CN114936891A - Hot area planning method and system based on simulated hot area - Google Patents
Hot area planning method and system based on simulated hot area Download PDFInfo
- Publication number
- CN114936891A CN114936891A CN202210347149.1A CN202210347149A CN114936891A CN 114936891 A CN114936891 A CN 114936891A CN 202210347149 A CN202210347149 A CN 202210347149A CN 114936891 A CN114936891 A CN 114936891A
- Authority
- CN
- China
- Prior art keywords
- picking
- target
- batch
- simulated
- warehouse
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0639—Item locations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to a hot zone planning method and a hot zone planning system based on a simulated hot zone, which relate to the technical field of e-commerce warehousing, and the method comprises the following steps: acquiring all target picking batches in a target warehouse and picking batch data corresponding to all the target picking batches within set time; calculating a first time difference according to the picking batch data; the first time difference is the absolute value of the difference between the time of executing the target picking batch in the target warehouse and the time of executing the target picking batch in the preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length; determining a maximum time difference according to the plurality of first time differences; determining a simulated hot area of a preset warehouse corresponding to the maximum time difference; and adjusting the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference. The invention determines the optimal hot area by setting the simulated hot area and calculating, thereby providing a basis for adjusting the commodity layout.
Description
Technical Field
The invention relates to the technical field of e-commerce warehousing, in particular to a hot zone planning method and system based on a simulated hot zone.
Background
The thermodynamic diagram displays a page area where visitors are keen and a geographical area where the visitors are located in a special highlight mode, and is widely applied to various fields such as webpage analysis and traffic analysis due to the remarkable intuitiveness of information. The warehousing field also gradually began to use thermodynamic diagrams to characterize rack warmth in warehouses.
The biggest difference between e-commerce warehousing and traditional warehousing is that: the e-commerce warehouse takes the performance of e-commerce orders as a core, has high performance timeliness requirements, and hot-zone optimization of e-commerce warehouse is an effective means for improving the performance efficiency of e-commerce warehouse. However, should it be planned how many SKUs (SKUs are physically indivisible units of minimal inventory) for different merchants entering the hot zone, are 50% or 30% of the total number of SKUs? Or 20%? No method or tool is currently available on the market to provide such decision-making basis to the merchant.
Disclosure of Invention
The invention aims to provide a hot zone planning method and system based on a simulated hot zone, which determine the optimal hot zone by setting the simulated hot zone to carry out goods picking calculation, thereby providing a basis for commodity layout adjustment and saving manpower and material resources.
In order to achieve the purpose, the invention provides the following scheme:
a hot zone planning method based on simulated hot zones, the hot zone planning method comprising:
acquiring all target picking batches in a target warehouse and picking batch data corresponding to all the target picking batches within set time; the picking batch data comprises at least a picking path length in the target picking batch; the picking path length is the value of the number of shelves from the picking starting point to the picking end point;
calculating a first time difference according to the picking batch data; the first time difference is an absolute value of a difference between a time at which the target picking batch is performed at the target warehouse and a time at which the target picking batch is performed at a preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length;
determining a maximum time difference according to a plurality of the first time differences;
determining a simulated hot zone of a preset warehouse corresponding to the maximum time difference according to the maximum time difference;
and adjusting the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference.
Optionally, the picking batch data further comprises the order quantity and picking duration in the target picking batch;
the calculating the first time difference according to the picking batch data specifically comprises:
calculating the average picking time length of the actually measured orders according to the picking time length and the order number;
obtaining the time length of picking the goods on the simulation order;
calculating the time saving of the average picking of the simulated hot area orders according to the average picking time of the measured orders and the average picking time of the simulated orders;
and calculating a first time difference according to the simulated hot area order average picking saving time and the simulated hot area covering order number.
Optionally, the calculating process of the simulated order picking duration specifically includes:
determining a first target picking batch group according to picking path lengths of a plurality of target picking batches and simulated hot areas of the preset warehouse; the first target picking lot group comprises a plurality of first target picking lots; the first target picking batch is a target picking batch for which picking operations can be performed within a simulated hot zone of the pre-determined warehouse;
calculating the total order number of the first target picking batch group;
calculating the total time length of the first target picking batch group for completing picking;
and calculating the average picking time length of the simulation order according to the total order number and the total time length.
