CN110020823A - A kind of storehouse assignment algorithm based on K-Means - Google Patents

A kind of storehouse assignment algorithm based on K-Means Download PDF

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CN110020823A
CN110020823A CN201910280617.6A CN201910280617A CN110020823A CN 110020823 A CN110020823 A CN 110020823A CN 201910280617 A CN201910280617 A CN 201910280617A CN 110020823 A CN110020823 A CN 110020823A
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sku
cluster
order
distance
storehouse assignment
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李志军
徐侃
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XINYI TECHNOLOGY Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

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Abstract

The invention discloses a kind of storehouse assignment algorithm based on K-Means, more particularly to logistic storage field, the mode that inventive algorithm combines the position sku Euclidean distance in order and K-Means method, to warehouse article, there are a product multidigits, the problem of which warehouse compartment of the preemption, is resolved after customer order flows into, K-Means is for given sample set, according to the distance between sample size, sample set is divided into k cluster, it allows the point in cluster closely to connect together as far as possible, and makes the distance between cluster big as far as possible.The present invention can allow the sku in " similar " customer order to concentrate on a certain region as far as possible, and the sku in " similarity " low customer order disperses as far as possible, significantly improves compared to random storehouse assignment or according to effect phase direct distribution effects.

Description

A kind of storehouse assignment algorithm based on K-Means
Technical field
The present invention relates to logistic storage technical fields, it is more particularly related to a kind of warehouse compartment based on K-Means Allocation algorithm.
Background technique
With the fast development of e-commerce in recent years, e-commerce is just gradually penetrating into the various aspects of people's life. Commodity can be carried out product multidigit distribution in order to avoid generating hot spot warehouse compartment by warehouse, when customer order inflow warehouse, in order Sku should be assigned to which warehouse compartment in warehouse, the sku of " similar " can be made to be distributed relatively close, and the sku of " difference " is distributed Farther out, so that order vehicle can complete order production in region as small as possible, without regard to generation congestion.
In actual production, when customer order flows into warehouse, storehouse assignment follows priority below:
1. in strict accordance with date of manufacture sequencing；
2. binding warehouse compartment described in kinds of goods (i.e. date of manufacture identical unbundling warehouse compartment is preferential)；
3. sorting according to warehouse compartment, as far as possible preemption same bank bit clear inventory；
Due to excessively having taken a fancy to the date of manufacture, lead to this in order to make a product multidigit of operation equilibrium as far as possible, at falseness A product multidigit.
One warehouse day order volume easily 5-6W be easy to make since the order of client has many characteristics, such as small lot, high frequency time At the part congestion of order vehicle, operation is unbalanced, and customer is more sensitive to express delivery arrival time under business environment, therefore to buyer The storehouse assignment of inflow order more stringent requirements are proposed.
Summary of the invention
In order to overcome the drawbacks described above of the prior art, the embodiment of the present invention provides a kind of warehouse compartment based on K-Means point With algorithm, existing in such a way that the position sku Euclidean distance in order and K-Means method combine to warehouse article One product multidigit, the problem of which warehouse compartment of the preemption, is resolved after customer order flows into, K-Means for given sample set, According to the distance between sample size, sample set is divided into k cluster, the point in cluster is allowed closely to connect together as far as possible, and is allowed Distance between cluster is big as far as possible；The sku in " similar " customer order can be allowed to concentrate on a certain region as far as possible, and " similarity " is low buys Sku in family's order disperses as far as possible, significantly improves compared to random storehouse assignment or according to effect phase direct distribution effects.
To achieve the above object, the invention provides the following technical scheme: a kind of storehouse assignment algorithm based on K-Means, Include the following steps:
S1, the inflow order of buyer is collected by the information of warehouse current commodity and in a period of time, and warehouse is worked as The information of preceding commodity includes commodity skuiPosition；
