reference sub-region, and a plurality of hit material boxes are obtained; based on the storage locations where the multiple hit material boxes are located and the number of tasks corresponding to the hit material boxes, the multiple hit material boxes are selected, the multiple reference sub-areas are subjected to area adjustment, and multiple target material boxes and multiple target sub-areas are obtained; the number of tasks corresponding to the hit material box is the number of articles in the target order satisfied by the hit material box; the plurality of target material boxes are used for completing a target order; and allocating the order tasks to the workstations in the corresponding target sub-regions. Dynamic partitioning based on order demands is realized, the balance degree of partitioning task load is improved, and the utilization rate of system transport capacity and order processing efficiency are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent warehousing technology, and in particular to an order distribution method, device, equipment, storage medium and program product. Background Art

[0002] With the continuous increase in storage area and the growing order volume, higher requirements are placed on the efficiency of order processing in the warehousing system. How to reduce the long-distance and cross-regional operations of robots is an urgent problem to be solved.

[0003] At present, static partitioning is usually used to divide the warehouse of the storage system into several fixed partitions, so that the robots can work in the same partition as much as possible, thereby improving the efficiency of the robots in handling material boxes and thus improving the efficiency of order processing. The static partitioning method is only applicable to scenarios with stable order structures and evenly distributed order demands. When the picking task volume corresponding to the order demand varies greatly in different partitions, there will be problems of insufficient or excessive capacity in some partitions, which affects the efficiency of order processing. Summary of the invention

[0004] The present disclosure provides an order distribution method, device, equipment, storage medium and program product, which realize dynamic partitioning based on order demand, improve the balance of partition task volume, thereby improving the utilization rate of system capacity and improving the efficiency of order processing.

[0005] In a first aspect, the present disclosure provides an order allocation method, comprising: dividing a storage area into multiple reference sub-areas; allocating virtual inventory of a target order to each reference sub-area to obtain multiple hit boxes; selecting multiple hit boxes based on the storage locations of the multiple hit boxes and the number of tasks corresponding to each hit box, and performing regional adjustments on multiple reference sub-areas to obtain multiple target boxes and multiple target sub-areas; wherein the number of tasks corresponding to the hit box is the number of items in the target order satisfied by the hit box; the multiple target boxes are partial boxes in the hit box, and the multiple target boxes are used to complete the target order; allocating order tasks to workstations in the corresponding target sub-areas, wherein the order tasks are picking tasks for the target orders satisfied by the target boxes in the corresponding target sub-areas.

[0006] In a possible implementation, based on the storage locations where multiple hit boxes are located and the number of tasks corresponding to each hit box, multiple hit boxes are selected and multiple reference sub-areas are adjusted to obtain multiple target boxes and multiple target sub-areas, including: using a clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected box in each pile, multiple rounds of selection of multiple hit boxes and multiple rounds of area adjustment of each pile are performed to obtain multiple target boxes and multiple target sub-areas; wherein, the initial value of the pile is the corresponding reference sub-area; the selected box is the box selected from the hit boxes in each round of area adjustment to complete the target order, the first distance corresponding to the selected box is the distance between the selected box and the center point of the box in the pile, and the center point of the box in the pile is the center of the storage location where each selected box in the pile is located; the number of tasks in the pile is the sum of the number of tasks corresponding to each selected box in the pile.

[0007] In one possible implementation, a clustering algorithm is used to perform multiple rounds of selection on hit bins and multiple rounds of regional adjustments on each pile based on an objective function between the number of task pieces in each pile and the first distance corresponding to each selected bin in each pile, so as to obtain multiple target bins and multiple target sub-areas, including: locking a first type of bin among the multiple hit bins as the target bins; the first type of bins are hit bins that must be selected to complete the target order; a clustering algorithm is used to perform multiple rounds of selection on the second type of bins and multiple rounds of regional adjustments on each pile based on an objective function between the number of task pieces in each pile and the first distance corresponding to each selected bin in each pile, so as to obtain multiple target bins and multiple target sub-areas; the second type of bins are hit bins other than the first type of bins.

[0008] In one possible implementation, a clustering algorithm is used to perform multiple rounds of selection on hit bins and multiple rounds of area adjustments on each pile based on an objective function between the number of tasks in each pile and the first distance corresponding to each selected bin in each pile, to obtain multiple target bins and multiple target sub-areas, including: a clustering algorithm is used to perform multiple rounds of selection on hit bins and multiple rounds of area adjustments on each pile based on an objective function between the number of tasks in each pile and the first distance corresponding to each selected bin in each pile, with the constraint that the adjusted number of tasks in each pile is greater than or equal to a first threshold, to obtain multiple target bins and multiple target sub-areas.

[0009] In a possible implementation, the order allocation method further includes: determining the required number of pieces at the workstation based on the required number of pieces to be shipped out of the warehousing system and the number of workstations; and determining the first threshold based on the required number of pieces at the workstation and the number of slots at a single workstation.

[0010] In one possible implementation, a first threshold is determined based on the number of pieces required by the workstation and the number of slots of a single workstation, including: calculating the ratio of the number of pieces required by the workstation to the number of slots of a single workstation to obtain a first number of pieces; calculating the product of a first preset coefficient and the number of pieces required by the workstation and the ratio of the product to the number of slots of a single workstation to obtain a second number of pieces; determining a first threshold based on the first number of pieces and the second number of pieces, wherein the first threshold is an integer greater than or equal to the first number of pieces and less than or equal to the second number of pieces.

[0011] In one possible implementation, the objective function is the sum of the target indexes of each pile; the target index of each pile is the sum of the first ratios corresponding to each selected material box in the pile, and the first ratio is the ratio of the first distance corresponding to the selected material box to the number of tasks in the pile.

[0012] In a possible implementation, the order allocation method further includes: calculating the product of a second preset coefficient and the required number of pieces of a workstation of the warehousing system, and the ratio thereof to the number of slots of a single workstation, to obtain a target number of pieces for the slots; and determining the number of reference sub-areas based on the rounded result of the ratio of the required number of pieces of the target order to the target number of pieces for the slots.

[0013] In one possible implementation, virtual inventory allocation of the target order is performed for each reference sub-area to obtain multiple hit material boxes, including: for each reference sub-area, selecting any workstation in the reference sub-area as the target workstation for executing the target order; based on the selected target workstation, determining multiple hit material boxes that meet the target order requirements from the storage area.

[0014] In a possible implementation, order tasks are assigned to workstations within the corresponding target sub-area, including: for each target sub-area, if the target sub-area includes the target workstation within the corresponding reference sub-area, the order task corresponding to the target sub-area is assigned to the target workstation; the target sub-area is obtained by adjusting the corresponding reference sub-area; if the target sub-area does not include the target workstation within the corresponding reference sub-area, the order task corresponding to the target sub-area is assigned to any workstation in the target sub-area.

[0015] In a second aspect, the present disclosure provides an order distribution device, including: a partitioning module, used to divide a storage area into multiple reference sub-areas; a virtual distribution module, used to perform virtual inventory distribution of a target order for each reference sub-area, and obtain multiple hit boxes; a partition adjustment module, used to select multiple hit boxes and perform area adjustment on multiple reference sub-areas based on the storage locations of the multiple hit boxes and the number of tasks corresponding to each hit box, and obtain multiple target boxes and multiple target sub-areas; wherein the number of tasks corresponding to the hit box is the number of items in the target order satisfied by the hit box; the multiple target boxes are part of the hit boxes, and the multiple target boxes are used to complete the target order; an order distribution module, used to distribute order tasks to workstations in the corresponding target sub-areas, wherein the order tasks are picking tasks for the target orders satisfied by the target boxes in the corresponding target sub-areas.

