CN109359905B - Automatic unmanned warehouse goods space allocation method and device and storage medium - Google Patents

Automatic unmanned warehouse goods space allocation method and device and storage medium Download PDF

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CN109359905B
CN109359905B CN201811075595.1A CN201811075595A CN109359905B CN 109359905 B CN109359905 B CN 109359905B CN 201811075595 A CN201811075595 A CN 201811075595A CN 109359905 B CN109359905 B CN 109359905B
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徐怡
刘则治
吴招富
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Guangdong Fitkits Technology Co ltd
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Abstract

The invention relates to an automatic unmanned warehouse goods space allocation method, device and storage medium. The method comprises the following steps: step 1, obtaining COI values of goods on all goods shelves in an automatic unmanned warehouse and running time of unmanned vehicles for transporting the goods from warehouse entry and exit places to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time; step 2, obtaining historical order information, and determining a second objective optimization function according to Euclidean distances among multiple goods on different shelves in the historical order information; step 3, determining a cargo space allocation scheme according to the first objective optimization function and the second objective optimization function; and 4, outputting the goods space allocation scheme. The technical scheme of the invention can improve the overall operation efficiency of the automatic unmanned storehouse and avoid corresponding resource waste.

Description

Automatic unmanned warehouse goods space allocation method and device and storage medium
Technical Field
The invention relates to the technical field of unmanned warehouses, in particular to a method and a device for automatically distributing goods positions of an unmanned warehouse and a storage medium.
Background
With the development of the e-commerce, higher requirements are put on goods distribution, and in recent years, an automatic unmanned warehouse is also appeared. In the automatic unmanned warehouse, different goods are placed on different goods shelves, after order information is received, an unmanned vehicle or AGV automatically drives to a corresponding goods position, and after goods taking is completed and the goods are placed on a vehicle body tray, the goods are carried to return to the warehouse-in and warehouse-out place. However, if the allocation of the goods space is not reasonable, that is, the corresponding goods are not present on the proper goods shelf, the working efficiency of the unmanned vehicle is reduced, and further, the operating efficiency of the whole unmanned storehouse is reduced. The current goods allocation is mainly carried out manually at random, so that the efficiency of the automatic unmanned warehouse in the operation process is low, and certain resource waste condition exists.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an automatic unmanned warehouse goods space allocation method, device and storage medium.
In a first aspect, the invention provides an automatic unmanned storehouse cargo space allocation method, which comprises the following steps:
step 1, obtaining COI values of goods on all goods shelves in an automatic unmanned warehouse and running time of unmanned vehicles for transporting the goods from warehouse entry and exit places to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time.
And 2, acquiring historical order information, and determining a second objective optimization function according to Euclidean distances among a plurality of goods on different shelves in the historical order information.
And 3, determining a cargo space allocation scheme according to the first objective optimization function and the second objective optimization function.
And 4, outputting the goods space allocation scheme.
In a second aspect, the present invention provides an automated unmanned storehouse cargo space allocation apparatus, the apparatus comprising:
the first processing module is used for acquiring COI values of goods on all goods shelves in the automatic unmanned warehouse and the running time of an unmanned vehicle for transporting the goods from an in-out place to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time.
And the second processing module is used for acquiring historical order information and determining a second target optimization function according to Euclidean distances among a plurality of types of goods on different shelves in the historical order information.
And the third processing module is used for determining a cargo space allocation scheme according to the first objective optimization function and the second objective optimization function.
And the output module is used for outputting the cargo space allocation scheme.
In a third aspect, the present invention provides an automated unmanned storehouse cargo space allocation apparatus, the apparatus comprising a memory and a processor; the memory for storing a computer program; the processor is configured to implement the automated unmanned warehouse location allocation method as described above when executing the computer program.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements an automated unmanned warehouse slot allocation method as described above.