S2, the order that storehouse assignment is completed is subjected to K-Means cluster, the specific steps are as follows:
S2.1, appropriate k value is chosen according to inflow quantity on order, k is randomly selected in the order that storehouse assignment is completed The position sku calculates other all sku and this k " at a distance from cluster " center " as cluster center；
S2.2, for each sku, be divided into its distance it is nearest " in the cluster where cluster " center ", for new Cluster calculates the new center of each cluster；
S3, imitate the phase in conjunction with commodity, calculate all positions sku and this k in an order " cluster " center " distance it is European away from From with；
Multiple sku place-centrics in S4, calculating step S3 are at a distance from cluster centers all in step S2；
S5, the certain weight of distance in step S3 and step S4 is assigned, the sku in the order is subjected to storehouse assignment；
S6, step S3-S5 is repeated, is completed until the inflow customer order in step S1 traverses.
In a preferred embodiment, in the step S1, commodity skuiPosition be set as N number of, product locations Matrix DiIt is as follows:
Wherein, matrix DiIn, behavior skuiThe position at place indicates that x, y are respectively the transverse and longitudinal coordinate of the sku with x and y.
In a preferred embodiment, in the step S2.1, other all sku and this k " cluster " center " is calculated Distance, using Euclidean distance formula:
Wherein, d is in order in (xim, yim) position skuiWith in (xjn, yjn) position skujThe distance between.
In a preferred embodiment, it in the step S2.2, when calculating the transverse and longitudinal coordinate at new center of new cluster, adopts With the transverse and longitudinal coordinate of the sku in new cluster to be sought to average mode.
In a preferred embodiment, in the step S3, specific steps are as follows: to all sku in an order Location matrix is traversed, and is calculated each sku and is in the case of different location at a distance from sku in other orders and l；
If sku in the orderjLocation matrix DjIn z row, that is, be located at transverse and longitudinal coordinate be (xjz, yjz) the sku imitate the phase Lower than a certain threshold value, then the sku storehouse assignment position is (xjz, yjz)；
For an order, there are distance and matrix L, matrix Ls are as follows:
L=(l1,l2,K,lm)
Wherein, lc(c ∈ [1, m]) be sku in the order be in the distance of a certain position with.
In a preferred embodiment, the step S4 specifically: calculate ldPlace-centric in the case of ∈ L and The Euclidean distance s at the cluster center of step S2d。
In a preferred embodiment, in the step S5, it is certain to assign distance in step S3 and step S4 Weight, i.e. calculating α ld+βsd, wherein α, β ∈ (0,1).
In a preferred embodiment, the step S6 specifically: customer order will be flowed into and press step S3-S5 one by one Storehouse assignment is carried out, is completed until flowing into customer order traversal.
Technical effect and advantage of the invention:
The present invention by the position sku Euclidean distance in order and K-Means method combine in the way of, warehouse article is deposited In a product multidigit, the problem of which warehouse compartment of the preemption, is resolved after customer order flows into, and K-Means is for given sample Collection, according to the distance between sample size, is divided into k cluster for sample set, the point in cluster is allowed closely to connect together as far as possible, and Make the distance between cluster big as far as possible；The sku in " similar " customer order can be allowed to concentrate on a certain region as far as possible, and " similarity " is low Sku in customer order disperses as far as possible, significantly improves compared to random storehouse assignment or according to effect phase direct distribution effects.
Detailed description of the invention
Fig. 1 is overall flow schematic diagram of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.
A kind of storehouse assignment algorithm based on K-Means according to figure 1, includes the following steps:
S1, the inflow order of buyer is collected by the information of warehouse current commodity and in a period of time, and warehouse is worked as The information of preceding commodity includes commodity skuiPosition, commodity skuiPosition be set as N number of, product locations matrix DiIt is as follows:
Wherein, matrix DiIn, behavior skuiThe position at place indicates that x, y are respectively the transverse and longitudinal coordinate of the sku with x and y；
S2, the order that storehouse assignment is completed is subjected to K-Means cluster, the specific steps are as follows:
S2.1, appropriate k value is chosen according to inflow quantity on order, k is randomly selected in the order that storehouse assignment is completed The position sku calculates other all sku and this k and " at a distance from cluster " center ", calculates other all sku and this k as cluster center A " distance of cluster " center ", using Euclidean distance formula:
Wherein, d is in order in (xim, yim) position skuiWith in (xjn, yjn) position skujThe distance between；