Optionally, the process of calculating the number of orders covered by the simulated hot zone specifically includes:
calculating the number of SKUs in the simulated hot zone according to a formula N (the number of bin positions of the average single shelf is 0.7);
determining the number of coverage orders of the simulated hot area according to the number of the SKUs in the simulated hot area and a TopN-SKU order coverage table; the TopN-SKU order cover table is used for representing the corresponding relation between the TopN occupancy and the cover order number in the simulation hot area; the TopN occupancy rate is the proportion of the number of orders completed by N SKUs to the total number of orders; n SKUs are the SKUs that are N top in rank of the heat of placement within the simulated hotspot.
Optionally, the acquiring all target picking batches in the target warehouse within the set time specifically includes:
acquiring all initial picking batches in a target warehouse within set time;
determining a first benchmark order quantity based on a mode principle according to the order quantities of all the initial picking batches;
removing the initial picking batches meeting a first preset condition to determine a plurality of intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first reference order quantity;
acquiring the picking duration of the intermediate picking batch;
removing the intermediate picking batches meeting a second preset condition to determine all target picking batches in a target warehouse; the second preset condition is that the picking time of the middle picking batch does not meet the set time requirement.
In order to achieve the purpose, the invention also provides the following technical scheme:
a thermal zone planning system based on simulated thermal zones, the thermal zone planning system comprising:
the picking batch data determining module is used for acquiring all target picking batches in the target warehouse within set time and picking batch data corresponding to all the target picking batches; the picking batch data comprises at least a picking path length in the target picking batch; the picking path length is the value of the number of shelves from the picking starting point to the picking end point;
the first time difference calculating module is used for calculating a first time difference according to the picking batch data; the first time difference is an absolute value of a difference between a time at which the target picking batch is performed at the target warehouse and a time at which the target picking batch is performed at a preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length;
a maximum time difference calculation module, configured to determine a maximum time difference according to the plurality of first time differences;
the simulated hot zone determining module is used for determining the simulated hot zone of the preset warehouse corresponding to the maximum time difference according to the maximum time difference;
and the hot area adjusting module is used for adjusting the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference.
Optionally, the picking batch data further comprises the order quantity and picking duration in the target picking batch;
the first time difference calculating module specifically includes:
the first time length calculation submodule is used for calculating the average picking time length of the actually measured orders according to the picking time length and the order quantity;
the second time length calculation submodule is used for acquiring the average picking time length of the analog order;
the order average goods-picking time length calculation submodule is used for calculating the simulation hot area order average goods-picking time length according to the measured order average goods-picking time length and the simulation order average goods-picking time length;
and the saving time length calculating submodule is used for calculating a first time difference according to the saving time length of the uniform picking of the simulated hot area orders and the number of the covered orders of the simulated hot area.
Optionally, the second duration calculating sub-module specifically includes:
the batch group determining unit is used for determining a first target picking batch group according to picking path lengths of a plurality of target picking batches and the simulated hot area of the preset warehouse; the first target picking batch group comprises a plurality of first target picking batches; the first target picking batch is a target picking batch for which picking operations can be performed within a simulated hot zone of the pre-determined warehouse;
a total order number calculating unit for calculating the total order number of the first target picking batch group;
the total duration calculating unit is used for calculating the total duration of the picking of the first target picking batch group;
and the order average picking time length calculating unit is used for calculating the simulation order average picking time length according to the total order number and the total time length.
Optionally, in terms of calculating the number of orders covered by the simulated hot zone, the saved duration calculating submodule specifically includes:
a SKU calculating unit for calculating the number of SKUs in the simulated hot zone according to a formula N (the number of bin positions of the average single shelf is 0.7);
the order number calculating unit is used for determining the coverage order number of the simulated hot area according to the number of the SKUs in the simulated hot area and a TopN-SKU order coverage table; the TopN-SKU order cover table is used for representing the corresponding relation between the TopN occupancy rate and the cover order number in the simulation hot area; the TopN occupancy rate is the proportion of the number of orders completed by N SKUs to the total number of orders; n SKUs are the SKUs that model the top N of the hotness ranking placed within the hotspot.
