CN113781135A - Order processing method and device, computing equipment and medium - Google Patents

Order processing method and device, computing equipment and medium Download PDF

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CN113781135A
CN113781135A CN202010853835.7A CN202010853835A CN113781135A CN 113781135 A CN113781135 A CN 113781135A CN 202010853835 A CN202010853835 A CN 202010853835A CN 113781135 A CN113781135 A CN 113781135A
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order
goods
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魏豫
付小龙
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Beijing Jingdong Qianshi Technology Co Ltd
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    • G06Q10/00—Administration; Management
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    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

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Abstract

The present disclosure provides an order processing method, including: acquiring at least one order to be processed, operation data of a first goods picking area and operation data of a second goods picking area; creating a first planning model according to at least one order to be processed, the operation data of the first picking area and the operation data of the second picking area; determining a flow direction allocation strategy for at least one to-be-processed order by using a first planning model; and picking the goods in the at least one order to be processed in the first goods picking area and the second goods picking area according to the flow direction distribution strategy. The disclosure also provides an order processing apparatus, a computing device and a medium.

Description

Order processing method and device, computing equipment and medium
Technical Field
The present disclosure relates to the field of warehouse logistics technologies, and in particular, to an order processing method, an order processing apparatus, a computing device, and a medium.
Background
With the development of the e-commerce industry, the demand of the e-commerce industry for the logistics warehouse processing capacity is higher and higher. The processing capacity of the single logistics warehouse is certain, and in order to improve the processing capacity of the logistics warehouse, the related art improves the order processing capacity by adding a supplementary picking area outside the main picking area of the logistics warehouse. The supplementary goods picking area has the same outward whole box moving function as the main goods picking area, so that the elasticity of the original hardware equipment can be increased, and the delivery efficiency is improved.
In implementing the disclosed concept, the inventors found that there are at least the following problems in the related art: the order allocation in the main picking area and the supplementary picking area is not reasonable, which results in low order processing efficiency.
Disclosure of Invention
In view of the above, the present disclosure provides an order processing method, an order processing apparatus, a computing device and a medium.
One aspect of the present disclosure provides an order processing method, including: acquiring at least one order to be processed, operation data of a first goods picking area and operation data of a second goods picking area; creating a first planning model based on the at least one order to be processed, the operational data of the first picking zone and the operational data of the second picking zone; determining a flow direction allocation strategy for the at least one order to be processed using the first planning model; and picking the goods in the at least one order to be processed in the first goods picking area and the second goods picking area according to the flow direction distribution strategy.
According to an embodiment of the present disclosure, the operation data of the first picking area includes remaining capacity of the first picking area, kind of goods and stock of goods in the first picking area; the operation data of the second picking area comprises the utilization rate of the second picking area, the goods type and the goods inventory in the second picking area and the storage space occupation information in the second picking area.
According to an embodiment of the present disclosure, the creating a first planning model from the at least one pending order and the operational data comprises: a first objective function is created according to the following formula:
Figure BDA0002644380830000021
wherein f1 is the first objective function, X1The total number of confluent orders assigned to the common handling of the first picking zone and the second picking zone, the X2To be allocated toThe number of first orders processed in the first picking area, X3Number of replenishment bins required for replenishment orders, X4The number of the unfilled places in the second picking area required to be occupied for the replenishment order, X5The number of the goods in the replenishment order is X6The number of the items in the second order distributed to the second picking area for processing, the
Figure BDA0002644380830000022
Is preset weight, if
Figure BDA0002644380830000023
Then
Figure BDA0002644380830000024
If it is not
Figure BDA0002644380830000025
Then
Figure BDA0002644380830000026
If it is not
Figure BDA0002644380830000027
Then
Figure BDA0002644380830000028
If it is not
Figure BDA0002644380830000029
Then
Figure BDA00026443808300000210
Wherein, the
Figure BDA00026443808300000212
Capacity of the first picking area, s is total quantity of goods in the at least one order to be processed, and
Figure BDA00026443808300000211
is the utilization rate of the second picking area, and z isA rate threshold; and creating a first constraint function according to the first constraint condition.
