CN115760305A - Intelligent multi-bin delivery method and system for electric business - Google Patents
Intelligent multi-bin delivery method and system for electric business Download PDFInfo
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Abstract
The invention relates to a delivery method and a delivery system for intelligent multi-bin electric business, wherein the method comprises the following steps: acquiring the quantity of ordered commodities; screening out a warehouse set with a corresponding order commodity; on the premise of meeting the transportation timeliness, a target function is constructed by taking the lowest cost as a target; and solving the objective function to obtain the delivery warehouse with the lowest cost. On the premise of meeting the transportation timeliness, the method takes the lowest cost as a target to construct an objective function, and solves the objective function to obtain an optimal delivery warehouse, so that the overall cost of the order commodity is lowest when the order commodity is delivered in multiple bins.
Description
Technical Field
The invention relates to the technical field of e-commerce delivery, in particular to a delivery method, a delivery system, electronic equipment and a computer-readable storage medium for e-commerce intelligent multi-warehouse delivery.
Background
With the high-speed development of electronic commerce and higher cost control requirements, many e-commerce enterprises generally adopt a multi-warehouse delivery mode so as to purchase goods locally and deliver goods nearby. However, such a shipping method shortens the shipping time, but does not take into consideration the transportation cost and the storage cost.
Disclosure of Invention
In order to solve the above problems, an object of the embodiments of the present invention is to provide a method and a system for intelligent multi-warehouse delivery for electric commerce.
A method for intelligent multi-bin shipping for electric commerce, comprising:
step 1: acquiring the quantity of ordered commodities;
step 2: screening out a warehouse set with a corresponding order commodity stock;
and step 3: on the premise of meeting the transportation timeliness, a target function is constructed by taking the lowest cost as a target;
and 4, step 4: and solving the objective function to obtain the delivery warehouse with the lowest cost.
Preferably, the objective function in step 2 is:
wherein N is the number of warehouses with inventory of ordered goods, OBLC i For freight of warehouse i, OBC i For the delivery fee of warehouse i, SC i The warehousing charge of warehouse i.
Preferably, the constraint conditions of the objective function in step 2 are:
wherein X i For the total number of warehouse i ex-warehouse, S i Total stock quantity for warehouse i, WS i Inventory quantity in warehouse, TS, for warehouse i i Number of stocks in transit for warehouse i, WX i Number of ex warehouse for warehouse i in warehouse inventory, TX i Number of exits, OBT, stocked for warehouse i in transit i For the delivery duration of warehouse i, IBLT i For the expected warehousing of warehouse iDuration, IBLTD i For redundant values of warehousing duration of warehouse i, OBLT i The time length of warehouse i for ex-warehouse transportation, T the time length of promised transportation and Q the number of ordered commodities.
Preferably, the step 4: solving the objective function to obtain a delivery warehouse with the lowest cost, wherein the method comprises the following steps:
solving the objective function by using a genetic algorithm to obtain a delivery warehouse with the lowest cost; in the variation step of the genetic algorithm, the adjustment coefficient of the variation proportion is as follows:
in the formula, α is an adjustment coefficient of the variation ratio.
Preferably, in the interleaving step of the genetic algorithm, the adjustment coefficient of the interleaving probability is:
in the formula, β is an adjustment coefficient of the crossover probability.
The invention also provides an intelligent multi-warehouse delivery system for the electric business, which comprises the following components:
the order quantity acquisition module is used for acquiring the quantity of order commodities;
the order commodity screening module is used for screening out a warehouse set with a corresponding order commodity stock;
the target function construction module is used for constructing a target function by taking the lowest cost as a target on the premise of meeting the transportation timeliness;
and the solving module is used for solving the objective function to obtain a delivery warehouse with the lowest cost.
The invention also provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are connected through the bus, and the computer program realizes the steps in the intelligent multi-bin shipping method for electric commerce when being executed by the processor.
The invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the above-mentioned steps in a method for intelligent multi-bin shipping for e-commerce.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a delivery method, a system, electronic equipment and a computer-readable storage medium for intelligent multi-bin electronic commerce, wherein the method comprises the following steps: acquiring the quantity of ordered commodities; screening out a warehouse set with a corresponding order commodity stock; on the premise of meeting the transportation timeliness, constructing an objective function by taking the lowest cost as a target; and solving the objective function to obtain the delivery warehouse with the lowest cost. On the premise of meeting the transportation timeliness, the method takes the lowest cost as a target to construct an objective function, and solves the objective function to obtain an optimal delivery warehouse, so that the overall cost during multi-warehouse delivery is the lowest.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a delivery method for intelligent multi-warehouse electric business provided by the invention.
