CN113689167A

CN113689167A - Material box distribution method and device, server, storage medium and warehousing system

Info

Publication number: CN113689167A
Application number: CN202110983344.9A
Authority: CN
Inventors: 喻润方; 艾鑫
Original assignee: Shenzhen Kubo Software Co Ltd
Current assignee: Shenzhen Kubo Software Co Ltd
Priority date: 2021-08-25
Filing date: 2021-08-25
Publication date: 2021-11-23

Abstract

The disclosure provides a bin distribution method, a bin distribution device, a server, a storage medium and a warehousing system. The bin distribution method is applied to a server in a warehousing system. The warehousing system comprises a goods shelf and an aerial robot, wherein the goods shelf is provided with a vertical track, and the aerial robot is used for moving to a storage position where a bin is stored along the vertical track to carry out bin taking operation; the method comprises the following steps: determining a to-be-taken object according to the to-be-processed order task; determining a vertical track where each bin in at least one bin containing the goods to be taken is located; and selecting the bin allocated to the order task from the at least one bin according to the vertical track where the at least one bin is located.

Description

Material box distribution method and device, server, storage medium and warehousing system

Technical Field

The application relates to the field of intelligent warehousing, in particular to a material box distribution method, a device, a server, a storage medium and a warehousing system.

Background

In the middle of traditional storage trade, be provided with the goods shelves region in the storage space, the goods shelves region includes a plurality of goods shelves, and each goods shelves includes multilayer storehouse position, and these storehouse positions are used for placing the workbin, are provided with the tunnel between two adjacent goods shelves. Meanwhile, the automatic conveying device is provided with robots special for conveying the workbin, and the robots can move automatically in a roadway to convey the workbin.

Along with the continuous development of technique, the robot is more intelligent, can combine to climb the goods shelves and realize climbing goods shelves, the workbin of taking, transport more functions such as workbin. Specifically, be provided with many vertical tracks on the goods shelves that can climb in tunnel one side, the robot can pass in the tunnel, reachs vertical track department, realizes climbing through vertical track.

However, generally only one robot is allowed to enter and exit at the same time in one vertical track. In the prior art, when the order is subjected to stock distribution, the uniformity of distributed bins is ignored, and the positions of the distributed bins are concentrated, the goods taking process of the robot can cause congestion, and the overall delivery efficiency of the system is low.

Disclosure of Invention

The present disclosure provides a bin distribution method, device, server, storage medium and warehousing system to solve the above technical problems.

In a first aspect, the present disclosure provides a bin distribution method, applied to a server in a warehousing system, where the warehousing system includes a shelf and an aerial robot, the shelf is provided with a vertical rail, and the aerial robot is used to move along the vertical rail to a storage location where bins are stored for taking the bins; the method comprises the following steps:

determining a to-be-taken object according to the to-be-processed order task;

determining a vertical track where each bin in at least one bin containing the goods to be taken is located;

and selecting the bin allocated to the order task from the at least one bin according to the vertical track where the at least one bin is located.

Optionally, selecting a bin allocated to the order task from the at least one bin according to the vertical track where the at least one bin is located includes:

and selecting the bin allocated to the order task from the at least one bin by calculating the number of bins allocated within the vertical track according to the vertical track in which the at least one bin is located.

and selecting the bin allocated to the order task from the at least one bin by calculating the variance of the number of bins allocated within the vertical track according to the vertical track in which the at least one bin is located.

Optionally, selecting a bin allocated to the order task from the at least one bin by calculating a variance of the number of bins allocated within the vertical track according to the vertical track in which the at least one bin is located, includes:

generating an initial feasible solution of the bin allocation corresponding to the order task according to a single optimization factor;

optimizing the initial feasible solution according to a plurality of optimization factors to obtain an optimized distribution result of the material box;

determining a bin allocated to the order task in at least one bin according to the bin allocation result;

wherein the plurality of optimization factors includes a variance of the number of bins allocated within the vertical track.

Optionally, the plurality of optimization factors further include at least one of: the number of bins allocated to the order task; the sum of the distances between the bin allocated to the order task and the operating platform; the sum of the weights of bins allocated to the order task;

the single optimization factor is any one of the plurality of optimization factors.

Optionally, generating an initial feasible solution of bin allocation corresponding to the order task according to a single optimization factor includes:

sorting the bins containing the types of goods according to the sequence from near to far from the operating platform aiming at each type of goods required by the order task;

for each kind of goods, sequentially selecting bins from the bins containing the kind of goods, and allocating the bins to the order task to obtain an initial feasible solution;

wherein the kind of goods contained in the bin allocated to the order task meets the quantity requirement of the order task.