[0016] In a third aspect, the present disclosure provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, so that the processor executes the method provided in the first aspect above.

[0017] In a fourth aspect, the present disclosure provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method provided in the first aspect above.

[0018] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which implements the method provided in the first aspect above when executed by a processor.

[0019] The present disclosure provides an order allocation method, device, equipment, storage medium and program product. In order to improve the balance of the picking task volume of the target order in each partition of the storage area, a dynamic partitioning method based on the order demand of the target order is provided. Specifically, the storage area is first divided into multiple reference sub-areas, such as an average division; then, the workstation in each reference sub-area is used as a simulated operation workstation for the order, and multiple hit material boxes that meet the order demand are obtained through virtual inventory allocation; the reference sub-area is adjusted and optimized by using the storage location where the hit material box is located and the number of tasks corresponding to each hit material box, so that the distribution of the target material boxes in the target sub-area obtained in the end is balanced and the number of tasks in each target sub-area is balanced; when allocating tasks, the picking tasks of the target order satisfied by the target material box in the target sub-area are allocated to the workstations in the target sub-area, taking the target sub-area as the unit. By allocating target orders to workstations in multiple sub-areas, the parallelism of order operations is improved and the order processing efficiency is improved. At the same time, the dynamic adjustment of partitions based on target order requirements improves the balance of picking tasks in different sub-areas, reduces the time required to complete order processing, overcomes the problem of idle or excess capacity in the warehousing system due to uneven task allocation, and improves the utilization rate of the system's capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] Figure 1 A schematic diagram of a storage system;

[0022] Figure 2 A flowchart of an order allocation method provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of a static partitioning result of a storage area provided in an embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of a reference sub-region adjustment process provided by an embodiment of the present disclosure;

[0025] Figure 5 A schematic diagram of a target order and its virtual inventory allocation result provided by an embodiment of the present disclosure;

[0026] Figure 6 A flowchart of another order allocation method provided by an embodiment of the present disclosure;

[0027] Figure 7 A schematic diagram of a virtual inventory allocation result A provided in an embodiment of the present disclosure;

[0028] Figure 8 A flowchart of another order allocation method provided by an embodiment of the present disclosure;

[0029] Fig. 9 A schematic diagram of the structure of an order distribution device provided by an embodiment of the present disclosure;

[0030] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure.

[0031] The above drawings have shown clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0033] In a warehousing system, in order to improve the efficiency of order processing and reduce the walking distance of robots transporting material boxes, it is usually necessary to divide the storage area into multiple sub-areas, and the robots should try to operate in the corresponding sub-areas.

[0034] For example, Figure 1 A schematic diagram of a storage system is shown in FIG. Figure 1 As shown, the storage area of ​​the warehousing system is provided with shelves, and the shelves include multiple storage locations for storing boxes. When executing an order for items to be shipped out of the warehouse, the warehousing system will select a hit box for the order so that the order can be completed by picking the hit box. After determining the hit box, the robot will move the hit box to the workstation for item sorting. In addition, a workstation can correspond to multiple slots, which are areas of the workstation used to cache order boxes. An order box is placed in each slot. When an order is sent to the workstation to execute item picking, the warehousing system will bind the order to the corresponding order box. When the box corresponding to the order is transported to the workstation, the picker or the automated picking device will pick the items required for the order in the box into the order box.

[0035] The warehouse system includes multiple workstations, such as Figure 1In the workstation 1 to workstation 4 in the workstation layout, in some workstation layout scenarios, the lateral distance between the workstations may be far. Therefore, if the order is sent to one of the workstations for item picking, the hit material box will be far away from the workstation, resulting in low efficiency in handling the hit material box. Therefore, the existing technology statically divides the storage area into multiple sub-areas, such as Figure 1 The robot can move boxes from sub-area 1 to sub-area 3 in the workstation of each sub-area, so as to solve the problem of reduced efficiency caused by long-distance transportation and cross-area scheduling.

[0036] The sub-areas obtained by static partitioning are fixed and unchanged. If the hit boxes corresponding to the order are evenly distributed in each sub-area, then static partitioning can greatly improve the order processing efficiency. However, in the scenario where the hit boxes corresponding to the order are unevenly distributed in each sub-area, static partitioning will result in insufficient or excessive transportation capacity (robots) in some sub-areas, resulting in low robot utilization.

[0037] In response to the above-mentioned problems, the embodiments of the present disclosure provide an order allocation method. On the basis of static partitioning, a partition dynamic adjustment strategy based on order demand is proposed, so that the number of hitting boxes between the adjusted sub-areas is balanced, and the total picking tasks corresponding to the hitting boxes in each sub-area are balanced, thereby improving the utilization rate of the warehousing system's transportation capacity and the order processing efficiency.

[0038] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0039] Figure 2 A flowchart of an order allocation method provided in an embodiment of the present disclosure. The order allocation method provided in this embodiment can be executed by any device in the warehouse system that has corresponding data processing capabilities, which can be a device specifically used for order allocation, or a device in the warehouse system responsible for other functions, such as a scheduling device, which can also be used to allocate robots to hit bins, etc.

[0040] like Figure 2 As shown, the order allocation method provided in this embodiment includes the following steps:

[0041] Step S201, dividing the storage area into multiple reference sub-areas.

[0042] The storage area is an area in the storage system used to store items, in which one or more rows of shelves can be placed. A row of shelves includes multiple storage locations distributed in a matrix. The space corresponding to the storage location can be a fixed size or a dynamic space that can flexibly change with the size of the material box to be stored.

[0043] Specifically, the storage area is divided into a plurality of reference sub-areas, and any division method may be used, such as average division, non-average division, and the like.

[0044] Exemplarily, the storage area may be divided into a plurality of reference sub-areas based on the number of workstations in the storage system, so that the number of workstations in each reference sub-area is as equal as possible.

[0045] In some embodiments, the number of reference sub-areas, such as A, may be given, so that when the storage area is statically partitioned, the storage area is divided into A reference sub-areas.

[0046] When the storage area is divided into A reference sub-areas, and the reference sub-areas are divided according to the number of workstations, if the number of workstations cannot be divided by the number of reference sub-areas, some reference sub-areas can share workstations. Figure 3 A schematic diagram of the static partitioning result of the storage area provided in the embodiment of the present disclosure, such as Figure 3 As shown, the storage system includes 6 workstations, namely workstation 311 to workstation 316, and each workstation is evenly distributed. Taking the number of reference sub-regions as 4 as an example, the 4 reference sub-regions r31 to r34 obtained by division are as follows: Figure 3 As shown, the workstations in the reference sub-regions r31 to r34 are: workstation 311 and workstation 312, workstation 312 and workstation 313, workstation 314 and workstation 315, workstation 315 and workstation 316. Among them, the reference sub-region r31 and the reference sub-region r32 share the workstation 312, and the reference sub-region r33 and the reference sub-region r34 share the workstation 315. If there are 5 reference sub-regions and 6 workstations, the result of the division may be that one of the workstations is shared by two adjacent reference sub-regions, and the remaining 5 workstations are evenly divided into each reference sub-region.

[0047] Step S202, allocate virtual inventory of the target order to each reference sub-area to obtain multiple hit material boxes.

[0048] The target order is any order to be sent to the workstation so that the workstation can pick items. The target order may include at least one order line, and each order line may include an item identifier and item quantity of an item required by the target order.

[0049] In the warehousing system, SKU (Stock Keeping Unit) is usually used as an item identifier to represent an item. One order line of the target order can correspond to one SKU, and different order lines are used to describe different SKUs required by the order and the corresponding quantity of the SKU.

