CN116011721A

CN116011721A - Method and device for grouping sheets

Info

Publication number: CN116011721A
Application number: CN202111229533.3A
Authority: CN
Inventors: 安柏楠; 董红宇; 柯恒; 陈明锟
Original assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2021-10-21
Filing date: 2021-10-21
Publication date: 2023-04-25

Abstract

The invention discloses a method and a device for grouping sheets, and relates to the technical field of computers. One embodiment of the method comprises the following steps: based on order picking travel time, screening initial seed orders and subsequent orders of the initial seed orders through a seed algorithm to obtain initial order combinations and corresponding estimated total picking time, taking an initial seed order single layer as an initial layer, searching and determining seed orders of subsequent layers of the initial layer by layer through a preset sequencing-skip variable neighborhood search algorithm, and after searching of all layers is completed, taking the finally determined order combination with optimal estimated total picking time as an aggregate list, and outputting the aggregate list and the corresponding estimated total picking time. According to the method and the device, the quality of the collection list and the efficiency of the group list can be improved, wrong collection or missing collection is prevented, the calculated amount and the calculated time are reduced, the labor cost is reduced, and the requirements of industrial-grade algorithms are met.

Description

Method and device for grouping sheets

Technical Field

The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for grouping sheets.

Background

When the small standard warehouse processes orders, partial orders are required to be combined in an order pool formed by the order to be picked, and then the combined orders are delivered to a picker for picking, so that the purposes of saving the picking time, reducing the labor cost and regulating the warehouse flow are achieved. At present, four schemes exist for group list: the first scheme is a manual order-forming mode, namely, the order-forming is mainly carried out by relying on manual experience, and special order-forming operators carry out order-forming at regular time according to the number of orders in an order pool and the area where the order storage is located and the principle of first-in-advance order-forming is adopted according to basic constraint conditions; the scheme II is a single-target group single mode, namely, according to basic constraint conditions, a single-target group single algorithm evaluates and calculates by means of single targets such as the number of crossing lanes, the number of storage areas, the number of storage digits and the like; the scheme III is a multi-objective optimization group single mode, namely the multi-objective optimization group single algorithm comprehensively considers the storage area number, the roadway number, the storage number and the like according to basic constraint conditions and distributes weights according to warehouse requirements; and the scheme IV is a group list mode represented by deep learning and machine learning, namely, the deep learning and machine learning models are used for estimating and clustering spatial distribution among storage sites in the warehouse according to basic constraint conditions, and an optimal collection list is calculated.

In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:

the scheme has the advantages that the single effect rate of a group is low, the labor cost is high, the optimal single effect of the collection is difficult to ensure, and the phenomena of error collection, missing collection and the like often occur; the aggregate simple substance quantity of the scheme II is poor, and the average picking time of the pickers is long; the third scheme adopts a plurality of types of process indexes to optimize, and each optimization target is generally distributed according to experience when weight distribution is carried out, so that deviation exists between the optimization targets and the correlation of the final evaluation indexes, and the single aggregate quality is poor; the four groups of single processes of the scheme are ideally simplified, the actual scene requirement of industrial engineering is ignored, the calculation is assisted by a solver, the calculated amount is extremely large, the calculation time is long (in the order of minutes or even hours), and the requirement of industrial algorithm (in the order of seconds or even minutes) is difficult to deal with.

Disclosure of Invention

In view of the above, the embodiment of the invention provides a method and a device for grouping, which can improve the quality of an aggregate list and the efficiency of the group list, prevent wrong collection or missing collection, reduce the calculated amount and the calculated time, reduce the labor cost and meet the requirements of industrial-grade algorithms.

To achieve the above object, according to one aspect of an embodiment of the present invention, there is provided a group singulation method.

A method of singulating, comprising: screening an initial seed order and subsequent orders of the initial seed order through a seed algorithm based on the order picking travel time to obtain an initial order combination and estimated total picking time of the initial order combination; the subsequent orders of the initial seed order are orders for picking after the initial seed order; and using the layer where the initial seed order is located as an initial layer, and searching and determining seed orders of all subsequent layers of the initial layer by layer through a preset sequencing skip variable neighborhood searching algorithm, wherein: defining a searched layer as a current layer during searching of each layer, sorting orders to be searched of the current layer based on picking travel time from a previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total picking time length through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total picking time length of the determined order combination as the seed order of the current layer; and after the search of all the layers is completed, combining the finally determined order with the optimal estimated total picking time length into a collection list, and outputting the collection list and the corresponding estimated total picking time length.

Optionally, the order-based picking travel time screens out an initial seed order and a subsequent order of the initial seed order by a seed algorithm, including: selecting an order with shortest picking travel time from a departure point as the initial seed order; when each subsequent order of the initial seed order is selected, ordering the orders to be selected, and selecting the order with the shortest picking travel time from the order to be selected last from the sequence formed by the orders to be selected under the condition that the order combination limiting condition is met.

Optionally, searching orders in the order sequence for multiple times according to a search rule, determining order combination and obtaining a corresponding estimated total picking time length through a seed algorithm based on the searched orders during each search, including: taking an order with the shortest picking travel time from the previous layer of seed orders in the order sequence as an alternative seed order of the current layer, and taking the estimated total picking time of the order combination determined based on the alternative seed order as the optimal estimated total picking time; determining a target search order in the order sequence according to the initial neighborhood action range, determining an order combination through the seed algorithm based on the target search order, and obtaining a corresponding estimated total picking time length; comparing the estimated total pick time of the order combination determined based on the target search order with the estimated total pick time of the order combination determined based on the candidate seed order; and under the condition that the estimated total picking time length of the order combination determined based on the target search order is smaller than the estimated total picking time length of the order combination determined based on the alternative seed order, updating the alternative seed order of the current layer to the target search order, and updating the optimal estimated total picking time length to the estimated total picking time length of the order combination determined based on the target search order, and then jumping to the step of determining the target search order in the order sequence according to the initial neighborhood action range so as to determine a new target search order and carrying out the next search.

Optionally, the method further comprises: and under the condition that the estimated total picking time length of the order combination determined based on the target search order is greater than or equal to the estimated total picking time length of the order combination determined based on the candidate seed order, judging whether the search termination condition of the current layer is currently reached, if not, changing the current neighborhood motion range, determining the target search order in the order sequence with the new neighborhood motion range, jumping to the step of determining the order combination based on the target search order through the seed algorithm and obtaining the corresponding estimated total picking time length, wherein the search termination condition of the current layer is that the current neighborhood motion range reaches the maximum value of the neighborhood motion change or all orders of the order sequence are searched.

Optionally, said changing the current neighborhood motion profile, determining a target search order in the order sequence with the new neighborhood motion profile, comprises: and automatically increasing the current neighborhood action range by a preset value to obtain a new neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the new neighborhood action range so as to determine the position of the target search order in the order sequence.

Optionally, after the determining whether the search termination condition of the current layer is currently reached, the method further includes: and if the search termination condition of the current layer is currently reached, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

Optionally, the determining the target search order in the order sequence according to the initial neighborhood action range includes: and acquiring the initial neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the initial neighborhood action range so as to determine the position of the target search order in the order sequence.

Optionally, the method further comprises: and if the target search order does not exist in the order sequence according to the initial neighborhood action range, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

Optionally, before the continuing searching to determine the seed order of the next layer of the current layer, the method includes: and confirming that the number of layers corresponding to the current layer is smaller than a preset neighborhood number of layers, wherein the neighborhood number of layers is the upper limit of the search number of layers set according to the maximum number of orders allowed by the aggregate list.