Optionally, optimizing the initial feasible solution according to a plurality of optimization factors to obtain an optimized bin distribution result, including:

constructing a total optimization function according to the weighted sum of the optimization factors;

and optimizing the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the total optimization function to obtain an optimized bin distribution result.

Optionally, optimizing the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the total optimization function to obtain an optimized bin distribution result, including:

repeatedly executing the following steps until the total optimization function meets a convergence condition to obtain an optimized bin distribution result:

adjusting the current feasible solution according to at least one optimization factor of the plurality of optimization factors except the single optimization factor to obtain an adjusted feasible solution;

and calculating to obtain a first result corresponding to the adjusted feasible solution according to a total optimization function, and determining whether to accept the adjusted feasible solution according to the first result and a second result, wherein the second result is a result corresponding to the current feasible solution calculated according to the total optimization function.

Optionally, adjusting the current feasible solution according to at least one of the optimization factors other than the single optimization factor to obtain an adjusted feasible solution, where the adjusting includes:

according to the current feasible solution, searching a target vertical track with the largest number of bins allocated to the order task and/or a first number of target bins with the smallest number of goods to be taken;

replacing at least one bin in the searched target vertical track with bins of other vertical tracks, and/or replacing the first number of target bins with a second number of bins to obtain an adjusted feasible solution;

wherein the second number is less than the first number.

Optionally, the method further includes:

according to the current feasible solution, searching a vertical rail with the least number of distributed bins, and searching a replacement bin from the vertical rail with the least number of bins; and/or the presence of a gas in the gas,

determining a second number of replacement bins from the at least one bin containing the most items to be taken;

wherein the replacement bin is to replace a bin in the current feasible solution.

determining a plurality of feasible solutions according to at least one of the following optimization factors: the number of bins allocated to the order task, the sum of the distances between the bins allocated to the order task and the operating platform, and the sum of the weights of the bins allocated to the order task;

calculating the variance of the number of the bins distributed in the vertical track corresponding to each feasible solution;

and determining the bin allocated to the order task according to the feasible solution with the minimum variance.

In a second aspect, the present disclosure provides a bin distribution device comprising:

the to-be-taken object determining module is used for determining the to-be-taken object according to the to-be-processed order task;

the vertical track determining module is used for determining a vertical track where each bin in at least one bin containing the goods to be taken is located;

and the bin selection module is used for selecting a bin allocated to the order task from the at least one bin according to the vertical track of the at least one bin.

Optionally, the bin selection module is specifically configured to:

Optionally, when the bin selection module selects a bin allocated to the order task from the at least one bin by calculating a variance of the number of bins allocated in the vertical rail according to the vertical rail in which the at least one bin is located, the bin selection module is specifically configured to:

Optionally, when the bin selection module generates an initial feasible solution of bin allocation corresponding to the order task according to a single optimization factor, the bin selection module is specifically configured to:

Optionally, the bin selection module is configured to, when optimizing the initial feasible solution according to a plurality of optimization factors to obtain an optimized bin distribution result, specifically:

Optionally, when the bin selection module optimizes the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the total optimization function to obtain an optimized bin distribution result, the bin selection module is specifically configured to:

Optionally, the bin selection module, when adjusting the current feasible solution according to at least one of the optimization factors other than the single optimization factor, to obtain an adjusted feasible solution, is specifically configured to:

wherein the second number is less than the first number.

Optionally, the bin distribution device further comprises a replacement bin determination module, configured to:

In a third aspect, the present disclosure provides a server comprising: a memory, a processor;

a memory for storing program instructions;

a processor for calling and executing program instructions in said memory to perform the method according to the first aspect.

In a fourth aspect, the present disclosure provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method of the first aspect when executed by a processor.

In a fifth aspect, the present disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.

In a sixth aspect, the present disclosure provides a warehousing system comprising: a rack, an aerial robot store, and the server of the third aspect.

The disclosure provides a bin distribution method, a bin distribution device, a server, a storage medium and a warehousing system. The bin distribution method is applied to a server in a warehousing system. The warehousing system comprises a goods shelf and an aerial robot, wherein the goods shelf is provided with a vertical track, and the aerial robot is used for moving to a storage position where a bin is stored along the vertical track to carry out bin taking operation; the method comprises the following steps: determining a to-be-taken object according to the to-be-processed order task; determining a vertical track where each bin in at least one bin containing the goods to be taken is located; and selecting the bin allocated to the order task from the at least one bin according to the vertical track where the at least one bin is located. According to the scheme, firstly, commodities (goods to be taken) corresponding to the order tasks to be processed need to be determined, then, each bin containing the corresponding goods to be taken and the vertical rail where the bin is located can be determined according to requirements, and then, the bins distributed to the order tasks can be selected from the bins containing the corresponding goods to be taken according to the condition of the vertical rail where the bins are located. The distribution condition of workbin on the vertical track on goods shelves has been considered to this scheme to carry out the distribution of workbin according to this, can guarantee the homogeneity of the workbin of distribution to a certain extent, avoid the position of the workbin of distribution too concentrate the robot that causes get goods in-process the jam rate height, and then guarantee the holistic shipment efficiency of system.

