CN113128924B - Cargo scheduling method, apparatus and computer readable storage medium - Google Patents
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Abstract
The invention discloses a cargo scheduling method, a cargo scheduling device and a computer readable storage medium, and relates to the technical field of logistics. The cargo scheduling method comprises the following steps: determining the association degree between different kinds of cargoes according to the historical data; determining the handling capacity of the manual picking area in the warehouse to the cargoes in the future period as the manual handling capacity according to the sum of the predicted demand of various cargoes in the future period and the picking capacity of the automatic picking area in the warehouse; according to the demand of each kind of goods in the future period and the association degree between different kinds of goods, a goods list of the manual picking area is established, the goods list comprises the kinds of the goods and the corresponding demand, and the sum of the demands of each kind of goods in the goods list is equal to the manual processing amount; and dispatching the cargoes according to the cargoes list. Therefore, the warehouse can meet production requirements in the peak period of warehouse-out, and the sorting efficiency of the warehouse is improved.
Description
Technical Field
The present invention relates to the field of logistics technologies, and in particular, to a cargo scheduling method, apparatus, and computer readable storage medium.
Background
The scale of the existing e-commerce warehouse gradually becomes larger, and in the traditional manual picking mode, automatic equipment is added for picking, so that the operation efficiency of the warehouse is improved, and the current man-machine mixed sorting mode is formed. The automated area may take different modes, such as a pick mode from truck to person based AGVs (Automated Guided Vehicle, automated guided vehicles) or from truck to robot, a pick mode from truck to person based multi-level shuttles or stackers or from truck to robot, etc. The manual area mainly adopts a traditional manual picking mode.
The operation efficiency of the automation area is high and stable. The manual area can be adjusted by temporarily increasing and decreasing staff, and the production flexibility is high. In the man-machine hybrid mode, the advantages of the two can be combined.
During peak sorting, such as mass and centralized delivery of cargoes, the delivery requirements are difficult to meet due to low production flexibility of the automation area, so that partial cargoes need to be transferred to the manual area, order diversion during peak delivery is realized, and production pressure of the automation area is reduced. Currently, the selection of goods and corresponding amounts transferred to an artificial area is largely dependent on staff based on past experience.
Disclosure of Invention
The inventor finds that the currently adopted mode has larger workload and higher complexity of data processing after analysis. The quantity of goods determined empirically is not accurate. If the amount of the goods transferred to the artificial area is small, the artificial area cannot effectively shunt the goods to be delivered out of the warehouse, so that the delivery efficiency is affected; if too much goods are transferred to the artificial area, after the delivery peak is over, the goods in the artificial area also need to be transported back to the automation area, and the production efficiency of the warehouse is also affected. Thus, the currently employed approach can lead to inefficient sorting.
One technical problem to be solved by the embodiment of the invention is as follows: how to improve the sorting efficiency of the warehouse.
According to a first aspect of some embodiments of the present invention, there is provided a cargo scheduling method, comprising: determining the association degree between different kinds of cargoes according to the historical data; determining the handling capacity of the manual picking area in the warehouse to the cargoes in the future period as the manual handling capacity according to the sum of the predicted demand of various cargoes in the future period and the picking capacity of the automatic picking area in the warehouse; according to the demand of each kind of goods in the future period and the association degree between different kinds of goods, a goods list of the manual picking area is established, the goods list comprises the kinds of the goods and the corresponding demand, and the sum of the demands of each kind of goods in the goods list is equal to the manual processing amount; and dispatching the cargoes according to the cargoes list.
In some embodiments, creating a list of items for the manual pick zone based on the demand of each item in the future period and the association between different items includes: sequencing the cargoes according to the order of the predicted demand of each cargo from big to small to obtain a cargo sequence; initializing a goods list of the manual picking area, and sequentially adding the goods in the goods sequence and the goods with the association degree larger than a preset value into the goods list until the sum of the demand amounts of the goods in the goods list reaches the manual processing amount.
