Product placement method and system

Abstract

The invention relates to a product placement method and system, and the method comprises the steps: setting single items with the single item type probability not lower than a probability threshold value as central positions, dividing a storage region into a plurality of central regions, and sequentially placing the single items into nearest vacancies corresponding to the central positions according to a highest probability principle to form a plurality of sub-regions, the ex-warehouse probability of the product combinations formed in the sub-regions is relatively high, and the ex-warehouse probability of the product combinations formed among the different sub-regions is relatively low, so that the sub-regions with the high probability are placed in a centralized manner, and the ex-warehouse execution efficiency of a warehouse manager is 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] This application relates to the technical field of inventory management, and particularly to a product placement method and system. Background Art

[0002] In logistics and trade or manufacturing enterprises, finished products that have been purchased and are awaiting sale or production completion are usually stored in a warehouse in advance for future outbound shipments. Taking the printer industry as an example, finished product inventory often includes several printers models with different types, several types of consumables and accessories with different models, gifts, etc.

[0003] In warehouse management practice, when finished products are warehoused, warehouse administrators often place the same type of products together and randomly place different types of products. The obvious defect of this placement method is that different products often leave the warehouse in the form of product combinations according to certain rules. Therefore, when warehouse administrators perform the outbound work according to the outbound order, in order to successfully obtain the product combination, they spend a lot of time in the search and movement process. For example, if there are different types of products on the same outbound order and these different types of products are placed in different inventory areas or on different shelves, it will inevitably reduce the efficiency of warehouse administrators when performing the outbound work.

[0004] Some manufacturers introduce information systems to improve efficiency. They first count which types of products have left the warehouse in combination historically and calculate the probability of their combination existence, and then make a two-dimensional product column combination probability table to determine the probability of any two products appearing in combination. Therefore, when manufacturers obtain finished products, the problem they face is how to place m products into n shelf vacancies so that the sum of the probabilities of the products placed and their adjacent products leaving the warehouse in combination is maximized. This method only considers the sum of the probabilities of product combinations leaving the warehouse, but ignores the situation that there may be common single products that are frequently shipped out in different product combinations or general-purpose products that meet all or most of the outbound order listings. In other words, when placing finished products, not only the overall probability of product combinations but also the individual probability of single products should be considered. Moreover, this method fails to consider the probability of products placed on non-empty shelves and the products to be placed on the vacant shelves appearing in combination. Summary of the Invention

[0005] In view of the problems in the prior art, this application proposes a product placement method to solve the problems in the prior art.

[0006] To achieve the above technical objectives, the technical solution of this application is as follows:

[0007] This application proposes a product placement method, including the following steps:

[0008] S1. Divide the inventory area into n sub - areas. Among them, the probability that single - item types belonging to the same sub - area are shipped out in a binary combination is not less than the preset first threshold P1, and the probability that single - item types belonging to different sub - areas are shipped out in a binary combination is lower than the preset second threshold P2, where n is a positive integer;

[0009] S2. Sort the single - item types to be placed in descending order of the shipping - out probability, select the first n single - item types whose probabilities are not less than the preset third threshold P3, and place the selected single - item types at the central positions of the n sub - areas. Among them, the remaining single - item types θ = Ψ / φ, where Ψ is all single - item types and φ is the selected single - item types;

[0010] S3. Select the single - item type with the highest probability among the remaining single - item types θ, form a binary combination with the n single - item types already placed at the central positions, and calculate the binary - combination probability;

[0011] S31. If there is a unique binary - combination probability not less than the preset first threshold P1, place the single - item type in the sub - area pointed to by the binary combination;

[0012] S32. If there are at least two binary - combination probabilities not less than the preset first threshold P1, place the single - item type in one of the sub - areas where there is a single - item type that forms a binary - combination probability not less than the preset second threshold P2 with the single - item type;

[0013] S33. If there is no binary - combination probability not less than the preset first threshold P1, increase the number of sub - areas by 1, and place the single - item type at the central position of the new sub - area;

[0014] S4. Continue to execute S3 - S33 until all single - item types are placed in the inventory area.

[0015] Optionally, n≥3.

[0016] Optionally, the preset first threshold P1 is 0.6 - 0.7.

[0017] Optionally, the preset second threshold P2 is 0.2 - 0.3.

[0018] Optionally, the preset third threshold P3 is 0.5 - 0.55.

[0019] Optionally, step S32 includes:

[0020] S321. If there are m binary - combination probabilities not less than the preset first threshold P1, then the m sub - areas are φi, where i = 1, 2, 3…m;

[0021] S322. Reorder the m sub-regions φi based on the single-item type at the central position of the sub-region φi in the order of decreasing single-item type probability to form a set family π, i.e., π = {φi; i = 1, 2... m}.

