Online warehousing method and device considering order sequence and warehouse capacity constraint

Abstract

The application discloses an online warehouse allocation method and device considering order sequence and warehouse capacity constraints, wherein the method comprises the following steps: acquiring warehouse capacity occupied by each order on average; acquiring a current order which needs to be responded currently, and calculating a modified profit of each warehouse responding to the order based on the current order, and determining a warehouse with the maximum modified profit; and modifying a shadow price of the warehouse capacity according to a warehouse allocation result obtained by the warehouse with the maximum modified profit until all orders are modified in a preset observation period, and determining a final warehouse allocation result. Thus, the technical problem that the global operation capacity of the store, the global inventory of the warehouse and the randomness of future orders are ignored in the related art, the calculation result cannot meet the global demand, and the algorithm solving efficiency is low is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of order fulfillment in the supply chain industry, in particular to an online warehouse allocation method and device considering order sequence and warehouse capacity constraints. BACKGROUND

[0002] Order fulfillment is one of the important links of modern supply chain and online e-commerce transactions, which refers to the whole link process from consumer ordering to completing the transaction, including order payment, order processing, goods delivery, distribution and many other links. Reducing the cost of order fulfillment has a profound impact on increasing the economic benefits of the supply chain and improving the service level of the supply chain. Among them, supply chain warehouse allocation is a very important problem, that is, the optimal warehouse allocation strategy through algorithm ensures the lowest supply chain cost, and the cost calculation includes product procurement price, warehouse operation fee, and rebate. The specific requirements of supply chain warehouse allocation include:

[0003] 1) Considering the influence of global warehouse capacity constraints, order category quantity, warehouse operation cost and other constraints on the optimization result, the overall review period (for example, one day) requires the warehouse allocation result to be as close to the global optimal result as possible;

[0004] 2) Some combination products / gifts must be sent out together, and the warehouse allocation result needs to meet this business constraint;

[0005] 3) For e-commerce platforms, real-time solution needs to be achieved in milliseconds in the transaction link.

[0006] The warehouse allocation method in the related art can receive orders online, and based on the current order product price and warehouse operation cost, solve the integer programming model of the current order to maximize the profit of the current order.

[0007] However, the warehouse allocation method in the related art only considers the immediate optimal solution, without considering the global operation capacity of the store, the global inventory of the warehouse and other constraints, ignoring the randomness of future orders, resulting in that the calculation result cannot meet the global demand, and the algorithm solving efficiency is low, which needs to be improved. SUMMARY

[0008] The present application provides an online warehouse allocation method and device considering order sequence and warehouse capacity constraints to solve the technical problems that the related art ignores the global operation capacity of the store, the global inventory of the warehouse and other constraints, and the randomness of future orders, resulting in that the calculation result cannot meet the global demand, and the algorithm solving efficiency is low.

[0009] The first aspect of the present application provides an online warehouse allocation method considering order sequence and warehouse capacity constraints, comprising the following steps: obtaining warehouse capacity occupied by each order on average; obtaining a current order to be responded to, and calculating a modified profit of each warehouse responding to the order based on the current order, determining a warehouse with the maximum modified profit; and modifying a shadow price of warehouse capacity according to a warehouse allocation result obtained by the warehouse with the maximum modified profit, until all orders are modified in a preset observation period, and determining a final warehouse allocation result.

[0010] Optionally, in an embodiment of the present application, the obtaining of the warehouse capacity occupied by each order on average comprises: establishing an objective function of a warehouse allocation problem global optimization model and a constraint condition of the warehouse allocation problem global optimization model; solving the warehouse allocation problem global optimization model based on the objective function and the constraint condition to obtain warehouse capacity consumed in the overall observation period; and calculating the warehouse capacity occupied by each order on average according to the warehouse capacity consumed in the overall observation period.

[0011] Optionally, in an embodiment of the present application, the objective function is:

[0012]

[0013] wherein, are decision variables, represents whether the product k in the current order i is responded to by the warehouse s, represents whether the product in the current order i is shipped by the warehouse s, is the quantity of the product k required by the order i, r (s,k) is the gross profit of the product k in the warehouse s, o s is the operating cost of the warehouse s.