Detailed Description
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are used merely for convenience of description and simplification of the description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise explicitly stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a shipping method for electric commerce intelligent multi-warehouse includes:
step 1: acquiring the quantity of ordered commodities;
step 2: screening out a warehouse set with a corresponding order commodity;
in the embodiment of the present invention, the objective function in step 2 is:
wherein N is the number of warehouses with inventory of ordered goods, OBLC i For freight of warehouse i, OBC i For the delivery fee of warehouse i, SC i The warehousing charge of warehouse i.
The constraints of the objective function are:
wherein, X i For the total number of warehouse i ex-warehouse, S i Total inventory quantity for warehouse i, WS i Inventory quantity in warehouse, TS, for warehouse i i Quantity of stock in transit for warehouse i, WX i Number of ex-warehouse for warehouse i in-warehouse inventory, TX i Quantity ex warehouse, OBT, in stock in warehouse i in transit i For the delivery duration of warehouse i, IBLT i For the estimated warehousing duration of warehouse i, IBLTD i Is a redundant value of the warehousing duration of warehouse i, OBLT i The time length of warehouse i for ex-warehouse transportation, T the time length of promised transportation and Q the number of ordered commodities.
And step 3: on the premise of meeting the transportation timeliness, a target function is constructed by taking the lowest cost as a target;
and 4, step 4: and solving the objective function to obtain the delivery warehouse with the lowest cost.
Further, the step 4 includes:
solving the objective function by using a genetic algorithm to obtain a delivery warehouse with the lowest cost; in the variation step of the genetic algorithm, the adjustment coefficient of the variation proportion is as follows:
in the formula, α is an adjustment coefficient of the variation ratio.
In the crossing step of the genetic algorithm, the adjustment coefficient of the crossing probability is as follows:
in the formula, β is an adjustment coefficient of the crossover probability.
The principle and process of the intelligent multi-warehouse delivery method for the electric business of the invention are further described in the following with specific embodiments:
after the customer completes the order placement, the system automatically screens out the warehouse set W = { i =1, \8230;, N } with the corresponding order in stock, and the stock quantity (including in-transit stock) of the order in each warehouse is S = { S = } 1 ,…,S i …,S N W = { WS }, where the inventory number in the library is WS = { WS = } 1 ,…,WS i …,WS N The stock quantity in transit is TS = { TS = } 1 ,…,TS i …,TS N }，S i ＝WS i +TS i 。
The inventory quantity set of each warehouse ex-warehouse is X = { X 1 ,…,X i …,X N W x, wherein the stock quantity in the library is WX = { WX = 1 ,…,WX i …,WX N The quantity of stock in transit is
TX＝{TX 1 ,…,TX i …,TX N }，X i ＝WX i +TX i . N is the number of warehouses that have this order in stock. T is the committed transit time, and the customer demand is Q.
The system optimization aims to minimize the overall cost of the system on the premise of meeting the overall transportation timeliness. If a plurality of warehouses (warehouses of the same type) are to be selected, for example, shipping time of the same order from the Taiyuan coordination warehouse and the Beijing coordination warehouse is the same, freight A1 and freight A2 are calculated according to the transportation cost of the order commodity from the Taiyuan coordination warehouse and the Beijing coordination warehouse, then the delivery fees of the order from the Taiyuan coordination warehouse and the Beijing coordination warehouse are respectively calculated to be B1 and B2, and the reduced warehousing fees are respectively C1 and C2. The lowest freight rate, freight rate and warehousing rate (A + B-C) is used as a delivery warehouse.
The optimization objective is
The constraints are:
1)X i <S i i =1, \ 8230, N, the number of ex-warehouse cannot exceed the number of inventory.
2)WS i >=0 and is an integer, TS i >=0 and is an integer, i =1, \ 8230, N, the inventories are all positive integers.
3)WS i >＝WX i >=0 and is an integer, TS i >＝TX i >=0 and is an integer, i =1, \ 8230, N, the stocks taken out of stock are all positive integers.