Drawings

In order to more clearly illustrate the technical solutions of the present disclosure or the prior art, the following briefly introduces the drawings needed to be used in the description of the embodiments or the prior art, and obviously, the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive labor.

Fig. 1 is a schematic diagram of an application scenario provided by the present disclosure;

FIG. 2 is a schematic view of a pallet configuration provided by the present disclosure;

fig. 3 is a schematic diagram of a robot climbing on a shelf provided by the present disclosure;

FIG. 4 is a flow chart of a bin distribution method according to an embodiment of the present disclosure;

FIG. 5 is a schematic illustration of a bin distribution process provided by an embodiment of the present disclosure;

FIG. 6 is a flow chart illustrating an optimization of a bin distribution structure according to an embodiment of the disclosure;

fig. 7 is a schematic structural diagram of a bin distribution device according to an embodiment of the disclosure;

fig. 8 is a schematic structural diagram of a server according to an embodiment of the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be described clearly and completely below with reference to the accompanying drawings in the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

In traditional storage scene, ordinary goods shelves do not support the robot to climb, and ordinary robot itself also can't climb the goods shelves. Therefore, the robot does not face the problem that the robot is jammed on the shelf to block the travel when the robot performs the task to pick up the goods, and the existing bin distribution method does not take the problems into consideration. When the existing bin distribution method is applied to a new scene, bin distribution can be realized, and in the process of executing tasks according to distribution results, the problem that the robot is jammed in a roadway or a vertical track due to over concentration of bin distribution corresponding to each task, so that the task execution efficiency is influenced, and further the overall ex-warehouse efficiency of the system is low can occur.

Accordingly, the present disclosure proposes a bin distribution method, device, server, storage medium and warehousing system. The influence of the roadway where the blanking box is located in the current shelf scene and the vertical track where the blanking box is located on the robot to execute the goods taking task is fully considered, so that the efficiency of material box distribution in the current shelf scene is improved.

Fig. 1 is a schematic diagram of an application scenario provided in the present disclosure. As shown in fig. 1, the application scenario includes a server 10, a shelf 20 storing a plurality of bins, and a plurality of robots 30.

The server 10 may be any type of electronic computing platform or device that acts as a control center for the overall warehousing system. According to actual requirements, the system can have corresponding storage space or computing capability to provide one or more application services or functions, such as receiving orders, distributing orders to operation consoles, issuing orders, generating pick-and-place tasks according to orders, distributing tasks to robots, issuing tasks, controlling robots to execute pick-and-place tasks, and the like.

The rack 20 is a structure for storing bins. Multiple racks may be provided in the warehousing system to store more bins. And a passable channel called a roadway is formed between every two shelves. Referring to fig. 2, fig. 2 is a schematic structural diagram of a shelf according to the present disclosure. As shown in FIG. 2, the racks 20 may have multiple levels, each level having multiple storage locations 201 that may be used to hold bins 202. A vertical rail 203 for the robot to climb is arranged on the vertical frame of each shelf 20, and the lower end of the vertical rail is connected to the roadway. The robot 30 can move up and down on the vertical rails 203 to reach the corresponding storage location to perform the corresponding pick-and-place task on the corresponding bin 202. A "bin" as referred to in this disclosure is an ensemble of objects stored in each bay, which may be a combination of merchandise and containers (e.g., boxes, trays, etc.).

The robot 30 is an automated device having a traveling mechanism that moves in a space of the stocker system (on the ground, on a rail, etc.) and that can carry a bin to perform a pick-and-place operation. Referring to fig. 3, fig. 3 is a schematic diagram of a robot climbing on a shelf presented in a side view provided by the present disclosure. As shown in fig. 3, a traveling mechanism 301 is provided on the robot 30, and the traveling mechanism 301 can be matched with the vertical tracks on the shelves a and B, so that the robot 30 vertically moves up and down along the vertical tracks in the space between the shelves a and B. Any suitable type of power system may be used for the running gear 301, among others. Robot 30 may load at least one bin at a time.

The server 10 can determine the types and the quantities of the commodities required for completing the orders according to the order conditions, and accordingly allocate corresponding bins for the orders, and further generate corresponding tasks. And assigns tasks to the robot 30. The robot can carry the workbin to the operation panel through carrying out the task, through the letter sorting operation on the operation panel, the commodity in the workbin of corresponding order is delivered from godown.