In some embodiments, determining the degree of association between different goods from the historical data includes: acquiring orders of the first goods and the second goods in the historical orders; and determining the association degree between the first goods and the second goods according to the reciprocal of the goods types in each order in which the first goods and the second goods are simultaneously appeared.
In some embodiments, the predicted demand for each type of good over the future period is equal to the product of the historical daily average demand for the type of good and a preset coefficient, the preset coefficient being determined based on the historical lift amplitude for the type of good or all types of good.
In some embodiments, scheduling the good according to the list of goods includes: determining goods to be put in storage in a goods list to serve as split-flow goods; for each of the split loads, determining the number of storage of the split loads in the automated picking area according to the automatic handling capacity of the same load and the storage of the same load in the automated picking area under the condition that the storage number of the same load in the automated picking area is smaller than the automatic handling capacity, so as to determine a first storage strategy, wherein the automatic handling capacity is the required amount in a first preset period, and the first preset period is before a future period; after the first warehousing strategy is determined, for the types of the cargos in the storage area which are not determined in the shunt cargos, determining the warehousing storage quantity of the same shunt cargos to the manual picking area according to the demand quantity of the same cargos in the manual picking area in a future period and the storage quantity of the same cargos in the current manual picking area so as to determine the second warehousing strategy.
In some embodiments, scheduling the good according to the list of goods further comprises: after the second warehousing strategy is determined, for the types of the cargos in the storage areas which are not determined in the split cargos, determining the warehousing storage quantity of the same split cargos to the automatic picking area according to the upper limit of the replenishment quantity of the same cargos in the automatic picking area and the storage quantity of the same cargos in the automatic picking area so as to determine a third warehousing strategy.
In some embodiments, scheduling the good according to the list of goods further comprises: and after the third warehousing strategy is determined, determining the cargoes which are not determined to be in the storage area in the shunt cargoes as cargoes which are warehoused in the centralized storage area.
In some embodiments, scheduling the good according to the list of goods further comprises: in a second preset period before the beginning of the future period, under the condition that the storage quantity of the cargoes in the manual sorting area is smaller than the demand quantity of the same cargoes in the future period, the same cargoes in the centralized storage area are allocated to the manual sorting area so that after the cargoes in the centralized storage area are allocated, the storage quantity of the cargoes in the manual sorting area reaches the demand quantity of the same cargoes in the future period; after the goods in the centralized storage area are allocated, under the condition that the storage quantity of the goods in the manual sorting area is still smaller than the demand quantity of the same goods in the future period, allocating the same goods in the automatic sorting area into the manual sorting area so that the quantity of the same goods in the manual sorting area reaches the demand quantity of the same goods in the future period.
In some embodiments, the picking capability of an automated picking zone within the warehouse is determined based on the number of workstations enabled for the automated picking zone for a future period of time, the length of picking work for each workstation, and the picking efficiency per unit time for each workstation.
According to a second aspect of some embodiments of the present invention, there is provided a cargo scheduling device comprising: the association degree determining module is configured to determine association degrees among different kinds of cargoes according to historical data; a manual throughput prediction module configured to determine a throughput of the goods in the future period of time by the manual picking area in the warehouse as a manual throughput based on a sum of the predicted demand amounts of the plurality of goods in the future period of time and a picking capability of the automatic picking area in the warehouse; the system comprises a goods list establishing module, a manual picking area selecting module and a manual picking area selecting module, wherein the goods list establishing module is configured to establish a goods list of a manual picking area according to the demand of each goods in a future period and the association degree between different goods, the goods list comprises the types of the goods and the corresponding demand, and the sum of the demands of each goods in the goods list is equal to the manual processing amount; and the dispatching module is configured to dispatch cargoes according to the cargoes list.
According to a third aspect of some embodiments of the present invention, there is provided a cargo scheduling device comprising: a memory; and a processor coupled to the memory, the processor configured to perform any of the foregoing cargo scheduling methods based on instructions stored in the memory.
According to a fourth aspect of some embodiments of the present invention, there is provided a computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements any of the foregoing cargo scheduling methods.