S2.2, for each sku, be divided into its distance it is nearest " in the cluster where cluster " center ", for new Cluster, calculates the new center of each cluster, and when calculating the transverse and longitudinal coordinate at new center of new cluster, using by the transverse and longitudinal of the sku in new cluster Coordinate seeks average mode；
S3, imitate the phase in conjunction with commodity, calculate all positions sku and this k in an order " cluster " center " distance it is European away from From with specific steps are as follows:
All sku location matrixs in one order are traversed, calculate each sku be in the case of different location with The distance and l of sku in other orders；
If sku in the orderjLocation matrix DjIn z row, that is, be located at transverse and longitudinal coordinate be (xjz, yjz) the sku imitate the phase Lower than a certain threshold value, then the sku storehouse assignment position is (xjz, yjz)；
For an order, there are distance and matrix L, matrix Ls are as follows:
L=(l1,l2,K,lm)
Wherein, lc(c ∈ [1, m]) be sku in the order be in the distance of a certain position with；
Multiple sku place-centrics in S4, calculating step S3 specially calculate at a distance from cluster centers all in step S2 ldThe Euclidean distance s at the cluster center of place-centric and step S2 in the case of ∈ Ld；
S5, the certain weight of distance in step S3 and step S4, i.e. calculating α l are assignedd+βsd, wherein α, β ∈ (0,1), Then the sku in the order is subjected to storehouse assignment；
S6, step S3-S5 is repeated, is completed until the inflow customer order in step S1 traverses, specifically: buyer will be flowed into Order presses step S3-S5 one by one and carries out storehouse assignment, completes until flowing into customer order traversal.
To make the object, technical solutions and advantages of the present invention clearer, the present invention is done further with reference to the accompanying drawing Description；
As shown in Figure 1, steps are as follows for realization of the invention:
S1, the inflow order of buyer is collected by the information of warehouse current commodity and in a period of time；
S2, the setting of algorithm initial parameter: k=2, effect phase are set as current time+30 days, α=β=0.5；
The order that storehouse assignment is completed is subjected to K-Means cluster；
S3, assume that cluster centre is respectively (1,1.5) and (2,3)；
Assuming that including sku in an order1And sku2, sku1Location matrix be D1:
sku2Location matrix D2:
If the sku in (2,3) position2Guarantor's date is spent less than current time+30 days, then sku2Storehouse assignment position be (2, 3)；Calculate the order distance and matrix L, L=(l1,l2)；
Wherein, l1For in the sku of (1,1) position1Sku with position in (2,3)2Distance and (due to there was only 2 kinds of sku, So "and" can be ignored):
l2For in the sku of (2,2) position1Sku with position in (2,3)2Distance and (due to there was only 2 kinds of sku, so "and" can be ignored):
S4, l is calculateddThe Euclidean distance s at all cluster centers in the place-centric in the case of ∈ L, with step S2d:
Wherein, l1In the case of place-centric are as follows:
l1In the case of place-centric and step (2) cluster center Euclidean distance s1, s1' be respectively as follows:
Wherein, l2In the case of place-centric are as follows:
l2In the case of place-centric and step (2) cluster center Euclidean distance s2, s2' be respectively as follows:
S5, l is calculated separately1In the case of α l1+βs1With α l1+βs1' and l2In the case of α l2+βs2With α l2+βs2', it determines most Whole storehouse assignment scheme；
S6, inflow customer order is traversed according to above-mentioned steps one by one, step S3-S5 is repeated, until in step S1 Inflow customer order traverse complete, customer order traversal completely after, export result.
The several points that should finally illustrate are: firstly, in the description of the present application, it should be noted that unless otherwise prescribed and It limits, term " installation ", " connected ", " connection " shall be understood in a broad sense, can be mechanical connection or electrical connection, be also possible to two Connection inside element, can be directly connected, and "upper", "lower", "left", "right" etc. are only used for indicating relative positional relationship, when The absolute position for being described object changes, then relative positional relationship may change；
Secondly: the present invention discloses in embodiment attached drawing, relates only to the structure being related to the embodiment of the present disclosure, other knots Structure, which can refer to, to be commonly designed, and under not conflict situations, the same embodiment of the present invention and different embodiments be can be combined with each other；
Last: the foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, all in the present invention Spirit and principle within, any modification, equivalent replacement, improvement and so on, should be included in protection scope of the present invention it It is interior.