[0014] Optionally, in an embodiment of the present application, the constraint condition of the warehouse allocation problem global optimization model comprises a product category shipping constraint, a warehouse capacity constraint and a decision variable correlation constraint, wherein,

[0015] the product category shipping constraint is:

[0016]

[0017] the warehouse capacity constraint is:

[0018]

[0019] wherein, v real(s,k) represents the inventory of the product k in the warehouse s;

[0020] the decision variable correlation constraint is:

[0021]

[0022]

[0023] Optionally, in an embodiment of the present application, the correction formula of the shadow price is:

[0024]

[0025] wherein, is the transpose of the kth row of w i is the transpose of the kth row of d, and a is a learning rate.

[0026] The second aspect embodiment of the present application provides an online warehousing device considering order sequence and warehouse capacity constraints, comprising: an acquisition module configured to acquire warehouse capacity occupied by each order on average; a calculation module configured to acquire a current order to be responded to, and calculate a correction profit of each warehouse responding to the order based on the current order, and determine a warehouse with the maximum correction profit; and a determination module configured to correct a shadow price of warehouse capacity according to a warehousing result obtained by the warehouse with the maximum correction profit, until all orders are corrected in a preset observation period, and determine a final warehousing result.

[0027] Optionally, in an embodiment of the present application, the acquisition module comprises: a constraint unit configured to establish a target function of a global optimization model of a warehousing problem and a constraint condition of the global optimization model of the warehousing problem; a modeling unit configured to solve the global optimization model of the warehousing problem based on the target function and the constraint condition, and obtain warehouse capacity consumed in an overall observation period; and a calculation unit configured to calculate the warehouse capacity occupied by each order on average according to the warehouse capacity consumed in the preset observation period.

[0028] Optionally, in an embodiment of the present application, the target function is:

[0029]

[0030] wherein, are decision variables, represents whether a product k in a current order i is responded to by a warehouse s, represents whether any product in the current order i is shipped by the warehouse s, is the number of the product k required by the order i, r (s,k) is the gross profit of the product k in the warehouse s, o s is the operating cost of the warehouse s.

[0031] ​Optionally, in an embodiment of the present application, the warehouse allocation problem global optimization model constraint conditions include category shipment constraints, warehouse capacity constraints and decision variable correlation constraints, wherein,

[0032] The category shipment constraint is:

[0033]

[0034] The warehouse capacity constraint is:

[0035]

[0036] wherein, v real(s,k) represents the inventory of the commodity k in the warehouse s;

[0037] The decision variable correlation constraint is:

[0038]

[0039]

[0040] Optionally, in an embodiment of the present application, the shadow price correction formula is:

[0041]

[0042] wherein, is the transpose of the kth row of w i is the transpose of the kth row of d, and a is a learning rate.

[0043] An embodiment of the third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the online warehouse allocation method considering order sequence and warehouse capacity constraints as described in the above embodiments.

[0044] An embodiment of the fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the online warehouse allocation method considering order sequence and warehouse capacity constraints as described above.

[0045] ​The embodiment of the application can obtain the average occupied warehouse capacity of each order and the current order needing to be responded, calculate the corrected profit of each warehouse responding to the order, determine the warehouse with the maximum corrected profit, and then obtain the shadow price of the corrected warehouse capacity of the warehouse result, until the corrected end of all orders in the investigation period, and determine the final warehouse result, so that the time sequence characteristics of the order, the global operation capacity of the store, the global inventory constraint of the warehouse and the randomness of the future order can be comprehensively considered in the instant warehouse allocation problem, the multi-objective optimization problem of the business is solved, the warehouse allocation result in the whole investigation period is approximated to the global optimum, the solving efficiency is improved, and the application prospect is wide. Therefore, the technical problems that the global operation capacity of the store, the global inventory constraint of the warehouse and the randomness of the future order are ignored in the related art, the calculation result cannot meet the global demand, and the algorithm solving efficiency is low are solved.

[0046] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and / or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0047] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A flowchart of an online warehouse allocation method considering order sequence and warehouse capacity constraint according to an embodiment of the application is provided;

[0049] Figure 2 A flowchart of an online warehouse allocation method considering order sequence and warehouse capacity constraint according to an embodiment of the application is provided;

[0050] Figure 3 An effect comparison diagram of an online warehouse allocation method considering order sequence and warehouse capacity constraint according to an embodiment of the application is provided;

[0051] Figure 4 A structural diagram of an online warehouse allocation device considering order sequence and warehouse capacity constraint according to an embodiment of the application is provided;

[0052] Figure 5 A structural diagram of an electronic device according to an embodiment of the application is provided. DETAILED DESCRIPTION

[0053] The embodiments of the application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.