The bin dispensing method provided by the present disclosure will be explained and illustrated in detail below using specific examples.

Fig. 4 is a flowchart of a bin distribution method according to an embodiment of the disclosure, and as shown in fig. 4, the method according to this embodiment may be applied to a server in a warehousing system, where the warehousing system includes a rack and an aerial robot, the rack is provided with a vertical rail, and the aerial robot is configured to move to a storage location where bins are stored along the vertical rail to perform a bin fetching operation. The bin distribution method of the embodiment comprises the following steps:

s401, determining the goods to be taken according to the order task to be processed.

The pending order task may be one order task or a plurality of order tasks. Each order includes at least one specific commodity and a corresponding quantity, and the specific commodity included in the order is referred to as a to-be-taken commodity in the present disclosure.

S402, determining a vertical rail where each bin in at least one bin containing goods to be taken is located.

After determining the goods (pickups) needed to complete the pending order, it may be determined at which locations in the warehouse the goods may be picked.

In the warehousing system, inventory of the goods is generally checked at regular time, and statistics on which goods are stored in each bin and the quantity of each goods are counted. Thus, by looking up the stock, it is possible to determine at least one bin containing the goods to be taken, as well as the shelf on which each bin is located, the corresponding vertical rail, and even the stock level on which it is located.

S403, selecting a bin to be allocated to the order task from the at least one bin according to the vertical track where the at least one bin is located.

And selecting the bin to be allocated to the order task according to the determined vertical track where the at least one bin is located.

When the bin is selected, the selection can be performed according to a certain standard, and the standard can be set by a user according to requirements. For example, the sum of the distances of the selected bins from the station is as small as possible, and/or the number of selected bins is as small as possible, and/or the variance of the number of selected bins per vertical rail is as small as possible. If there are already bins allocated (allocated bins) that have not been removed by the robot before the allocation is performed this time, then a selection criterion may be established such that after the allocation is completed, the sum of the distances of all allocated bins from the operating floor is as small as possible, and/or the number of all allocated bins is as small as possible, and/or the variance of the number of all allocated bins within each vertical track is as small as possible, etc.

The bin distribution method provided by the embodiment is applied to the server in the warehousing system. The warehousing system comprises a goods shelf and an aerial robot, wherein the goods shelf is provided with a vertical track, and the aerial robot is used for moving to a warehouse position where a material box is stored along the vertical track to carry out box taking operation. The method comprises the following steps: determining a to-be-taken object according to the to-be-processed order task; determining a vertical track where each bin in at least one bin containing goods to be taken is located; selecting a bin from the at least one bin to assign to the order task based on the vertical rail on which the at least one bin is located. According to the scheme, firstly, commodities (goods to be taken) corresponding to the order tasks to be processed need to be determined, then, each bin containing the corresponding goods to be taken and the vertical rail where the bin is located can be determined according to requirements, and then, the bins distributed to the order tasks can be selected from the bins containing the corresponding goods to be taken according to the condition of the vertical rail where the bins are located. The distribution condition of workbin on the vertical track on goods shelves has been considered to this scheme to carry out the distribution of workbin according to this, can guarantee the homogeneity of the workbin of distribution to a certain extent, avoid the position of the workbin of distribution too concentrate the robot that causes get goods in-process the jam rate height, and then guarantee the holistic shipment efficiency of system.

In some embodiments, the selecting, according to the vertical track where the at least one bin is located, a bin to be allocated to the order task from the at least one bin may specifically include: and selecting the bin to be allocated to the order task from the at least one bin by calculating the number of bins allocated within the vertical track according to the vertical track in which the at least one bin is located.

The bins containing the items to be picked may be distributed among the various positions of the vertical rails of the racks, and the type and number of items to be picked in each bin may not be necessary. Thus, there may be many combinations of bins that can meet the requirements of the order task. One of the options is to select the number of bins that have been allocated in each vertical track. For example, bins have been allocated for other order tasks and have not been taken by the robot before the bin allocation method is performed this time. These bins are referred to as "dispensing bins". On the basis, the number of distributed bins in the vertical rail where each bin containing goods to be taken is located is inquired, and the bins in which the vertical rail is specifically selected are determined according to the number of the distributed bins, so that the total number of the distributed bins in each vertical rail does not exceed a preset value after the bins are distributed at this time through calculation.

The greater the number of bins allocated within a track, the higher the probability of congestion occurring when the robot performs a bin picking task. By calculating the number of the distributed material boxes in the vertical rails, the number of the distributed material boxes in each vertical rail is small, the excessive number of the distributed material boxes in some rails is avoided, congestion is avoided, and the whole delivery efficiency is improved.