Optionally, in terms of acquiring all target picking batches in the target warehouse within the set time, the picking batch data determining module specifically includes:
the initial picking batch determining submodule is used for acquiring all initial picking batches in the target warehouse within set time;
the benchmark order determining submodule is used for determining a first benchmark order quantity based on a mode principle according to the order quantities of all the initial picking batches;
an intermediate picking batch determining sub-module for removing the initial picking batches satisfying a first preset condition to determine a plurality of intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first reference order quantity;
the picking duration obtaining submodule is used for obtaining the picking duration of the middle picking batch;
the target picking batch determining submodule is used for removing the intermediate picking batches meeting a second preset condition so as to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the middle picking batch does not meet the set time requirement.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
firstly, acquiring all target picking batches in a target warehouse and corresponding picking batch data, and calculating the difference value between the time for executing a certain target picking batch in the target warehouse and the time for executing the target picking batch in a preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length. And then, determining the maximum time difference according to the time difference calculated by different target picking batches, determining the corresponding simulated hot area of the preset warehouse according to the maximum time difference, and further adjusting the commodity layout in the target warehouse according to the simulated hot area of the preset warehouse.
According to the picking batch data of the warehouse, the optimal hot area is determined by performing hot area simulation picking, so that the commodity layout is optimized, the picking efficiency is further improved, and manpower and material resources are saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a hot zone planning method based on simulated hot zones according to the present invention;
fig. 2 is a schematic structural diagram of a hot zone planning system based on simulated hot zones according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a hot zone planning method and a hot zone planning system based on a simulated hot zone.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Example 1
In e-commerce warehousing planning, goods with high sales volume or picking frequency are intensively placed in a certain area to complete most order picking in a small area, and the area with high picking efficiency is a hot area.
As shown in fig. 1, the present embodiment provides a hot zone planning method based on a simulated hot zone, including:
step 1001, all initial picking batches in the target warehouse within a set time are obtained.
Step 1002, determining a first benchmark order quantity based on a mode principle according to the order quantities of all the initial picking batches. Specifically, the mode of the number of orders of the initial picking lot is generally the desired number of orders for a single picking lot set by the merchant, and picking lots smaller than this number are not referred to, and therefore it is necessary to determine the first base order number, i.e., determine the mode in all orders.
Step 1003, removing the initial picking batches meeting first preset conditions to determine a plurality of intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first reference order quantity.
And 1004, acquiring the picking duration of the intermediate picking batch.
Step 1005, removing the intermediate picking batches meeting a second preset condition to determine all target picking batches in the target warehouse; the second preset condition is that the picking duration of the middle picking batch does not meet the set time requirement.
Specifically, the picking batch data further comprises the order number and picking duration in the target picking batch; the step 200 specifically includes:
step 2001, calculating the average picking duration of the measured orders according to the picking duration and the order quantity.
Step 2002, obtain the length of time for picking the order of the simulation. The step 2002 specifically includes:
(1) determining a first target picking lot group according to picking path lengths of a plurality of the target picking lots and simulated hot areas of the preset warehouse; the first target picking batch group comprises a plurality of first target picking batches; the first target picking batch is a target picking batch for which picking operations can be performed within the simulated hot zone of the pre-determined warehouse.
(2) Calculating the total amount of orders of the first target picking batch group.
(3) And calculating the total time length of the first target picking batch group for completing picking.
(4) And calculating the average picking time length of the simulated order according to the total order number and the total time length. Specifically, the simulated order average picking duration is the total duration of picking completed by the first target picking batch group/the total order number of the first target picking batch group.
And step 2003, calculating the time saving time for simulating the uniform picking of the hot area orders according to the measured uniform picking time length of the orders and the simulated uniform picking time length of the orders. Specifically, the simulated hot-zone order picking saving time length is measured order picking time length-simulated order picking time length.