Some of the embodiments of the above invention have the following advantages or benefits: according to the invention, partial cargoes can be shunted to the manual picking area for processing by predicting the demand of cargoes in the future period and analyzing the association degree between cargoes, so that the warehouse can meet the production demand in the warehouse-out peak period, and the sorting efficiency of the warehouse is improved.
Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 illustrates a flow diagram of a cargo scheduling method according to some embodiments of the invention.
Fig. 2 shows a flow diagram of a cargo scheduling method according to further embodiments of the invention.
Fig. 3 illustrates a schematic diagram of a cargo scheduling device according to some embodiments of the invention.
Fig. 4 shows a schematic structural view of a cargo scheduling device according to further embodiments of the invention.
Fig. 5 shows a schematic structural view of a cargo scheduling device according to further embodiments of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Fig. 1 illustrates a flow diagram of a cargo scheduling method according to some embodiments of the invention. As shown in fig. 1, the cargo scheduling method of this embodiment includes steps S102 to S108.
In step S102, the degree of association between different cargoes is determined from the history data.
The history data is data recorded in association with the history of the shipment, such as history order data, history waybill data, and the like. The association degree is used for measuring the association degree of different kinds of cargoes when the cargoes are delivered. When two cargoes are frequently taken out of the warehouse together, the association degree of the two cargoes is higher, and the cargoes are required to be placed in a similar area during storage.
In some embodiments, determining the degree of association between different goods from the historical data includes: acquiring orders of the first goods and the second goods in the historical orders; and determining the association degree between the first goods and the second goods according to the reciprocal of the goods types in each order in which the first goods and the second goods are simultaneously appeared. For example, the degree of association r ij between the good i and the good j is calculated using equation (1).
In equation (1), O ij represents a collection of historical orders for which orders for good i and good j occur simultaneously; the |o| represents the category number of the goods in each order in the set O ij. It can be seen that the more orders the goods i and the goods j are simultaneously present, the fewer the types of the goods in the orders the goods i and the goods j are simultaneously present, the higher the association degree of the goods i and the goods j is.
In step S104, the amount of the load handled by the manual picking area in the warehouse in the future period is determined as the manual handling amount based on the sum of the predicted demand amounts of the plurality of loads in the future period and the picking capability of the automatic picking area in the warehouse.
In some embodiments, the future period is a period of expected occurrence of a peak to the library. Such as a promotional period, a change period, a spring period, etc., of the e-commerce platform.
In some embodiments, the demand of the good is predicted from its daily average demand over a future period. In some embodiments, the predicted demand for each type of good over the future period is equal to the product of the historical daily average demand for the type of good and a preset coefficient, the preset coefficient being determined based on the historical lift amplitude for the type of good or all types of good. The demand may be reflected by an index such as sales, and orders.
For example, assuming that the average daily demand of a certain item s for the latest period is Q s, the predicted demand Q s of the item s for the future period may be expressed by the formula (2).
Qs＝qs(1+η) (2)
In formula (2), η represents the history lifting amplitude. If the good s has a promotion plan in a future period, the demand rise amplitude of the good s can be set as a value of eta in a period having the same attribute as the "future period" (for example, the period is the same as the e-commerce promotion period and the period is the same as the holiday period); if the good s has no promotion plan in the future period, the demand rise amplitude of all kinds of goods can be regarded as a value of η in a period having the same attribute as the "future period".
In some embodiments, the picking capability of an automated picking zone within the warehouse is determined based on the number of workstations enabled for the automated picking zone for a future period of time, the length of picking work for each workstation, and the picking efficiency per unit time for each workstation. For example, the picking capability C may be calculated using equation (3).
C＝W·T·E (3)
In equation (3), W represents the number of stations enabled by the automated picking zone for the future period, T represents the picking duration of each station, and E represents the picking efficiency per unit time for each station.
The amount of treatment G of the goods in the manual picking area during the future period of time may be expressed by the formula (4).
G＝∑sQs-C (4)
In formula (3), s represents the representation of the type of the item, Q s represents the demand of the item s in the future period, and C represents the picking capability of the automated picking zone. Thus, demand that cannot be met by automated picking areas for future periods of time can be handled by manual picking areas.