In other embodiments, the selecting a bin from the at least one bin to assign to the order task according to the vertical rail on which the at least one bin is located may include: selecting a bin from the at least one bin to assign to the order task by calculating a variance of the number of bins assigned within the vertical track based on the vertical track in which the at least one bin is located.

The variance in the number of bins allocated within each vertical rail may characterize the uniformity of the bins allocated between the various rails. The smaller the variance, the higher the uniformity; the larger the variance, the lower the uniformity, i.e. the more concentrated the bins that can be dispensed are in a partial vertical track. By calculating the variance of the number of the distributed work bins in the vertical rails, the number of the distributed work bins in each vertical rail can be uniform, the problem that the jam rate is high in the goods taking process of the robot due to the fact that the positions of the distributed work bins are too concentrated is avoided, and the overall goods discharging efficiency of the system is guaranteed.

In a specific embodiment, the selecting a bin to be assigned to the order task from the at least one bin by calculating a variance of the number of bins assigned within the vertical track based on the vertical track in which the at least one bin is located may include: generating an initial feasible solution of bin allocation corresponding to the order task according to a single optimization factor; optimizing the initial feasible solution according to a plurality of optimization factors to obtain an optimized distribution result of the material box; determining a bin allocated to the order task in at least one bin according to the bin allocation result; wherein the plurality of optimization factors includes a variance of the number of bins allocated within the vertical track.

In the present embodiment, bin allocation is performed using "variance in the number of bins allocated within the vertical track" as one of the optimization factors.

Wherein a single optimization factor may be included among the plurality of optimization factors. The "variance of the number of bins allocated within a vertical track" may be used as a single optimization factor, or may be used as one of a plurality of optimization factors other than the single optimization factor.

Firstly, a feasible solution of bin allocation corresponding to the order task is generated according to a single optimization factor. An optimization objective may be set for a single optimization factor, and the feasible solution may satisfy this optimization objective. When "variance of the number of bins allocated within a vertical track" is a single optimization factor, the optimization target may be set to a particular variance value.

And then, optimizing the initial feasible solution according to a plurality of optimization factors to obtain an optimized distribution result of the bin, and adjusting a distribution scheme based on other optimization factors on the basis of considering a single optimization factor to enable other factors to achieve a more ideal optimization target.

The bin allocation result actually indicates which bins are allocated to the order task.

In the embodiment, the problem of bin allocation is converted into the problem of finding a more optimal solution by generating the initial feasible solution and optimizing the initial feasible solution, so that on one hand, a plurality of optimization factors can be comprehensively considered to generate an allocation scheme, and on the other hand, the calculated amount is relatively small.

In some implementations, in addition to the optimization factor of "variance of number of bins allocated within a vertical track," the plurality of optimization factors may include at least one of: the number of bins allocated to the order task; the sum of the distances of the distributed bins from the operating table; sum of weight of bin dispensed; the single optimization factor is any one of the plurality of optimization factors.

The fewer bins are allocated to order tasks, the fewer robots are occupied, and the probability of congestion on roadways or vertical tracks is reduced.

The smaller the sum of the distances between the distributed bins and the operating floor, the less time is spent in transporting the bins to the operating floor, and the faster and more efficient the delivery is.

The smaller the sum of the weight of the distributed bins, the less energy is consumed by the robot to carry the bins. Meanwhile, the smaller the sum of the weights is, the fewer goods irrelevant to the order task are in the material box, the sorting difficulty can be reduced, and the delivery efficiency is improved.

Of course, the above optimization factors are only examples, and the user may propose other optimization factors by combining the actual scene and the actual requirements, or may set the specific content of a single optimization factor and multiple optimization factors by himself.

Through the selection and the cooperation of each optimization factor, the requirement of bin distribution under different scenes can be met, and the applicability of the scheme disclosed by the invention is wider.

In one particular embodiment, the "distance of the assigned bin from the station" is taken as the single optimization factor. Correspondingly, the generating of the initial feasible solution of bin allocation corresponding to the order task according to the single optimization factor may specifically include: according to each kind of goods required by the order task, sorting bins containing the kind of goods in sequence from near to far away from an operation platform; for each kind of goods, selecting a plurality of bins from the bins filled with the kind of goods in sequence, and respectively allocating the bins to order tasks to obtain an initial feasible solution; wherein the goods of the kind contained in the allocated bins meet the quantity requirement of the order task.

That is, bin allocation is performed separately for each kind of goods. The method comprises the steps of firstly, sorting bins containing certain types of goods according to the distance from an operation table, and then selecting a plurality of bins with the closest distances according to the sorting result to be distributed to order tasks. The selected bin can at least meet the quantity requirements of the order task for that type of goods.