[0050] Virtual inventory allocation simulates the process of selecting a workstation as the workstation to perform the item picking task corresponding to the target order, and performing inventory allocation to obtain a set of hit boxes corresponding to the target order, which meets the needs of the target order.

[0051] For each reference sub-area obtained by division, any workstation in the reference sub-area is used as a workstation for simulating the execution of the target order, and a set of hit material boxes corresponding to the target order is obtained by allocating virtual inventory to the target order; each reference sub-area is traversed to obtain multiple sets of hit material boxes, that is, the aforementioned multiple hit material boxes are obtained. Each set of hit material boxes includes at least one hit material box.

[0052] For example, Table 1 is a list of hit bins for target order 1. Target order 1 includes two order lines, namely, 500 pairs of shoes a and 1000 pairs of socks b, that is, target order 1 requires 500 pairs of shoes a and 1000 pairs of socks b. Assume that the storage area is divided into three reference sub-areas, namely, reference sub-areas r1 to reference sub-areas r3. For the order line where shoes a is located in target order 1, when the workstations in reference sub-areas r1 to r3 are used as objects for virtual inventory allocation, the hit bins obtained are: bin 1 to bin 10, bin 2, bin 5, bin 21 to bin 28, bin 9, bin 31 to bin 39; for the order line where socks b is located in target order 1, when the workstations in reference sub-areas r1 to r3 are used as objects for virtual inventory allocation, the hit bins obtained are: bin 12 to bin 17, bin 13 to bin 19, bin 13 to bin 19. The boxes listed in the cells other than the header in Table 1 can meet the requirements of the order lines where the corresponding items are located, such as boxes 1 to 10 include at least 500 pairs of shoes a.

[0053] Table 1 List of hit boxes for target order 1

[0054]

[0055] Step S203, based on the storage locations of the multiple hit material boxes and the number of tasks corresponding to each hit material box, multiple hit material boxes are selected and multiple reference sub-areas are adjusted to obtain multiple target material boxes and multiple target sub-areas.

[0056] Among them, the number of tasks corresponding to the hit material box is the number of items in the target order that the hit material box satisfies; multiple target material boxes are part of the material boxes in the hit material box, and multiple target material boxes are used to complete the target order.

[0057] The target sub-area is the sub-area finally obtained after the reference sub-area is dynamically adjusted, and the number of the target sub-areas is the same as the number of the reference sub-areas. The target bin is the hit bin finally selected to complete the item picking task corresponding to the target order.

[0058] After obtaining multiple hit boxes for the target order through multiple rounds of virtual inventory allocation, it is necessary to make multiple adjustments to the reference sub-areas based on the distribution of the hit boxes and the number of tasks corresponding to the hit boxes, and adjust the selected boxes during the process of adjusting the reference sub-areas, so that the distribution and quantity of the target boxes between the target sub-areas are balanced, or the distribution and quantity of the target boxes between the target sub-areas are balanced and the number of tasks are balanced.

[0059] A set of hit boxes for completing the target order can be selected multiple times through an iterative process, i.e., based on the location of the hit box and the number of tasks corresponding to the hit box, and the reference sub-area is scaled at the same time, so that the density of the selected boxes selected between the adjusted reference sub-areas is equal and the number of tasks in each adjusted reference sub-area is as equal as possible. The selected box is the hit box selected for completing the target order in each iteration; the number of tasks in the adjusted reference sub-area is the sum of the number of tasks corresponding to all selected boxes in the adjusted reference sub-area.

[0060] The number of iterations can be set, and the adjusted reference sub-area and selected bin output from the last iteration are used as the target sub-area and target bin.

[0061] In each iteration, adjustments are made based on the reference sub-region adjusted in the previous iteration.

[0062] After obtaining the adjusted reference sub-region corresponding to an iteration, the number of selected material boxes in the reference sub-region adjusted for this iteration can be counted and the distance between each selected material box and the center point of the material box can be calculated. Based on these two parameters, the density of the selected material boxes in the adjusted reference sub-region is determined. If the density of the selected material boxes in each adjusted reference sub-region is equivalent, that is, the difference is small, there is no need to continue the iteration, and the loop is ended in advance, and the adjusted reference sub-regions and the selected material boxes therein are output as the target sub-regions and target material boxes; if there is a large difference in the density of the selected material boxes in each adjusted reference sub-region, the next iteration is used to continue the hit material box selection and reference sub-region adjustment, wherein the center point of the material box is the center of the storage location of all the selected material boxes in the corresponding reference sub-region, that is, the center point of the material box is the center of the storage location of all the selected material boxes in the corresponding reference sub-region.

[0063] The clustering algorithm and the objective function can be used to perform multiple rounds of selection of hit bins and multiple rounds of adjustment of reference sub-regions, and obtain target bins and target sub-regions. The information of multiple hit bins (such as the storage location corresponding to the hit bins and the SKU in the target order corresponding to the hit bins, etc.) and the number of tasks corresponding to each hit bin can be input into the clustering module, and the clustering module outputs multiple target bins and multiple target sub-regions through multiple rounds of hit bin selection and reference sub-region adjustment based on the clustering algorithm and the designed objective function. Clustering is performed with the goal of minimizing the objective function to obtain a clustering result, that is, multiple target bins and multiple target sub-regions.

[0064] In some embodiments, the objective function can be a function of the distance between the selected material box and the center point of the material box in each adjusted reference sub-area obtained after this iteration. By setting the objective function, the sum of the walking distances required to transport the material boxes between the target sub-areas is made close, thereby achieving a balance in the transporting tasks.

[0065] In other embodiments, the objective function may be a function of the distance between the selected material box and the center point of the material box in the adjusted reference sub-area obtained after this iteration, and the number of tasks corresponding to the adjusted reference sub-area obtained after this iteration, so as to balance the handling tasks and sorting tasks in each target sub-area.

[0066] Step S204: Allocate the order task to the workstation in the corresponding target sub-area.

[0067] The order task is a partial picking task in the target order satisfied by the target bin in the corresponding target sub-area.

[0068] Specifically, the target material box in each target sub-area is a material box used to complete the target order, and the order task corresponding to the target sub-area is a task of picking some items in the target order completed by the target sub-area through the target material box.

[0069] For example, the target order includes order lines 1 to 3, the target sub-area includes target sub-area M1 to target sub-area M3, and the target material box includes material box L1 to material box L10, wherein material box L1 to material box L3 are located in the target sub-area M1, and are used to complete the picking task of order line 1 in the target order; material box L4 to material box L7 are located in the target sub-area M2, and are used to complete the task of order line 2 in the target order and the picking task of the first part of order line 3; material box L8 to material box L10 are located in the target sub-area M3, and are used to complete the picking task of the second part of order line 3 in the target order. Then the order task corresponding to the target sub-area M1 is the picking task of order line 1 in the target order, the order task corresponding to the target sub-area M2 is the picking task of order line 2 in the target order and the picking task of the first part of order line 3, and the order task corresponding to the target sub-area M3 is the picking task of the second part of order line 3 in the target order.

[0070] The order task corresponding to the target sub-area can be allocated to any idle workstation in the target sub-area, or to a workstation located in the target sub-area that is selected during virtual inventory allocation.

[0071] Specifically, if there is a workstation selected when allocating virtual inventory in the target sub-region, and the workstation has an available slot, the order task corresponding to the target sub-region will be preferentially allocated to the workstation; if there is a workstation selected when allocating virtual inventory in the target sub-region, but the workstation has no available slot, the order task corresponding to the target sub-region can be allocated to the workstation after the slot of the workstation is released, or the order task corresponding to the target sub-region is allocated to other workstations with available slots in the target sub-region. If the target sub-region does not include the workstation selected when allocating virtual inventory, the order task corresponding to the target sub-region is allocated to any workstation with an available slot in the target sub-region; if there is no workstation with an available slot, the order task corresponding to the target sub-region is allocated to the workstation with a slot released in the target sub-region after the slot of the workstation is released.