In step S106, a cargo list of the manual picking area is established according to the demand of each cargo in the future period and the association degree between different cargoes, wherein the cargo list includes the types of cargoes and the corresponding demand, and the sum of the demands of each cargo in the cargo list is equal to the manual handling capacity.
In some embodiments, the goods are ordered in order of the predicted demand for each good from big to small to obtain a sequence of goods; initializing a goods list of the manual picking area, and sequentially adding the goods in the goods sequence and the goods with the association degree larger than a preset value into the goods list until the sum of the demand amounts of the goods in the goods list reaches the manual processing amount.
For example, the resulting cargo sequence is A, B, C … … in order of demand from large to small. Firstly, adding goods A into a goods list, and then judging whether the sum of the demand amounts of the goods in the goods list reaches the manual processing amount or not; if not, continuing to add the goods M with the association degree with the goods A being larger than the preset value into the goods list, and continuing the process of judging the demand; if the end condition has not been reached, the addition of good B to the list of goods continues, and so on.
If the relevance between cargoes is not considered when cargoes are split, a confluence order may appear, namely, one part of the order is produced in an automatic sorting area, the other part of the order is produced in a manual area, and then cargoes sorted in the two areas are required to be confluently packed, so that the operation cost is increased. By diverting the goods and their related goods, which are expected to be in high demand during the future time period, to the manual sorting area, the generation of the confluent order can be reduced, the operation cost can be reduced, and the sorting efficiency can be improved.
In step S108, the goods are scheduled according to the goods list.
For example, in the goods warehousing stage, basic information such as the name, the type and the like of goods to be warehoused is obtained by means of code scanning, goods bill reading and the like; then, inquiring whether the goods are in a goods list from a database; determining the storage position of the goods according to the query result and the corresponding warehousing strategy, and sending a carrying instruction to carrying equipment corresponding to the storage position; the handling equipment performs loading and inventory input on the goods so as to update data in the warehouse management system.
Through the method of the embodiment, partial cargoes can be shunted to the manual picking area for processing by predicting the demand of cargoes in the future period and analyzing the association degree between cargoes, so that the warehouse can meet production demands in the warehouse-out peak period, and the sorting efficiency of the warehouse is improved.
After obtaining the list of goods that need to be sorted to the manual picking area, the following method may be used to implement the dispatch of the goods.
For convenience of description, the goods to be put in storage appearing in the goods list are referred to as split goods.
In some embodiments, for each of the diverted loads, determining a quantity of the same diverted load to be stocked into the automated picking area based on an automatic throughput of the same load and a quantity of the same load to be stocked into the automated picking area, where the automatic throughput is a demand within a first preset time period, the first preset time period preceding a future time period, where the automatic throughput is less than the automatic throughput for each of the diverted loads.
Since daily sorting work is still largely done by the automated sorting area before the peak of the warehouse-out arrives. Thus, in a first preset period before the future period, the production requirements of the daily sorting work are first fulfilled. In a first warehousing strategy, the amount of stocked and stored shunt goods s to an automated picking areaFor example by equation (5).
In the formula (5), D s represents the number of the cargoes s arrived at this time and the total amount of the split cargoes s; j s denotes the number of cargos s that the automated sorting area is currently also able to store; t m represents the duration between the current time and the preset future period start time; q s represents the demand of the good s within a first preset period; k s denotes the amount of storage of the item s currently in the automated sorting area.
In some embodiments, after determining the first warehousing strategy, determining, for the types of the cargos in the split cargos in which the storage area is not determined, the number of warehousing the same split cargos to the manual picking area according to the demand of the same cargos in the manual picking area in a future period and the number of the same cargos stored in the current manual picking area, so as to determine the second warehousing strategy.
Thus, after automated production before arrival of a peak of delivery is satisfied, the inventory of the manual sorting area can be gradually replenished. And the distribution of the cargoes is completed in the warehouse-in stage of the cargoes, so that the operation times of adjusting the storage position of the cargoes again after warehouse-in is reduced, and the efficiency of the operation in the warehouse is improved.