Optionally, determining an order combination by the seed algorithm based on the target search order, and obtaining a corresponding estimated total picking time length, including: obtaining a determined order subset from each layer of seed orders and the target search orders before the current layer; selecting a subsequent order of the target search order, wherein the subsequent order of the target search order is an order for picking after the target search order; when each order subsequent to the target search order is selected, sequencing the remaining orders, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the sequence of the remaining orders; and obtaining an order combination determined based on the target search order and a corresponding estimated total picking time length from each order in the order subset and each subsequent order of the target search order, wherein the estimated total picking time length of the order combination is the sum of the picking time lengths of all orders in the order combination.

Optionally, before the order-based picking travel time and the initial seed order are screened out by the seed algorithm, the method includes: judging whether a special storage order exists currently or not, and generating a first order combination based on the special storage order under the condition that the special storage order exists; the obtaining the initial order combination and the estimated total picking time length of the initial order combination comprises the following steps: selecting an order with shortest picking travel time from a departure point as the initial seed order, and adding the initial seed order into the first order combination under the condition that the order combination limiting condition is met; selecting subsequent orders of the initial seed order, wherein when each subsequent order of the initial seed order is selected, sorting all the orders to be selected, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the order to be selected last time from the order sequence to be selected, and adding the order into the first order combination; and obtaining the initial order combination and the estimated total picking time length of the initial order combination according to the final first order combination and the corresponding estimated total picking time length.

Optionally, the order combination constraint includes: one or more of maximum load weight allowed by the aggregate list, maximum load volume, upper order quantity limit, class, batch number, grade, CPU maximum computing power.

According to another aspect of an embodiment of the present invention, a group bill apparatus is provided.

A group singulation apparatus comprising: the initial order combination generating module is used for screening an initial seed order and subsequent orders of the initial seed order through a seed algorithm based on the order picking travel time to obtain an initial order combination and estimated total picking time of the initial order combination; the subsequent orders of the initial seed order are orders for picking after the initial seed order; the group order module is used for searching and determining seed orders of all subsequent layers of the initial layer by taking the layer where the initial seed order is located as the initial layer through a preset sequencing-skip variable neighborhood searching algorithm, wherein: defining a searched layer as a current layer during searching of each layer, sorting orders to be searched of the current layer based on picking travel time from a previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total picking time length through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total picking time length of the determined order combination as the seed order of the current layer; and after the search of all the layers is completed, combining the finally determined order with the optimal estimated total picking time length into a collection list, and outputting the collection list and the corresponding estimated total picking time length.

Optionally, the initial order combination generating module is further configured to: selecting an order with shortest picking travel time from a departure point as the initial seed order; when each subsequent order of the initial seed order is selected, ordering the orders to be selected, and selecting the order with the shortest picking travel time from the order to be selected last from the sequence formed by the orders to be selected under the condition that the order combination limiting condition is met.

Optionally, the group unit module is further configured to: taking an order with the shortest picking travel time from the previous layer of seed orders in the order sequence as an alternative seed order of the current layer, and taking the estimated total picking time of the order combination determined based on the alternative seed order as the optimal estimated total picking time; determining a target search order in the order sequence according to the initial neighborhood action range, determining an order combination through the seed algorithm based on the target search order, and obtaining a corresponding estimated total picking time length; comparing the estimated total pick time of the order combination determined based on the target search order with the estimated total pick time of the order combination determined based on the candidate seed order; and under the condition that the estimated total picking time length of the order combination determined based on the target search order is smaller than the estimated total picking time length of the order combination determined based on the alternative seed order, updating the alternative seed order of the current layer to the target search order, and updating the optimal estimated total picking time length to the estimated total picking time length of the order combination determined based on the target search order, and then jumping to the step of determining the target search order in the order sequence according to the initial neighborhood action range so as to determine a new target search order and carrying out the next search.

Optionally, the group unit module is further configured to: and under the condition that the estimated total picking time length of the order combination determined based on the target search order is greater than or equal to the estimated total picking time length of the order combination determined based on the candidate seed order, judging whether the search termination condition of the current layer is currently reached, if not, changing the current neighborhood motion range, determining the target search order in the order sequence with the new neighborhood motion range, jumping to the step of determining the order combination based on the target search order through the seed algorithm and obtaining the corresponding estimated total picking time length, wherein the search termination condition of the current layer is that the current neighborhood motion range reaches the maximum value of the neighborhood motion change or all orders of the order sequence are searched.

Optionally, the group unit module is further configured to: and automatically increasing the current neighborhood action range by a preset value to obtain a new neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the new neighborhood action range so as to determine the position of the target search order in the order sequence.

Optionally, the group unit module is further configured to: and if the search termination condition of the current layer is currently reached, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

Optionally, the group unit module is further configured to: and acquiring the initial neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the initial neighborhood action range so as to determine the position of the target search order in the order sequence.

Optionally, the group unit module is further configured to: and if the target search order does not exist in the order sequence according to the initial neighborhood action range, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

Optionally, the group unit module is further configured to: and confirming that the number of layers corresponding to the current layer is smaller than a preset neighborhood number of layers, wherein the neighborhood number of layers is the upper limit of the search number of layers set according to the maximum number of orders allowed by the aggregate list.

Optionally, the group unit module is further configured to: obtaining a determined order subset from each layer of seed orders and the target search orders before the current layer; selecting a subsequent order of the target search order, wherein the subsequent order of the target search order is an order for picking after the target search order; when each order subsequent to the target search order is selected, sequencing the remaining orders, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the sequence of the remaining orders; and obtaining an order combination determined based on the target search order and a corresponding estimated total picking time length from each order in the order subset and each subsequent order of the target search order, wherein the estimated total picking time length of the order combination is the sum of the picking time lengths of all orders in the order combination.

Optionally, the initial order combination generating module is further configured to: judging whether a special storage order exists currently or not, and generating a first order combination based on the special storage order under the condition that the special storage order exists; selecting an order with shortest picking travel time from a departure point as the initial seed order, and adding the initial seed order into the first order combination under the condition that the order combination limiting condition is met; selecting subsequent orders of the initial seed order, wherein when each subsequent order of the initial seed order is selected, sorting all the orders to be selected, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the order to be selected last time from the order sequence to be selected, and adding the order into the first order combination; and obtaining the initial order combination and the estimated total picking time length of the initial order combination according to the final first order combination and the corresponding estimated total picking time length.

According to yet another aspect of an embodiment of the present invention, an electronic device is provided.

An electronic device, comprising: one or more processors; and the memory is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the group list method provided by the embodiment of the invention.

According to yet another aspect of an embodiment of the present invention, a computer-readable medium is provided.

A computer readable medium having stored thereon a computer program which when executed by a processor implements a group ordering method provided by an embodiment of the invention.

One embodiment of the above invention has the following advantages or benefits: based on order picking travel time, initial seed orders and subsequent orders of the initial seed orders are screened through a seed algorithm, initial order combination and corresponding estimated total picking time are obtained, an initial seed order single layer is taken as an initial layer, seed orders of subsequent layers of the initial layer are determined through a preset sequencing jump variable neighborhood search (SR-VNS) algorithm, each layer of orders in the aggregate list is determined through layer-by-layer search, specifically, when each layer of orders in the aggregate list is determined through searching, ascending sorting is carried out according to the travel time between storage positions of the orders in the aggregate list, order combination is determined through the seed algorithm, and order combination with optimal estimated total picking time is obtained, searching is carried out in each layer firstly when searching is carried out, searching is continued to the next layer when the current layer search does not obtain a solution which is better than the current solution, searching is carried out in the current neighborhood range without returning to the previous layer neighborhood, the order is greatly saved, the total picking time is calculated according to the order combination with optimal estimated total picking time, the aggregate order combination is calculated according to the total picking time is calculated, the aggregate order total picking time is reduced, the aggregate demand is met or the aggregate quality is calculated, the aggregate quality is greatly reduced, the aggregate picking time is calculated, and the aggregate quality is calculated is required.

Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

FIG. 1 is a schematic diagram of the main steps of a singulation method in accordance with one embodiment of the present invention;

FIG. 2 is a schematic illustration of a group unit flow according to one embodiment of the invention;

FIG. 3 is a group-wise flow diagram of an SR-VNS (sequenced-jump variable neighborhood search) framework in accordance with one embodiment of the invention;

FIG. 4 is a schematic illustration of a seed algorithm-based group unit flow according to one embodiment of the invention;

FIGS. 5 and 6 are schematic illustrations of a group unit flow based on an SR-VNS framework and a seed algorithm in accordance with one embodiment of the invention;

FIG. 7 is a schematic diagram of the main modules of a group singulation apparatus in accordance with one embodiment of the present invention;

FIG. 8 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;

fig. 9 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram of main steps of a grouping method according to an embodiment of the present invention, and as shown in fig. 1, the grouping method according to an embodiment of the present invention mainly includes the following steps S101 to S102.

Step S101: screening an initial seed order and subsequent orders of the initial seed order through a seed algorithm based on the order picking travel time to obtain an initial order combination and estimated total picking time of the initial order combination; subsequent orders for the initial seed order are orders that are picked after the initial seed order;

step S102: and searching layer by layer to determine seed orders of subsequent layers of the initial layer by using a preset sequencing skip variable neighborhood search algorithm by taking the layer where the initial seed order is located as an initial layer, and after searching of all layers is completed, combining the finally determined order combination with the optimal estimated total picking time length as a collection list and outputting the collection list and the corresponding estimated total picking time length.

Wherein: defining the searched layer as the current layer during each layer of searching, sorting the orders to be searched of the current layer based on the order picking travel time from the previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total order picking time through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total order picking time of the determined order combination as the seed order of the current layer.

Before the orders are assembled, the orders enter a warehouse management system, express information, commodity types and quantity of the warehouse are determined by the order management system, and the orders are sent to an order pool of the warehouse management system to wait for the ordered orders to be picked.

The pick travel time of an order is the time that the sorter walks between two storage locations. That is, the pick travel time between orders is specifically referred to as the pick travel time between the storage locations where the orders are located.

The picking time length is the total picking time length of the sorter between the two storage positions, and comprises the sum of the picking travel time and the picking time. The picking time is the time that the picker performs the picking operation at one storage location.

The estimated total pick time for the aggregate order is the sum of the pick times for all orders in the aggregate order. The order picking time length can be calculated by the order picking travel time and the order picking time, and the order picking travel time and the order picking time can be determined according to the storage position of the ordered commodity. The total picking time length can be estimated through the picking time length of all orders in the collection list, namely the estimated total picking time length of the collection list.

The seed order will eventually be placed into the aggregate sheet generated after the group sheet is completed.

The current layer to-be-searched order refers to the remaining orders in the order pool before searching and determining the seed order of the current layer, and does not comprise the seed orders of all layers before the determined current layer. In some embodiments, the orders are also ordered based on special stock orders before the initial seed order is determined, and then the current tier of orders to be searched also does not include these special stock orders.

The order sequence is obtained by sequencing the orders to be searched in the current layer, specifically, the order sequence is obtained by sequencing the order sequence from short to long according to the order picking travel time of the seed order in the previous layer.

Based on the order picking travel time, the initial seed order and the subsequent orders of the initial seed order are screened out through a seed algorithm, and the method specifically comprises the following steps: selecting an order with shortest picking travel time from a departure point as an initial seed order; when each subsequent order of the initial seed order is selected, ordering the orders to be selected, and selecting the order with shortest picking travel time from the sequences formed by the orders to be selected under the condition that the order combination limiting condition is met.

Searching orders in the order sequence for multiple times according to a searching rule, determining order combination by a seed algorithm based on the searched orders during each searching to obtain corresponding estimated total picking time, and specifically comprising the following steps: taking an order with the shortest picking travel time from the previous layer of seed orders in the order sequence as an alternative seed order of the current layer, and taking the estimated total picking time length of the order combination determined based on the alternative seed order as the optimal estimated total picking time length; determining target search orders in the order sequence according to the initial neighborhood action range, determining order combination through a seed algorithm based on the target search orders, and obtaining corresponding estimated total picking time; comparing the estimated total pick time of the order combination determined based on the target search order with the estimated total pick time of the order combination determined based on the alternate seed order; in the case that the estimated total picking time length of the order combination determined based on the target search order is smaller than the estimated total picking time length of the order combination determined based on the candidate seed order, updating the candidate seed order of the current layer to the target search order, updating the optimal estimated total picking time length to the estimated total picking time length of the order combination determined based on the target search order, and then jumping to the step of determining the target search order in the order sequence according to the initial neighborhood action range so as to determine a new target search order and conduct the next search.

And under the condition that the estimated total picking time length of the order combination determined based on the target search order is greater than or equal to the estimated total picking time length of the order combination determined based on the alternative seed order, judging whether the current search termination condition of the current layer is reached, if not, changing the current neighborhood action range, determining the target search order in the order sequence with the new neighborhood action range, jumping to the step of determining the order combination based on the target search order through a seed algorithm, and obtaining the corresponding estimated total picking time length, so as to perform the next search, wherein the current search termination condition of the current layer is that the current neighborhood action range reaches the maximum value of the neighborhood action change or all orders of the searched order sequence.

Changing the current neighborhood action range, and determining a target search order in the order sequence by using the new neighborhood action range, wherein the method specifically comprises the following steps of: and (3) automatically increasing the current neighborhood action range by a preset value (for example, automatically increasing by 1), obtaining a new neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the new neighborhood action range so as to determine the position of the target search order in the order sequence.

And judging whether the current search termination condition is reached or not, if so, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

Determining a target search order in an order sequence according to the initial neighborhood action range, wherein the target search order comprises the following specific steps: and acquiring an initial neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the initial neighborhood action range so as to determine the position of the target search order in the order sequence.

If the target search order does not exist in the order sequence according to the initial neighborhood action range, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

Before continuing searching and determining the seed order of the next layer of the current layer, confirming that the number of layers corresponding to the current layer is smaller than a preset neighborhood number of layers, wherein the neighborhood number of layers is the upper limit of the searching number of layers set according to the maximum number of orders allowed by the aggregate list.

Based on the target search order, determining an order combination through a seed algorithm, and obtaining a corresponding estimated total picking time length, wherein the method specifically comprises the following steps: seed orders and target search orders of all layers in front of the current layer are used for obtaining a determined order subset; selecting a subsequent order of the target search order, wherein the subsequent order of the target search order is an order for picking after the target search order; when each order subsequent to the target search order is selected, sequencing the remaining orders, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the sequence of the remaining orders; and obtaining an order combination determined based on the target search order and a corresponding estimated total picking time length from each order in the order list and each subsequent order of the target search order, wherein the estimated total picking time length of the order combination is the sum of the picking time lengths of all orders in the order combination.

In one embodiment, based on the order picking travel time, before the initial seed order and the subsequent orders of the initial seed order are screened by the seed algorithm, whether a special place order exists currently can be judged, and in the case that the special place order exists, a first order combination is generated based on the special place order.