The automatic unmanned warehouse goods location allocation method, the automatic unmanned warehouse goods location allocation device and the storage medium have the advantages that relevant historical data can be called from a warehouse management system database of the automatic unmanned warehouse, COI values of goods on all goods shelves in the warehouse, namely, Order volume indexes (Cube-Per-Order Index) and time of the unmanned vehicle running to different goods shelves are obtained, and a first target optimization function is determined based on the two parameters so as to optimize the total running time of the unmanned vehicle. Meanwhile, since one piece of order information generally includes a plurality of kinds of commodity information, that is, SKUs (Stock Keeping Unit), different commodities can be located on different shelves, and a second objective optimization function is determined according to the euclidean distance between the commodities so as to optimize the total travel distance of the unmanned vehicle. The first objective optimization function and the second objective optimization function can enable the unmanned vehicle for taking goods in the warehouse to take time factors and distance factors into consideration when the unmanned vehicle runs, namely energy consumption factors, when the future goods cargo space is allocated, so that the overall running efficiency of the automatic unmanned warehouse is improved, and corresponding resource waste is avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an automated unmanned warehouse cargo space allocation method according to an embodiment of the present invention;
fig. 2 is a block diagram of an automated unmanned warehouse cargo space allocation apparatus according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, an automated unmanned warehouse cargo space allocation method according to an embodiment of the present invention includes:
step 1, obtaining COI values of goods on all goods shelves in an automatic unmanned warehouse and running time of unmanned vehicles for transporting the goods from warehouse entry and exit places to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time.
And 2, acquiring historical order information, and determining a second objective optimization function according to Euclidean distances among a plurality of goods on different shelves in the historical order information.
And 3, determining a cargo space allocation scheme according to the first objective optimization function and the second objective optimization function.
And 4, outputting the goods space allocation scheme.
In this embodiment, the relevant historical data may be called from the warehouse management system database of the automated unmanned warehouse, the COI values of the items on all shelves in the warehouse, that is, the Order volume Index (Cube-Per-Order Index), and the time for the unmanned vehicle to run to different shelves are obtained, and the first objective optimization function is determined based on the above two parameters, so as to optimize the total running time of the unmanned vehicle. Meanwhile, since one piece of order information generally includes a plurality of kinds of commodity information, that is, SKUs (Stock Keeping Unit), different commodities can be located on different shelves, and a second objective optimization function is determined according to the euclidean distance between the commodities so as to optimize the total travel distance of the unmanned vehicle. The first objective optimization function and the second objective optimization function can enable the unmanned vehicle for taking goods in the warehouse to take time factors and distance factors into consideration when the unmanned vehicle runs, namely energy consumption factors, when the future goods cargo space is allocated, so that the overall running efficiency of the automatic unmanned warehouse is improved, and corresponding resource waste is avoided.
Preferably, the automatic unmanned storehouse comprises I rows and J columns of shelves.
The planar structure of the conventional automated unmanned storehouse is rectangular, so that the shelves are often arranged in an array form in I rows and J columns, wherein any row number can be represented by I, and any column number can be represented by J.
The specific implementation of the step 1 comprises the following steps:
the COI value of the item is determined according to a first formula.
The first formula is: COIij＝Cij/fij。
Wherein, COIijCOI value, C, representing items on row i and column j shelvesijRepresenting the inventory capacity required for the total storage of items on the i-th and j-th shelves, fijIndicating the ex-warehouse frequency of the goods on the ith row and the jth column, I is equal to {1, …, I }, and J is equal to {1, …, J }.
The COI value obtained through the first formula can reflect the ratio of the maximum stock quantity of a certain kind of goods to the turnover rate of the goods, the picking time of the goods can be reduced to a certain extent in the optimization process, and the execution efficiency of the warehouse is improved.
The first objective optimization function is determined according to a second formula.
The second formula is: q ═ tij×COIij。
Wherein, tijThe running time of the unmanned vehicle from the warehouse-in and warehouse-out place to the goods shelf positioned on the ith row and the jth column is shown.
Since the values of I and J are determined by the total number of rows I and the total number of columns J, Q is an array having I × J elements. And since Q is the product of the COI value of the goods and the running time of the unmanned vehicle, the product can reflect the sorting efficiency of the goods and the running time of the unmanned vehicle.
Preferably, the historical order information includes n items.
Due to the rule of full package postal, most consumers often choose to purchase multiple items simultaneously when shopping on the same e-commerce platform, that is, one order usually includes multiple items, that is, multiple SKUs.
The specific implementation of the step 2 comprises the following steps:
determining the second objective optimization function according to a third formula.
The third formula is: l ═ Σg，z∈nlg，z。
Wherein lg，zRepresenting the euclidean distance between any two of the n items.
Since the order includes n items, i.e., n SKUs, they can be made SKUs respectively1，SKU2,…,SKUnEach SKU may not be located on the same shelf. That is, in order to complete the goods in the same order at once, the unmanned vehicle may need to move among a plurality of racks. Because the same goods may be placed on different goods shelves, in order to reduce the total running distance of the unmanned vehicle, the total Euclidean distance between the goods in the order is used as the constraint condition for the movement of the unmanned vehicle through the second objective optimization function, so that the total running distance of the unmanned vehicle can be reduced, the corresponding total energy consumption can be reduced, and unnecessary resource waste can be avoided.