In the second warehousing strategy, the amount of the shunt goods s stored in the warehouse of the manual picking areaFor example by equation (6).
In formula (6), M s represents the predicted demand of the goods s in a preset future period; h s represents the amount of storage of the item s currently in the manual pick area. The meaning of the other parameters refers to the relevant description of equation (5).
In some embodiments, after determining the second warehousing policy, determining, for the types of the cargos in the split cargos for which the storage area is not determined, an amount of warehousing storage of the same split cargos to the automated picking area according to an upper limit of a replenishment amount of the same cargos in the automated picking area and a storage amount of the same cargos in the automated picking area, so as to determine the third warehousing policy.
After the goods requirements of the manual picking area in the future period are met, if the goods to be put in storage still remain, the goods to be put in storage can be continuously distributed to the automatic picking area for storage, so that the operation times of carrying the goods in the manual picking area to the automatic picking area after the delivery peak is finished are reduced, and the efficiency of the operation in the warehouse is improved.
In a third warehousing strategy, the amount of stocked and stored diverted goods s into the automated picking areaFor example by equation (7).
In equation (7), T b represents the upper limit on the pick days of the automated pick zone. The meaning of the other parameters is described with reference to the associated equations (5) and (6).
In some embodiments, after determining the third warehousing policy, the shipment in the undetermined storage area of the split shipment is determined to be a shipment warehoused to the centralized storage area. Thus, the goods exceeding the required amount and the storage capacity of the respective areas can be stored as a whole.
By the method of the embodiment, the warehousing flow direction of the goods can be judged in the warehousing stage of the goods, and the corresponding warehousing strategy is determined. Therefore, the inventory of each area can be adjusted gradually before the arrival of the peak period of warehouse-out, the working efficiency of the warehouse is improved, and the sorting efficiency and the storage space utilization rate are also improved.
When the peak of delivery is approaching, the goods in the warehouse can be further internally adjusted so that the storage amount of the manual sorting area reaches the required amount.
In some embodiments, in a second preset period before the start of the future period, in the case where the storage amount of the same kind of goods in the manual sorting area is smaller than the demand amount of the same kind of goods in the future period, the same kind of goods in the centralized storage area are allocated to the manual sorting area, so that after the goods in the centralized storage area are allocated, the storage amount of the goods in the manual sorting area reaches the demand amount of the same kind of goods in the future period. For example, the number of loads s allocated from the centralized storage area to the manual picking areaCalculated using equation (8).
In formula (8), M s represents the predicted demand of the goods s in a preset future period; h s represents the amount of storage of the item s currently in the manual pick area; b s denotes several stored quantities of the goods s.
In some embodiments, after the goods in the centralized storage area are allocated, in a case where the storage quantity of the goods in the manual sorting area is still smaller than the demand quantity of the same goods in the future period, allocating the same goods in the automatic sorting area into the manual sorting area so that the quantity of the same goods in the manual sorting area reaches the demand quantity of the same goods in the future period. For example, the number of orders for goods s ordered by automated picking areas to manual picking areasCalculated using equation (9).
In equation (9), K s represents the amount of storage of the item s currently in the automated sorting area. The meaning of the other parameters is shown in formula (8).
In some embodiments, the duration of the second period is less than the duration of the first period. For example, the distribution of the warehoused goods is started one month before the arrival of the peak, and the goods in the warehouse are scheduled several days before the arrival of the peak. Therefore, the goods can be directly shunted by warehousing preferentially, and then fine adjustment is realized by internal scheduling, so that the efficiency of warehouse operation is improved. An embodiment of scheduling cargo is described below exemplarily with reference to fig. 2.
Fig. 2 shows a flow diagram of a cargo scheduling method according to further embodiments of the invention. As shown in fig. 2, the cargo scheduling method of this embodiment takes a certain cargo S in the scheduled cargo list as an example, and includes steps S202 to S212.
In step S202, for the item S to be stocked in the item list, the number of stocked storage of the item S to the automated picking area is determined.