[0072] The order allocation method provided by the embodiment of the present disclosure provides a dynamic partitioning method based on the order demand of the target order in order to improve the balance of the picking task volume of the target order in each partition of the storage area. Specifically, the storage area is first divided into multiple reference sub-areas, such as equal division; then, the workstation in each reference sub-area is used as a simulated operation workstation for the order, and multiple hit material boxes that meet the order demand are obtained through virtual inventory allocation; the reference sub-area is adjusted and optimized by using the storage location where the hit material box is located and the number of tasks corresponding to each hit material box, so that the distribution of the target material boxes in the target sub-area obtained in the end is balanced and the number of tasks in each target sub-area is balanced; when allocating tasks, the picking tasks of the target order satisfied by the target material box in the target sub-area are allocated to the workstations in the target sub-area, taking the target sub-area as the unit. By allocating target orders to workstations in multiple sub-areas, the parallelism of order operations is improved and the order processing efficiency is improved. At the same time, the dynamic adjustment of partitions based on target order requirements improves the balance of picking tasks in different sub-areas, reduces the time required to complete order processing, overcomes the problem of idle or excess capacity in the warehousing system due to uneven task allocation, and improves the utilization rate of the system's capacity.

[0073] Optionally, based on the storage locations of multiple hit boxes and the number of tasks corresponding to each hit box, multiple hit boxes are selected and multiple reference sub-areas are adjusted to obtain multiple target boxes and multiple target sub-areas, including: utilizing a clustering algorithm, based on an objective function between the number of tasks in each pile and the first distance corresponding to each selected box selected from each pile, multiple rounds of selections are performed on multiple hit boxes and multiple rounds of area adjustments are performed on multiple piles to obtain multiple target boxes and multiple target sub-areas.

[0074] Among them, each reference sub-area corresponds to a pile, the initial value of the pile is the corresponding reference sub-area, and the subsequent value is the area obtained by the corresponding reference sub-area after each round of adjustment; the selected material box is the material box selected from the hit material box in each round of area adjustment to complete the target order; the first distance corresponding to the selected material box is the distance between the selected material box and the center point of the material box in the pile, and the center point of the material box in the pile is the center of the storage location of each selected material box in the pile.

[0075] Exemplarily, the clustering algorithm may be a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, a K-means (K-means) clustering algorithm, or the like.

[0076] In each iteration of the clustering algorithm, a group of hit bins that can complete the target order needs to be selected, that is, a group of selected bins. The group of selected bins selected in the last iteration is the multiple target bins.

[0077] The goal of the clustering algorithm is to find a way to divide the heap (or cluster) and the centroid so that the sum of the distances between each selected hit bin in the heap and the centroid of the heap, that is, the sum of the first distances of the selected hit bins, is minimized, and the difference in the number of tasks in each heap is minimized. By designing the corresponding objective function, the value of the objective function is gradually optimized at each iteration to obtain the clustering result, that is, multiple target bins and multiple target sub-areas. The centroid of the heap is the center point of the bin in the heap.

[0078] In the objective function, the distance can be represented by Euclidean distance, the term corresponding to the distance can be the sum of the squares of the distance, and the term corresponding to the number of tasks in the pile can be represented by variance, standard deviation, etc.

[0079] Taking the K-means clustering algorithm as an example, the value of K (the number of cluster centers) is the number of reference sub-regions. In the first iteration, each reference sub-region is used as the initial value of the pile, and one hit material box is selected from each reference sub-region as the initial centroid of each pile, and the remaining hit material boxes that complete the target order are selected to obtain a group of selected material boxes; the first distance between each selected material box and the centroid of each pile is calculated, and the selected material box is classified into the pile where the nearest centroid is located, and the value of the objective function is calculated based on the storage location of the selected material box in each pile and the corresponding number of tasks; in the next iteration, the centroid of each pile obtained in the previous iteration is updated, and the centroid of the pile is specifically updated to the center point of the storage location of each selected material box in the pile, and a round of hit material box selection and objective function calculation is performed again, and the cycle is repeated until the end condition is met, such as the centroid of the pile does not change for multiple consecutive iterations, or the value of the objective function calculated for multiple consecutive iterations is lower than the preset value and the difference is small, or the upper limit of the number of iterations is reached. The selected bins of the last iteration are the target bins, and the target sub-regions are determined based on the piles obtained in the last iteration.

[0080] Exemplarily, the shape of the target sub-region may be limited to a rectangle, so that each target sub-region is obtained based on the storage location of each selected material box in each cluster obtained in the last iteration.

[0081] Figure 4 A schematic diagram of a reference sub-region adjustment process provided by an embodiment of the present disclosure, such as Figure 4 As shown, Figure 4 Taking six reference sub-regions, namely reference sub-regions r41 to reference sub-regions r46, as an example, multiple hit bins are obtained through six virtual inventory allocations of the target order ( Figure 4 The circles in the figure represent the hit bins. Through the clustering algorithm, multiple target bins are obtained as follows: Figure 4 As shown by the solid dots in the middle, the circled multiple target bins form a pile. Based on the location of the target bins in each pile, an adjustment result is obtained, namely, the target sub-areas M41 to M46 are as follows: Figure 4 As shown, the target sub-regions M41 and M44 are the same as the corresponding reference sub-regions, namely, reference sub-regions r41 and r44, the target sub-regions M42 and M46 are larger than the corresponding reference sub-regions, namely, reference sub-regions r42 and r46, and the target sub-regions M43 and M45 are smaller than the corresponding reference sub-regions, namely, reference sub-regions r43 and r45.

[0082] By utilizing clustering algorithms and objective functions, the hit bin selection and reference sub-area adjustment are achieved through multiple iterations. By continuously reducing the value of the objective function, the local optimal solution can be quickly found, which improves the efficiency of bin selection and area adjustment. At the same time, it can improve the distribution balance of the final selected target bin and the balance of the picking task volume in each target sub-area, thereby improving the accuracy of area adjustment.

[0083] Optionally, the objective function is the sum of the target indexes of each pile; the target index of each pile is the sum of the first ratios corresponding to each selected material box in the pile, and the first ratio is the ratio of the first distance corresponding to the selected material box to the number of tasks in the pile.

[0084] The objective function J() can be expressed as:

[0085]

[0086] Where A is the number of reference sub-regions or target sub-regions; x i is the i-th selected bin; C j represents the jth heap, x i ∈C j Indicates that the material box x is selected i Located in the jth pile; u j For the jth pile C j The center of mass, C j The center point of the material box; N j For the jth pile C j The number of tasks, n i For the selected bin x i The corresponding number of tasks.

[0087] Optionally, a clustering algorithm is used to perform multiple rounds of selection on the hit bins and multiple rounds of area adjustments on the multiple piles based on the objective function between the number of task pieces in each pile and the first distance corresponding to each selected bin in each pile, so as to obtain multiple target bins and multiple target sub-areas, including: locking the first type of bins among the multiple hit bins as target bins; the first type of bins are the hit bins that must be selected to meet the target order; a clustering algorithm is used to perform multiple rounds of selection on the second type of bins and multiple rounds of area adjustments on the multiple piles based on the objective function between the number of task pieces in each pile and the first distance corresponding to each selected bin in each pile, so as to obtain multiple target bins and multiple target sub-areas; the second type of bins are the hit bins other than the first type of bins.

[0088] Specifically, the first type of material boxes mentioned above are the hit material boxes necessary to meet the picking task of the target items in the target order, that is, the hit material boxes included in the combination of various hit material boxes that meet the picking task of the target items in the target order.