An initial first order combination may be defined first, and special stock orders may be placed into the first order combination one by one as the first order combination is generated, the first order combination being dynamically updated as new special stock orders are placed.

When a special storage order is placed in the first order combination each time, whether the order combination limiting condition is met or not can be judged first, and the fact that the order combination limiting condition is met currently is confirmed.

The special storage order is an order in a special storage cabinet, wherein the special storage cabinet is a type of storage position existing in a warehouse, the position of the special storage position is not fixed in the warehouse, and goods are required to be picked and delivered preferentially.

Obtaining an initial order combination and an estimated total pickup time of the initial order combination may specifically include: selecting an order with shortest picking travel time from a starting point as an initial seed order, and adding the initial seed order into a first order combination under the condition that the order combination limiting condition is met; selecting subsequent orders of the initial seed order, wherein when each subsequent order of the initial seed order is selected, sorting all the orders to be selected, selecting the order with the shortest picking travel time from the order to be selected last time from the order to be selected sequence under the condition that the limiting condition of order combination is met, and adding the order to the first order combination; and obtaining the initial order combination and the estimated total picking time length of the initial order combination according to the final first order combination and the corresponding estimated total picking time length.

The order combination constraints may include: one or more of maximum load weight allowed by the aggregate list, maximum load volume, upper order quantity limit, class, batch number, grade, CPU maximum computing power.

When seed orders of subsequent layers of an initial layer are determined by layer-by-layer searching, a group order strategy of SR-VNS (sequencing jump variable neighborhood search framework) fused with a seed algorithm is adopted, and a scattered order in a current warehouse can be effectively combined by combining a storage position picking travel time prediction method, so that a collection order with the minimum current total picking time length is obtained, and the defect that the existing artificial group order and single-target and multi-target group single-mode collection order effect is poor is overcome. The SR-VNS framework of the embodiment of the invention reduces invalid loop search while ensuring excellent calculation effect.

The VNS (variable neighborhood search framework, variable Neighborhood Search) framework is a local search algorithm, and uses neighborhood structures formed by different actions to perform alternate search, so that a good balance between concentration and evacuation is achieved, and in the existing VNS framework, if a solution better than the current solution is found in a certain neighborhood search, the first neighborhood needs to be skipped to restart the search, so that the calculated amount is large, and the industrial data scene is difficult to deal with.

The SR-VNS framework provided by the embodiment of the invention is improved on the basis of the existing VNS framework, when each layer of orders in the aggregate list are determined by searching, ascending order sorting is carried out according to the storage interval travel time between the order and the previous layer of orders, order combination is determined by utilizing a seed algorithm, and the order combination with optimal estimated total picking time is obtained.

FIG. 2 is a schematic block diagram of a single flow according to one embodiment of the invention. As shown in fig. 2, an order pool and order information thereof at the current moment are obtained, an aggregate order Λ is initialized, the initial first order combination is defined by the initial aggregate order Λ, and the aggregate order Λ can be dynamically updated as orders are added; judging whether an order in the special storage cabinet exists in the order pool or not, wherein the order in the special storage cabinet is a special storage order; if a special storage order exists, continuing to judge whether the addition of the order exceeds the current order collecting limiting condition, wherein the order collecting limiting condition is an order combination limiting condition, which can be simply called limiting condition; if the limit condition is not exceeded, the order is ordered Adding the current aggregate list lambda, updating aggregate list parameters, updating an order pool, acquiring order parameters, and returning to the step of judging whether an order in a special storage cabinet exists in the current order pool; if the limit condition is exceeded, the batch aggregation list Λ and the total picking time delta are output _total Updating the order pool, acquiring order parameters, and returning to the step of initializing the aggregate order Λ; if the order pool does not have an order in the special storage cabinet, the SR-VNS order grouping strategy of the fusion seed algorithm is adopted for the current order pool to perform order grouping, and the batch aggregate order lambda and the total order picking time delta are output _total Updating the order pool, acquiring order parameters, and returning to the step of initializing the aggregate order Λ. The output aggregate list lambda is the aggregate list obtained after the group list of the embodiment of the invention is completed, and the total picking time delta is output _total I.e., the estimated total pick time of the assembled sheets after the completion of the group sheets.

Examples of order pools and portions thereof are shown in table 1, relating to order number, name, quantity, bin number, weight, volume, lot number, grade, etc. information. An example of a set of single parameters is shown in table 2.

TABLE 1

TABLE 2

Fig. 3 is a schematic illustration of a group single flow based on an SR-VNS framework, according to one embodiment of the invention. The method comprises the steps of screening and determining initial order combinations, obtaining estimated total picking time of the initial order combinations, screening initial candidate seed orders s and subsequent picking orders of the initial candidate seed orders from an order pool according to picking travel time, and particularly sorting all orders in the order pool according to picking travel time from a starting point, wherein the starting point is a picking entrance set in a warehouse and is different from warehouse to warehouse. Selecting a storage order with shortest picking travel time from a starting point from the ordered orders as an initial candidate sub-order, ordering the rest orders in the order pool according to the order from the initial candidate sub-order when selecting a subsequent first order from the initial candidate sub-order, wherein the rest orders do not comprise the initial candidate sub-order, selecting an order closest to the initial candidate sub-order based on the ordered order, taking the order as the subsequent first order from the initial candidate sub-order, and selecting the subsequent orders according to the picking travel time of the subsequent first order from the initial candidate sub-order when selecting a subsequent second order from the initial candidate sub-order, and so on. The sorting is performed according to the order of the order picking travel time from short to long, and in the order picking process, if a limit condition is exceeded after a certain order is added, the order selecting operation is finished, and a collection order and a corresponding estimated total order picking time length are output.

Designing SR-VNS framework parameters, specifically, according to the order quantity phi in an order pool and the maximum quantity phi of orders allowed by a collective order _max Designing the neighborhood layer number N and the neighborhood action change maximum value N of the SR-VNS framework _v Taking the order of the storage position with the shortest travel time at the pick inlet as an initial candidate seed order s, determining an initial order combination and a corresponding estimated total pick time length according to the method, taking the estimated total pick time length of the initial order combination as an initial solution, and defining i=1 to enable N _i (S) is the i-th layer neighborhood. The parameters of a set of SR-VNS frameworks are shown in Table 3, wherein the number of neighbor layers N should be less than or equal to the maximum number of orders Φ allowed in the aggregate sheet _max The larger the value is, the better the single group single effect is, but the longer the calculation time is, the more commonly can be set as phi _max Half of the value; neighborhood motion variance maximum N _v Should be smaller than the number of orders in the order pool Φ, the larger the value is, the better the group of single effects is, but the longer the calculation time is, the more can be set to 3, 4 and 5.

TABLE 3 Table 3

At N _i And (S) searching by combining a seed algorithm to obtain a current optimal solution S, wherein i represents the number of layers corresponding to the current layer, and the seed order of the ith layer can be determined through the step, and the optimal solution S is the current optimal estimated total picking time length obtained in the process of determining the seed order of the ith layer. The currently optimal estimated total pick time is an estimated total pick time for the order combination determined based on the seed order at the i-th layer. The order combination is determined based on the seed order of the ith layer, specifically, on the basis of each seed order determined before the ith layer, the subsequent orders of the seed order of the ith layer are further determined based on the seed order of the ith layer through a seed algorithm, so that one order combination comprising each seed order determined before the ith layer, the seed order of the ith layer and the subsequent orders of the seed order of the ith layer is obtained.