In step S204, if there are still cargos in the undetermined storage area among the cargos S to be put in storage, the number of the cargos S put in storage to the manual picking area is determined.
In step S206, if there are still cargos in the undetermined storage area among the cargos S to be stocked, the number of the cargos S stocked in the automated picking area is determined again.
In step S208, if there are still cargoes in the undetermined storage area among the cargoes S to be put in storage, the cargoes are put in storage in the centralized storage area.
In step S210, if the stored quantity of the goods S in the manual sorting area is smaller than the required quantity of the goods S in the future period, the goods S in the centralized storage area are allocated to the manual sorting area.
In step S212, if the stored quantity of items S in the manual picking area is still less than the demand quantity of items S in the future period, the items S of the automated picking area are allocated to the manual picking area.
By the method of the embodiment, the efficiency of warehouse operation can be improved.
An embodiment of the cargo scheduling device of the present invention is described below with reference to fig. 3.
Fig. 3 illustrates a schematic diagram of a cargo scheduling device according to some embodiments of the invention. As shown in fig. 3, the cargo scheduling device 30 of this embodiment includes: a degree of association determination module 310 configured to determine a degree of association between different kinds of goods from the history data; a manual throughput prediction module 320 configured to determine a throughput of the goods in the future period of time by the manual picking area in the warehouse as a manual throughput based on a sum of the predicted demand amounts of the plurality of goods in the future period of time and a picking capability of the automatic picking area in the warehouse; a goods list creation module 330 configured to create a goods list of the manual picking area according to a required amount of each goods in a future period and a degree of association between different goods, the goods list including a kind of the goods and a corresponding required amount, a sum of the required amounts of each of the goods in the goods list being equal to the manual handling amount; the scheduling module 340 is configured to schedule the goods according to the list of goods.
In some embodiments, the shipment list creation module 330 is further configured to sort the shipments in order of the predicted demand for each shipment from greater to lesser to obtain a shipment sequence; initializing a goods list of the manual picking area, and sequentially adding the goods in the goods sequence and the goods with the association degree larger than a preset value into the goods list until the sum of the demand amounts of the goods in the goods list reaches the manual processing amount.
In some embodiments, the relevancy determination module 310 is further configured to obtain orders for both the first and second items in the historical orders; and determining the association degree between the first goods and the second goods according to the reciprocal of the goods types in each order in which the first goods and the second goods are simultaneously appeared.
In some embodiments, the predicted demand for each type of good over the future period is equal to the product of the historical daily average demand for the type of good and a preset coefficient, the preset coefficient being determined based on the historical lift amplitude for the type of good or all types of good.
In some embodiments, the scheduling module 340 is further configured to determine the goods to be warehoused that are present in the list of goods as split-loads; for each of the split loads, determining the number of storage of the split loads in the automated picking area according to the automatic handling capacity of the same load and the storage of the same load in the automated picking area under the condition that the storage number of the same load in the automated picking area is smaller than the automatic handling capacity, so as to determine a first storage strategy, wherein the automatic handling capacity is the required amount in a first preset period, and the first preset period is before a future period; after the first warehousing strategy is determined, for the types of the cargos in the storage area which are not determined in the shunt cargos, determining the warehousing storage quantity of the same shunt cargos to the manual picking area according to the demand quantity of the same cargos in the manual picking area in a future period and the storage quantity of the same cargos in the current manual picking area so as to determine the second warehousing strategy.
In some embodiments, the scheduling module 340 is further configured to determine, for the types of the loads in the split loads for which the storage area is not determined, a third warehousing policy based on an upper limit of the replenishment quantity of the same load in the automated picking area and a storage quantity of the same load in the automated picking area after determining the second warehousing policy.
In some embodiments, the scheduling module 340 is further configured to determine, after determining the third warehousing policy, the good of the split good for which the storage area is not determined as a good to be warehoused to the centralized storage area.