[0054] The online warehouse allocation method and device considering order sequence and warehouse capacity constraints of the embodiments of the present application are described below with reference to the accompanying drawings. In view of the technical problems in the related art that the global operation capacity of the store, the global inventory of the warehouse, and the randomness of future orders are ignored, resulting in a calculation result that cannot meet the global demand and low algorithm solving efficiency, the present application provides an online warehouse allocation method considering order sequence and warehouse capacity constraints. In the method, the average warehouse capacity occupied by each order and the current order to be responded to can be obtained, the corrected profit of each warehouse responding to the order is calculated, the warehouse with the maximum corrected profit is determined, and the shadow price of the warehouse capacity of the determined warehouse is obtained. Until the correction of all orders in the observation period is completed, the final warehouse allocation result is determined. Therefore, the time sequence characteristics of the order, the global operation capacity of the store, the global inventory constraint of the warehouse, and the randomness of future orders can be considered in the real-time warehouse allocation problem, the multi-objective optimization problem of the business is solved, the warehouse allocation result in the overall observation period approaches the global optimum, the solving efficiency is improved, and the application prospect is wide. Thus, the technical problems in the related art that the global operation capacity of the store, the global inventory of the warehouse, and the randomness of future orders are ignored, resulting in a calculation result that cannot meet the global demand and low algorithm solving efficiency are solved.

[0055] Specifically, Figure 1 The flowchart of the online warehouse allocation method considering order sequence and warehouse capacity constraints provided by the embodiments of the present application is shown.

[0056] As Figure 1 shown, the online warehouse allocation method considering order sequence and warehouse capacity constraints includes the following steps:

[0057] In step S101, the average warehouse capacity occupied by each order is obtained.

[0058] In the actual execution process, the present application can preset an observation period, such as one day, and predict the order quantity value of the preset observation period, and then calculate the average warehouse capacity occupied by each order.

[0059] Optionally, in an embodiment of the present application, obtaining the average warehouse capacity occupied by each order includes: establishing a target function of a global optimization model of a warehouse allocation problem and a constraint condition of the global optimization model of the warehouse allocation problem; solving the global optimization model of the warehouse allocation problem based on the target function and the constraint condition to obtain the predicted warehouse capacity consumed in the overall observation period; and calculating the average warehouse capacity occupied by each order based on the predicted warehouse capacity consumed in the preset observation period.

[0060] Specifically, the embodiment of the present application can define the order quantity prediction value of the preset observation period as N, initialize the shadow price of the warehouse capacity constraint as p=0, and define the warehouse capacity available in the preset observation period as a matrix v real The actual quantity of goods sold in the actual observation period should always be less than the available inventory v real The order consumption warehouse capacity in the preset observation period is defined as a matrix v, where v can be obtained by global simulation, and the order sequence used in simulation can be sampled from the order of the previous observation period. The global optimization solution obtains the predicted consumption warehouse capacity v in the overall observation period, where v reafl The dimensions of v and v are both K×S, K is the total number of commodity types, and S is the total number of warehouses.

[0061] Optionally, in an embodiment of the present application, the objective function is:

[0062]

[0063] wherein, are decision variables, represents whether the commodity k in the current order i is responded by the warehouse s, represents whether there is a commodity in the current order i shipped by the warehouse s, is the quantity of commodity k required by order i, r (s ,k) is the gross profit of commodity k in the warehouse s, o s is the operating cost of the warehouse s.

[0064] Further, the embodiment of the present application can establish the objective function of the global optimization model of the warehousing problem to maximize the warehousing gross profit and minimize the warehouse operating cost as the optimization objective:

[0065]

[0066] wherein, are decision variables, represents whether the commodity k in the current order i is responded by the warehouse s, represents that the commodity k in the order i will be responded by the warehouse s, represents that the commodity k in the order i will not be responded by the warehouse s, represents whether there is a commodity in the current order i shipped by the warehouse s, is the quantity of commodity k required by order i, is the gross profit of commodity k in the warehouse s, is the operating cost of the warehouse s, is the warehousing gross profit of order i, The warehousing operation cost consumption for the order i.