[0022] S323. Select the element φj with the highest probability in the set family π. If there exists a single-item type y1 ∈ φj and P(x, y1) > P1, then place x into φj; if not, take φ(j + 1) and continue to judge. When there still exists a single-item type y2 ∈ φj and P(x, y2) < P2, then increment the number of the sub-regions by 1 and place x at the central position of the new sub-region, where x is the single-item type to be placed.

[0023] Optionally, the step S4 includes:

[0024] S41. Place the unplaced single-item types near the placed single-item types in the order of the nearest west side, the nearest east side, the nearest south side, and the nearest north side.

[0025] Optionally, when the nearest west-side vacancy, the nearest east-side vacancy, the nearest south-side vacancy, or the nearest north-side vacancy at the first central position of the placement area is at the boundary of the placement area, the boundary of the placement area extends eastward, westward, southward, or northward by itself.

[0026] The present application also provides a product placement system, including:

[0027] A partitioning module, configured to partition an inventory area into n sub-regions, where the probability of the single-item types belonging to the same sub-region being shipped out in a binary combination is not lower than a preset first threshold P1, and the probability of the single-item types belonging to different sub-regions being shipped out in a binary combination is lower than a preset second threshold P2, and n is a positive integer;

[0028] A sorting module, configured to sort the single-item types to be placed in the order of decreasing shipping probability, select the first n single-item types with probabilities not lower than a preset third threshold P3, and place the selected single-item types at the central positions of the n sub-regions, where the remaining single-item types θ = Ψ / φ, Ψ is the set of all single-item types, and φ is the selected single-item types;

[0029] A placement module, configured to select the single-item type with the highest probability in the remaining single-item types θ, form a binary combination with the n single-item types placed at the central positions, and calculate the binary combination probability;

[0030] A first placement sub-module, configured to, if there exists a unique binary combination with a probability not lower than the preset first threshold P1, place the single-item type into the sub-region pointed to by the binary combination;

[0031] Placement sub-module two, which is used to place the single product type into one of the sub-regions if there are at least two binary combination probabilities not lower than the preset first threshold P1, and there is a single product type in the sub-region that forms a binary combination probability not lower than the preset second threshold P2 with the single product type;

[0032] Placement sub-module three, which is used to add 1 to the number of the sub-regions and place the single product type at the center position of the new sub-region if there is no binary combination probability not lower than the preset first threshold P1;

[0033] The loop module continues to execute S3 - S33 until all single product types are placed in the inventory area.

[0034] A product placement method provided by an embodiment of the present application sets the single products with single product type probabilities not lower than the preset probability threshold as the center positions respectively, so the inventory area is divided into several central areas, and the single products are placed in the nearest empty positions corresponding to the central positions in sequence according to the principle of the highest probability, forming several sub-regions. Since the probability of the product combinations formed inside the sub-regions for outbound is relatively high, and the probability of the product combinations formed between different sub-regions for outbound is relatively low, the present application concentrates the product combinations with high probabilities, greatly improving the efficiency of the warehouse administrator in performing the outbound work. In addition, the present application comprehensively considers the overall probability of the product combinations and the individual probability of the single products, overcoming the single thinking of only considering the overall probability in the prior art. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 It is a flowchart of a product placement method described in an embodiment of the present application. Detailed Embodiments

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0038] As Figure 1 shown, in the first aspect of the present application, a product placement method includes the following:

[0039] Step S1: Divide the inventory area into n sub - areas. Among them, the probability that the single - product types belonging to the same sub - area are shipped out in binary combinations is not less than the preset first threshold P1, and the probability that the single - product types belonging to different sub - areas are shipped out in binary combinations is lower than the preset second threshold P2. n is a positive integer.

[0040] Among them, the probability that the single - product types in the same sub - area are shipped out in binary combinations is much greater than the probability that the single - product types in different sub - areas are shipped out in binary combinations. Therefore, in this application, the preset first threshold P1 can be set to 0.6, the preset second threshold P2 can be set to 0.2, and n is 3. For the convenience of understanding:

[0041] Suppose there are 8 outbound orders in total, and the specific product types are as shown in Table 1 below:

[0042]

[0043] Table 1

[0044] According to the product - type distribution of each outbound order, calculate the correlation between product types to obtain a probability form. Among them, the single - product type probabilities are as shown in Table 2 below:

[0045] Single item type A B C D E Probability 0.75 0.75 0.625 0.75 0.375

[0046] Table 2

[0047] The combination - type probabilities are as shown in Table 3 below:

[0048] Combination type CD BC AC AE BE DE Probability 0.625 0.375 0.125 0.25 0.25 0.125

[0049] Table 3

[0050] Step S2: Sort the single - product types to be placed in descending order of outbound probability, select the first n single - product types with probabilities not less than the preset third threshold P3, and place the selected single - product types at the central positions of the n sub - areas. Among them, the remaining single - product types θ = Ψ / φ, where Ψ is the set of all single - product types and φ is the set of the selected single - product types.