According to an embodiment of the present disclosure, the first constraint comprises at least one of the following constraints: each of the at least one pending order is assignable to only one of the first picking area and the second picking area; the quantity of goods in the order processed by the second goods picking area is less than the stock of goods in the second goods picking area; the quantity of goods in the order processed by the second goods picking area is less than the stock quantity of goods in the first goods picking area; the number of the bins which can be stored in each underfilled storage position in the second goods picking area is not more than 3; one bin corresponds to one storage position; the total number of the replenishment bins without selecting part of storage positions is less than the number of empty storage positions; and the inventory of goods in the second picking area is larger than the safety inventory.
According to an embodiment of the present disclosure, the flow direction allocation policy is configured to classify each order of the at least one to-be-processed order as the first order, the second order, the merge order, or the to-be-filled order; the picking the items in the at least one order to be processed in the first picking area and the second picking area according to the flow direction distribution strategy comprises the following steps: moving the goods in the order to be restocked from the first goods picking area to the second goods picking area; executing the goods picking operation of the first order by the first picking area, executing the goods picking operation of the second order and the order to be replenished by the second picking area, and executing the goods picking operation of the confluent order jointly by the first picking area and the second picking area.
According to an embodiment of the present disclosure, the second picking zone includes a fixed buffer zone and a temporary buffer zone; the moving the items in the replenishment order from the first pick-up area to the second pick-up area comprises: transporting goods needing replenishment to the temporary buffer area; and periodically transporting the goods in the temporary buffer area to the fixed buffer area. The executing of the goods picking operation of the second order and the to-be-restocked order by the second picking area comprises: and picking the goods in the second order and the order to be restocked in the fixed buffer area.
According to an embodiment of the present disclosure, the method further comprises: creating a second planning model according to the orders to be processed in the second picking area and second operation data of the second picking area; dividing the orders to be processed in the second order picking area into at least one order set according to the second planning model, and determining picking points corresponding to the goods in the at least one order set; and picking the goods in the orders to be processed in the second picking area according to the at least one order set and the picking point.
According to an embodiment of the present disclosure, the creating a second planning model according to the orders to be processed in the second picking zone and the second operation data of the second picking zone includes: creating the second objective function according to the following formula:
f2＝min{Y1+Y2+Y3}
wherein f2 is a second objective function, Y1Area enclosing an image for the picking point, said Y2For the number of picking points, said Y3Is the total volume of the goods in the order set; and creating a second constraint function according to the second constraint condition.
According to an embodiment of the present disclosure, the second constraint comprises at least one of the following constraints: the total volume of the items in the order set is greater than a first volume threshold; the total volume of items in the order set is less than a second volume threshold; the number of the orders to be processed in the second picking area is smaller than an order number threshold value; the quantity of goods in the orders to be processed in the second goods picking area is less than the goods quantity threshold value; the quantity of each goods in the orders to be processed in the second goods picking area is less than the inventory quantity of the goods in the second goods picking area; the number of the bins which can be stored in each underfilled storage position in the second goods picking area is not more than 3; and one bin corresponds to one storage location.
Another aspect of the present disclosure provides an order processing apparatus including: the acquisition module is used for acquiring at least one order to be processed, the operation data of the first goods picking area and the operation data of the second goods picking area; a creation module for creating a first planning model based on the at least one order to be processed, the operational data of the first picking zone and the operational data of the second picking zone; a determining module for determining a flow direction allocation strategy for the at least one order to be processed using the first planning model; and the picking module is used for picking the goods in the at least one order to be processed in the first goods picking area and the second goods picking area according to the flow direction distribution strategy.
Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, the flow direction distribution strategy of the orders to be processed is determined through the first mathematical programming model, so that the orders are reasonably distributed to the first picking area and the second picking area according to the flow direction distribution strategy, the capacity of the first picking area and the second picking area is better utilized, and the production efficiency is improved.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary application scenario in which an order processing method may be applied, according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow diagram of an order processing method according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart of an order processing method according to an embodiment of the present disclosure;
FIG. 4 schematically shows a block diagram of an order processing apparatus according to an embodiment of the present disclosure; and
FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides an order processing method and a device capable of applying the order processing method. The method comprises the following steps: acquiring at least one order to be processed, operation data of a first goods picking area and operation data of a second goods picking area; creating a first planning model according to at least one order to be processed, the operation data of the first picking area and the operation data of the second picking area; determining a flow direction allocation strategy for at least one to-be-processed order by using a first planning model; and picking the goods in the at least one order to be processed in the first goods picking area and the second goods picking area according to a flow direction distribution strategy.