[0067] Optionally, in an embodiment of the present application, the constraint conditions of the warehousing problem global optimization model include category delivery constraints, warehouse capacity constraints and decision variable correlation constraints.

[0068] The category delivery constraints are:

[0069]

[0070] The warehouse capacity constraints are:

[0071]

[0072] wherein v real(s,k) is the inventory of the commodity k in the warehouse s.

[0073] The decision variable correlation constraints are:

[0074]

[0075]

[0076] As a possible implementation manner, the embodiment of the present application can establish constraint conditions of the warehousing problem global optimization model, specifically including:

[0077] The category delivery constraints are:

[0078]

[0079] It is indicated that only one warehouse can be selected to deliver a certain commodity in a single order.

[0080] The warehouse capacity constraints are:

[0081]

[0082] It is indicated that the total amount of a certain commodity delivered from a certain warehouse cannot exceed the inventory of the commodity in the warehouse, wherein is the inventory of the commodity k in the warehouse s, which is the s-th row and k-th column of the matrix v real .

[0083] The decision variable correlation constraints are:

[0084]

[0085]

[0086] It is indicated that if the warehouse s responds to the commodity in the order i, it constitutes an operation on the warehouse, so that If the warehouse s does not respond to any commodity in the order i, then

[0087] The embodiment of the application can obtain the global optimization model of the fixed warehouse problem, which aims to solve the objective function of the global optimization model of the fixed warehouse problem, and the constraint of the correlation between the category delivery, warehouse capacity and decision variable, to obtain the consumed warehouse capacity in the whole observation period Thus, the matrix v is obtained, and the s-th row and the k-th column of v is v (s,k) .

[0088] The average warehouse capacity occupied by each order is obtained: the warehouse capacity occupied by each order is d=v / N, wherein the dimension of d is consistent with that of v, that is, K×S, K is the total number of goods, and S is the total number of warehouses.

[0089] In step S102, the current order that needs to be responded to is obtained, and the corrected profit of each warehouse responding to the order is calculated based on the current order, and the warehouse with the maximum corrected profit is determined.

[0090] In the actual execution process, the embodiment of the application can obtain the order that needs to be responded to online, calculate the corrected profit of each warehouse responding to the order, and constantly select the warehouse with the maximum corrected profit to satisfy all goods in the current order; the operation cost of each warehouse is defined as o, and o s is a vector composed of o, and the matrix H=v>0 indicating whether the warehouse capacity is remaining is initialized, and the specific steps are as follows:

[0091]

[0092] Further, the required quantity vector of each good in the order i is q i , which is a vector composed of q , and the dimension is K×1.

[0093] The corrected profit of each warehouse is calculated:

[0094] g=max(o)-o-Trace(H·q i )p,

[0095] wherein the dimensions of g, o and p are all S×1, the max(*) function is used to obtain the maximum element in the vector, and the Trace(*) function is used to obtain the diagonal matrix with the vector elements as the diagonal elements.

[0096] The embodiment of the application can select the warehouse corresponding to the maximum element in the corrected profit g, and the serial number of the warehouse is s, which is used to respond to the order. After responding to the order, the warehouse capacity is reduced, which is represented as w s is deducted from the remaining capacity v i,s of the warehouse, wherein v s is the s-th column of v s , and w i,sAll dimensions are K×1.

[0097] If some items in order i cannot be shipped from the selected warehouse due to insufficient warehouse capacity, this embodiment of the application can construct a vector q for the items in the order that cannot be fulfilled by that warehouse and their required quantities. i =q i -w i,s And repeat the above steps; if the response for all items in the order is completed, then q i =∑ s w i,s Construct matrix w i Its dimensions are K×S, and its s-th column is w. i,s .

[0098] In step S103, the shadow price of warehouse capacity is adjusted based on the order placement result obtained from the warehouse with the highest adjusted profit, until all orders within the preset observation period are adjusted, and the final order placement result is determined.