And step 2004, calculating a first time difference according to the average picking time saving duration of the simulated hot area orders and the number of the simulated hot area coverage orders. Specifically, the process of calculating the number of orders covered by the simulated hot zone specifically includes:
calculating the number of SKUs in the simulated hot zone according to a formula N (the number of bin positions of an average single shelf is 0.7); determining the number of coverage orders of the simulated hot area according to the number of the SKUs in the simulated hot area and a TopN-SKU order coverage table; the TopN-SKU order cover table is used for representing the corresponding relation between the TopN occupancy and the cover order number in the simulation hot area; the TopN occupancy rate is the proportion of the number of orders completed by N SKUs to the total number of orders; n SKUs are the SKUs that are N top in rank of the heat of placement within the simulated hotspot.
And step 400, determining a simulated hot zone of a preset warehouse corresponding to the maximum time difference according to the maximum time difference.
And 500, adjusting the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference.
In a specific embodiment, the hot zone planning method based on the simulated hot zone specifically includes:
firstly, all initial picking batches in 7 days of a target warehouse, picking path lengths, picking duration and order numbers of all the initial picking batches are obtained, and all the initial picking batches are cleaned, specifically: (1) the mode of all order quantities is determined and only the initial picking batches with order quantities equal to the mode are retained. (2) Removing picking batches with picking time duration less than 3 minutes or more than 60 minutes, wherein the remaining initial picking batches are the target picking batches obtained from the target warehouse; the order picking time is less than 3 minutes, and the order picking time is more than 60 minutes, so that the distorted data can be caused by misoperation of the staff. Through the data cleaning steps, the data adopted during hot zone simulation can be more accurately attached to reality.
For convenience of calculation, a "walking index" is defined in this embodiment, shelves in a general warehouse all have shelf numbers, and according to the difference of the system in setting the numbers, a picker will continue picking goods according to a certain shelf sequence. Unlike the shelf number, the walk index is the picking order code of the full-bin shelf, and generally increases one by one in integer units starting from 1, and the maximum walk index is generally equal to the total number of the shelves in the warehouse. The length of the picking path is the difference between the start-point walking index and the end-point walking index of a certain picking batch, so as to represent the length of the path taken by the picking batch when picking in the bin.
Then, sorting the picking path lengths of all picking batches in a descending order, calculating the difference between the time of executing the target picking batch in the preset warehouse and the time of executing the target picking batch in the target warehouse from the target picking batch with the smallest picking path length. The preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length. Examples are: when the picking path length of the shortest path is 10 shelves, the simulated hot area in the preset warehouse is also 10 shelves, and then the time for picking can be saved more than the current time for picking when the simulated hot area is 10 shelves.
Specifically, when the number of shelves in the simulated hot zone is 10, the picking path length of each picking batch in the existing data is used to determine the target picking batch with the picking path length not exceeding 10 shelves, that is, all the batches with the picking path length less than or equal to the number of shelves in the simulated hot zone (10 shelves) are selected, that is, the batches can be completed within the size of the simulated hot zone. Note: the batch path length may be less than or equal to the simulated hot zone size, and no specific start and end positions of the hot zone are contemplated herein.
The SKU number N that the simulated hot zone can hold is calculated according to the formula N (bin number of average single shelf 0.7). Among these, the reason for 0.7 is: the goods in the hot area are usually sold in large quantity and need large storage space, so the number of the bin positions which can be accommodated by a single shelf in the hot area is 70% of the number of the bin positions of the single shelf in the warehouse, and the actual storage condition is met.
And calculating and obtaining a TopN-SKU order coverage table of the merchant according to the order structure of the merchant. Specifically, for a certain merchant, when only selecting the first N hottest SKUs, calculating the number of orders that can be completed by these SKUs, wherein different N correspond To different orders, and according To the corresponding relation, To can be constructed p An N-SKU order overlay table as shown in the following figure:
| TopN SKU | amount of covered order |
| 1 | 303 |
| 2 | 1857 |
| 3 | 3385 |
| 4 | 3920 |
| 5 | 4398 |
| 6 | 4898 |
The number of orders that the simulated hot zone can theoretically handle when the hottest N SKUs are distributed in the simulated hot zone, that is, the simulated hot zone cover order number, is obtained through the TopN-SKU order cover table.