At N _i Sequentially determining new candidate seed orders in the allowed neighborhood action quantity, calculating the corresponding optimal solutions S ', namely comparing the sizes of the S ' and the S values, converting the solutions with smaller values into better solutions, updating S=S ' if S is still the better solution, changing the candidate seed orders in the corresponding neighborhood action range based on the candidate seed orders, and updating N _i (S) and then at N again _i And (S) searching in the neighborhood of the layer according to the new alternative seed order and the seed algorithm. Judging whether i is smaller than or equal to N of the neighborhood layer number, if i is larger than N, taking the latest alternative seed order as a seed order of the neighborhood layer, and outputting a collection order corresponding to the seed order at the moment and an optimal solution S; if i is less than or equal to N, N is reserved _i The corresponding order in the current neighborhood calculated in (S) is then made i=i+1, at the new N _i And (3) continuously calculating the optimal solution S 'calculated based on the current layer candidate seed order within the allowable neighborhood action number in the step (S), comparing the S' value with the S value, and judging whether the solution is a better solution or not, namely repeating the steps. Until the final aggregate list Λ and the corresponding optimal solution S are output.

FIG. 4 is a schematic illustration of a seed algorithm-based group unit flow according to one embodiment of the invention. As shown in fig. 4, an initial seed order is selected and an aggregate order Λ is initialized, and referring to the description of the above embodiment, the initialized aggregate order Λ is not a final group order result, but is defined as an initial first order combination, where the number of orders may be 0, and the final aggregate order Λ is a group order result of the embodiment of the present invention as the aggregate order Λ is added to the order. And adding the initial seed order into the aggregate list lambda, updating aggregate list parameters, wherein the initial seed order is the seed order of the initial layer, and sorting the to-be-searched orders of the current layer according to the seed order of the last layer. Judging whether the current order pool has an order meeting the limiting condition and capable of being added into the aggregate list, and if not, outputting the aggregate list Λ and the total picking time delta total; if so, selecting the order of the storage position meeting the limiting condition and having the smallest estimated picking travel time from the last storage position, adding the order into the collection list, and updating the information of the collection list and the total picking time of the collection list. Judging whether the current collection list reaches a limiting condition after the order is added, and outputting the collection list lambda and the total picking time delta total if the current collection list reaches the limiting condition; if the limiting condition is not met, updating the order pool, acquiring order parameters, and returning to the step of judging whether the current order pool has the order meeting the limiting condition and capable of being added into the aggregate list, namely repeating the process. Wherein, according to the actual requirements of each warehouse, the limiting conditions can include: whether the aggregate sheet exceeds the maximum load weight of the pickers after the order is added; whether the aggregate sheet exceeds the maximum carrying volume of the pickers after the order is added; whether the aggregate order exceeds the upper limit of the aggregate order quantity after the order is added; the same kind of goods but different batch numbers and different grades of goods can not be added into the same collection list; whether the CPU maximum computing power is exceeded. The limitation may specifically include only one of them or the condition. Preferably, the order may be added to the aggregate sheet if the above-described respective restrictions are satisfied simultaneously.

Fig. 5 and 6 are schematic diagrams of a group-wise flow based on an SR-VNS framework and a seed algorithm according to an embodiment of the present invention, and it should be noted that fig. 5 and 6 are part of the flow respectively, and the two together form a complete flow chart. The embodiment of the invention aims at optimizing the total picking time of the collection list and makes a perfect orderGrouping scattered orders in the single pool, and outputting a final aggregate order lambda and a corresponding estimated total picking time delta _total . According to fig. 5, the current order pool and order parameters are obtained, and a collective order Λ is initialized, wherein the number of orders in the initialized order collective is currently 0. At the same time, the estimated total picking time delta _total =0, i=1. The algorithm variables and meanings are shown in table 4.

TABLE 4 Table 4

And judging whether an order in the special storage cabinet exists or not.

If an order of a special storage cabinet exists, whether the addition of the order exceeds an aggregate limit condition is continuously judged, wherein the aggregate limit condition is the order combination limit condition, namely the limit condition for short in the embodiment of the invention. If the limiting condition is not exceeded, adding the order into the current aggregate list lambda, updating aggregate list parameters and an order pool, acquiring order parameters, and returning to the step of whether the order of the special storage cabinet exists in the current order pool, namely repeating the process to add the subsequent order of the special storage cabinet; if the limit condition is exceeded, the group order is completed, and the batch aggregate order lambda and the total picking time delta are output _total 。

If no order of the special storage cabinet exists, the corresponding estimated picking time length of the order of the special storage cabinet is delta _{t_x} Delta is calculated _{t_x} Then judging whether the current collection list reaches the limit condition, if so, completing the group list, outputting the batch of collection list lambda and the estimated total picking time delta _total The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, selecting the order of the storage position with the shortest picking travel time from the departure point as the initial seed order, and correspondingly estimating the picking time to be delta _{t_0} Adding the initial seed order to the current aggregate order Λ, updating delta _total ＝Δ _{t_0} +Δ _{t_x} . If the first determination is made as to whether or not there is an order in the special storage cabinet, and if the determination result is that there is no order in the special storage cabinet, Δ _{t_x} =0. If the order in the special storage cabinet is not judged for the first time, and the judgment result is that the order in the special storage cabinet is not present, delta _{t_x} The sum of the estimated picking time for each particular bin order that has been added to the aggregate order Λ.

Then judging whether the current collection list reaches the limiting condition, if not, updating the order pool, selecting the order which meets the limiting condition and has the shortest picking travel time from the last selected order through a seed algorithm, adding the current collection list, and recording the picking time length of the selected order as delta _{t_i} Simultaneously updating the estimated total picking time of the current collection list, namely, enabling delta _total ＝Δ _total +Δ _{t_i} It should be noted that, the picking travel time between orders specifically refers to the picking travel time between storage locations where the orders are located; if the limit condition is met, completing the group list, and outputting the current collection list lambda and the optimal total picking time delta _best . i=1, the last bin described above is the bin corresponding to the initial seed order.

Continuing to determine whether the current aggregate order has reached a limit or whether there are no joinable orders in the order pool. If the limit condition is not met and the order which can be added into the order pool still exists, i=i+1 is set, the order pool and the information thereof are updated at the same time, and the storage order which meets the limit condition and has the shortest estimated picking travel time from the last storage is continuously selected to be added into the current aggregate; if the limit condition is reached, the step of updating the optimal estimated total picking time length and setting the relevant parameters in fig. 6 is performed.

According to FIG. 6, following the flow of FIG. 5, the optimal estimated total picking time is updated and related parameters are set, and the optimal estimated total picking time is updated by specifically commanding delta _best ＝Δ _total The delta is _total Is the estimated total pick duration for the current aggregate sheet obtained by the process of fig. 5. The parameters set in this step include o _loc 、u、o _N Specifically let o _loc ＝1,u＝0,o _N ＝1，o _loc =1 indicates: in order of short to long pick travel time based on order from last layer of seedsAfter ordering the orders to be searched of the current layer to obtain an order sequence, taking the order ranked first in the order sequence as the initial alternative seed order of the current layer, and passing o _loc The position of the alternative seed order to the current tier in the order sequence may be located. o (o) _N =1 indicates that the neighborhood motion range is 1.

Extraction of delta _{t_0} 、Δ _{t_u} Obtaining delta _{t_u} Corresponding aggregate list, let i=1, where Δ _{t_0} Is the estimated picking time for the initial seed order. Delta _{t_u} For the total estimated picking time of each order that has been added to the aggregate order at and before the nth tier, delta _{t_u} The corresponding aggregate form is the aggregate form formed by the seed order of the u layer and each order added before the u layer. In the subsequent cycle, with the self-increase of u, Δ _{t_u} Delta _{t_u} The corresponding aggregate sheet is dynamically changing.