Claims (8)

1. a kind of storehouse assignment algorithm based on K-Means, characterized by the following steps:
S1, the inflow order of buyer is collected by the information of warehouse current commodity and in a period of time, and the current quotient in warehouse The information of product includes commodity skuiPosition；
S2, the order that storehouse assignment is completed is subjected to K-Means cluster, the specific steps are as follows:
S2.1, appropriate k value is chosen according to inflow quantity on order, k sku is randomly selected in the order that storehouse assignment is completed It sets as cluster center, and calculates other all sku and this k " at a distance from cluster " center "；
S2.2, for each sku, be divided into its distance it is nearest " in the cluster where cluster " center ", for new cluster, meter Calculate the new center of each cluster；
S3, imitate the phase in conjunction with commodity, calculate in an order all positions sku and this k " Euclidean distance of cluster " center " distance and；
Multiple sku place-centrics in S4, calculating step S3 are at a distance from cluster centers all in step S2；
S5, the certain weight of distance in step S3 and step S4 is assigned, the sku in the order is subjected to storehouse assignment；
S6, step S3-S5 is repeated, is completed until the inflow customer order in step S1 traverses.
2. a kind of storehouse assignment algorithm based on K-Means according to claim 1, it is characterised in that: the step S1 In, commodity skuiPosition be set as N number of, product locations matrix DiIt is as follows:
Wherein, matrix DiIn, behavior skuiThe position at place indicates that x, y are respectively the transverse and longitudinal coordinate of the sku with x and y.
3. a kind of storehouse assignment algorithm based on K-Means according to claim 2, it is characterised in that: the step In S2.1, calculate other all sku and this k " at a distance from cluster " center ", using Euclidean distance formula:
Wherein, d is in order in (xim, yim) position skuiWith in (xjn, yjn) position skujThe distance between.
4. a kind of storehouse assignment algorithm based on K-Means according to claim 3, it is characterised in that: the step In S2.2, when calculating the transverse and longitudinal coordinate at new center of new cluster, using the transverse and longitudinal coordinate of the sku in new cluster to be sought to average side Formula.
5. a kind of storehouse assignment algorithm based on K-Means according to claim 4, it is characterised in that: the step S3 In, specific steps are as follows: all sku location matrixs in an order are traversed, each sku is calculated and is in different location feelings Under condition at a distance from sku in other orders and l；
If sku in the orderjLocation matrix DjIn z row, that is, be located at transverse and longitudinal coordinate be (xjz, yjz) the sku effect phase be lower than A certain threshold value, then the sku storehouse assignment position is (xjz, yjz)；
For an order, there are distance and matrix L, matrix Ls are as follows:
L=(l1,l2,K,lm)
Wherein, lc(c ∈ [1, m]) be sku in the order be in the distance of a certain position with.
6. a kind of storehouse assignment algorithm based on K-Means according to claim 5, it is characterised in that: the step S4 Specifically: calculate ldThe Euclidean distance s at the cluster center of place-centric and step S2 in the case of ∈ Ld。
7. a kind of storehouse assignment algorithm based on K-Means according to claim 6, it is characterised in that: the step S5 In, assign the certain weight of distance in step S3 and step S4, i.e. calculating α ld+βsd, wherein α, β ∈ (0,1).
8. a kind of storehouse assignment algorithm based on K-Means according to claim 7, it is characterised in that: the step S6 Specifically: customer order will be flowed into and press step S3-S5 progress storehouse assignment one by one, completed until flowing into customer order traversal.
CN201910280617.6A 2019-04-09 2019-04-09 A kind of storehouse assignment algorithm based on K-Means Pending CN110020823A (en)

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Application publication date: 20190716