And calculating the measured order picking time length according to the formula, wherein the measured order picking time length is equal to the picking time length of the current batch/the order quantity of the current batch.
And calculating the average picking time length of the simulated order according to the formula, wherein the average picking time length of the simulated order is the total processing time length of the batch which can be completed in the simulated hot area size/the total number of the batches which can be completed in the simulated hot area size.
And calculating the simulated hot area order average picking time length according to the formula simulated hot area order average picking time length-the simulated single average picking time length.
And calculating the time saved when the current simulated hot area is set by the formula first time difference, namely the time saved when the simulated hot area single is picked and the hot area covering order number, compared with the time saved when the current target warehouse is picked.
The above calculation is repeated until the target picking batch with the longest picking path is also calculated. The number of shelves of the simulated hot areas set in different preset warehouses and the corresponding first time difference of the simulated hot areas during picking are placed in a chart to obtain a query table, and the maximum time difference and the simulated hot areas of the preset warehouses corresponding to the maximum time difference, namely the optimal hot areas, are determined according to the query table. And finally, adjusting the positions of the commodities on the shelf of the target warehouse according to the size of the obtained optimal hot area, so that N commodities before the hotness ranking are concentrated in the same channel or the same area, and therefore when picking, a worker does not need to run all over the whole warehouse, can quickly finish picking in a certain channel or a plurality of channels, and the picking efficiency is greatly improved.
Example 2
As shown in fig. 2, the present embodiment provides a hot zone planning system based on simulated hot zones, including:
the picking batch data determining module 101 is configured to obtain all target picking batches in the target warehouse and picking batch data corresponding to all the target picking batches within a set time; the picking batch data comprises at least a picking path length in the target picking batch; the pick path length is the number of shelves from the pick start point to the pick end point.
Specifically, the picking batch data determining module 101 includes an initial picking batch determining sub-module, a benchmark order determining sub-module, an intermediate picking batch determining sub-module, a picking duration obtaining sub-module and a target picking batch determining sub-module in terms of obtaining all target picking batches in the target warehouse within a set time.
The initial picking batch determining submodule is used for acquiring all initial picking batches in the target warehouse within set time. And the benchmark order determining submodule is used for determining a first benchmark order quantity based on a mode principle according to the order quantities of all the initial picking batches. The intermediate picking batch determining submodule is used for removing the initial picking batches meeting first preset conditions to determine a plurality of intermediate picking batches; the first predetermined condition is that the order quantity of the initial picking batch is not equal to the first reference order quantity. The picking duration obtaining submodule is used for obtaining the picking duration of the middle picking batch. The target picking batch determining submodule is used for removing the intermediate picking batches meeting a second preset condition so as to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the middle picking batch does not meet the set time requirement.
A first time difference calculating module 201, configured to calculate a first time difference according to the picking lot data; the first time difference is an absolute value of a difference between a time at which the target picking batch is performed at the target warehouse and a time at which the target picking batch is performed at a preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length.
The picking batch data further comprises the order quantity and picking duration in the target picking batch; the first time difference calculation module 201 includes a first time length calculation submodule, a second time length calculation submodule, a single average saved time length calculation submodule, and a saved time length calculation submodule.
The first time length calculating submodule is used for calculating the average picking time length of the measured orders according to the picking time length and the order quantity; the second time length calculating submodule is used for acquiring the average picking time length of the analog order; the order average goods-picking time length calculation submodule is used for calculating the simulation hot-area order average goods-picking time length according to the measured order average goods-picking time length and the simulation order average goods-picking time length; the saving time length calculating submodule is used for calculating a first time difference according to the simulated hot area order average picking saving time length and the simulated hot area covering order number.
Preferably, the second duration calculating submodule specifically includes:
the batch group determining unit is used for determining a first target picking batch group according to picking path lengths of a plurality of target picking batches and the simulated hot area of the preset warehouse; the first target picking batch group comprises a plurality of first target picking batches; the first target picking batch is a target picking batch for which picking operations can be performed within the simulated hot zone of the pre-determined warehouse.