And updating the order pool, and sorting the orders to be searched of the (u+1) layer according to the ascending order of the picking travel time from short to long based on the determined seed orders of the u th layer to obtain an order sequence. In this example, the (u+1) layer is the current layer. The order with the shortest picking travel time from the seed order of the u th layer in the order sequence is used as the candidate seed order of the (u+1) th layer.

And judging whether a target search order exists in the order sequence. The target search order is an order in the order sequence that is equidistant from the (u+1) layer's alternate seed order by an amount equal to the neighborhood action range.

If there is no target search order currently in the order sequence, the candidate seed order of the (u+1) layer is the last order in the order sequence, then the neighborhood action range is restored to the initial value and the layer number corresponding to the current search is increased by 1, specifically, o can be caused to be _N =1, u=u+1. Judging whether seed orders of all layers are searched and determined, namely judging whether u is smaller than N-1, if so, adding o _loc Restoring to the initial value o _loc =1, then jump to extract Δ _{t_0} 、Δ _{t_u} Obtaining delta _{t_u} Step of letting i=1 for corresponding aggregate order to continue searching to determine seed order of next layerThe method comprises the steps of carrying out a first treatment on the surface of the If the seed orders of all the layers are searched and determined, outputting the current collection list and the optimal estimated total picking time delta of the current collection list _best 。

If there is a target search order currently in the order sequence, the target search order is added to the aggregate order, specifically to add delta, if the constraint condition is satisfied _{t_u} Corresponding aggregate list, target search order estimated picking time length is delta _{t_i} Updating the estimated total picking time delta of the collection list _total The sum of the estimated picking time lengths for all orders that have currently joined the aggregate sheet.

Then judging whether the aggregate orders added with the target search orders reach the limiting condition or whether the aggregate orders can be added in the order pool are not available, if the limiting condition is not met and the aggregate orders can be added in the order pool, letting i=i+1, continuing to select the next order of the target search orders to add the aggregate orders, specifically, updating the order pool, selecting the order meeting the limiting condition and having the shortest order picking travel time from the last selected order to add the current aggregate orders through a seed algorithm, and storing the estimated order picking time between the storage positions as delta _{t_i} Simultaneously updating the estimated total picking time delta of the current collection list _total ＝Δ _total +Δ _{t_i} And then returns to the step of judging whether the aggregate list after the target search order is added reaches the limit condition or whether the aggregate list can be added in the order pool. If the limiting condition is met or no order capable of being added into the order pool exists, obtaining the estimated total picking time delta of the current aggregate on the basis of the sum of the estimated picking time of the current orders in the aggregate _variable ，Δ _variable ＝Δ _total 。

Comparing the estimated total pick time of the order combination determined based on the target search order with the estimated total pick time of the order combination determined based on the alternate seed order, namely: will delta _variable And delta _best And (5) comparing.

If delta _variable Less than delta _best Representing determination based on target search ordersIf the total picking time length of the order combination is shorter, updating the optimal estimated total picking time length to be the estimated total picking time length delta of the order combination _variable Order delta _best ＝Δ _variable And restore the current neighborhood motion region to the initial neighborhood motion region (let o _N =1), and updating the (u+1) level of candidate seed orders to the current target search order (let o _loc ＝o _loc +o _N ) Then, the step of determining whether the target search order exists in the order sequence is skipped, and it should be noted that, the step of skipping to the step of determining whether the target search order exists in the order sequence is to determine a new target search order, and if the new target search order exists, determining a new aggregate order based on the new target search order.

If delta _variable Greater than or equal to delta _best If the total order picking time of the order combination determined based on the alternative seed order is shorter, the current neighborhood action range is automatically increased by 1 to obtain a new neighborhood action range, namely o _N ＝o _N +1. Then judging whether the search termination condition of the current layer is reached, specifically, o _N Whether or not it is smaller than N _v And o is o _loc +o _N Whether or not less than phi- (u+1), i.e., whether or not the current neighborhood search action range maximum has been exceeded in the neighborhood search, and whether or not all orders for the (u+1) level have been fully calculated. If the search termination condition of the current layer is not reached, i.e. o _N Less than N _v And o is o _loc +o _N And if the target search order is smaller than phi- (u+1), determining a new target search order in the order sequence through the operation of changing the neighborhood action range, and then jumping to the step of judging whether the target search order exists in the order sequence so as to judge whether the target search order can be searched according to the new neighborhood action range, and further continuing searching in the current layer under the condition that the target search order exists. If the search termination condition of the current layer has been reached, i.e. o is not satisfied _N Less than N _v And o is o _loc +o _N Less than phi- (u+1), let o _N =1, u=u+1, then determining whether u is less than N-1, i.e. determining whether it has beenSearching to determine seed orders of all layers, if u is smaller than N-1, then let o _loc =1, then jump to extract Δ _{t_0} 、Δ _{t_u} Obtaining delta _{t_u} A step of letting i=1 for the corresponding aggregate order to continue searching to determine the seed order of the next layer; if u is greater than or equal to N-1, indicating that the seed orders of all layers are searched and determined, outputting the current aggregate order lambda and the optimal total picking time delta _best 。

Aiming at the actual industrial scene, the embodiment of the invention fuses an SR-VNS framework and a seed algorithm, combines a method for estimating the pick travel time between storage positions, balances the calculation time and the optimization precision of a group list process, and improves the calculation speed and the quality of a collection list so as to meet the industrial engineering requirement. In addition, the orders to be searched are sorted according to the ascending order of the picking travel time in each neighborhood search, orders with short picking travel time among storage locations are collected together in each search, the current optimal solution corresponding to the seed orders can be quickly and efficiently constructed based on a seed algorithm in each neighborhood, the quality of the local optimal solution is improved, meanwhile, based on an SR-VNS framework, the next sub-optimal solution seed order is added in the current collection list in the next neighborhood range of the seed orders which are optimally sorted, therefore, the quality of the local optimal solution in each cycle search can be guaranteed in each neighborhood search process, and the collection list result obtained by the embodiment of the invention can be converged to the global optimal solution, so that the group list effect is better. The SR-VNS framework provided by the invention eliminates random neighborhood setting and bidirectional neighborhood setting in the existing VNS framework, is based on sequencing unidirectional neighborhood, searches sequentially and orderly in the neighborhood action calculation, reserves the current layer seed order result after the current layer calculation is completed and jumps to the next layer search, and the subsequent search does not return to the calculated neighborhood layer, so that invalid neighborhood search range and steps can be reduced while the quality of the aggregate list is ensured, and compared with the traditional VNS framework, the algorithm calculation time complexity is greatly reduced, and the method is suitable for engineering scene application.

Fig. 7 is a schematic diagram of main modules of a group unit according to an embodiment of the present invention, and as shown in fig. 7, a group unit 700 according to an embodiment of the present invention mainly includes: an initial order combination generation module 701 and a group order module 702.

The initial order combination generating module 701 is configured to screen an initial seed order and a subsequent order of the initial seed order through a seed algorithm based on a picking travel time of the order, so as to obtain an initial order combination and an estimated total picking time of the initial order combination; subsequent orders for the initial seed order are orders that are picked after the initial seed order.