4)OBT i <T, if WX i >0,TX i =0, the delivery time length satisfies the commitment time length when all the stocks are delivered from the stock in the warehouse, i =1, \ 8230, N.
5)IBLT i +IBLTD i +OBLT i <T, if TX i >0, when the warehouse-in and warehouse-out are carried out from the in-transit warehouse, the warehouse-in and warehouse-out duration meets the commitment duration, i =1, \ 8230, and N. And (3) the time redundancy value of the estimated arrival time (estimated warehousing time + ex-warehouse transportation time) + warehousing time of the in-transit stocks of the corresponding warehouse also meets the time redundancy requirement, and the corresponding warehouses of the warehouses are also taken as delivery warehouses.
6)sum{X 1 ,…,X i …,X N }＝Q。
Wherein, OBT i = warehouse i warehouse out Time (Outbound Time)
OBLT i = warehouse i shipment Time (Outbound Logistics Time)
IBLT i = expected warehouse entry Time of warehouse i (Inbound Logistics Time)
IBLTD i = warehouse entry duration redundancy value of warehouse i (Inbound Logi statistics Time Delta)
OBLC i = freight of warehouse i (Outbound Logi fuels Cost)
OBC i = warehouse-out charge (Outbound Cost)
SC i Storage fee of Storage i (Storage Cost)
The logic of the estimated warehousing duration and the ex-warehouse transportation duration is as follows:
1. prediction of warehousing time IBLT i Transport means (land transport, air transport) determined by a combination of a shipping warehouse or a factory-located county area (prefecture or district of province, city, or prefecture) and a transport mode (prefecture or district of province, city, or prefecture) of the warehouse, and recorded in the system database. Referring to table 1, note that the shipping age may be adjusted for different periods of time (e.g., the shipping age may change for stormy weather).
2. Long OBLT of delivery i The system is determined by the combination of the county and district (county or district of provincial and city) where the warehouse is located and the county and district (county or district of provincial and city) where the client is located and the transportation mode (land transportation, air transportation), and is recorded by the system database. Referring to Table 1, note that the age of transportation may be adjusted for different periods of time (e.g., transportation in heavy rain weather)
The aging may vary).
3. The system firstly matches the receiving address (ex-warehouse time length) and the delivery address (in-warehouse time length) with the county-level distribution areas of the warehouses, and if the county-level areas are not matched, the receiving address and the delivery address are sequentially upward compatible, and the system is pushed to the provincial level.
TABLE 1
The time redundancy value of the warehousing time length is estimated based on the standard deviation of the actual transportation time lengths of the lines of different origin province and city counties and destination province and city counties, and is obtained by weighting the execution standard deviation of the latest 3 months and the standard deviation of the same month of the last year. Time redundancy value IBLTD of warehousing duration i Standard deviation of the wire of the first 3 months + standard deviation of the wire of the same month of the previous year 3 x 30% = standard deviation of the wire of the first 3 months recorded by the information system 3 x 70% >. Note that the standard deviation 3 is a case where 3 times the standard deviation covers 99% or more, assuming that the distribution of the transportation time length is a normal distribution. 70% +30% =100%, 70%% and 30% are empirical values.
Since the optimization process cannot be evaluated by methods such as a simplex method, a genetic algorithm is required for solving. The solving process is as follows:
1) Initially: the set of initial solutions is 50, each solution being WX 1j ＝{WX 1j1 ,
TX 1j1 ,…,WX 1ji ,TX 1ji ,…WX 1jN ,TX 1jN }，j＝1,…,50,WX 1ji And TX 1ji >0
And is a positive integer, WS i >WX 1ji ,TS i >TX 1ji ,i＝1,…,N。
2) Iteration: calculating corresponding target values E of 50 initial solutions j According to 1/E j As a criterion whether the solution can reach the next generation. Assume that the current generation K.
3) Mutation: WX in 10 solutions for the K-th generation at random kji ,TX kji Is changed.
Modified synchronization to synchronize another WX in the solution kji ,TX kji Is adjusted so as to satisfy sum { X } 1 ,…,X i …,X N } = Q.10 is an empirical value.
A.WX kji There are 1 strong limits to the number of inventories in the warehouse that cannot exceed this SKU.