And the total order calculating unit is used for calculating the total order number of the first target picking batch group.
And the total duration calculating unit is used for calculating the total duration of the picking of the first target picking batch group.
And the order average picking time length calculating unit is used for calculating the simulation order average picking time length according to the total order number and the total time length.
In a specific embodiment, in the aspect of calculating the number of orders covered by the simulated hot zone, the saved time period calculating submodule comprises a SKU calculating unit and a SKU calculating unit.
The SKU calculation unit is used for calculating the number of SKUs in the simulated hot zone according to a formula N (the number of bin positions of an average single shelf is 0.7). The order number calculating unit is used for determining the coverage order number of the simulated hot area according to the number of the SKUs in the simulated hot area and a TopN-SKU order coverage table; the TopN-SKU order cover table is used for representing the corresponding relation between the TopN occupancy rate and the cover order number in the simulation hot area; the TopN occupancy rate is the proportion of the number of orders completed by N SKUs to the total number of orders; n SKUs are the SKUs that model the top N of the hotness ranking placed within the hotspot.
A maximum time difference calculating module 301, configured to determine a maximum time difference according to a plurality of the first time differences;
and a simulated hot zone determining module 401, configured to determine, according to the maximum time difference, a simulated hot zone of the preset warehouse corresponding to the maximum time difference.
And a hot area adjusting module 501, configured to adjust the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A hot zone planning method based on a simulated hot zone is characterized by comprising the following steps:
acquiring all target picking batches in a target warehouse and picking batch data corresponding to all the target picking batches within set time; the picking batch data comprises at least a picking path length in the target picking batch; the picking path length is the value of the number of shelves from the picking starting point to the picking end point;
calculating a first time difference according to the picking batch data; the first time difference is an absolute value of a difference between a time at which the target picking batch is performed at the target warehouse and a time at which the target picking batch is performed at a preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length;
determining a maximum time difference according to a plurality of the first time differences;
determining a simulated hot zone of a preset warehouse corresponding to the maximum time difference according to the maximum time difference;
and adjusting the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference.
2. The hot-zone planning method based on simulated hot-zones according to claim 1, characterized in that the picking batch data further comprises the order quantity and picking duration in the target picking batch;
the calculating the first time difference according to the picking batch data specifically comprises:
calculating the average picking time length of the actually measured orders according to the picking time length and the order number;
obtaining the time length of picking the goods on the simulation order;
calculating the time saving of the average picking of the simulated hot area orders according to the average picking time of the measured orders and the average picking time of the simulated orders;
and calculating the first time difference according to the average picking time saving time of the simulated hot area orders and the simulated hot area covering orders.
3. The hot-zone planning method based on simulated hot-zone as claimed in claim 2, wherein the calculation process of the simulated order-by-order picking duration specifically comprises:
determining a first target picking batch group according to picking path lengths of a plurality of target picking batches and simulated hot areas of the preset warehouse; the first target picking batch group comprises a plurality of first target picking batches; the first target picking batch is a target picking batch for which picking operations can be performed within a simulated hot zone of the pre-determined warehouse;
calculating the total order number of the first target picking batch group;
calculating the total time length of the first target picking batch group for completing picking;
and calculating the average picking time length of the simulation order according to the total order number and the total time length.
4. The hot-zone planning method based on simulated hot-zones as claimed in claim 2, wherein the calculation process of the number of orders covered by the simulated hot-zones specifically comprises:
calculating the number of SKUs in the simulated hot zone according to a formula N (the number of bin positions of an average single shelf is 0.7);
determining the number of coverage orders of the simulated hot area according to the number of the SKUs in the simulated hot area and a TopN-SKU order coverage table; the TopN-SKU order cover table is used for representing the corresponding relation between the TopN occupancy rate and the cover order number in the simulation hot area; the TopN occupancy rate is the proportion of the number of orders completed by N SKUs to the total number of orders; n SKUs are the SKUs that are N top in rank of the heat of placement within the simulated hotspot.