Fig. 1 schematically illustrates an exemplary application scenario 100 in which the order processing method may be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the application scenario 100 according to this embodiment may include a first picking zone 10, a second picking zone 20.
The first picking area 10 may be, for example, an unmanned picking area, and may be used as a main picking area for automatically processing orders in an order pool. The second pick-up area 20 may be, for example, a fluent shelf pick-up area. The second picking area 20 may be disposed on one side of the first picking area 10 as a supplemental picking area to the first picking area 10. The second picking area 20 has the same outward whole-box transferring function as the first picking area 10, and can process orders independently, and the second picking area 20 can process combined orders together with the first picking area 10. The second picking area 20 may be configured as a fixed buffer area for storing goods to be restocked in advance and a temporary buffer area for storing goods to be restocked in advance, so as to make up for the capacity shortage of the first picking area 10 when large promotion and daily peak value occur.
FIG. 2 schematically shows a flow chart of an order processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes acquiring at least one order to be processed, operational data for a first pick zone, and operational data for a second pick zone at operation S210.
According to an embodiment of the present disclosure, each of the at least one pending order contains at least one item to be picked out of the warehouse. The operation data of the first pick-up area may include, for example, a remaining capacity of the first pick-up area, a kind of goods in the first pick-up area, and an inventory quantity of goods.
The operation data of the second picking area may include, for example, a usage rate of the second picking area, a kind and an inventory of goods in the second picking area, and a storage space occupation information in the second picking area. The bin occupancy information may include, for example, empty bins, the type and quantity of items in occupied bins.
Then, in operation S220, a first planning model is created based on the at least one order to be processed, the operational data of the first picking zone and the operational data of the second picking zone.
According to an embodiment of the present disclosure, the first planning model may, for example, include a first objective function and a first constraint function. The method of creating the first planning model is further described below.
For convenience, in this embodiment, the order assigned to the first picking area for processing is referred to as a first order, the order assigned to the second picking area for processing is referred to as a second order, and the order assigned to the first picking area and the second picking area for processing together is referred to as a merged order.
According to an embodiment of the present disclosure, the first objective function may be created according to the following formula:
Figure BDA0002644380830000071
where f1 is a first objective function, X1Total number of confluent orders, X, assigned to common handling of the first and second picking zones2For the number of first orders allocated to processing in the first picking zone, X3Number of replenishment bins required for replenishment orders, X4The number of unfilled places, X, in the second pick-up zone to be taken up for replenishment orders5Number of items in order for replenishment, X6For the number of items in the second order processed assigned to the second pick-up area,
Figure BDA0002644380830000072
the weight is preset to be a preset weight,
if it is not
Figure BDA0002644380830000073
Then
Figure BDA0002644380830000074
If it is not
Figure BDA0002644380830000075
Then
Figure BDA0002644380830000076
If it is not
Figure BDA0002644380830000077
Then
Figure BDA0002644380830000078
If it is not
Figure BDA0002644380830000079
Then
Figure BDA00026443808300000710
Wherein, is
Figure BDA00026443808300000711
Capacity of the first picking area, s is the total quantity of goods in at least one order to be processed,
Figure BDA00026443808300000712
is the utilization of the second pick-up area and z is the utilization threshold.
According to an embodiment of the present disclosure, a first constraint function may be created according to a first constraint condition. Wherein the first constraint may comprise at least one of the following constraints, for example:
(1) each of the at least one pending order can be assigned to only one of the first picking zone and the second picking zone.
(2) The quantity of goods in the order processed by the second goods picking area is less than the stock quantity of goods in the second goods picking area.
(3) The quantity of goods in the order processed by the second goods picking area is less than the stock quantity of goods in the first goods picking area.
(4) The number of the bins which can be stored in each underfilled storage position in the second goods picking area is not more than 3;
(5) one bin corresponds to one storage location.
(6) The total number of the replenishment bins without the selected part of the storage positions is less than the number of the empty storage positions.
(7) The inventory of goods in the second goods picking area is larger than the safety inventory.
In operation S230, a flow direction allocation strategy for at least one pending order is determined using the first planning model.