In the case of bin allocation for each type of goods, all of the allocable bins may be involved in the ordering, taking into account that there may be more than one type of goods in each bin.

The bin allocation process is illustrated in fig. 5 by way of example with a virtual bin allocation scenario. And determining the goods to be taken as 4 goods A and 4 goods B according to the order task. The five material boxes containing goods to be taken are respectively a material box a (comprising 3B), a material box B (comprising 2B), a material box c (comprising 1A and 2B), a material box d (comprising 3A), a material box e (comprising 4A), and the distances between the material boxes and the operation table are ordered from short to long as a < B < c < d < e. The bin containing the goods A is sorted into c, d and e according to the sequence from near to far from the operation table, and the c and the d are selected and distributed to order tasks; the bins containing goods B are sorted in order from near to far from the console as a, B, c, and a and B are selected for assignment to the order task. The initial feasible solution includes a, b, c, d.

In this example, it can be seen that c contains not only a but also B, and the number of B is the same as B, so that B can be deleted in the subsequent optimization step, and the order requirement can still be met, but the overall bin number is reduced. This is the sense of optimization.

In some embodiments, the optimizing the initial feasible solution according to a plurality of optimization factors to obtain the optimized bin distribution result includes: constructing a total optimization function according to the weighted sum of the optimization factors; and optimizing the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the total optimization function to obtain an optimized bin distribution result.

The overall optimization function may be expressed in the form of a sum of the products of the scores and the weights of the plurality of optimization factors. The overall optimization function is primarily used to indicate the direction of optimization, where the score for each optimization factor then indicates the degree of optimization over that optimization factor. The optimization aims to make the score of each optimization factor as high as possible, and the value of the total optimization function as high as possible.

By constructing a total optimization function and optimizing the initial feasible solution according to the total optimization function, a more optimized bin distribution result can be searched by integrating a plurality of optimization factors.

In some embodiments, the optimizing the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the total optimization function to obtain an optimized bin distribution result includes: repeatedly executing the following steps until the total optimization function meets the convergence condition to obtain an optimized bin distribution result: adjusting the current feasible solution according to at least one optimization factor except for a single optimization factor in the multiple optimization factors to obtain an adjusted feasible solution; and calculating according to the total optimization function to obtain a first result corresponding to the adjusted feasible solution, and determining whether to accept the adjusted feasible solution according to the first result and a second result, wherein the second result is a result corresponding to the current feasible solution calculated according to the total optimization function.

The optimization process of bin allocation results is described in connection with fig. 6. Firstly, according to at least one optimization factor except for a single optimization factor in a plurality of optimization factors, adjusting the current feasible solution to obtain an adjusted feasible solution. Secondly, calculating a total optimization function corresponding to the adjusted feasible solution to obtain a first result; calculating a total optimization function corresponding to the current feasible solution to obtain a second result; if the first result is greater than the second result, accepting the adjusted feasible solution; otherwise, the current feasible solution is still maintained. And further judging whether the total optimization function meets the convergence condition, if so, ending the circulation, and taking the current feasible solution as the optimized bin distribution result, otherwise, continuing the circulation.

During the first iteration, the current feasible solution is the initial feasible solution; after receiving the adjusted feasible solution, the adjusted feasible solution becomes the current feasible solution.

Wherein, the convergence condition of the overall optimization function may include: the total optimization function value is greater than or equal to a preset value, and/or the value of each optimization factor in the total optimization function is greater than or equal to a preset value.

The adjusting the current feasible solution according to at least one of the optimization factors other than the single optimization factor to obtain an adjusted feasible solution may specifically include: according to the current feasible solution, searching a target vertical track with the largest number of bins allocated to the order task and/or a first number of target bins with the smallest number of goods to be taken; replacing at least one bin in the searched target vertical track with bins of other vertical tracks, and/or replacing a first number of target bins with a second number of bins to obtain an adjusted feasible solution; wherein the second number is less than the first number.

The specific adjustment direction (i.e. the optimization direction of the optimization factor) may be such that the variance of the distribution bin between the vertical rails is reduced. Specifically, the vertical rail (target vertical rail) with the largest number of distribution bins is searched in the current feasible solution, and at least one bin in the target vertical rail is replaced by a bin in the other vertical rails. The number of bins in the vertical rail that distributes the largest number of bins is reduced and variance may be reduced.

The specific adjustment direction may also be such as to reduce the total number of dispensing bins. Specifically, a first number of bins (target bins) with the smallest number of goods to be taken is searched in the current feasible solution, and then the first number of target bins is replaced by a second number of bins. The second quantity is less than the first quantity, but the goods to be taken in the second quantity of the material boxes can meet the requirement of the order on the goods to be taken after being replaced.