[0089] For example, if SKU1 is required in the target order, when SKU1 only exists in bin a, and SKU1 is not stored in the other bins in the storage area, then bin a is a first-class bin. Or, if the demand for SKU1 in the target order is to pick out 100 pieces of SKU10, the bins storing SKU10 in the storage area include bin a, bin b, and bin c, where bin a stores 20 pieces of SKU10, bin b stores 90 pieces of SKU10, and bin c stores 30 pieces of SKU10. No matter which combination of bin a, bin b, and bin c is selected to complete the task of picking SKU10 in the target order, bin b is required, such as selecting a combination of bin a and bin b, or selecting a combination of bin b and bin c. Therefore, bin b is a first-class bin, and bin a and bin c are second-class bins.

[0090] When running the iterative process corresponding to the clustering algorithm, in the order allocation scenario, there may be some hit bins, that is, the first-class bins are necessary choices for the picking tasks that meet the target order's order lines, that is, the hit bins that must be selected to meet the picking tasks of a certain order line of the target bin. In this iterative process, the first-class bins must be determined as the selected bins each time. In order to improve efficiency, the first-class bins can be directly locked as the target bins, so that when selecting the hit bins, only the hit bins that meet the picking tasks of the remaining order lines of the target order need to be selected from the remaining hit bins, that is, the second-class bins. For the second-class bins, since there are multiple choices, it is necessary to determine the better choice based on the objective function at each iteration.

[0091] For example, Figure 5 A schematic diagram of a target order and its virtual inventory allocation result provided by an embodiment of the present disclosure, such as Figure 5As shown, the target order 50 has three order lines, namely order line 1 to order line 3. The SKU and quantity required by each order line are as follows Figure 5 As shown. Through virtual inventory allocation, bins 51 to 59 are determined as hit bins, and the inventory status of each hit bin is as follows Figure 5 As shown. It can be seen that there is only one choice that satisfies the picking task of SKU1 in order line 1, that is, bin 56. Then bin 56 is a first-class bin. When using the clustering algorithm to select the hit bin, bin 56 can be locked, that is, bin 56 is directly determined as the target bin, that is, bin 56 needs to be selected as the selected bin in each iteration. At the same time, in each iteration, the remaining hit bins that meet the order line 2 and order line 3 of the target order 50 are selected from bins 51 to 55, and bins 57 to 59, such as bins 52, 54, 58 and 59.

[0092] Figure 6 A flowchart of another order allocation method provided in an embodiment of the present disclosure. Figure 2 On the basis of the illustrated embodiment, the steps of virtual inventory allocation, area adjustment and order task allocation are refined, constraints of the clustering process are added, and a step of determining the number of reference sub-areas is added.

[0093] like Figure 6 As shown, the order allocation method provided in this embodiment may specifically include the following steps:

[0094] Step S601, calculate the product of the second preset coefficient and the required number of pieces of the workstation of the storage system, and the ratio of the number of slots of a single workstation to obtain the target number of pieces of the slot.

[0095] Among them, the required number of pieces of the workstation of the warehousing system is the production capacity requirement of the workstation in the warehousing system. Specifically, it can be the number of pieces required to be completed by a single workstation per unit time, such as the number of picked pieces that the workstation needs to complete per hour, or it can be the ratio of the statistical hourly business flow of the entire warehousing system to the number of workstations in the warehousing system.

[0096] The second preset coefficient is a known coefficient, and specifically may be a coefficient greater than 1, so as to ensure the production capacity requirement of the workstation.

[0097] The number of slots of a single workstation may be an average number of slots of each workstation in the storage system, or when a large number of workstations with the same number of slots are included, the number of slots of the workstation may be the number of slots of the single workstation.

[0098] That is to say, the expression of the target number of pieces in a slot can be: preset coefficient × required number of pieces in the workstation / number of slots in a single workstation.

[0099] Step S602: Determine the number A of reference sub-areas based on the rounded result of the ratio of the required number of pieces of the target order to the target number of pieces of the slot.

[0100] The number of pieces required for the target order is the sum of the number of pieces of various items required for the target order, that is, the sum of the number of pieces of each SKU in all order lines of the target order.

[0101] The rounding result of a value can be obtained through operations such as rounding up, rounding down, and rounding up, such as using the ceil function, floor function, and round function.

[0102] Exemplarily, the expression of the number A of reference sub-areas is: ceil(required number of pieces of target order / target number of pieces of slot), where the ceil function is a rounding-up function, that is, the value is rounded up to the nearest integer, such as the rounding result of ceil(4.5) is 5.

[0103] By determining the number of reference sub-areas A, the reference sub-areas are divided based on the demand of the target order and the requirement of the workstation capacity, so that the divided sub-areas can improve the parallelism of the target order operations and improve the processing efficiency of the target order while meeting the system capacity requirements.

[0104] Step S603: divide the storage area into A reference sub-areas.

[0105] The storage area of ​​the storage system can be divided into A reference sub-areas in an even manner. The sizes of the reference sub-areas are as similar as possible, and each reference sub-area has at least one workstation.

[0106] Step S604: for each reference sub-area, select any workstation in the reference sub-area as a target workstation for executing the target order.

[0107] After A reference sub-areas are obtained, for each reference sub-area, any workstation in the reference sub-area is selected as the target workstation for simulating the execution of the target order, so that the hit material box required for the target order is allocated to the target workstation through virtual inventory allocation.

[0108] Step S605 , based on the selected target workstation, multiple hit material boxes that meet the target order requirements are determined from the storage area through virtual inventory allocation.

[0109] After selecting the target workstation of each reference sub-area, multiple hit boxes that meet the order requirements of the target order are allocated to the target workstation through virtual inventory allocation. Since there are A reference sub-areas, it is necessary to obtain A groups of hit boxes through A virtual inventory allocations. Different groups of hit boxes can include overlapping hit boxes. Each group of hit boxes in the A group of hit boxes can meet all order requirements of the target order.

[0110] Specifically, each time virtual inventory is allocated, a group of hit material boxes is determined based on the location of the target workstation and the inventory situation in the storage area. On the premise of meeting the order requirements of the target order, the transportation distance of the selected group of hit material boxes from the target workstation should be as short as possible.

[0111] Each time virtual inventory is allocated, it can be done order line by order line, that is, a hit material box is allocated to the order line of each target order to obtain a set of hit material boxes that meet the target order.

[0112] Take A as 4 as an example, Figure 7 A schematic diagram of the A-time virtual inventory allocation result provided by the embodiment of the present disclosure, such as Figure 7 As shown, in the 1st to 4th virtual inventory allocation, workstations 71, 73, 75 and 76 are selected as target workstations respectively, and four groups of hit boxes are obtained, wherein a group of hit boxes corresponding to workstation 71 includes boxes 711 to 719, a group of hit boxes corresponding to workstation 73 includes boxes 718, 719 and boxes 721 to 725, a group of hit boxes corresponding to workstation 75 includes boxes 722, 724 and boxes 731 to 736, and a group of hit boxes corresponding to workstation 76 includes boxes 741 to 747. It can be seen that repeated hit boxes may be selected in different virtual inventory allocations, and multiple hit boxes corresponding to the target order are obtained through 4 virtual inventory allocations, namely boxes 711 to 719, boxes 721 to 725, boxes 731 to 736 and boxes 741 to 747.

[0113] Step S606, using a clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected material box in each pile, multiple rounds of selection are performed on the hit material boxes and multiple rounds of area adjustments are performed on each pile, with the constraint condition that the number of tasks in each pile after adjustment is greater than or equal to the first threshold, to obtain multiple target material boxes and multiple target sub-areas.