5. The hot zone planning method based on simulated hot zones as claimed in claim 1, wherein said obtaining all target picking batches in the target warehouse within the set time specifically comprises:
acquiring all initial picking batches in a target warehouse within a set time;
determining a first benchmark order quantity based on a mode principle according to the order quantities of all the initial picking batches;
removing the initial picking batches meeting a first preset condition to determine a plurality of intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first reference order quantity;
acquiring the picking duration of the intermediate picking batch;
removing the intermediate picking batches meeting a second preset condition to determine all target picking batches in a target warehouse; the second preset condition is that the picking duration of the middle picking batch does not meet the set time requirement.
6. A thermal zone planning system based on simulated thermal zones, the thermal zone planning system comprising:
the picking batch data determining module is used for acquiring all target picking batches in the target warehouse within set time and picking batch data corresponding to all the target picking batches; the picking batch data comprises at least a picking path length in the target picking batch; the picking path length is the value of the number of shelves from the picking starting point to the picking end point;
the first time difference calculating module is used for calculating a first time difference according to the picking batch data; the first time difference is an absolute value of a difference between a time at which the target picking batch is performed at the target warehouse and a time at which the target picking batch is performed at a preset warehouse; the preset warehouse is a warehouse provided with a simulated hot area, and the shelf number value in the simulated hot area is equal to the picking path length;
a maximum time difference calculation module, configured to determine a maximum time difference according to the plurality of first time differences;
the simulated hot zone determining module is used for determining the simulated hot zone of the preset warehouse corresponding to the maximum time difference according to the maximum time difference;
and the hot area adjusting module is used for adjusting the commodity hot area in the target warehouse according to the simulated hot area of the preset warehouse corresponding to the maximum time difference.
7. The hot zone planning system based on simulated hot zones according to claim 6, characterized in that the picking batch data further comprises the order quantity and picking duration in the target picking batch;
the first time difference calculating module specifically includes:
the first time length calculation submodule is used for calculating the average picking time length of the actually measured orders according to the picking time length and the order quantity;
the second time length calculation submodule is used for acquiring the average picking time length of the simulated order;
the order average goods-picking time length calculation submodule is used for calculating the simulation hot area order average goods-picking time length according to the measured order average goods-picking time length and the simulation order average goods-picking time length;
and the saving time length calculating submodule is used for calculating a first time difference according to the saving time length of the uniform picking of the simulated hot area orders and the number of the covered orders of the simulated hot area.
8. The system according to claim 7, wherein the second duration calculation sub-module comprises:
the batch group determining unit is used for determining a first target picking batch group according to picking path lengths of a plurality of target picking batches and the simulated hot area of the preset warehouse; the first target picking batch group comprises a plurality of first target picking batches; the first target picking batch is a target picking batch for which picking operations can be performed within a simulated hot zone of the pre-determined warehouse;
a total order number calculating unit for calculating the total order number of the first target picking batch group;
the total duration calculating unit is used for calculating the total duration of the picking of the first target picking batch group;
and the order average picking time length calculating unit is used for calculating the simulation order average picking time length according to the total order number and the total time length.
9. The hot-zone planning system based on simulated hot-zones as claimed in claim 7, wherein the saved time period calculation sub-module specifically comprises in terms of calculation of the number of orders covered by the simulated hot-zones:
a SKU calculating unit for calculating the number of SKUs in the simulated hot zone according to a formula N (the number of bin positions of the average single shelf is 0.7);
the order number calculating unit is used for determining the coverage order number of the simulated hot area according to the number of the SKUs in the simulated hot area and a TopN-SKU order coverage table; the TopN-SKU order cover table is used for representing the corresponding relation between the TopN occupancy and the cover order number in the simulation hot area; the TopN occupancy rate is the proportion of the number of orders completed by N SKUs to the total number of orders; n SKUs are the SKUs that are N top in rank of the heat of placement within the simulated hotspot.