B.TX kji There are 1 strong limits to the value of (c), which cannot exceed the number of stock in transit for this SKU in the warehouse.
C. In order to improve the mutation efficiency, the mutation probability before the first 20 generations, 21 generations to 40 generations and 41 generations is 10%,20% and 30% so as to prevent local optimality.
D. From a practical operational perspective, business preferences are centralized rather than over-distributed. For example, if 100 goods are sold, the business would prefer to leave 100 from 1 or 2 warehouses, rather than 20 from 5 warehouses each. So for WX i +TX i The larger the sum of (2), WX kji ,TX kji The higher the probability of (a) becomes. I.e. WX kji ,TX kji Relative to WX i ,TX i The proportional adjustment coefficient of (a) is processed as follows:
the inventory quantity of the order (including the inventory in transit) of each warehouse is as follows:
S＝{S 1 ,…,S i …,S N }，S i ＝WS i +TS i the customer demand is Q.
4) And (3) crossing: randomly solving WX in 2 solutions of the K generation kji ,TX kji Is exchanged, modified synchronization is used to synchronize another WX in the solution kji ,TX kji Is adjusted so as to satisfy sum { X } 1 ,…,X i …,X N } = Q. The process proceeds 5 times to cross 10 values per generation.
10 is an empirical value.
A. To improve the efficiency of crossover, the crossover probability before the first 20 generations, 21 generations to 40 generations, and 41 generations later, is 10%,20, 30%, so as to prevent local optimality.
B. Similar variation, in order to prevent the warehouse-out quantity of the warehouse with large stock quantity from being too small after crossing, 2 solutions are adopted: WX kji ,TX kji Value of (A) and WX kji ,TX kji The adjustment coefficient of the probability of value crossing of (1) is as follows:
5) Calculating the minimum E in the set of target values corresponding to the current solution set k The iteration continues until the current E k And E k-1 The ratio of the difference is less than 1%, and the algorithm is ended.
The invention fully considers the stock of in-transit purchase and in-transit dump, improves the turnover rate of the stock, and greatly reduces the transportation cost and the storage cost on the premise of ensuring the transportation timeliness.
The invention also provides an intelligent multi-warehouse delivery system for the electric business, which comprises the following components:
the order quantity acquisition module is used for acquiring the quantity of order commodities;
the order commodity screening module is used for screening out a warehouse set with a corresponding order commodity stock;
the target function construction module is used for constructing a target function by taking the lowest cost as a target on the premise of meeting the transportation timeliness;
and the solving module is used for solving the objective function to obtain the delivery warehouse with the lowest cost.
Compared with the prior art, the beneficial effect of the delivery system for the intelligent multi-warehouse of the electric business is the same as that of the delivery method for the intelligent multi-warehouse of the electric business in the technical scheme, and the detailed description is omitted here.
The embodiment of the invention also provides electronic equipment, which comprises a bus, a transceiver, a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, each process of the embodiment of the intelligent multi-bin delivery method for the e-commerce business is realized, the same technical effect can be achieved, and the details are not repeated for avoiding repetition.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements each process of the foregoing embodiment of the method for intelligent multi-bin shipping for electric commerce, and can achieve the same technical effect, and in order to avoid repetition, the details are not repeated here.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and the present invention shall be covered by the claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. A delivery method for intelligent multi-bin electric business is characterized by comprising the following steps:
step 1: acquiring the quantity of ordered commodities;
and 2, step: screening out a warehouse set with a corresponding order commodity;
and 3, step 3: on the premise of meeting the transportation timeliness, a target function is constructed by taking the lowest cost as a target;
and 4, step 4: and solving the objective function to obtain a delivery warehouse with the lowest cost.
2. The intelligent multi-bin shipping method for electric business according to claim 1, wherein the objective function in step 2 is:
wherein N is the number of warehouses with inventory of ordered goods, OBLC i For freight of warehouse i, OBC i For the delivery fee of warehouse i, SC i The warehousing charge of warehouse i.
3. The intelligent multi-bin shipping method for electric business according to claim 2, wherein the constraints of the objective function in step 2 are:
wherein X i For the total number of warehouse i ex-warehouse, S i Total stock quantity for warehouse i, WS i Quantity of inventory in warehouse, TS, for warehouse i i Quantity of stock in transit for warehouse i, WX i Number of ex warehouse for warehouse i in warehouse inventory, TX i Warehouse-out for warehouse i in-transit stockQuantity, OBT i For the delivery duration of warehouse i, IBLT i For the estimated warehousing duration of warehouse i, IBLTD i For redundant values of warehousing duration of warehouse i, OBLT i The time length of warehouse i for ex-warehouse transportation, T the time length of promised transportation and Q the number of ordered commodities.
4. The intelligent multi-bin shipping method for electric commerce according to claim 3, wherein the step 4: solving the objective function to obtain a delivery warehouse with the lowest cost, wherein the method comprises the following steps:
solving the objective function by using a genetic algorithm to obtain a delivery warehouse with the lowest cost; in the variation step of the genetic algorithm, the adjustment coefficient of the variation proportion is as follows:
in the formula, α is an adjustment coefficient of the variation ratio.
5. The intelligent multi-bin shipping method for electric business according to claim 4, wherein in the crossing step of the genetic algorithm, the adjustment coefficient of the crossing probability is as follows:
in the formula, β is an adjustment coefficient of the crossover probability.
6. An intelligent multi-bin shipping system for electrical commerce, comprising:
the order quantity acquisition module is used for acquiring the quantity of order commodities;
the order commodity screening module is used for screening out a warehouse set with warehoused corresponding order commodities;
the target function construction module is used for constructing a target function by taking the lowest cost as a target on the premise of meeting the transportation timeliness;
and the solving module is used for solving the objective function to obtain a delivery warehouse with the lowest cost.
7. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps of a method for intelligent multi-bin shipping for electric commerce according to any one of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for intelligent multi-bin shipping for electric commerce according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117391593A (en) * | 2023-12-13 | 2024-01-12 | 广东精工智能***有限公司 | Intelligent warehouse article scheduling management method and system based on Internet of things |
| CN118229198A (en) * | 2024-05-23 | 2024-06-21 | 深圳琼景科技有限公司 | Method, device and system for controlling warehouse separation and packaging |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109359759A (en) * | 2018-08-07 | 2019-02-19 | 深圳市易达云科技有限公司 | Intelligence divides storehouse method, equipment and computer readable storage medium |
| CN109816294A (en) * | 2017-11-22 | 2019-05-28 | 上海德启信息科技有限公司 | A kind of determination method and apparatus of shipping room |
| CN112215530A (en) * | 2019-07-11 | 2021-01-12 | 北京京东尚科信息技术有限公司 | Bin selection method and device |
| CN112990590A (en) * | 2021-03-26 | 2021-06-18 | 中国科学技术大学 | E-commerce logistics transfer optimization method and system under background of network freight platform |
| CN113408771A (en) * | 2020-03-16 | 2021-09-17 | 北京京东振世信息技术有限公司 | Article delivery scheduling method, device and storage medium |
-
2022
- 2022-11-30 CN CN202211521847.5A patent/CN115760305B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109816294A (en) * | 2017-11-22 | 2019-05-28 | 上海德启信息科技有限公司 | A kind of determination method and apparatus of shipping room |
| CN109359759A (en) * | 2018-08-07 | 2019-02-19 | 深圳市易达云科技有限公司 | Intelligence divides storehouse method, equipment and computer readable storage medium |
| CN112215530A (en) * | 2019-07-11 | 2021-01-12 | 北京京东尚科信息技术有限公司 | Bin selection method and device |
| CN113408771A (en) * | 2020-03-16 | 2021-09-17 | 北京京东振世信息技术有限公司 | Article delivery scheduling method, device and storage medium |
| CN112990590A (en) * | 2021-03-26 | 2021-06-18 | 中国科学技术大学 | E-commerce logistics transfer optimization method and system under background of network freight platform |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117391593A (en) * | 2023-12-13 | 2024-01-12 | 广东精工智能***有限公司 | Intelligent warehouse article scheduling management method and system based on Internet of things |
| CN117391593B (en) * | 2023-12-13 | 2024-05-03 | 广东精工智能***有限公司 | Intelligent warehouse article scheduling management method and system based on Internet of things |
| CN118229198A (en) * | 2024-05-23 | 2024-06-21 | 深圳琼景科技有限公司 | Method, device and system for controlling warehouse separation and packaging |
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