[0114] The reference sub-area obtained by division is regarded as the initial value of the pile. Through the clustering algorithm, each pile is adjusted multiple times. When adjusting the area of ​​each pile in each iteration, it is also necessary to ensure that the number of tasks in each pile after adjustment is not too low to meet the business flow requirements of the warehousing system. In this way, when using the clustering algorithm to select the hit material box and adjust the pile, the constraint condition on the number of tasks in the pile should also be set so that the number of tasks in each pile after each adjustment meets the constraint condition.

[0115] The constraint condition is specifically that the number of tasks in each pile after adjustment is greater than or equal to a first threshold, that is, the number of tasks in each pile obtained in each iteration is at least the first threshold. The first threshold may be preset, such as based on experience, or obtained by calculation.

[0116] Optionally, the order allocation method further includes: determining the required number of pieces for a workstation based on the required number of pieces to be shipped out of the warehousing system and the number of workstations; determining a first threshold based on the required number of pieces for the workstation and the number of slots of a single workstation. The required number of pieces for shipping out of the warehousing system is the number of pieces required to be shipped out of the warehousing system per unit time, which is a given value; the number of workstations is the number of workstations set up in the warehousing system for item sorting and shipping out of the warehousing system. The required number of pieces for a workstation is the number of pieces required to be completed by a single workstation per unit time.

[0117] Specifically, the required number of pieces at a workstation may be the ratio of the required number of pieces to be shipped out of the storage system to the number of workstations. The first threshold may be determined based on the ratio of the required number of pieces at a workstation to the number of slots at a single workstation, such as the result of rounding the ratio.

[0118] For a storage system with multiple workstations that are undifferentiated, the number of slots in each workstation is the same, and the number of slots in a single workstation can be the number of slots in any workstation.

[0119] For a warehousing system with multiple workstations with different numbers of slots, one solution is to determine the number of slots of a single workstation as the number of slots of most workstations or as the average number of slots of each workstation; another solution is to determine different first thresholds for target workstations with different numbers of slots, so that the first thresholds in the constraints in the heap clustering process for different target workstations are different, that is, the number of slots of a single workstation used when calculating the first threshold is the number of slots of the target workstation in the heap.

[0120] Assume that the warehouse system requires 400 pieces to be shipped out per hour, that is, the number of pieces required to be shipped out is 400, and the warehouse system has 8 workstations responsible for the shipping task, so the number of pieces required to be shipped out by the workstation is 50. Assume that the number of slots of target workstation 1 is 8, and the number of slots of target workstation 2 is 10, then the constraint condition of the pile where target workstation 1 is located can be that the number of task pieces in the pile is greater than or equal to 7, and the constraint condition of the pile where target workstation 2 is located is that the number of task pieces in the pile is greater than or equal to 5.

[0121] In some other embodiments, the first threshold may also be a coefficient greater than 1, such as the ratio of the product of 1.5 and the number of pieces required by the workstation to the number of slots of a single workstation.

[0122] By calculating the first threshold, the minimum number of tasks in each pile after the area adjustment is ensured, avoiding the situation where the number of tasks in a certain pile is too small, which leads to waste of transportation capacity, and improves the utilization rate of the system transportation capacity.

[0123] Optionally, a first threshold is determined based on the number of pieces required by the workstation and the number of slots of a single workstation, including: calculating the ratio of the number of pieces required by the workstation to the number of slots of a single workstation to obtain a first number of pieces; calculating the product of a first preset coefficient and the number of pieces required by the workstation and the ratio of the product to the number of slots of a single workstation to obtain a second number of pieces; determining the first threshold based on the first number of pieces and the second number of pieces, wherein the first threshold is an integer greater than or equal to the first number of pieces and less than or equal to the second number of pieces.

[0124] The first number of pieces is: ceil (the number of pieces required by the workstation / the number of slots for a single workstation), and the second number of pieces is: ceil (the first preset coefficient × the number of pieces required by the workstation / the number of slots for a single workstation). Since the first preset coefficient is greater than 1, the second number of pieces is greater than the first number of pieces. The first number of pieces is the minimum value required for the number of tasks in the pile. The first threshold value can select any integer between the first number of pieces and the second number of pieces, such as the first number of pieces, the second number of pieces, or other values ​​in between. The first preset coefficient and the second preset coefficient are both coefficients greater than 1, and the two can be equal or unequal.

[0125] Taking the first preset coefficient as 1.5, the required number of pieces of the workstation as 50, and the number of slots of a single workstation as 8 as an example, the first number of pieces is 7, the second number of pieces is 10, and the first threshold can be 7, 8, 9 or 10.

[0126] By setting the first number of pieces and the second number of pieces as the limits of the first threshold, the flexibility of determining the first threshold is improved. By determining a larger first threshold, the number of tasks in each target area is larger, thereby avoiding waste of transportation capacity. By determining a smaller first threshold, better material box selection and area adjustment methods can be found, thereby improving the accuracy of area adjustment.

[0127] Step S607: if the target sub-area includes a target workstation in the corresponding reference sub-area, the order task corresponding to the target sub-area is allocated to the target workstation.

[0128] The target sub-region is obtained by adjusting the corresponding reference sub-region.

[0129] Step S608: If the target sub-region does not include the target workstation in the corresponding reference sub-region, the order task corresponding to the target sub-region is allocated to any workstation in the target sub-region.

[0130] After clustering is completed and multiple target bins for completing the target order are obtained, as well as multiple target sub-areas with balanced target bin distribution and balanced task volume, the order tasks corresponding to each target sub-area need to be allocated to the workstations in each target sub-area.

[0131] When allocating order tasks, since the reference sub-area is adjusted to the target sub-area by shrinking or enlarging, there are two situations: one is that the target sub-area obtained by adjusting the reference sub-area still includes the target workstation of the reference sub-area, and the other is that the target sub-area obtained by adjusting the reference sub-area does not include the target workstation of the reference sub-area. Among them, the target workstation is the workstation selected when allocating virtual inventory.

[0132] In the case where the target sub-area includes a target workstation in the corresponding reference sub-area, when allocating order tasks, the order tasks corresponding to the target sub-area can be directly allocated to the target workstation, so that the target workstation can complete operations such as item sorting and item delivery of this part of the order tasks. If the target workstation has no available slots, it is possible to wait for the slots of the target workstation to be released, that is, after there are available slots, the corresponding order tasks can be allocated to the target workstation. Available slots can be understood as idle slots, that is, slots that are not bound to any order.

[0133] In the case where the target sub-area does not include the target workstation in the corresponding reference sub-area, that is, the target workstation in the reference sub-area corresponding to the target sub-area is located in other target sub-areas, then when allocating order tasks, the order task corresponding to the target sub-area can be allocated to any workstation in the target sub-area, so that the workstation can complete the operations such as item sorting and item delivery of this part of the order task. Specifically, the corresponding order task can be allocated to any workstation with an available slot in the target sub-area. If there are no available slots in all workstations in the target sub-area, you can wait for the slot of a certain workstation to be released, that is, after there is an available slot, the corresponding order task can be allocated to the workstation.

[0134] In this embodiment, the number of reference sub-areas is adaptively calculated using the required number of pieces of the target order and the required number of pieces of the workstations of the warehousing system, thereby realizing static partitioning based on order requirements and improving the accuracy of determining the number of partitions, thereby avoiding too many partitions, which results in the clustering process taking too long, and avoiding too few partitions, which results in reduced order processing parallelism and affects order processing efficiency. Through multiple virtual inventory allocations, as many hit bins as possible are found to provide sufficient data samples for subsequent clustering analysis, thereby improving the accuracy of the clustering process. During clustering, constraints on the number of tasks in a pile are added to the clustering algorithm, thereby avoiding the problem of too low number of tasks in some piles, which results in idle robots and low system capacity utilization in the target sub-area corresponding to the pile. Through the clustering algorithm, static partitioning is optimized and dynamic partitioning is realized, so that tasks in different partitions are balanced and order processing efficiency is improved.

[0135] The warehousing system will continuously receive pending orders, and different orders can be used as target orders to achieve the allocation of multiple pending orders.

[0136] Optionally, the order allocation method further includes: acquiring pending orders from the warehousing system; sorting the pending orders in descending order of priority; and determining each pending order as a target order in turn based on the sorting result.

[0137] The priority of an order can be determined based on the urgency of the order, the remaining time for order processing, the time the order was received, etc.

[0138] After sorting the pending orders according to priority, take out the first-ranked pending order as the target order, perform reference sub-area division, virtual inventory allocation, material box selection and area adjustment, obtain multiple target material boxes and multiple target sub-areas, and perform order task allocation; then, take out the first-ranked pending order from the remaining pending orders as the target order, and so on, until all pending orders are allocated.

[0139] Figure 8 A flowchart of another order allocation method provided by the embodiment of the present disclosure is shown as follows: Figure 8 As shown, the order allocation method mainly includes the following steps:

[0140] Step S801, obtaining all pending orders in the warehousing system.

[0141] Step S802: sort all pending orders according to their priority from high to low.

[0142] Step S803, taking out the first-ranked pending order as the target order.

[0143] Step S804: Calculate the number A of reference sub-areas based on the required number of pieces of the target order.

[0144] Step S805, evenly divide the storage area into A reference sub-areas, and select any target workstation in each reference sub-area to perform virtual inventory allocation for the target order to obtain a virtual inventory allocation result, which includes the hit material box, the storage location of the hit material box, and the number of tasks corresponding to the hit material box.

[0145] Step S806: According to the virtual inventory allocation result and the first threshold N, hit material box selection and reference sub-area area adjustment are performed.

[0146] Each reference sub-area corresponds to a pile. The hit material box that must be selected for the SKU in the target order is locked, that is, the first type of material box is locked. According to the pile task balance, the hit material box selection and the area scaling of the reference sub-area are performed to obtain multiple target sub-areas and multiple target material boxes, so that the density of the hit material boxes selected between piles is equivalent, the number of tasks in the piles is equivalent, and it is necessary to ensure that the number of tasks in each pile is greater than or equal to the first threshold N.

[0147] Step S807, order task allocation, the order task may be allocated to any workstation with available slots in the corresponding target sub-area.

[0148] Among them, the order task is the picking task of the target order satisfied by the target material box in the corresponding target sub-area. If, during allocation, the target sub-area includes the target workstation selected during virtual allocation, and the target workstation has an available slot, the order task corresponding to the target sub-area will be allocated to the target workstation first; if there is no workstation with an available slot in the target sub-area, the order task corresponding to the target sub-area will be allocated to the workstation after the slot of the workstation in the target sub-area is released.

[0149] Fig. 9 A schematic diagram of the structure of an order distribution device provided by an embodiment of the present disclosure is shown in FIG. Fig. 9 As shown, the order allocation device provided in this embodiment includes: a partition module 910, a virtual allocation module 920, a partition adjustment module 930 and an order allocation module 940.

[0150] Among them, the partitioning module 910 is used to divide the storage area into multiple reference sub-areas; the virtual allocation module 920 is used to perform virtual inventory allocation of target orders for each reference sub-area to obtain multiple hit boxes; the partition adjustment module 930 is used to select hit boxes and perform regional adjustments on multiple reference sub-areas based on the storage locations of multiple hit boxes and the number of tasks corresponding to each hit box, to obtain multiple target boxes and multiple target sub-areas; wherein the number of tasks corresponding to the hit box is the number of items in the target order satisfied by the hit box; multiple target boxes are partial boxes in the hit box, and multiple target boxes are used to complete the target order; the order allocation module 940 is used to allocate order tasks to workstations in the corresponding target sub-areas, wherein the order tasks are picking tasks for the target orders satisfied by the target boxes in the corresponding target sub-areas.

[0151] Optionally, the partition adjustment module 930 is specifically used to: utilize a clustering algorithm, based on the objective function between the number of task pieces in each pile and the first distance corresponding to each selected material box in each pile, perform multiple rounds of selection on multiple hit material boxes and perform multiple rounds of area adjustment on each pile to obtain multiple target material boxes and multiple target sub-areas; the initial value of the pile is the corresponding reference sub-area; the selected material box is the material box selected from the hit material boxes in each round of area adjustment to complete the target order, the first distance corresponding to the selected material box is the distance between the selected material box and the center point of the material box in the pile, and the center point of the material box in the pile is the center of the storage location where each selected material box in the pile is located; the number of task pieces in the pile is the sum of the number of task pieces corresponding to each selected material box in the pile.

[0152] Optionally, the partition adjustment module 930 is specifically used to: lock the first type of boxes among multiple hit boxes as target boxes; the first type of boxes are the hit boxes that must be selected to complete the target order; use a clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected box in each pile, perform multiple rounds of selection on the second type of boxes and perform multiple rounds of area adjustments on each pile to obtain multiple target boxes and multiple target sub-areas; the second type of boxes are the hit boxes other than the first type of boxes.

[0153] Optionally, the partition adjustment module 930 is specifically used to: utilize a clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected material box in each pile, perform multiple rounds of selection on the hit material boxes and perform multiple rounds of area adjustments on each pile, with the constraint condition that the number of tasks in each pile after adjustment is greater than or equal to the first threshold, to obtain multiple target material boxes and multiple target sub-areas.

[0154] Optionally, the order distribution device also includes a first threshold determination module, which is used to: determine the required number of pieces at the workstation based on the required number of pieces to be shipped out of the warehousing system and the number of workstations; determine the first threshold based on the required number of pieces at the workstation and the number of slots of a single workstation.

[0155] Optionally, the first threshold determination module is specifically used to: determine the required number of pieces at the workstation based on the required number of pieces to be shipped out of the warehousing system and the number of workstations; calculate the ratio of the required number of pieces at the workstation to the number of slots for a single workstation to obtain a first number of pieces; calculate the product of a first preset coefficient and the required number of pieces at the workstation, and the ratio to the number of slots for a single workstation to obtain a second number of pieces; determine a first threshold based on the first number of pieces and the second number of pieces, wherein the first threshold is an integer greater than or equal to the first number of pieces and less than or equal to the second number of pieces.

[0156] Optionally, the objective function is the sum of the target indexes of each pile; the target index of each pile is the sum of the first ratios corresponding to each selected material box in the pile, and the first ratio is the ratio of the first distance corresponding to the selected material box to the number of tasks in the pile.

[0157] Optionally, the order allocation device also includes a partition quantity determination module, which is used to: calculate the product of a second preset coefficient and the required number of pieces of a workstation in the warehousing system, and the ratio of the number of slots in a single workstation to obtain the target number of slots; determine the number of reference sub-areas based on the rounded result of the ratio of the required number of pieces of the target order to the target number of slots.

[0158] Optionally, the virtual allocation module 920 is specifically used to: for each reference sub-area, select any workstation in the reference sub-area as the target workstation for executing the target order; based on the selected target workstation, determine multiple hit material boxes that meet the requirements of the target order from the storage area.

[0159] Optionally, the order allocation module 940 is specifically used for: for each target sub-area, if the target sub-area includes the target workstation in the corresponding reference sub-area, the order task corresponding to the target sub-area is allocated to the target workstation; the target sub-area is obtained by adjusting the corresponding reference sub-area; if the target sub-area does not include the target workstation in the corresponding reference sub-area, the order task corresponding to the target sub-area is allocated to any workstation in the target sub-area.