10. The hot zone planning system according to claim 6, wherein the picking batch data determining module comprises in particular, in terms of all target picking batches in a target warehouse within a set time of acquisition:
the initial picking batch determining submodule is used for acquiring all initial picking batches in the target warehouse within set time;
the benchmark order determining submodule is used for determining a first benchmark order quantity based on a mode principle according to the order quantities of all the initial picking batches;
an intermediate picking batch determining sub-module for removing the initial picking batches satisfying a first preset condition to determine a plurality of intermediate picking batches; the first preset condition is that the order quantity of the initial picking batch is not equal to the first reference order quantity;
the picking duration obtaining submodule is used for obtaining the picking duration of the middle picking batch;
the target picking batch determining submodule is used for removing the intermediate picking batches meeting a second preset condition so as to determine all target picking batches in the target warehouse; the second preset condition is that the picking time of the middle picking batch does not meet the set time requirement.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210347149.1A CN114936891A (en) | 2022-04-01 | 2022-04-01 | Hot area planning method and system based on simulated hot area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210347149.1A CN114936891A (en) | 2022-04-01 | 2022-04-01 | Hot area planning method and system based on simulated hot area |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114936891A true CN114936891A (en) | 2022-08-23 |
Family
ID=82862900
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210347149.1A Pending CN114936891A (en) | 2022-04-01 | 2022-04-01 | Hot area planning method and system based on simulated hot area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114936891A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115423404A (en) * | 2022-09-06 | 2022-12-02 | 上海聚货通电子商务有限公司 | Automatic partitioning method and system for sorting area of E-commerce warehouse |
-
2022
- 2022-04-01 CN CN202210347149.1A patent/CN114936891A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115423404A (en) * | 2022-09-06 | 2022-12-02 | 上海聚货通电子商务有限公司 | Automatic partitioning method and system for sorting area of E-commerce warehouse |
| CN115423404B (en) * | 2022-09-06 | 2023-09-12 | 上海聚货通电子商务有限公司 | Automatic partitioning method and system for sorting area of e-commerce warehouse |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Agrawal et al. | Estimating negative binomial demand for retail inventory management with unobservable lost sales | |
| Bollapragada et al. | Myopic heuristics for the random yield problem | |
| US20100138273A1 (en) | Repeatability index to enhance seasonal product forecasting | |
| US7213007B2 (en) | Method for forecasting using a genetic algorithm | |
| CN108389073A (en) | Automatic calculating method and system, the electronic equipment and storage medium of commodity price | |
| JPH0567119A (en) | Merchandise analyzing system | |
| AU2010249271B2 (en) | Product assortment planning system and method utilizing scaled performance metric values | |
| CN109583660B (en) | Method for realizing dynamic goods picking strategy | |
| US20100169165A1 (en) | Method for updating regression coefficients in a causal product demand forecasting system | |
| CN114936891A (en) | Hot area planning method and system based on simulated hot area | |
| JP7208029B2 (en) | Placement optimization system and placement optimization method | |
| CN103631801B (en) | A kind of method and device that merchandise news is provided | |
| CN108470261B (en) | Ordering method and device | |
| CN102663128B (en) | Recommending system of large-scale collaborative filtering | |
| Tan | Managing manufacturing risks by using capacity options | |
| CN111325509A (en) | Data processing method and device, storage medium and electronic device | |
| CN108229739B (en) | Crop yield prediction method, terminal and computer readable storage medium | |
| CN104915721A (en) | Order production period and output measuring method | |
| CN113435805A (en) | Article storage information determining method, device, equipment and storage medium | |
| Budiani | Inventory Management of Broilers in PD Mugi Jaya Using Economic Order Quantity (EOQ) and Silver Meal Method | |
| Wasusri et al. | An Application Of Discrete Event Simulation On Order Picking Strategies: A Case Study Of Footwear Warehouses. | |
| JP6877378B2 (en) | Product layout planning system and product layout planning method | |
| CN108256966A (en) | A kind of distributed commodity tracing method based on block chain | |
| Maruyama et al. | Improved efficiency of warehouse picking by co-optimization of order batching and storage location assignment | |
| CN111639274A (en) | Online commodity intelligent sorting method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |