Inventory partitioning method and system

Abstract

The invention relates to an inventory partitioning method and system. A storage area can be divided into a plurality of sub-areas; due to the fact that the ex-warehouse probability of the product combinations formed in the sub-areas is relatively high, and the ex-warehouse probability of the product combinations formed between the different sub-areas is relatively low, the product combinations with the high probability are placed in a centralized mode, and the ex-warehouse work execution efficiency of a warehouse administrator can be greatly improved. The overall probability of the product combination and the individual probability of the single product are comprehensively considered, and the single thinking that only the overall probability is considered in the prior art is overcome.

Description

Technical Field

[0001] The present application relates to the technical field of inventory partitioning, and in particular to an inventory partitioning method and system. Background Art

[0002] In logistics, trade or manufacturing companies, finished products that have been purchased and are ready for sale or have been completed are usually stored in the warehouse in advance for future shipment. Taking the printer industry as an example, the finished product inventory often includes several different models of printing machines, several different models of consumables and accessories, gifts, etc.

[0003] In warehouse management practice, when finished products are put into storage, warehouse managers often put the same type of products together, and different types of products are randomly placed. The obvious defect of this placement method is that different products are often shipped out in the form of product combinations according to certain rules. Therefore, when warehouse managers perform outbound work according to outbound orders, in order to successfully obtain product combinations, they spend a lot of time in the process of searching and moving. For example, if there are different types of products on the same outbound order, if these different types of products are placed in different inventory areas or different shelves, it will inevitably reduce the efficiency of warehouse managers in performing outbound work. Summary of the invention

[0004] In view of the problems in the prior art, the present application proposes an inventory partitioning method and system to solve the problems in the prior art.

[0005] In order to achieve the above technical objectives, the technical solution of this application is as follows:

[0006] This application proposes a stock partitioning method, comprising the following steps:

[0007] S1, query all the delivery orders within μ months from the current time and generate a probability table, which includes the probability of a single product type appearing in all delivery orders and the probability of a combination type of two products forming a binary combination appearing in the delivery order, where μ is a positive integer;

[0008] S2, selecting all single product types Ψ in all outbound orders whose single product type probabilities are not less than a first preset probability threshold, and combining any two product types in all single product types Ψ to form a binary combination set Φ1, taking the intersection of the binary combination set Φ1 and the binary combination in the probability table to generate a binary combination set Φ2;

[0009] S3, selecting all binary combination type sets whose binary combination type probabilities are lower than a second preset probability threshold in the binary combination set Φ2, and filtering out single product types with small probabilities in all binary combination type sets to form a single product type set W;

[0010] S4, taking the intersection Ψn W of all the single product types Ψ and the single product type set W; calculating the complement Ω of the intersection ΨnW in all the single product types Ψ, that is, complement Ω=Ψ / (Ψn W), and the number of single product types in the complement Ω is Num(Ω);

[0011] S5, divide the warehouse area into Num(Ω) sub-areas with all the product types in the remainder set Ω as the center.

[0012] Optionally, μ is a positive integer not less than 12.

[0013] Optionally, the first preset probability threshold is 0.6-0.7.

[0014] Optionally, the second preset probability threshold is 0.2-0.3.

[0015] Optionally, Num(Ω) is a positive integer not less than 1.

[0016] The present application also proposes a product inventory partitioning system, comprising:

[0017] The product probability module is used to query all the delivery orders within μ months from the current time and generate a probability table, which includes the probability of a single product type appearing in all delivery orders and the probability of a combination type of two products forming a binary combination appearing in all delivery orders, where μ is a positive integer;

[0018] A binary set module is used to select all single product types Ψ in all outbound orders whose single product type probability is not less than a first preset probability threshold, and combine any two product types in all single product types Ψ to form a binary combination set Φ1, and take the intersection of the binary combination set Φ1 and the binary combination in the probability table to generate a binary combination set Φ2;

[0019] A single product set module selects all binary combination type sets whose binary combination type probabilities are lower than a second preset probability threshold in the binary combination set Φ2, and selects single product types with small probabilities in all binary combination type sets to form a single product type set W;

[0020] A remainder module is used to obtain the intersection Ψn W of all the single product types Ψ and the single product type set W; calculate the complement Ω of the intersection Ψn W in all the single product types Ψ, that is, the complement Ω=Ψ / (Ψn W), and the number of single product types in the complement Ω is Num(Ω);

[0021] The partitioning module is used to divide the warehouse area into Num(Ω) sub-areas with all the item types in the complement set Ω as the center.

[0022] The inventory partitioning method provided in the embodiment of the present application can divide the storage area into several sub-areas; since the probability of product combinations formed within a sub-area being released from the warehouse is relatively high, and the probability of product combinations formed between different sub-areas being released from the warehouse is relatively low, the present application places product combinations with high probabilities in a centralized manner, which can greatly improve the efficiency of warehouse managers in performing outbound work. In addition, the present application comprehensively considers the overall probability of product combinations and the individual probability of single products, overcoming the single thinking of only considering the overall probability in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 This is a flow chart of an inventory partitioning method described in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0026] like Figure 1 As shown, in a first aspect of the present application, a method for inventory partitioning includes the following:

[0027] Step S1: query all the delivery orders within μ months from the current time and generate a probability table, which includes the probability of a single product type appearing in all delivery orders and the probability of a combination type of two products forming a binary combination appearing in all orders.

[0028] Among them, there are different types of items in all outbound orders, and there is at least one type of item. Items of the same type are placed in the same space in the warehouse; when there are no less than two types of items, any two types of items are combined to form a binary combination. The item type probability is the frequency of a type of item appearing in the statistical outbound orders. If the frequency is high, it means that the item is shipped many times and is popular. The combination type probability is the probability of two types of items appearing in the same outbound order. If the probability is high, it means that the two types of items are highly correlated and often shipped at the same time, that is, the frequency of simultaneous sales is high. The probability table records all existing item type probabilities and combination type probabilities in all outbound orders in the last μ months; μ is set to 12, that is, the statistical time of all outbound orders is 12 months, which can make the item type probability and combination type probability in the probability table more accurate.

[0029] Assume that there are 8 outbound delivery orders in total, and the specific product types are as shown in Table 1:

[0030]

[0031] Table 1

[0032] According to the distribution of product types of each delivery order, the correlation between product types is calculated to obtain a probability table; among which, the probability of single product type is as follows in Table 2:

[0033] Product Type A B C D E Probability 0.75 0.75 0.625 0.75 0.375

[0034] Table 2

[0035] The probability of the combination type is shown in Table 3:

[0036] Combination Type CD BC AD AC Probability 0.625 0.375 0.625 0.125

[0037] Table 3

[0038] Step S2: select all the single product types Ψ in all the delivery orders whose single product type probability is not less than the first preset probability threshold, and combine any two product types in the said all single product types Ψ to form a binary combination set Φ1, take the intersection of the said binary combination set Φ1 and the binary combination in the probability table to generate a binary combination set Φ2.

[0039] The first preset probability threshold is set to 0.6. All item types Ψ are: A, B, C, D. The binary combination set Φ1 is: AB, AC, AD, BC, BD, CD. Calculate the intersection of the binary combination set Φ1 and the binary combination in the probability table:

[0040] {AB, AC, AD, BC, BD, CD}∩{CD, BC, AD, AC}={AC, AD, BC, CD}, that is, the binary combination set Φ2 is: AC, AD, BC, CD.

[0041] Step S3: select all binary combination type sets in the binary combination set Φ2 whose binary combination type probabilities are lower than a second preset probability threshold, and select single product types with small probabilities from all binary combination type sets to form a single product type set W.

[0042] The second preset probability threshold is set to 0.2. The set of all binary combination types is: AC. In addition, the probability of item type A in AC is greater than the probability of item type C; then the item type set W is: C.

[0043] Step S4: Take the intersection Ψn W of all the single product types Ψ and the single product type set W; calculate the complement Ω of the intersection Ψn W in all the single product types Ψ, that is, the complement Ω=Ψ / (ΨnW), and the number of single product types in the complement Ω is Num(Ω).

[0044] Among them, all single product types Ψ are: A, B, C, D. The single product type set W is: C.

[0045] Calculate the intersection Ψn W of all product types Ψ and the product type set W:

[0046] {A, B, C, D}∩{C}={C};

[0047] Calculate the complement Ω of the intersection Ψn W in all product types Ψ:

[0048] Ω=Ψ / (Ψn W)={A, B, D}, that is, Num(Ω) is 3.

[0049] Step S5: Divide the warehouse area into Num(Ω) sub-areas with all the product types in the remainder set Ω as the center.

[0050] In this embodiment, the warehouse area can be divided into three sub-areas, and item types A, B, and D are placed in the center of the area respectively. Other item types can be placed near item type A in sequence according to the principle of high and low probability of combination types. This product inventory partitioning method can make the product placement in the warehouse more convenient and the outbound efficiency higher.

[0051] In another embodiment, assuming that there are 8 outbound delivery orders, the specific product types are as shown in Table 4:

[0052]

[0053] Table 4

[0054] According to the distribution of product types of each delivery order, the correlation between product types is calculated to obtain a probability table; among which, the probability of single product type is as follows in Table 5:

[0055] Product Type A B C D E Probability 0.875 0.75 0.5 0.625 0.625

[0056] Table 5

[0057] The probability of the combination type is shown in Table 6:

[0058] Combination Type CD BC AD AE Probability 0.625 0.375 0.625 0.125

[0059] Table 6

[0060] In this embodiment, the first preset probability threshold is set to 0.6. All item types Ψ are: A, B, D, E. The binary combination set Φ1 is: AB, AD, AE, BD, BE, DE. Calculate the intersection of the binary combination set Φ1 and the binary combination in the probability table:

[0061] {AB, AD, AE, BD, BE, DE}∩{CD, BC, AD, AE}={AD, AE}, that is, the binary combination set Φ2 is: AD, AE.

[0062] The second preset probability threshold is set to 0.2. The set of all binary combination types is: AE. In addition, the probability of item type A in AE is greater than the probability of item type E; then the item type set W is: E.

[0063] Among them, all single product types Ψ are: A, B, D, E. The single product type set W is: E.

[0064] Calculate the intersection Ψn W of all product types Ψ and the product type set W:

[0065] {A, B, D, E}∩{E}={E};

[0066] Calculate the complement Ω of the intersection Ψn W in all product types Ψ:

[0067] Ω=Ψ / (Ψn W)={A, B, D}, that is, Num(Ω) is 3.

[0068] In this embodiment, the warehouse area can be divided into three sub-areas, and item types A, B, and D are placed in the center of each area. Other item types can be placed near item type A in order according to the probability of the combination type.

[0069] In a second aspect of the present application, a stock partitioning system comprises:

[0070] The product probability module is used to query all the delivery orders within μ months from the current time and generate a probability table, which includes the probability of a single product type appearing in all delivery orders and the probability of a combination type of two products forming a binary combination appearing in all delivery orders, where μ is a positive integer;

[0071] A binary set module is used to select all single product types Ψ in all outbound orders whose single product type probability is not less than a first preset probability threshold, and combine any two product types in all single product types Ψ to form a binary combination set Φ1, and take the intersection of the binary combination set Φ1 and the binary combination in the probability table to generate a binary combination set Φ2;

[0072] A single product set module selects all binary combination type sets whose binary combination type probabilities are lower than a second preset probability threshold in the binary combination set Φ2, and selects single product types with small probabilities in all binary combination type sets to form a single product type set W;

[0073] A remainder module is used to obtain the intersection Ψn W of all the single product types Ψ and the single product type set W; calculate the complement Ω of the intersection Ψn W in all the single product types Ψ, that is, the complement Ω=Ψ / (Ψn W), and the number of single product types in the complement Ω is Num(Ω);

[0074] The partitioning module is used to divide the warehouse area into Num(Ω) sub-areas with all the item types in the complement set Ω as the center.

[0075] In this embodiment, the inventory partitioning system can place product combinations with high probability in corresponding sub-areas, which can greatly improve the efficiency of warehouse managers in performing outbound work.

[0076] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the description of the present application, it should be understood that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0078] In the description of the present application, unless otherwise clearly specified and limited, for example, it can be a fixed connection, a detachable connection, or an integrated connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, for ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0079] It should be noted that, in the description of the present application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0080] The above is a detailed introduction to the scheme provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for inventory partitioning, It is characterized in that The following steps are involved: S1, query all the delivery orders within μ months from the current time and generate a probability table, which includes the probability of a single product type appearing in all delivery orders and the probability of a combination type of two products forming a binary combination appearing in all delivery orders, where μ is a positive integer; S2, selecting all single product types Ψ in all outbound orders whose single product type probabilities are not less than a first preset probability threshold, and combining any two product types in all single product types Ψ to form a binary combination set Φ1, taking the intersection of the binary combination set Φ1 and the binary combination in the probability table to generate a binary combination set Φ2; S3, selecting all binary combination type sets whose binary combination type probabilities are lower than a second preset probability threshold in the binary combination set Φ2, and filtering out single product types with small probabilities in all binary combination type sets to form a single product type set W; S4, taking the intersection Ψn W of all the single product types Ψ and the single product type set W; calculating the complement Ω of the intersection Ψn W in all the single product types Ψ, that is, complement Ω=Ψ / (Ψn W), and the number of single product types in the complement Ω is Num(Ω); S5, divide the warehouse area into Num(Ω) sub-areas with all the product types in the remainder Ω as the center.

2. The inventory partitioning method according to claim 1, Features: The μ is a positive integer not less than 12.

3. The inventory partitioning method according to claim 1, Features: The first preset probability threshold is 0.6-0.

7.

4. The inventory partitioning method according to claim 1, Features: The second preset probability threshold is 0.2-0.

3.

5. The inventory partitioning method according to claim 1, Features: The Num(Ω) is a positive integer not less than 1.

6. A stock partitioning system, It is characterized in that include: The product probability module is used to query all the delivery orders within μ months from the current time and generate a probability table, which includes the probability of a single product type appearing in all delivery orders and the probability of a combination type of two products forming a binary combination appearing in all delivery orders, where μ is a positive integer; A binary set module is used to select all single product types Ψ in all outbound orders whose single product type probability is not less than a first preset probability threshold, and combine any two product types in all single product types Ψ to form a binary combination set Φ1, and take the intersection of the binary combination set Φ1 and the binary combination in the probability table to generate a binary combination set Φ2; A single product set module selects all binary combination type sets whose binary combination type probabilities are lower than a second preset probability threshold in the binary combination set Φ2, and selects single product types with small probabilities in all binary combination type sets to form a single product type set W; A remainder module is used to obtain the intersection Ψn W of all the single product types Ψ and the single product type set W; calculate the complement Ω of the intersection Ψn W in all the single product types Ψ, that is, the complement Ω=Ψ / (Ψn W), and the number of single product types in the complement Ω is Num(Ω); The partitioning module is used to divide the warehouse area into Num(Ω) sub-areas with all the item types in the complement set Ω as the center.

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