[0160] Optionally, the order distribution device also includes an order sorting module, which is used to: obtain pending orders from the warehousing system; sort the pending orders in order of order priority from high to low; and determine each pending order as a target order in turn based on the sorting result.

[0161] An order allocation device provided in this embodiment can execute the order allocation method provided in any of the above embodiments. The implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0162] Fig.10 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Fig.10As shown, the electronic device provided in this embodiment includes: a processor 1001, and a memory 1002 communicatively connected to the processor 1001; the memory 1002 stores computer execution instructions, and the processor 1001 executes the computer execution instructions stored in the memory 1002 to implement the order allocation method provided in any embodiment of the present disclosure.

[0163] The specific implementation process of the processor 1001 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0164] Optionally, the electronic device further includes a communication component, through which the electronic device communicates with other devices such as a robot, an order-taking device, etc. The order-taking device is a device responsible for receiving and managing orders processed by the agent. The processor 1001, the memory 1002, and the communication component are connected via a bus.

[0165] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0166] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk storage.

[0167] The communication component of the electronic device can send wireless signals (for example, wifi signals or radio signals such as 4G / 5G) to the robot, and can also receive wireless signals sent by the robot.

[0168] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present disclosure is not limited to only one bus or one type of bus.

[0169] The present disclosure also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0170] The present disclosure also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0171] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0172] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0173] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0174] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0176] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0177] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0178] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure, are not limited to the precise structures described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An order allocation method, characterized in that: include: Divide the storage area into multiple reference sub-areas; Allocating virtual inventory of target orders to each of the reference sub-areas to obtain multiple hit boxes; Based on the storage locations where the multiple hit boxes are located and the number of tasks corresponding to each of the hit boxes, the multiple hit boxes are selected and the multiple reference sub-areas are adjusted to obtain multiple target boxes and multiple target sub-areas; wherein the number of tasks corresponding to the hit box is the number of items in the target order satisfied by the hit box; the multiple target boxes are some of the hit boxes, and the multiple target boxes are used to complete the target order; The order task is allocated to a workstation in the corresponding target sub-area, wherein the order task is a picking task of the target order satisfied by the target container in the corresponding target sub-area.

2. The method according to claim 1, characterized in that: The method of selecting the multiple hit boxes and adjusting the multiple reference sub-areas based on the storage locations of the multiple hit boxes and the number of tasks corresponding to each of the hit boxes to obtain multiple target boxes and multiple target sub-areas includes: Using a clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected bin in each pile, multiple rounds of selection are performed on the multiple hit bins and multiple rounds of area adjustment are performed on the multiple piles to obtain the multiple target bins and the multiple target sub-areas; Among them, the initial value of the pile is the corresponding reference sub-area; the selected material box is the material box selected from the hit material box in each round of area adjustment to complete the target order, the first distance corresponding to the selected material box is the distance between the selected material box and the center point of the material box in the pile, and the center point of the material box in the pile is the center of the storage location where each of the selected material boxes in the pile is located; the number of tasks in the pile is the sum of the number of tasks corresponding to each of the selected material boxes in the pile.

3. The method according to claim 2, characterized in that The method utilizes a clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected bin in each pile, to perform multiple rounds of selection on the hit bins and multiple rounds of region adjustment on the multiple piles to obtain the multiple target bins and the multiple target sub-regions, including: Locking a first type of material box among the multiple hit material boxes as the target material box; the first type of material box is a hit material box that must be selected to complete the target order; Utilizing the clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each of the selected boxes in each pile, multiple rounds of selection are performed on the second category boxes and multiple rounds of area adjustments are performed on each pile to obtain the multiple target boxes and the multiple target sub-areas; the second category boxes are the hit boxes other than the first category boxes.

4. The method according to claim 2, characterized in that: The clustering algorithm is used to perform multiple rounds of selection on the hit bins and multiple rounds of region adjustment on each pile based on the objective function between the number of tasks in each pile and the first distance corresponding to each selected bin in each pile, to obtain the multiple target bins and the multiple target sub-regions, including: Utilizing the clustering algorithm, based on the objective function between the number of tasks in each pile and the first distance corresponding to each of the selected material boxes in each pile, multiple rounds of selection are performed on the hit material boxes and multiple rounds of area adjustments are performed on each pile, with the constraint condition that the number of tasks in each pile after adjustment is greater than or equal to the first threshold, to obtain the multiple target material boxes and the multiple target sub-areas.

5. The method according to claim 4, characterized in that The method further comprises: Determine the required number of pieces for a workstation based on the required number of pieces to be shipped out of the warehouse system and the number of workstations; The first threshold is determined based on the number of pieces required by the workstation and the number of slots of a single workstation.

6. The method according to claim 5, characterized in that The determining the first threshold based on the number of pieces required by the workstation and the number of slots of a single workstation includes: Calculate the ratio of the required number of pieces of the workstation to the number of slots of the single workstation to obtain a first number of pieces; Calculate the ratio of the product of the first preset coefficient and the required number of pieces of the workstation to the number of slots of the single workstation to obtain a second number of pieces; The first threshold is determined according to the first number of pieces and the second number of pieces, wherein the first threshold is an integer greater than or equal to the first number of pieces and less than or equal to the second number of pieces.

7. The method according to any one of claims 2 to 6, characterized in that: The objective function is the sum of the target indexes of each pile; the target index of each pile is the sum of the first ratios corresponding to the selected boxes in the pile, and the first ratio is the ratio of the first distance corresponding to the selected box to the number of tasks in the pile.

8. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Calculate the ratio of the product of the second preset coefficient and the required number of pieces of the workstation of the storage system and the number of slots of a single workstation to obtain the target number of pieces of the slot; The number of the reference sub-areas is determined based on a rounded result of a ratio of the required number of pieces of the target order to the target number of pieces of the slot.

9. The method according to any one of claims 1 to 6, characterized in that: The step of allocating virtual inventory of target orders to each of the reference sub-areas to obtain a plurality of hit boxes includes: For each of the reference sub-areas, selecting any one workstation in the reference sub-area as a target workstation for executing the target order; Based on the selected target workstation, the plurality of hit containers that meet the target order requirements are determined from the storage area.

10. The method according to claim 9, characterized in that The step of allocating the order task to the corresponding workstation in the target sub-area includes: For each of the target sub-areas, if the target sub-area includes the target workstation in the corresponding reference sub-area, the order task corresponding to the target sub-area is allocated to the target workstation; the target sub-area is obtained by adjusting the corresponding reference sub-area; If the target sub-area does not include the target workstation in the corresponding reference sub-area, the order task corresponding to the target sub-area is allocated to any workstation in the target sub-area.

11. An order distribution device, characterized in that: include: A partitioning module is used to divide the storage area into multiple reference sub-areas; A virtual allocation module, used for allocating virtual inventory of target orders to each of the reference sub-areas to obtain multiple hit material boxes; A partition adjustment module is used to select the multiple hit boxes and perform regional adjustment on the multiple reference sub-areas based on the storage locations where the multiple hit boxes are located and the number of tasks corresponding to each of the hit boxes, so as to obtain multiple target boxes and multiple target sub-areas; wherein the number of tasks corresponding to the hit box is the number of items in the target order satisfied by the hit box; the multiple target boxes are some of the hit boxes, and the multiple target boxes are used to complete the target order; An order allocation module is used to allocate order tasks to workstations in the corresponding target sub-area, wherein the order tasks are picking tasks for the target orders satisfied by the target bins in the corresponding target sub-area.

12. An electronic device comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 11.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 10 when executed by a processor.

14. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 10 when being executed by a processor.

Images