According to the embodiment of the disclosure, by solving the first planning model, the flow direction allocation strategy can be obtained. The flow allocation policy may be used to classify each of the at least one pending order as a first order, a second order, a merge order, or a pending replenishment order.
In operation S240, items in at least one pending order are picked in the first pick zone and the second pick zone according to the flow direction allocation policy.
According to an embodiment of the present disclosure, the operation S240 may include, for example, moving the items in the order to be restocked from the first picking area to the second picking area, then performing the item picking operation of the first order by the first picking area, performing the item picking operation of the second order and the order to be restocked by the second picking area, and performing the item picking operation of the merged order by the first picking area and the second picking area together.
According to another embodiment of the present disclosure, a fixed buffer and a temporary buffer may be configured in the second pick zone. And transporting the goods needing replenishment to the temporary buffer area, and then periodically transporting the goods in the temporary buffer area to the fixed buffer area. When the goods picking operation of the second order and the order to be replenished needs to be carried out in the second picking area, the goods in the second order and the order to be replenished are picked in the fixed buffer area.
According to the embodiment of the disclosure, the flow direction distribution strategy of the orders to be processed is determined through the first mathematical programming model, so that the orders are reasonably distributed to the first picking area and the second picking area according to the flow direction distribution strategy, the capacity of the first picking area and the second picking area is better utilized, and the production efficiency is improved.
FIG. 3 schematically shows a flow chart of an order processing method according to another embodiment of the present disclosure.
As shown in fig. 3, the method may further include operations S310 to S330 in addition to operations S210 to S240.
In operation S310, a second planning model is created according to the order to be processed in the second picking zone and second operation data of the second picking zone.
According to an embodiment of the present disclosure, the second planning model may, for example, include a second objective function and a second constraint function.
According to an embodiment of the present disclosure, the second objective function may be created according to the following formula:
f2＝min{Y1+Y2+Y3}
where f2 is the second objective function, Y1Enclosing the area of the image for the sorting point, Y2For sorting the number of points, Y3Is the total volume of the items in the order set; and
according to an embodiment of the present disclosure, a second constraint function may be created according to a second constraint. Wherein the second constraint may comprise at least one of the following constraints, for example:
(1) the total volume of items in the order set is greater than a first volume threshold.
(2) The total volume of items in the order set is less than the second volume threshold.
(3) The number of orders to be processed in the second picking area is less than the order number threshold.
(4) The quantity of the goods in the orders to be processed in the second goods picking area is less than the goods quantity threshold value.
(5) The quantity of each type of goods in the orders to be processed in the second goods picking area is less than the inventory quantity of the goods in the second goods picking area.
(6) Each underfilled storage location in the second pick area may hold no more than 3 bins.
(7) One bin corresponds to one storage location.
In operation S320, the orders to be processed in the second picking area are divided into at least one order set according to the second planning model, and picking points corresponding to the items in the at least one order set are determined.
According to the embodiment of the disclosure, an order location strategy may be determined by solving the second planning model, and the order location strategy may be used to represent a dividing method for dividing the order to be processed into the order set and a position of a pick point corresponding to an item in the order set.
In operation S330, items in the order to be processed in the second picking area are picked according to the at least one order set and the picking point.
According to the embodiment of the disclosure, the order sets can be generated one at a time according to the divided order sets and the positions of the picking points corresponding to each order set until the to-be-processed order set in the second picking area is empty. Each collection includes a collection of orders and a collection of pickpoints.
According to the embodiment of the disclosure, in the order processing process of the second picking area, if a bin for replenishing goods exists at the picking point, an emergency replenishment task is generated for the bin. And when the urgent replenishment task is completely completed, issuing a picking task of the collection list.
According to the method disclosed by the embodiment of the disclosure, the flow direction distribution strategy and the order location strategy are respectively determined in two steps, so that the capacity of the first sorting area can be utilized to the maximum extent, and the capacity of the second sorting area can be utilized reasonably. When the capacity of the first sorting area is insufficient, the order flow of the first sorting area and the second sorting area can be reasonable. The confluence output of the first sorting area and the second sorting area and the number of replenishment bins of the first sorting area to the second sorting area are reduced, and the production timeliness is improved. And the number of the storage positions in the second sorting area can be reasonably occupied, so that the explosion is avoided.
The methods shown in FIGS. 2-3 are further described below with reference to specific embodiments. Those skilled in the art will appreciate that the following example embodiments are only for the understanding of the present disclosure, and the present disclosure is not limited thereto.
In this embodiment, the first picking area is a sirius picking area, and the second picking area is a fluent goods shelf area.
Firstly, data such as related data of an order to be processed and operation data of a first picking area and a second picking area are obtained.
According to the embodiment of the disclosure, the remaining capacity of the sirius area can be calculated according to the following formula:
Figure BDA0002644380830000101
wherein the number of order pieces to be picked (unit: piece) in the Tianlang machine area is s, the collection of picking work stations is w, the picking efficiency (according to the statistics of historical data, piece/hour) of the work stations is thetaiThe current time is t from the production end time length (unit: hour).
According to the embodiment of the present disclosure, if
Figure BDA0002644380830000102
The remaining capacity of the sirius area is sufficient. Otherwise, the residual capacity is insufficient.
According to embodiments of the present disclosure, the utilization of the leverage shelf may be calculated according to the following formula:
Figure BDA0002644380830000111
wherein, the total storage digit is s, and the empty storage digit is w.
Illustratively, in this embodiment, the usage threshold is 0.8 if
Figure BDA0002644380830000112
The utilization rate of the circulation utilization shelf is too high, otherwise, the utilization rate is normal.
A first planning model is then created based on data relating to the orders to be processed, operational data for the first pick zone and the second pick zone, and the like. Wherein the optimization objectives of the first planning model include:
(1) the number of goods supplementing boxes of the Tianlang in the fluent goods shelf area is as small as possible.
(2) Reducing the interflow production order.
(3) And under the condition of sufficient capacity of the machine area, selecting the machine area to have more orders as much as possible.
(4) And under the condition of insufficient capacity of the machine area, selecting as many orders in the fluent area as possible.
(5) The unfixed products in the fluent region are removed as much as possible and are less supplemented.
(6) The quantity of the fixed articles on the fluent shelf is supplemented to the upper limit of the replenishment as much as possible.
(7) When the utilization rate of the fluent goods shelf is too high, the number of empty storage positions occupied by the fluent goods shelf during goods replenishment is as small as possible.
In this embodiment, the parameters of the first planning model are as follows:
d: collecting orders;
s: a set of items (set of items in D);
t: a turnover box set (a material box of a sirius machine area);
p: a collection of less than full storage locations in a fluent shelf area;
θs: sku does not belong to the fixed commodity set in the fluency area;
Figure BDA0002644380830000113
the number of stored material boxes of the partially empty storage positions;
Mds: the demand quantity of the commodity s in the order D belongs to D;
Ots: the inventory of the commodities s in the turnover box T belongs to T;
Msr: the inventory quantity of commodities S in the fluent goods shelf area is S, and S belongs to S;
Msb: inventory of goods s in the Tianlang aircraft region;
Wr: the number of hollow storage positions in the fluent shelf area;
Qrs: the fluent goods shelf area aims at the upper limit of replenishment of the commodities s;
in this embodiment, the variables of the first planning model objective function are as follows:
γd: order d whether to select the production in the fluency area, gammadE to {0, 1}, selecting to be 1, otherwise, 0;
βd: whether the order d selects the production of the machine area or not;
αpt: whether the bin t selects a storage position P, P belongs to P, sku (commodity identification) corresponding to P and t is equal, and alphaptE to {0, 1}, selecting to be 1, otherwise, 0;
πt: whether the transfer box t is selected, pitE to {0, 1}, selecting to be 1, otherwise, 0;
in this embodiment, the objective function of the first planning model may be:
Figure BDA0002644380830000121
in this embodiment, the constraint function of the first planning model may include:
constraint (1): gamma rayd+βd≤1，
Figure BDA0002644380830000122
Constraint (2): sigma-gammad*Mds≤∑πt*Ots+Msr，
Figure BDA0002644380830000123
Constraint (3): sigma betad*Mds≤∑(1-πt)*Ots+Msb，
Figure BDA0002644380830000124
Constraint (4):
Figure BDA0002644380830000125
constraint (5): sigma alphapt≤1，
Figure BDA0002644380830000126
Constraint (6): sigma pit-∑αpt≤Wr
Constraint (7): sigma pit*θs*Ots+Msr-∑γd*Mds≥Qrs，
Figure BDA0002644380830000127
Wherein, the constraint (1) indicates that the order can only select one area to produce. Constraint (2) indicates that the order goods produced in the fluent region are less in demand than the fluent region inventory. And the constraint (3) indicates that the order commodity volume produced in the machine area is less than the machine area inventory. Constraint (4) indicates that the bin count for a less full flow zone bin p cannot exceed 3. Constraint (5) indicates that only one fluent region reservoir p can be selected for a bin t. Constraint (6) indicates that the total number of replenishment bins without selected partial bins is less than the number of empty bins. Constraint (7) represents the restocking amount + the existing amount-the order demand > safety stock of the fixed sku in the fluency area.
By solving the first planning model, the following flow direction assignment results are obtained: tianlang district production order set sigma betadFluent shelf area production order collection∑γdConfluent order set (D-sigma gamma)d-∑βd) Generating work-up task of emergency material box sigma pit。
Then, the material box of the urgent replenishment task is moved from the Tianlang area to the second picking area, and then the production order set sigma beta is picked from the Tianlang areadAnd a confluent order collection (D-sigma gamma)d-∑βd) Goods picked in the Zhongtianlang area.
Meanwhile, the fluent goods shelf area collects sigma gamma according to the orderdConfluent order set (D-sigma gamma)d-∑βd) And collection of replenishment bins ∑ πtAnd selecting an optimal combination, namely an aggregation list, from the production order aggregation of the goods shelf area. The destination of the replenishment bin is based on the selection of the aggregated sheet.
More specifically, a second planning model is created. Wherein the optimization objectives of the second planning model may include: the sorting boxes of the collection list are full as much as possible; the number of orders cannot exceed the upper limit; the order cannot be split; the picking path of the fluent shelf area is shortest; the use volume is more than w1 and less than 100%; the number of commodities in the order does not exceed the upper limit of the number of commodities.
In this embodiment, the parameters of the second planning model are as follows:
d: order collection (collection of orders produced by the fluent shelf section that flow to post-allocation);
s: a set of items (items in D);
t: a bin set for emergency replenishment;
Pt: a picking point set (a set of stock box storage points in a fluent shelf area library);
Pe: the fluent shelf area may have a collection of available storage sites.
Pe: the fluent shelf area is not full of the collection of storage sites.
Figure BDA0002644380830000131
Number of stocked bins in less than full storage positions
Vs: the standard volume of a commodity S belongs to S;
XpYp: choosing the coordinate point of point P, where P belongs to Pt；
Mds: the demand quantity of the commodity s of the order D belongs to D;
Ots: the inventory of commodities s of the turnover box T belongs to T;
Ops: the inventory of the commodities s at the sorting point p;
w 1: a lower volume threshold;
w 2: penalty terms (for balancing pick area and pick volume);
w 3: an order number upper threshold;
w 4: a picking amount upper limit threshold;
t: total volume of the bin;
in this embodiment, the variables of the objective function of the second planning model are as follows:
γd: whether order d is selected, γdE to {0, 1}, selecting to be 1, otherwise, 0;
αp: whether the selection point p is selected, alphapE to {0, 1}, selecting to be 1, otherwise, 0;
πt: whether the transfer box t is selected, pitE to {0, 1}, selecting to be 1, otherwise, 0;
αptwhether the bin t selects the storage position p or not; (p is a reservoir not full of fluency and sku for p is equal to sku for t)
In this embodiment, the objective function of the second planning model may be:
Figure BDA0002644380830000141
in this embodiment, the constraint function of the second planning model may include:
constraint (1): sigma Vs*Mds*γd＞w1T
Constraint (2): sigma Vs*Mds*γd≤T
Constraint (3): sigma-gammad＜w3
Constraining(4)：∑γd*Mds＜w4
Constraint (5):
Figure BDA0002644380830000156
constraint (6):
Figure BDA0002644380830000152
constraint (7): sigma alphapt≤1，
Figure BDA0002644380830000153
Constraint (8): sigma alphapt-1＜αp，
Figure BDA0002644380830000154
Constraint (9):
Figure BDA0002644380830000155
wherein constraint (1) represents the lower volume limit. Constraint (2) represents the upper volume limit. The constraint (3) indicates an order number upper limit. Constraint (4) represents the upper limit of the total number of items. Constraint (5) indicates that the picked quantity of the item s is less than the stock quantity. Constraint (6) indicates that the bin count for a less full flow zone bin p cannot exceed 3. Constraint (7) indicates that only one fluent region reservoir p can be selected for a bin t. Constraints (8) indicate whether the pick point is picked, and a bin selection selects a point. Constraint (9) indicates the number of empty bin selections, i.e., the number of bins that have not been selected for completion.
And then, solving the second planning model to obtain order and positioning sorting points contained in the collection sheet, namely a sheet-combination positioning strategy. The order positioning strategy may be cycled through, generating one order at a time, until the pending order collection for the second picking area is empty. And if the sorting point has a material box for replenishment, generating an emergency replenishment task aiming at the material box. And when the urgent replenishment task is completely completed, issuing a picking task of the collection list.
Fig. 4 schematically shows a block diagram of an order processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 4, apparatus 400 may include an acquisition module 410, a creation module 420, a determination module 430, and a culling module 440.
The obtaining module 410 is configured to obtain at least one order to be processed, operation data of the first picking area, and operation data of the second picking area.
A creation module 420 for creating a first planning model based on at least one order to be processed, operational data of the first picking zone and operational data of the second picking zone.
A determining module 430 is configured to determine a flow direction allocation strategy for the at least one pending order using the first planning model.
A picking module 440 for picking items in at least one pending order in the first picking zone and the second picking zone according to the flow direction allocation policy.
According to the embodiment of the disclosure, the flow direction distribution strategy of the orders to be processed is determined through the first mathematical programming model, so that the orders are reasonably distributed to the first picking area and the second picking area according to the flow direction distribution strategy, the capacity of the first picking area and the second picking area is better utilized, and the production efficiency is improved.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any number of the acquisition module 410, the creation module 420, the determination module 430, and the culling module 440 may be combined in one module for implementation, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 410, the creating module 420, the determining module 430, and the sorting module 440 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the obtaining module 410, the creating module 420, the determining module 430 and the culling module 440 may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.
FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 5 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 5, a computer system 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. The processor 501 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 501 may also include onboard memory for caching purposes. Processor 501 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.
In the RAM 503, various programs and data necessary for the operation of the system 500 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. The processor 501 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 502 and/or the RAM 503. Note that the programs may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, system 500 may also include an input/output (I/O) interface 505, input/output (I/O) interface 505 also being connected to bus 504. The system 500 may also include one or more of the following components connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program, when executed by the processor 501, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include ROM 502 and/or RAM 503 and/or one or more memories other than ROM 502 and RAM 503 described above.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (12)

1. An order processing method, comprising:
acquiring at least one order to be processed, operation data of a first goods picking area and operation data of a second goods picking area;
creating a first planning model based on the at least one order to be processed, the operational data of the first picking zone and the operational data of the second picking zone;
determining a flow direction allocation strategy for the at least one order to be processed using the first planning model; and
picking the goods in the at least one order to be processed in the first picking area and the second picking area according to the flow direction distribution strategy.
2. The method according to claim 1, wherein the operation data of the first picking area includes remaining capacity of the first picking area, kind of goods and stock of goods in the first picking area;
the operation data of the second picking area comprises the utilization rate of the second picking area, the goods type and the goods inventory in the second picking area and the storage space occupation information in the second picking area.
3. The method of claim 2, wherein said creating a first planning model from said at least one pending order and said operational data comprises:
a first objective function is created according to the following formula:
Figure FDA0002644380820000011
wherein f1 is the first objective function, X1The total number of confluent orders assigned to the common handling of the first picking zone and the second picking zone, the X2For the number of first orders assigned to the first picking zone, the X3Number of replenishment bins required for replenishment orders, X4The number of the unfilled places in the second picking area required to be occupied for the replenishment order, X5The number of the goods in the replenishment order is X6The number of the items in the second order distributed to the second picking area for processing, the
Figure FDA0002644380820000012
Is preset weight, if
Figure FDA0002644380820000013
Then
Figure FDA0002644380820000014
If it is not
Figure FDA0002644380820000015
Then
Figure FDA0002644380820000016
If it is not
Figure FDA0002644380820000017
Then
Figure FDA0002644380820000018
If it is not
Figure FDA0002644380820000019
Then
Figure FDA00026443808200000110
Wherein, the
Figure FDA00026443808200000111
Capacity of the first picking area, s is total quantity of goods in the at least one order to be processed, and
Figure FDA0002644380820000021
is the usage rate of the second picking area, and z is a usage rate threshold; and
according to the first constraint condition, a first constraint function is created.
4. The method of claim 3, wherein the first constraint comprises at least one of:
each of the at least one pending order is assignable to only one of the first picking area and the second picking area;
the quantity of goods in the order processed by the second goods picking area is less than the stock of goods in the second goods picking area;
the quantity of goods in the order processed by the second goods picking area is less than the stock quantity of goods in the first goods picking area;
the number of the bins which can be stored in each underfilled storage position in the second goods picking area is not more than 3;
one bin corresponds to one storage position;
the total number of the replenishment bins without selecting part of storage positions is less than the number of empty storage positions; and
the inventory of goods in the second goods picking area is larger than the safety inventory.
5. The method of claim 3, wherein the flow direction allocation policy is to classify each of the at least one pending order as the first order, the second order, the merge order, or the pending replenishment order;
the picking the items in the at least one order to be processed in the first picking area and the second picking area according to the flow direction distribution strategy comprises the following steps:
moving the goods in the order to be restocked from the first goods picking area to the second goods picking area;
executing the goods picking operation of the first order by the first picking area, executing the goods picking operation of the second order and the order to be replenished by the second picking area, and executing the goods picking operation of the confluent order jointly by the first picking area and the second picking area.
6. The method of claim 5, wherein the second pick zone comprises a fixed buffer zone and a temporary buffer zone;
the moving the items in the replenishment order from the first pick-up area to the second pick-up area comprises:
transporting goods needing replenishment to the temporary buffer area; and
periodically transporting goods in the temporary buffer area to the fixed buffer area;
the executing of the goods picking operation of the second order and the to-be-restocked order by the second picking area comprises:
and picking the goods in the second order and the order to be restocked in the fixed buffer area.
7. The method of claim 2, further comprising:
creating a second planning model according to the orders to be processed in the second picking area and second operation data of the second picking area;
dividing the orders to be processed in the second order picking area into at least one order set according to the second planning model, and determining picking points corresponding to the goods in the at least one order set; and
and picking the goods in the orders to be processed in the second picking area according to the at least one order set and the picking point.
8. The method of claim 7, wherein the creating a second planning model from the orders to be processed for the second pickup region and second operational data for the second pickup region comprises:
creating the second objective function according to the following formula:
f2＝min{Y1+Y2+Y3}
wherein f2 is a second objective function, Y1Area enclosing an image for the picking point, said Y2For the number of picking points, said Y3Is the total volume of the goods in the order set; and
and creating a second constraint function according to the second constraint condition.
9. The method of claim 8, wherein the second constraint comprises at least one of:
the total volume of the items in the order set is greater than a first volume threshold;
the total volume of items in the order set is less than a second volume threshold;
the number of the orders to be processed in the second picking area is smaller than an order number threshold value;
the quantity of goods in the orders to be processed in the second goods picking area is less than the goods quantity threshold value;
the quantity of each goods in the orders to be processed in the second goods picking area is less than the inventory quantity of the goods in the second goods picking area;
the number of the bins which can be stored in each underfilled storage position in the second goods picking area is not more than 3; and
one bin corresponds to one storage location.
10. An order processing apparatus comprising:
the acquisition module is used for acquiring at least one order to be processed, the operation data of the first goods picking area and the operation data of the second goods picking area;
a creation module for creating a first planning model based on the at least one order to be processed, the operational data of the first picking zone and the operational data of the second picking zone;
a determining module for determining a flow direction allocation strategy for the at least one order to be processed using the first planning model; and
a picking module for picking the goods in the at least one order to be processed in the first picking area and the second picking area according to the flow direction distribution strategy.
11. A computing device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 9.
12. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 9.
CN202010853835.7A 2020-08-21 2020-08-21 Order processing method and device, computing equipment and medium Pending CN113781135A (en)

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