In some embodiments, the scheduling module 340 is further configured to allocate the same kind of goods in the centralized storage area into the manual sorting area in a case where the storage amount of the same kind of goods in the manual sorting area is smaller than the demand amount of the same kind of goods in the future period within a second preset period before the start of the future period, so that after allocating the goods in the centralized storage area, the storage amount of the goods in the manual sorting area reaches the demand amount of the same kind of goods in the future period; after the goods in the centralized storage area are allocated, under the condition that the storage quantity of the goods in the manual sorting area is still smaller than the demand quantity of the same goods in the future period, allocating the same goods in the automatic sorting area into the manual sorting area so that the quantity of the same goods in the manual sorting area reaches the demand quantity of the same goods in the future period.
In some embodiments, the picking capability of an automated picking zone within the warehouse is determined based on the number of workstations enabled for the automated picking zone for a future period of time, the length of picking work for each workstation, and the picking efficiency per unit time for each workstation.
Fig. 4 shows a schematic structural view of a cargo scheduling device according to further embodiments of the invention. As shown in fig. 4, the cargo scheduling device 40 of this embodiment includes: a memory 410 and a processor 420 coupled to the memory 410, the processor 420 being configured to perform the cargo scheduling method of any of the foregoing embodiments based on instructions stored in the memory 410.
The memory 410 may include, for example, system memory, fixed nonvolatile storage media, and the like. The system memory stores, for example, an operating system, application programs, boot Loader (Boot Loader), and other programs.
Fig. 5 shows a schematic structural view of a cargo scheduling device according to further embodiments of the invention. As shown in fig. 5, the cargo scheduling device 50 of this embodiment includes: memory 510 and processor 520 may also include input-output interfaces 530, network interfaces 540, storage interfaces 550, and the like. These interfaces 530, 540, 550, as well as the memory 510 and the processor 520, may be connected by a bus 560, for example. The input/output interface 530 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, a touch screen, etc. Network interface 540 provides a connection interface for various networking devices. The storage interface 550 provides a connection interface for external storage devices such as SD cards, U discs, and the like.
An embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements any one of the cargo scheduling methods described above.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (11)
1. A cargo scheduling method comprising:
Determining the association degree between different kinds of cargoes according to the historical data;
Determining the handling capacity of the manual picking area in the warehouse to the cargoes in the future period as the manual handling capacity according to the sum of the predicted demand of various cargoes in the future period and the picking capacity of the automatic picking area in the warehouse;
Establishing a goods list of the manual picking area according to the demand of each goods in a future period and the association degree between different goods, wherein the goods list comprises the types of the goods and the corresponding demand, and the sum of the demand of each goods in the goods list is equal to the manual processing amount;
Scheduling the goods according to the goods list, including: determining goods to be put in storage in a goods list to serve as split-flow goods; for each of the split loads, determining the number of storage of the same split load in the automated picking area according to the automatic handling capacity of the same load and the storage of the same load in the automated picking area under the condition that the storage number of the same load in the automated picking area is smaller than the automatic handling capacity, so as to determine a first storage strategy, wherein the automatic handling capacity is the required amount in a first preset time period, and the first preset time period is before the future time period; after the first warehousing strategy is determined, for the types of cargos in the storage area which are not determined in the shunt cargos, determining the warehousing storage quantity of the same shunt cargos to the manual picking area according to the demand quantity of the same cargos in the manual picking area in the future period and the storage quantity of the same cargos in the current manual picking area so as to determine the second warehousing strategy.
2. The method for dispatching cargos of claim 1, wherein the creating the list of cargos in the manual picking area according to the demand of each kind of cargos in the future period and the association degree between different cargos comprises:
Sequencing the cargoes according to the order of the predicted demand of each cargo from big to small to obtain a cargo sequence;
initializing a goods list of the manual picking area, and sequentially adding the goods in the goods sequence and the goods with the association degree larger than a preset value into the goods list until the sum of the demand amounts of the goods in the goods list reaches the manual processing amount.
3. The cargo scheduling method of claim 1, wherein the determining a degree of association between different cargoes from the historical data comprises:
Acquiring orders of the first goods and the second goods in the historical orders;
And determining the association degree between the first goods and the second goods according to the reciprocal of the goods types in each order of the first goods and the second goods which occur simultaneously.