The grouping module 702 is configured to search layer by layer to determine seed orders of subsequent layers of the initial layer by using a layer where the initial seed order is located as the initial layer through a preset variable neighborhood search algorithm for sequencing and skipping, where: defining the searched layer as the current layer during each layer of searching, sorting the orders to be searched in the current layer based on the picking travel time from the previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total picking time length through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total picking time length of the determined order combination as the seed order of the current layer in each order searched in the current layer; and after the search of all the layers is completed, combining the finally determined order with the optimal estimated total picking time length into a collection list, and outputting the collection list and the corresponding estimated total picking time length.

The initial order combination generation module 701 is specifically configured to: selecting an order with shortest picking travel time from a departure point as an initial seed order; when each subsequent order of the initial seed order is selected, ordering the orders to be selected, and selecting the order with shortest picking travel time from the sequences formed by the orders to be selected under the condition that the order combination limiting condition is met.

The group list module 702 is specifically configured to: taking an order with the shortest picking travel time from the previous layer of seed orders in the order sequence as an alternative seed order of the current layer, and taking the estimated total picking time length of the order combination determined based on the alternative seed order as the optimal estimated total picking time length; determining target search orders in the order sequence according to the initial neighborhood action range, determining order combination through a seed algorithm based on the target search orders, and obtaining corresponding estimated total picking time; comparing the estimated total pick time of the order combination determined based on the target search order with the estimated total pick time of the order combination determined based on the alternate seed order; in the case that the estimated total picking time length of the order combination determined based on the target search order is smaller than the estimated total picking time length of the order combination determined based on the candidate seed order, updating the candidate seed order of the current layer to the target search order, updating the optimal estimated total picking time length to the estimated total picking time length of the order combination determined based on the target search order, and then jumping to the step of determining the target search order in the order sequence according to the initial neighborhood action range so as to determine a new target search order and conduct the next search.

The group list module 702 is also configured to: and under the condition that the estimated total picking time length of the order combination determined based on the target search order is greater than or equal to the estimated total picking time length of the order combination determined based on the alternative seed order, judging whether the current search termination condition of the current layer is reached, if not, changing the current neighborhood action range, determining the target search order in the order sequence with the new neighborhood action range, jumping to the step of determining the order combination based on the target search order through a seed algorithm, and obtaining the corresponding estimated total picking time length, so as to perform the next search, wherein the current search termination condition of the current layer is that the current neighborhood action range reaches the maximum value of the neighborhood action change or all orders of the searched order sequence.

The group list module 702 is also configured to: and automatically increasing the current neighborhood action range by a preset value to obtain a new neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the new neighborhood action range so as to determine the position of the target search order in the order sequence.

The group list module 702 is also configured to: if the search termination condition of the current layer is reached currently, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

The group list module 702 is also configured to: and acquiring an initial neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the initial neighborhood action range so as to determine the position of the target search order in the order sequence.

The group list module 702 is also configured to: if the target search order does not exist in the order sequence according to the initial neighborhood action range, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

The group list module 702 is also configured to: and confirming that the number of layers corresponding to the current layer is smaller than a preset neighborhood number of layers, wherein the neighborhood number of layers is the upper limit of the search number of layers set according to the maximum number of orders allowed by the aggregate list.

The group list module 702 is also configured to: seed orders and target search orders of all layers in front of the current layer are used for obtaining a determined order subset; selecting a subsequent order of the target search order, wherein the subsequent order of the target search order is an order for picking after the target search order; when each order subsequent to the target search order is selected, sequencing the remaining orders, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the sequence of the remaining orders; and obtaining an order combination determined based on the target search order and a corresponding estimated total picking time length from each order in the order list and each subsequent order of the target search order, wherein the estimated total picking time length of the order combination is the sum of the picking time lengths of all orders in the order combination.

The initial order combination generation module 701 is further configured to: judging whether a special storage order exists currently, and generating a first order combination based on the special storage order under the condition that the special storage order exists; selecting an order with shortest picking travel time from a starting point as an initial seed order, and adding the initial seed order into a first order combination under the condition that the order combination limiting condition is met; selecting subsequent orders of the initial seed order, wherein when each subsequent order of the initial seed order is selected, sorting all the orders to be selected, selecting the order with the shortest picking travel time from the order to be selected last time from the order to be selected sequence under the condition that the limiting condition of order combination is met, and adding the order to the first order combination; and obtaining the initial order combination and the estimated total picking time length of the initial order combination according to the final first order combination and the corresponding estimated total picking time length.

The order combination constraint includes: one or more of maximum load weight allowed by the aggregate list, maximum load volume, upper order quantity limit, class, batch number, grade, CPU maximum computing power.

Fig. 8 illustrates an exemplary system architecture 800 in which the group singulation method or group singulation device of embodiments of the present invention may be applied.

As shown in fig. 8, a system architecture 800 may include

terminal devices

801, 802, 803, a network 804, and a server 805. The network 804 serves as a medium for providing communication links between the

terminal devices

801, 802, 803 and the server 805. The network 804 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

A user may interact with the server 805 through the network 804 using the

terminal devices

801, 802, 803 to receive or send messages or the like. Various communication client applications such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the

terminal devices

801, 802, 803.

The

terminal devices

801, 802, 803 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 805 may be a server providing various services, such as a background management server (by way of example only) that provides support for shopping-type websites browsed by users using the

terminal devices

801, 802, 803. The background management server may analyze and process the received data such as the product information query request, and feedback the processing result (e.g., the target push information, the product information—only an example) to the terminal device.

It should be noted that, the method of grouping the components provided in the embodiment of the present invention is generally executed by the server 805, and accordingly, the grouping device is generally disposed in the server 805.

It should be understood that the number of terminal devices, networks and servers in fig. 8 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 9, there is illustrated a schematic diagram of a computer system 900 suitable for use in implementing a terminal device or server in accordance with an embodiment of the present invention. The terminal device or server shown in fig. 9 is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.

As shown in fig. 9, the computer system 900 includes a Central Processing Unit (CPU) 901, which can execute various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the system 900 are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.

The following components are connected to the I/O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 901.

The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor comprises an initial order combination generation module and a group order module. The names of these modules do not limit the module itself in some cases, for example, the initial order combination generation module may also be described as "a module for screening an initial seed order and subsequent orders of the initial seed order by a seed algorithm based on a pick travel time of the order, resulting in an initial order combination and an estimated total pick duration of the initial order combination".

As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: screening an initial seed order and subsequent orders of the initial seed order through a seed algorithm based on the order picking travel time to obtain an initial order combination and estimated total picking time of the initial order combination; the subsequent orders of the initial seed order are orders for picking after the initial seed order; and using the layer where the initial seed order is located as an initial layer, and searching and determining seed orders of all subsequent layers of the initial layer by layer through a preset sequencing skip variable neighborhood searching algorithm, wherein: defining a searched layer as a current layer during searching of each layer, sorting orders to be searched of the current layer based on picking travel time from a previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total picking time length through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total picking time length of the determined order combination as the seed order of the current layer; and after the search of all the layers is completed, combining the finally determined order with the optimal estimated total picking time length into a collection list, and outputting the collection list and the corresponding estimated total picking time length.

According to the technical scheme of the embodiment of the invention, based on order picking travel time, an initial seed order and subsequent orders of the initial seed order are screened through a seed algorithm, initial order combination and corresponding estimated total picking time are obtained, an initial seed order single layer is used as an initial layer, seed orders of subsequent layers of the initial layer are determined through a preset sequencing skip variable neighborhood search algorithm in a layer-by-layer mode, after searching of all layers is completed, the finally determined order combination with the optimal estimated total picking time is used as a collection order, and the collection order and the corresponding estimated total picking time are output. The method can improve the quality of the collection list and the efficiency of the group list, prevent wrong collection or missing collection, reduce the calculated amount and the calculated time, reduce the labor cost and meet the requirements of industrial-grade algorithms.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of singulating, comprising:

screening an initial seed order and subsequent orders of the initial seed order through a seed algorithm based on the order picking travel time to obtain an initial order combination and estimated total picking time of the initial order combination; the subsequent orders of the initial seed order are orders for picking after the initial seed order;

and using the layer where the initial seed order is located as an initial layer, and searching and determining seed orders of all subsequent layers of the initial layer by layer through a preset sequencing skip variable neighborhood searching algorithm, wherein: defining a searched layer as a current layer during searching of each layer, sorting orders to be searched of the current layer based on picking travel time from a previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total picking time length through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total picking time length of the determined order combination as the seed order of the current layer; and after the search of all the layers is completed, combining the finally determined order with the optimal estimated total picking time length into a collection list, and outputting the collection list and the corresponding estimated total picking time length.

2. The method of claim 1, wherein the order-based pick travel time, screening an initial seed order and subsequent orders for the initial seed order by a seed algorithm, comprises:

selecting an order with shortest picking travel time from a departure point as the initial seed order;

when each subsequent order of the initial seed order is selected, ordering the orders to be selected, and selecting the order with the shortest picking travel time from the order to be selected last from the sequence formed by the orders to be selected under the condition that the order combination limiting condition is met.

3. The method of claim 1, wherein searching orders in the order sequence for multiple times according to a search rule, determining order combinations by a seed algorithm based on the searched orders at each search and obtaining corresponding estimated total pickup time length, comprises:

taking an order with the shortest picking travel time from the previous layer of seed orders in the order sequence as an alternative seed order of the current layer, and taking the estimated total picking time of the order combination determined based on the alternative seed order as the optimal estimated total picking time;

Determining a target search order in the order sequence according to the initial neighborhood action range, determining an order combination through the seed algorithm based on the target search order, and obtaining a corresponding estimated total picking time length;

comparing the estimated total pick time of the order combination determined based on the target search order with the estimated total pick time of the order combination determined based on the candidate seed order;

and under the condition that the estimated total picking time length of the order combination determined based on the target search order is smaller than the estimated total picking time length of the order combination determined based on the alternative seed order, updating the alternative seed order of the current layer to the target search order, and updating the optimal estimated total picking time length to the estimated total picking time length of the order combination determined based on the target search order, and then jumping to the step of determining the target search order in the order sequence according to the initial neighborhood action range so as to determine a new target search order and carrying out the next search.

4. A method according to claim 3, further comprising:

and under the condition that the estimated total picking time length of the order combination determined based on the target search order is greater than or equal to the estimated total picking time length of the order combination determined based on the candidate seed order, judging whether the search termination condition of the current layer is currently reached, if not, changing the current neighborhood motion range, determining the target search order in the order sequence with the new neighborhood motion range, jumping to the step of determining the order combination based on the target search order through the seed algorithm and obtaining the corresponding estimated total picking time length, wherein the search termination condition of the current layer is that the current neighborhood motion range reaches the maximum value of the neighborhood motion change or all orders of the order sequence are searched.

5. The method of claim 4, wherein said changing the current neighborhood motion profile, determining a target search order in the order sequence with the new neighborhood motion profile, comprises:

and automatically increasing the current neighborhood action range by a preset value to obtain a new neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the new neighborhood action range so as to determine the position of the target search order in the order sequence.

6. The method of claim 4, wherein after determining whether the search termination condition of the current layer is currently reached, further comprising:

and if the search termination condition of the current layer is currently reached, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

7. A method according to claim 3, wherein said determining a target search order in said order sequence according to an initial neighborhood action range comprises:

and acquiring the initial neighborhood action range, and obtaining the distance between the candidate seed order and the target search order in the order sequence according to the initial neighborhood action range so as to determine the position of the target search order in the order sequence.

8. The method as recited in claim 7, further comprising:

and if the target search order does not exist in the order sequence according to the initial neighborhood action range, taking the latest alternative seed order of the current layer as the seed order of the current layer, and then continuing searching to determine the seed order of the next layer of the current layer.

9. The method of claim 6 or 8, wherein the continuing the search before determining the seed order for the next layer of the current layer comprises:

and confirming that the number of layers corresponding to the current layer is smaller than a preset neighborhood number of layers, wherein the neighborhood number of layers is the upper limit of the search number of layers set according to the maximum number of orders allowed by the aggregate list.

10. The method of claim 3, wherein determining order combinations based on the target search orders by the seed algorithm and resulting in corresponding estimated total pick times comprises:

obtaining a determined order subset from each layer of seed orders and the target search orders before the current layer;

selecting a subsequent order of the target search order, wherein the subsequent order of the target search order is an order for picking after the target search order; when each order subsequent to the target search order is selected, sequencing the remaining orders, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the sequence of the remaining orders;

And obtaining an order combination determined based on the target search order and a corresponding estimated total picking time length from each order in the order subset and each subsequent order of the target search order, wherein the estimated total picking time length of the order combination is the sum of the picking time lengths of all orders in the order combination.

11. The method of claim 1, wherein the order-based pick travel time, prior to screening an initial seed order and subsequent orders for the initial seed order by a seed algorithm, comprises:

judging whether a special storage order exists currently or not, and generating a first order combination based on the special storage order under the condition that the special storage order exists;

the obtaining the initial order combination and the estimated total picking time length of the initial order combination comprises the following steps:

selecting an order with shortest picking travel time from a departure point as the initial seed order, and adding the initial seed order into the first order combination under the condition that the order combination limiting condition is met;

selecting subsequent orders of the initial seed order, wherein when each subsequent order of the initial seed order is selected, sorting all the orders to be selected, and under the condition that the order combination limiting condition is met, selecting the order with the shortest picking travel time from the order to be selected last time from the order sequence to be selected, and adding the order into the first order combination;

And obtaining the initial order combination and the estimated total picking time length of the initial order combination according to the final first order combination and the corresponding estimated total picking time length.

12. The method of any one of claims 2, 10, 11, wherein the order combination constraint comprises: one or more of maximum load weight allowed by the aggregate list, maximum load volume, upper order quantity limit, class, batch number, grade, CPU maximum computing power.

13. A singulation apparatus, comprising:

the initial order combination generating module is used for screening an initial seed order and subsequent orders of the initial seed order through a seed algorithm based on the order picking travel time to obtain an initial order combination and estimated total picking time of the initial order combination; the subsequent orders of the initial seed order are orders for picking after the initial seed order;

the group order module is used for searching and determining seed orders of all subsequent layers of the initial layer by taking the layer where the initial seed order is located as the initial layer through a preset sequencing-skip variable neighborhood searching algorithm, wherein: defining a searched layer as a current layer during searching of each layer, sorting orders to be searched of the current layer based on picking travel time from a previous layer of seed orders to obtain an order sequence, searching the orders in the order sequence for a plurality of times according to a searching rule, determining order combination and obtaining corresponding estimated total picking time length through a seed algorithm during each searching based on the searched orders, and taking the order with the optimal estimated total picking time length of the determined order combination as the seed order of the current layer; and after the search of all the layers is completed, combining the finally determined order with the optimal estimated total picking time length into a collection list, and outputting the collection list and the corresponding estimated total picking time length.

14. An electronic device, comprising:

one or more processors;

a memory for storing one or more programs,

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-12.

15. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method according to any of claims 1-12.