Preferably, the specific implementation of step 3 includes:
and I multiplied by J Q values in the first target optimization function are arranged according to the size sequence, and goods corresponding to the Q values are distributed to goods positions from near to far away from the warehouse entry and exit place according to the sequence from small to large of the Q values.
When the Q values corresponding to the goods on the shelves in different rows and different columns are the same, distributing a plurality of goods with the same Q values to goods positions from near to far away from the warehousing-in and warehousing-out place according to the sequence from small to large of the L values in the second objective optimization function.
Because Q includes I multiplied by J elements, based on the first objective optimization function, the elements, namely a plurality of Q values are arranged according to the size sequence, if the value is smaller, the comprehensive value of the goods picking time and the unmanned vehicle running time is smaller, and the goods corresponding to the Q value with the smaller value are distributed to the goods shelves close to the warehouse-in and warehouse-out place. The total running time of the unmanned vehicle can be reduced as much as possible under a new goods space distribution scheme, the picking time of corresponding goods is considered, and the overall running efficiency of the automatic unmanned warehouse is improved.
If a plurality of identical Q values appear, the total Euclidean distance between the goods in the order comprising the goods can be compared for the goods corresponding to the identical Q values based on the second objective optimization function, if the Euclidean distance is minimum, the running distance of the unmanned vehicle under the distribution scheme is also minimum, and at the moment, the goods corresponding to the minimum total Euclidean distance are distributed to the optional goods shelf closest to the warehousing and ex-warehousing place. The distribution scheme can give consideration to the total running time and distance of the unmanned vehicle during goods taking, effectively improves the overall running efficiency of the automatic unmanned cabin, and reduces unnecessary energy consumption.
As shown in fig. 2, an automatic unmanned storehouse cargo space allocation device according to an embodiment of the present invention includes:
the first processing module is used for acquiring COI values of goods on all goods shelves in the automatic unmanned warehouse and the running time of an unmanned vehicle for transporting the goods from an in-out place to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time.
And the second processing module is used for acquiring historical order information and determining a second target optimization function according to Euclidean distances among a plurality of types of goods on different shelves in the historical order information.
And the third processing module is used for determining a cargo space allocation scheme according to the first objective optimization function and the second objective optimization function.
And the output module is used for outputting the cargo space allocation scheme.
Preferably, the automated unmanned warehouse includes I rows and J columns of shelves, and the first processing module is specifically configured to:
the COI value of the item is determined according to a first formula.
The first formula is: COIij＝Cij/fij。
Wherein, COIijCOI value, C, representing items on row i and column j shelvesijRepresenting the inventory capacity required for the total storage of items on the i-th and j-th shelves, fijIndicating the ex-warehouse frequency of the goods on the ith row and the jth column, I is equal to {1, …, I }, and J is equal to {1, …, J }.
The first objective optimization function is determined according to a second formula.
The second formula is: q ═ tij×COIij。
Wherein, tijThe running time of the unmanned vehicle from the warehouse-in and warehouse-out place to the goods shelf positioned on the ith row and the jth column is shown.
Preferably, the historical order information includes n kinds of goods, and the second processing module is specifically configured to:
determining the second objective optimization function according to a third formula.
The third formula is: l ═ Σg，z∈nlg，z。
Wherein lg，zRepresenting the euclidean distance between any two of the n items.
Preferably, the third processing module is specifically configured to:
and I multiplied by J Q values in the first target optimization function are arranged according to the size sequence, and goods corresponding to the Q values are distributed to goods positions from near to far away from the warehouse entry and exit place according to the sequence from small to large of the Q values.
When the Q values corresponding to the goods on the shelves in different rows and different columns are the same, distributing a plurality of goods with the same Q values to goods positions from near to far away from the warehousing-in and warehousing-out place according to the sequence from small to large of the L values in the second objective optimization function.
In another embodiment of the present invention, an automated unmanned aerial vehicle storage space allocation apparatus includes a memory and a processor. The memory is used for storing the computer program. The processor is configured to implement the automated unmanned warehouse location allocation method as described above when executing the computer program.