Here, only two adjustment directions are exemplified, and of course, other adjustment manners may be set based on other optimization factors to achieve other optimization goals.

In the method in the above embodiment, when replacing the bin, the method may further include: according to the current feasible solution, searching a vertical rail with the least number of distributed bins, and searching a replacement bin from the vertical rail with the least number of bins; and/or, determining a second number of replacement bins from at least one bin containing the most goods to be taken; wherein the replacement bin is used to replace bins in the currently feasible solution.

In the case of a reduction in the variance of the distribution bins between the vertical rails corresponding to the first adjustment direction, when determining a replacement bin, the replacement bin can be determined from the bins not distributed therein by finding the vertical rail having the smallest number of distributed bins including the distribution bin in the currently feasible solution.

In the second adjustment direction, the total number of dispensing bins is reduced, and in determining the replacement bins, a second number of replacement bins may be determined from at least one bin that contains the most items to be taken from among the unassigned bins. Of course, it is also possible to select a bin from the unassigned bins containing the goods to be picked that is capable of meeting the replacement requirements. The former directly selects the bin containing the most items to be picked, which supports the highest probability of success in replacing bins to find a more optimal allocation plan.

The condition of the replacement bin is that the sum of the number of goods to be taken contained in all the replacement bins and the number of goods to be taken contained in other non-replaced bins in the current feasible solution can meet the requirement of the order task on the goods to be taken.

In other embodiments, the selecting a bin to be assigned to the order task from the at least one bin by calculating a variance of the number of bins assigned within the vertical track according to the vertical track in which the at least one bin is located may also include: determining a plurality of feasible solutions according to at least one of the following optimization factors: the number of bins allocated to the order task, the sum of the distances of the allocated bins from the operating floor, and the sum of the weights of the allocated bins; calculating the variance of the number of the bins distributed in the vertical track corresponding to each feasible solution; and determining the bin allocated to the order task according to the feasible solution with the minimum variance.

The feasible solutions are determined directly according to at least one optimization factor, then the variance of the number of the bins distributed to the vertical rails corresponding to the feasible solutions is calculated respectively, and the feasible solution with the minimum variance is selected as a distribution scheme for bin distribution according to the variance calculation result.

Compared with the scheme, the iterative process is reduced, and the calculation process of the bin distribution scheme is simplified.

In other scenarios, the initial feasible solution may be determined using the number of bins allocated to the order task, or the sum of the distances of the allocated bins from the operating floor, or the sum of the weights of the allocated bins as a single optimization factor; and optimizing by combining a single optimization factor and other optimization factors as the initial feasible solution of multiple optimization factors to obtain the optimized bin distribution result.

Fig. 7 is a schematic structural diagram of a bin distribution device according to an embodiment of the disclosure, and as shown in fig. 7, the bin distribution device 700 according to this embodiment may include: a goods to be taken determining module 701, a vertical track determining module 702 and a bin selecting module 703.

A to-be-picked object determining module 701, configured to determine, according to the to-be-processed order task, an object to be picked;

a vertical rail determining module 702, configured to determine a vertical rail where each bin of at least one bin containing goods to be taken is located;

a bin selection module 703 for selecting a bin from the at least one bin to assign to the order task based on the vertical rail on which the at least one bin is located.

Optionally, the bin selection module 703 is specifically configured to:

and selecting the bin to be allocated to the order task from the at least one bin by calculating the number of bins allocated within the vertical track according to the vertical track in which the at least one bin is located.

Optionally, the bin selection module 703 is specifically configured to:

selecting a bin from the at least one bin to assign to the order task by calculating a variance of the number of bins assigned within the vertical track based on the vertical track in which the at least one bin is located.

Optionally, the bin selecting module 703 is specifically configured to, when selecting a bin to be allocated to the order task from the at least one bin by calculating a variance of the number of bins allocated in the vertical track according to the vertical track where the at least one bin is located:

generating an initial feasible solution of bin allocation corresponding to the order task according to a single optimization factor;

Optionally, the plurality of optimization factors further includes at least one of: the number of bins allocated to the order task; the sum of the distances of the distributed bins from the operating table; sum of weight of bin dispensed;

the single optimization factor is any one of a plurality of optimization factors.

Optionally, when the bin selection module 703 generates an initial feasible solution of bin allocation corresponding to the order task according to a single optimization factor, the method is specifically configured to:

according to each kind of goods required by the order task, sorting bins containing the kind of goods in sequence from near to far away from an operation platform;

for each kind of goods, sequentially selecting bins from the bins containing the goods of the kind, and allocating the bins to order tasks to obtain an initial feasible solution;

wherein the goods of the kind contained in the allocated bin meet the quantity requirements of the order task.