4. The cargo scheduling method according to claim 1, wherein the predicted demand of each cargo in the future period is equal to a product of a historical daily average demand of the kind of cargo and a preset coefficient determined according to a historical lift amplitude of the kind of cargo or all kinds of cargo.
5. The cargo scheduling method of claim 1, wherein the scheduling cargo according to the cargo list further comprises:
After the second warehousing strategy is determined, for the types of cargos in which the storage area is not determined in the split cargos, determining the warehousing storage quantity of the same split cargos to the automatic picking area according to the upper limit of the replenishment quantity of the same cargos in the automatic picking area and the storage quantity of the same cargos in the automatic picking area so as to determine a third warehousing strategy.
6. The cargo scheduling method of claim 5, wherein the scheduling cargo according to the cargo list further comprises:
And after the third warehousing strategy is determined, determining the cargoes which are not determined to be in the storage area in the shunt cargoes as cargoes which are warehoused in the centralized storage area.
7. The cargo scheduling method according to any one of claims 1 to 6, the scheduling cargo according to the cargo list further comprising:
In a second preset period before the beginning of the future period, under the condition that the storage quantity of the cargoes in the manual sorting area is smaller than the demand quantity of the same cargoes in the future period, the same cargoes in the centralized storage area are allocated to the manual sorting area so that after the cargoes in the centralized storage area are allocated, the storage quantity of the cargoes in the manual sorting area reaches the demand quantity of the same cargoes in the future period;
After the goods in the centralized storage area are allocated, under the condition that the storage quantity of the goods in the manual sorting area is still smaller than the demand quantity of the same goods in the future period, allocating the same goods in the automatic sorting area into the manual sorting area, so that the quantity of the goods of the type in the manual sorting area reaches the demand quantity of the goods of the type in the future period.
8. The method of cargo scheduling of claim 1, wherein the picking capacity of the automated picking zone within the warehouse is determined based on the number of workstations activated by the automated picking zone for a future period of time, the length of picking work for each workstation, and the picking efficiency per unit time for each workstation.
9. A cargo scheduling device comprising:
the association degree determining module is configured to determine association degrees among different kinds of cargoes according to historical data;
A manual throughput prediction module configured to determine a throughput of the goods in the future period of time by the manual picking area in the warehouse as a manual throughput based on a sum of predicted demand amounts of the plurality of goods in the future period of time and a picking capability of the automatic picking area in the warehouse;
A goods list establishing module configured to establish a goods list of a manual picking area according to the demand of each goods in a future period and the association degree between different goods, wherein the goods list comprises the types of the goods and the corresponding demand, and the sum of the demands of each goods in the goods list is equal to the manual processing amount;
a scheduling module configured to schedule goods according to the goods list, comprising: determining goods to be put in storage in a goods list to serve as split-flow goods; for each of the split loads, determining the number of storage of the same split load in the automated picking area according to the automatic handling capacity of the same load and the storage of the same load in the automated picking area under the condition that the storage number of the same load in the automated picking area is smaller than the automatic handling capacity, so as to determine a first storage strategy, wherein the automatic handling capacity is the required amount in a first preset time period, and the first preset time period is before the future time period; after the first warehousing strategy is determined, for the types of cargos in the storage area which are not determined in the shunt cargos, determining the warehousing storage quantity of the same shunt cargos to the manual picking area according to the demand quantity of the same cargos in the manual picking area in the future period and the storage quantity of the same cargos in the current manual picking area so as to determine the second warehousing strategy.
10. A cargo scheduling device comprising:
a memory; and
A processor coupled to the memory, the processor configured to perform the cargo scheduling method of any of claims 1-8 based on instructions stored in the memory.
11. A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the cargo scheduling method of any one of claims 1 to 8.
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| CN117952522B (en) * | 2024-03-26 | 2024-06-07 | 瑞熙(苏州)智能科技有限公司 | Warehouse entry management method and system based on data processing |
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