In another embodiment of the invention, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the automated unmanned warehouse cargo space allocation method as described above.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example" or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (8)

1. An automated unmanned storehouse cargo space allocation method, the method comprising:
step 1, obtaining COI values of goods on all goods shelves in an automatic unmanned warehouse and running time of unmanned vehicles for transporting the goods from warehouse entry and exit places to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time;
step 2, obtaining historical order information, and determining a second objective optimization function according to Euclidean distances among multiple goods on different shelves in the historical order information;
step 3, determining a cargo space allocation scheme according to the first objective optimization function and the second objective optimization function,
the specific implementation of the step 3 comprises the following steps:
i multiplied by J Q values in the first target optimization function are arranged according to the size sequence, and goods corresponding to the Q values are distributed to goods positions from near to far away from the warehouse entry and exit place according to the sequence from small to large of the Q values;
when the Q values corresponding to the goods on the shelves in different rows and different columns are the same, distributing a plurality of goods with the same Q values to goods positions from near to far away from the warehousing-in and warehousing-out place according to the sequence from small to large of the L values in the second objective optimization function;
and 4, outputting the goods space allocation scheme.
2. The automatic unmanned storehouse cargo space allocation method according to claim 1, wherein the automatic unmanned storehouse comprises I rows and J columns of shelves, and the specific implementation of the step 1 comprises:
determining a COI value for the item according to a first formula,
the first formula is: COIij＝Cij/fij，
Wherein, COIijCOI value, C, representing items on row i and column j shelvesijRepresenting the inventory capacity required for the total storage of items on the i-th and j-th shelves, fijThe warehouse-out frequency of goods on the ith row and the jth column is shown, I belongs to {1, …, I }, and J belongs to {1, …, J };
determining the first objective optimization function according to a second formula,
the second formula is: q ═ tij×COIij，
Wherein, tijThe running time of the unmanned vehicle from the warehouse-in and warehouse-out place to the goods shelf positioned on the ith row and the jth column is shown.
3. The automated unmanned warehouse cargo space allocation method according to claim 2, wherein the historical order information comprises n types of goods, and the step 2 is implemented by:
determining the second objective optimization function according to a third formula,
the third formula is: l ═ Σg，z∈nlg，z，
Wherein lg，zRepresenting the euclidean distance between any two of the n items.
4. An automated unmanned storehouse cargo space allocation device, the device comprising:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for acquiring COI values of goods on all goods shelves in an automatic unmanned warehouse and the running time of an unmanned vehicle for transporting the goods from an in-out place to all the goods shelves respectively, and determining a first target optimization function according to the COI values of the goods and the running time;
the second processing module is used for acquiring historical order information and determining a second target optimization function according to Euclidean distances among a plurality of goods on different shelves in the historical order information;
a third processing module, configured to determine a cargo space allocation plan according to the first objective optimization function and the second objective optimization function, where the third processing module is specifically configured to:
i multiplied by J Q values in the first target optimization function are arranged according to the size sequence, and goods corresponding to the Q values are distributed to goods positions from near to far away from the warehouse entry and exit place according to the sequence from small to large of the Q values;
when the Q values corresponding to the goods on the shelves in different rows and different columns are the same, distributing a plurality of goods with the same Q values to goods positions from near to far away from the warehousing-in and warehousing-out place according to the sequence from small to large of the L values in the second objective optimization function;
and the output module is used for outputting the cargo space allocation scheme.
5. The automated unmanned aerial vehicle storage space allocation device of claim 4, wherein the automated unmanned aerial vehicle storage space comprises a total of I rows and J columns of shelves, and the first processing module is specifically configured to:
determining a COI value for the item according to a first formula,
the first formula is: COIij＝Cij/fij，
Wherein, COIijCOI value, C, representing items on row i and column j shelvesijRepresenting the inventory capacity required for the total storage of items on the i-th and j-th shelves, fijThe warehouse-out frequency of goods on the ith row and the jth column is shown, I belongs to {1, …, I }, and J belongs to {1, …, J };
determining the first objective optimization function according to a second formula,
the second formula is: q ═ tij×COIij，
Wherein, tijThe running time of the unmanned vehicle from the warehouse-in and warehouse-out place to the goods shelf positioned on the ith row and the jth column is shown.
6. The automated unmanned aerial vehicle storage space allocation device of claim 5, wherein the historical order information comprises n items, and the second processing module is specifically configured to:
determining the second objective optimization function according to a third formula,
the third formula is: l ═ Σg，z∈nlg，z，
Wherein lg，zRepresenting the euclidean distance between any two of the n items.
7. An automated unmanned storehouse cargo space allocation device, comprising a memory and a processor;
the memory for storing a computer program;
the processor, when executing the computer program, for implementing the automated unmanned warehouse slot allocation method of any of claims 1 to 4.
8. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the automated unmanned warehouse slot allocation method according to any one of claims 1 to 3.
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