[0051] Among them, the third threshold P3 is set to 0.5, and the first 3 single - product types with probabilities not less than P3 are: A, B, D. Three sub - areas are formed with single - product type A, single - product type B, and single - product type D as the centers respectively. Ψ = {A, B, C, D, E}, φ = {A, B, D}, θ = {C, E}.

[0052] Among them, θ = Ψ / φ means taking the complement set of the set composed of the selected single - product types from the set composed of all single - product types.

[0053] Step S3: Select the single - product type with the highest probability among the remaining single - product types θ, form binary combinations with the n single - product types placed at the central positions, and calculate the binary - combination probabilities.

[0054] Among them, the single product type with the highest probability in θ is C. The single product type C forms binary combinations with the single product types A, B, and D respectively: AC, BC, CD, and their probabilities are calculated respectively:

[0055] P(AC) = 0.125, P(BC) = 0.375, P(CD) = 0.625.

[0056] Step S31, if there is a unique binary combination with a probability not lower than the preset first threshold P1, then place the single product type into the sub-region pointed to by the binary combination;

[0057] Step S32, if there are at least two binary combinations with probabilities not lower than the preset first threshold P1, then place the single product type into one of the sub-regions, and there is a single product type in the sub-region that forms a binary combination with the single product type with a probability not lower than the preset second threshold P2;

[0058] Step S321, if there are m binary combinations with probabilities not lower than the preset first threshold P1, then the m sub-regions are φi, where i = 1, 2, 3... m;

[0059] Step S322, based on the single product type at the central position of the sub-region φi, reorder the m sub-regions φi in descending order of the single product type probability to form a set family π, that is, π = {φi; i = 1, 2... m};

[0060] Step S323, select the element φj with the highest probability in the set family π. If there exists a single product type y1 ∈ φj and P(x, y1) > P1, then place x into φj; if not, take φ(j + 1) and continue to judge. When there still exists a single product type y2 ∈ φj and P(x, y2) > P2, then increase the number of the sub-regions by 1 and place x at the central position of the new sub-region, where x is the single product type to be placed;

[0061] Step S33, if there is no binary combination with a probability not lower than the preset first threshold P1, then increase the number of the sub-regions by 1 and place the single product type at the central position of the new sub-region;

[0062] Among them, P(CD) > P1, then place the single product type C into the sub-region with the single product type D at the central position.

[0063] Step S4, continue to execute steps S3 - S33 until all single product types are placed into the inventory area.

[0064] Among them, the remaining single product type θ is E. The single product type E forms binary combinations with the single product types A, B, and D respectively: AE, BE, DE, and their probabilities are calculated respectively:

[0065] P(AE) = 0.25, P(BE) = 0.25, P(DE) = 0.125. Since P(AE), P(BE), and P(DE) are all lower than P1, the number of sub-regions is 4, and the single product type E is placed at the center position of the new sub-region.

[0066] In another embodiment, assume there are 8 outgoing delivery orders in total, and the specific product types are as shown in Table 4 below:

[0067]

[0068]

[0069] Table 4

[0070] According to the product type distribution of each outgoing delivery order, calculate the correlation between product types to obtain a probability form; among them, the single product type probabilities are as shown in Table 5 below:

[0071] Single item type A B C D E Probability 0.875 0.75 0.5 0.625 0.5

[0072] Table 5

[0073] The combined type probabilities are as shown in Table 6:

[0074] Combination type CD BC AC AE BE DE Probability 0.625 0.625 0.125 0.125 0.125 0.125

[0075] Table 6

[0076] In this embodiment, the third threshold P3 is set to 0.5. The first 3 single product types with probabilities not lower than P3 are: A, B, D. Three sub-regions are formed with single product type A, single product type B, and single product type D as the centers respectively. Ψ = {A, B, C, D, E}, φ = {A, B, D}, θ = {C, E}.

[0077] Among them, the single product type with the highest probability in θ is C. The single product type C forms binary combinations with single product type A, single product type B, and single product type D respectively: AC, BC, CD, and calculate their probabilities respectively:

[0078] P(AC) = 0.125, P(BC) = 0.625, P(CD) = 0.625.

[0079] Among them, since there are 2 binary combination probabilities not lower than the preset first threshold P1, the sub-regions φ1 and φ2 can be obtained as the binary combination BC and the binary combination CD.

[0080] Taking the single-item type B in the binary combination BC and the single-item type D in the binary combination CD as benchmarks respectively, sort them in the order of the probability of the single-item type from high to low. The probability of the single-item type B is greater than the probability of the single-item type D. Therefore, the set family π = {φ1, φ2}, the central position of the sub-region φ1 is B, and the central position of the sub-region φ2 is D. Thus, select the sub-region φ1 with the central position B.

[0081] Since there is a probability that P(BC) is greater than P1, C is placed in the sub-region φ1.

[0082] Similarly, the remaining single-item type θ is E. The single-item type E forms binary combinations with the single-item types A, B, and D respectively: AE, BE, and DE, and calculate their probabilities respectively:

[0083] P(AE) = 0.125, P(BE) = 0.125, P(DE) = 0.125. P(AE), P(BE), and P(DE) are all lower than P2. Then the number of sub-regions is 4, and the single-item type E is placed at the central position of the new sub-region.

[0084] In one embodiment, the single-item types that have not been placed are placed near the single-item types that have been placed in the order of priority of west, east, south, and north.

[0085] In this embodiment, according to the requirements of the normal warehouse out-of-stock, usually the nearest west side of the central position is the most convenient orientation for the warehouse keeper, and the nearest east side, nearest south side, and nearest north side are the second, third, and fourth convenient orientations in turn, so as to further improve the out-of-stock efficiency.

[0086] In one embodiment, when the nearest west-side vacancy, nearest east-side vacancy, nearest south-side vacancy, or nearest north-side vacancy of the first central position of the placement area is at the boundary of the placement area, the boundary of the placement area extends eastward, westward, southward, or northward by itself.

[0087] In this embodiment, when the nearest west-side vacancy, nearest east-side vacancy, nearest south-side vacancy, and nearest north-side vacancy of the first central position of the placement area are at the boundary of the placement area, extend the placement area in the most adjacent direction, and the single item can be placed in the vacancy of the most adjacent shelf.

[0088] In the second aspect of the present application, a product placement system includes:

[0089] A zoning module for dividing the inventory area into n sub-regions, where the probability of the single-item types belonging to the same sub-region to be out of stock in binary combinations is not lower than a preset first threshold P1, and the probability of the single-item types belonging to different sub-regions to be out of stock in binary combinations is lower than a preset second threshold P2, and n is a positive integer;

[0090] A sorting module, configured to sort the types of single items to be placed in descending order of the outbound probability, select the top n types of single items with probabilities not lower than a preset third threshold P3, and place the selected types of single items at the central positions of the n sub-regions, where the remaining types of single items θ = Ψ / φ, Ψ is the set of all types of single items, and φ is the selected types of single items;

[0091] A placement module, configured to select the type of single item with the highest probability from the remaining types of single items θ, form a binary combination with the n types of single items already placed at the central positions, and calculate the binary combination probability;

[0092] A placement sub-module one, configured to, if there is a unique binary combination with a probability not lower than a preset first threshold P1, place the type of single item in the sub-region pointed to by the binary combination;

[0093] A placement sub-module two, configured to, if there are at least two binary combinations with probabilities not lower than a preset first threshold P1, place the type of single item in one of the sub-regions, where there is a type of single item in the sub-region that forms a binary combination with the type of single item with a probability not lower than a preset second threshold P2;

[0094] A placement sub-module three, configured to, if there is no binary combination with a probability not lower than a preset first threshold P1, increase the number of the sub-regions by 1, and place the type of single item at the central position of the new sub-region;

[0095] A loop module, continue to execute S3 - S33 until all types of single items are placed in the inventory area.

[0096] In this embodiment, the above placement method can be adopted by combining software and hardware, which can greatly improve the outbound efficiency of products, reduce the work intensity of warehouse management personnel, and save production costs.

[0097] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] 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 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 construed as a limitation to the present application.

[0099] In the description of the present application, unless otherwise clearly specified and defined, for example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements or the interaction relationship between two elements. Unless otherwise clearly defined, for those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0100] 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 actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the said element.

[0101] The above has introduced in detail the solutions provided by the embodiments of the present application. Specific examples are used herein to elaborate the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A product placement method, characterized in that, it includes the following steps: S1. Divide the inventory area into n sub - areas. Among them, the probability of the single - product types belonging to the same sub - area being shipped out in a binary combination is not less than the preset first threshold P1, and the probability of the single - product types belonging to different sub - areas being shipped out in a binary combination is lower than the preset second threshold P2, where n is a positive integer; S2. Sort the single - product types to be placed in descending order of the shipping - out probability, select the first n single - product types with probabilities not less than the preset third threshold P3, and place the selected single - product types at the central positions of the n sub - areas. Among them, the remaining single - product types θ = Ψ / φ, where Ψ is all single - product types and φ is the selected single - product types; S3. Select the single - product type with the highest probability among the remaining single - product types θ, form a binary combination with the n single - product types already placed at the central positions, and calculate the binary - combination probability; S31. If there is a unique binary - combination probability not less than the preset first threshold P1, place the single - product type in the sub - area pointed to by the binary combination; S32. If there are at least two binary - combination probabilities not less than the preset first threshold P1, place the single - product type in one of the sub - areas where there is a single - product type that forms a binary - combination probability not less than the preset second threshold P2 with the single - product type; S33. If there is no binary - combination probability not less than the preset first threshold P1, increase the number of sub - areas by 1, and place the single - product type at the central position of the new sub - area; S4. Continue to execute S3 - S33 until all single - product types are placed in the inventory area.

2. The product placement method according to claim 1, characterized in that: n≥3.

3. The product placement method according to claim 1, characterized in that: The preset first threshold P1 is 0.6 - 0.

7.

4. The product placement method according to claim 1, characterized in that: The preset second threshold P2 is 0.2 - 0.

3.

5. The product placement method according to claim 1, characterized in that: The preset third threshold P3 is 0.5 - 0.

55.

6. The product placement method according to claim 1, characterized in that: The step S32 includes: S321. If there are m binary - combination probabilities not less than the preset first threshold P1, then the m sub - areas are φi, where i = 1, 2, 3…m; S322. Based on the single - product types at the central positions of the sub - areas φi, re - sort the m sub - areas φi in descending order of the single - product - type probability to form a set family π, that is, π = {φi; i = 1, 2…m}; S323. Select the element φj with the highest probability in the set family π. If there exists a single - product type y1∈φj and P(x, y1) > P1, then place x in φj; if not, take φ(j + 1) and continue to judge. When there still exists a single - product type y2∈φj and P(x, y2) < P2, increase the number of sub - areas by 1, and place x at the central position of the new sub - area, where x is the single - product type to be placed.

7. The product placement method according to claim 1, It is characterized in that: The step S4 includes: S41. Place the single-item types that have not been placed near the placed single-item types in the order of the nearest west side, the nearest east side, the nearest south side, and the nearest north side.

8. The product placement method according to claim 1, It is characterized in that: When the nearest west-side vacancy, the nearest east-side vacancy, the nearest south-side vacancy, or the nearest north-side vacancy at the first central position of the placement area is at the boundary of the placement area, the boundary of the placement area extends eastward, westward, southward, or northward by itself.

9. A product placement system, It is characterized in that It includes: A zoning module for dividing the inventory area into n sub-areas, where the probability of the single-item types belonging to the same sub-area being shipped out in a binary combination is not less than a preset first threshold P1, and the probability of the single-item types belonging to different sub-areas being shipped out in a binary combination is lower than a preset second threshold P2, and n is a positive integer; A sorting module for sorting the single-item types to be placed in descending order of the shipping probability, selecting the first n single-item types with probabilities not less than a preset third threshold P3, and placing the selected single-item types at the central positions of the n sub-areas, where the remaining single-item types θ = Ψ / φ, Ψ is all single-item types, and φ is the selected single-item types; A placement module for selecting the single-item type with the highest probability among the remaining single-item types θ, forming a binary combination with the n single-item types placed at the central positions, and calculating the binary combination probability; A first placement sub-module for placing the single-item type in the sub-area pointed to by the binary combination if there is a unique binary combination probability not less than the preset first threshold P1; A second placement sub-module for placing the single-item type in one of the sub-areas if there are at least two binary combination probabilities not less than the preset first threshold P1, and there is a single-item type in the sub-area that forms a binary combination probability not less than the preset second threshold P2 with the single-item type; A third placement sub-module for adding 1 to the number of the sub-areas and placing the single-item type at the central position of the new sub-area if there is no binary combination probability not less than the preset first threshold P1; A loop module for continuing to execute S3 - S33 until all single-item types are placed in the inventory area.

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