[0099] As one possible approach, this embodiment of the application can adjust the shadow price of warehouse capacity based on the current order's allocation results. The above steps are repeated for each order until all orders within a preset observation period are satisfied, thereby determining the final allocation result. This embodiment of the application can solve the allocation problem online using pure matrix calculations, avoiding iterative and matrix inversion calculations during online solutions. This meets the real-time requirements of online e-commerce transactions and comprehensively considers the temporal characteristics of orders, the global operational capabilities of stores, the global inventory constraints of warehouses, and the randomness of future orders, achieving an allocation result within the preset observation period that approximates the global optimum.

[0100] Optionally, in one embodiment of this application, the formula for correcting the shadow price is:

[0101]

[0102] in, For w i The transpose of the k-th row, Let be the transpose of the k-th row of d, and α be the learning rate.

[0103] Specifically, in this embodiment of the application, the shadow price of warehouse capacity can be adjusted based on the current order's booking results, and the shadow price of warehouse capacity constraints can be adjusted based on the response status of order i. The adjustment formula is as follows:

[0104]

[0105] in, For w i The transpose of the k-th row, Let be the transpose of the k-th row of d. and The dimensions are all S×1, and α is the learning rate, which can generally be selected as...

[0106] Combination Figure 2 and Figure 3 As shown, an embodiment is used to illustrate in detail the working principle of the online booking method of this application that takes into account order sequence and warehouse capacity constraints.

[0107] like Figure 2 As shown, embodiments of this application may include the following steps:

[0108] Step S201: Establish a global optimization model for the inventory allocation problem and solve for the warehouse capacity consumed by orders within a preset observation period. In actual implementation, this embodiment of the application can preset the observation period, such as one day, and calculate the average warehouse capacity occupied by each order by obtaining the predicted order quantity value for the preset observation period.

[0109] Specifically, in this embodiment of the application, the predicted order quantity for a preset observation period can be defined as N, the shadow price for initializing warehouse capacity constraints can be p = 0, and the warehouse capacity available for the preset observation period can be defined as matrix v. real The actual quantity of goods sold during the actual observation period should always be less than the available inventory v. real The warehouse capacity consumed by orders within a preset observation period is defined as matrix v, where v can be obtained through global simulation. The simulated order sequence can use samples of orders from the previous observation period. Global optimization is used to obtain the expected warehouse capacity consumption v within the overall observation period, where v real Both v and v have dimensions K×S, where K is the total number of product types and S is the total number of warehouses.

[0110] Furthermore, embodiments of this application can establish an objective function for a global optimization model of the position-fixing problem, with the optimization objectives being to maximize position-fixing gross profit and minimize warehouse operation costs:

[0111]

[0112] in, All are decision variables. This indicates whether product k in the current order i is responded to by warehouse s. This means that item k in order i will be handled by warehouse s. This means that item k in order i is not responded to by warehouse s. This indicates whether there are any items in the current order i that have been shipped from warehouse s. For order i, the quantity of product k required is... The gross profit of the commodity k in the warehouse s, The operating cost of the warehouse s, The warehouse gross profit of the order i, The warehouse operating cost consumption of the order i.

[0113] As a possible implementation, the embodiment of the application can establish the constraint conditions of the global optimization model of the warehouse allocation problem, specifically including:

[0114] Category delivery constraint:

[0115]

[0116] It is indicated that only one warehouse can be selected for delivery of a certain commodity in a single order.

[0117] Warehouse capacity constraint:

[0118]

[0119] It is indicated that the total amount of a certain commodity delivered from a certain warehouse cannot exceed the inventory of the commodity in the warehouse, wherein It is indicated that the inventory of the commodity k in the warehouse s is the s-th row and the k-th column of the matrix v real .

[0120] Decision variable association constraint:

[0121]

[0122]

[0123] It is indicated that if the warehouse s responds to the commodity in the order i, it constitutes an operation to the warehouse, so that If the warehouse s does not respond to any commodity in the order i, then

[0124] The embodiment of the application can establish the global optimization model of the warehouse allocation problem with the objective function of solving the global optimization model of the warehouse allocation problem as the target, and the category delivery, warehouse capacity and decision variable association as the constraints, to obtain the warehouse capacity consumed in the overall review period Thus, the matrix v is obtained, and the s-th row and the k-th column of v are v (s,k) .