Optionally, the bin selection module 703 is specifically configured to, when optimizing the initial feasible solution according to a plurality of optimization factors to obtain an optimized bin distribution result:

Optionally, the bin selection module 703 is specifically configured to, when optimizing the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the total optimization function to obtain an optimized bin distribution result:

repeatedly executing the following steps until the total optimization function meets the convergence condition to obtain an optimized bin distribution result:

adjusting the current feasible solution according to at least one optimization factor except for a single optimization factor in the multiple optimization factors to obtain an adjusted feasible solution;

and calculating according to the total optimization function to obtain a first result corresponding to the adjusted feasible solution, and determining whether to accept the adjusted feasible solution according to the first result and a second result, wherein the second result is a result corresponding to the current feasible solution calculated according to the total optimization function.

Optionally, the bin selection module 703 is configured to, when adjusting the current feasible solution according to at least one optimization factor of the multiple optimization factors except for the single optimization factor to obtain an adjusted feasible solution, specifically:

replacing at least one bin in the searched target vertical track with bins of other vertical tracks, and/or replacing a first number of target bins with a second number of bins to obtain an adjusted feasible solution;

wherein the second number is less than the first number.

Optionally, bin dispensing apparatus 700 further comprises a replacement bin determination module 704 configured to:

wherein the replacement bin is used to replace bins in the currently feasible solution.

determining a plurality of feasible solutions according to at least one of the following optimization factors: the number of bins allocated to the order task, the sum of the distances of the allocated bins from the operating floor, and the sum of the weights of the allocated bins;

The apparatus of this embodiment may be configured to perform the method of any of the above embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

Fig. 8 is a schematic structural diagram of a server according to an embodiment of the present disclosure, and as shown in fig. 8, a server 800 of the present disclosure includes: memory 801, processor 802.

A memory 801 for storing program instructions;

the processor 802 is configured to call and execute the program instructions in the memory 801 to perform the method according to any of the embodiments described above, which achieves similar principles and technical effects, and is not described herein again.

The present disclosure also provides a warehousing system, comprising: racks, aerial robot stores, and servers in the above embodiments.

The present disclosure also provides a computer-readable storage medium, which stores a computer program, which, when executed by a processor, implements the method of any of the above embodiments.

The present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of any of the above embodiments.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Claims

1. The bin distribution method is characterized by being applied to a server in a warehousing system, wherein the warehousing system comprises a goods shelf and an aerial robot, the goods shelf is provided with a vertical rail, and the aerial robot is used for moving to a storage position where bins are stored along the vertical rail to carry out bin taking operation; the method comprises the following steps:

determining a to-be-taken object according to the to-be-processed order task;

2. The method of claim 1, wherein selecting a bin from the at least one bin for assignment to the order task based on the vertical rail on which the at least one bin is located comprises:

3. The method of claim 1, wherein selecting a bin from the at least one bin for assignment to the order task based on the vertical rail on which the at least one bin is located comprises:

4. A method according to claim 3, wherein selecting a bin from the at least one bin for assignment to the order task by calculating a variance in the number of bins assigned within a vertical track based on the vertical track in which the at least one bin is located comprises:

5. The method of claim 4, wherein the plurality of optimization factors further comprises at least one of: the number of bins allocated to the order task; the sum of the distances between the bin allocated to the order task and the operating platform; the sum of the weights of bins allocated to the order task;

6. The method of claim 4, wherein generating an initial feasible solution for bin allocation for the order task based on a single optimization factor comprises:

7. The method of any one of claims 4-6, wherein optimizing the initial feasible solution based on a plurality of optimization factors to obtain an optimized bin allocation result comprises:

8. The method of claim 7, wherein optimizing the initial feasible solution based on a heuristic search algorithm according to the initial feasible solution and the overall optimization function to obtain an optimized bin distribution result comprises:

9. The method of claim 8, wherein adjusting the current feasible solution according to at least one of the optimization factors other than the single optimization factor to obtain an adjusted feasible solution comprises:

wherein the second number is less than the first number.

10. The method of claim 9, further comprising:

11. A method according to claim 3, wherein selecting a bin from the at least one bin for assignment to the order task by calculating a variance in the number of bins assigned within a vertical track based on the vertical track in which the at least one bin is located comprises:

12. A bin distribution device, comprising:

13. A server, comprising: a memory, a processor;

a memory for storing program instructions;

a processor for calling and executing program instructions in said memory, performing the method of any of claims 1-11.

14. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-11.

15. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any of claims 1-11 when executed by a processor.

16. A warehousing system, comprising: a rack, an aerial robotic store, and the server of claim 13.