[0125] Step S202: Obtain the average warehouse capacity occupied by each order. Obtain the average warehouse capacity occupied by each order: for each warehouse, the average warehouse capacity d = v / N of each commodity is obtained, wherein the dimension of d is consistent with v, which is K×S, K is the total number of commodity categories, and S is the total number of warehouses.

[0126] Step S203: obtaining the order that needs to be responded currently. In actual execution process, the embodiment of the present application can obtain the order that needs to be responded currently on line, calculate the modified profit of responding the order by each warehouse, and constantly select the warehouse with the maximum modified profit to satisfy all goods in the current order; define the operation cost of each warehouse as o, and o s The vector composed of the warehouse capacity, initialize the matrix H=v>0 indicating whether the warehouse capacity is left, and the specific steps are as follows:

[0127]

[0128] Further, the required quantity vector of each good in the order i is q i (the vector composed of ), and the dimension is Kx1. Step S204: calculating the modified profit of each warehouse and warehousing. The modified profit of each warehouse is calculated:

[0129] g=max(o)-o-Trace(H-q i p),

[0130] wherein the dimensions of g, o and p are all Sx1, the max(*) function is used to obtain the maximum element in the vector, and the Trace(*) function is used to obtain the diagonal matrix with the vector elements as the diagonal elements.

[0131] The embodiment of the present application can select the warehouse corresponding to the maximum element in the modified profit g, and the serial number of the warehouse is s, which is used to respond the order. After responding the order, the warehouse capacity is reduced, which is represented as deducting the order good quantity w s that can be satisfied from the warehouse left capacity v i,s , wherein v s is the s-th column of v s , and the dimensions of v i,s and w i are both Kx1.

[0132] Step S205: whether there is a good that cannot be satisfied by the selected warehouse. If part of the goods in the order i cannot be sent out by the selected warehouse due to insufficient warehouse capacity, step S206 is entered; if all the goods in the order are responded, step S207 is entered, and q i =∑ s w i,s , and the matrix w i is constructed, and the dimension is KxS, and the s-th column is w i,s .

[0133] Step S206: the goods that cannot be satisfied in the order constitute the order that needs to be responded. The embodiment of the present application can construct the vector q i =q i -wi,s Then return to step S203.

[0134] Step S207: Adjust the shadow price of warehouse capacity. Specifically, in this embodiment, the shadow price of warehouse capacity can be adjusted based on the current order's booking results, and the shadow price of warehouse capacity constraints can be adjusted based on the response status of order i. The adjustment formula is as follows:

[0135]

[0136] in, For w i The transpose of the k-th row, Let be the transpose of the k-th row of d. and The dimensions are all S×1, and α is the learning rate, which can generally be selected as...

[0137] Step S208: Whether all orders within the evaluation period are satisfied. In this embodiment of the application, steps S203-S207 above can be repeated for each order until all orders within the preset evaluation period are satisfied.

[0138] In actual implementation, such as Figure 3 The diagram shown is a comparison of traditional methods in related technologies with the algorithm of the embodiments of this application.

[0139] The online warehousing method considering order sequence and warehouse capacity constraints proposed in this application can obtain the average warehouse capacity occupied by each order and the current order that needs to be responded to. It then calculates the adjusted profit for each warehouse responding to the order, determines the warehouse with the largest adjusted profit, and adjusts the shadow price of the warehouse capacity based on the warehousing result. This process continues until all orders within the observation period have been adjusted, determining the final warehousing result. This allows for a comprehensive consideration of the temporal characteristics of orders, the global operational capacity of stores, the global inventory constraints of warehouses, and the randomness of future orders in the real-time warehousing problem. It solves the multi-objective optimization problem of business operations, achieving a warehousing result that approximates the global optimum within the overall observation period, improving solution efficiency and demonstrating broad application prospects. Therefore, it solves the technical problems in related technologies that neglect constraints such as the global operational capacity of stores, the global inventory of warehouses, and the randomness of future orders, leading to calculation results that cannot meet global requirements and low algorithm solution efficiency.

[0140] Next, referring to the accompanying drawings, an online booking device considering order sequence and warehouse capacity constraints is described according to an embodiment of this application.

[0141] Figure 4 This is a block diagram of an online booking device that takes into account order sequence and warehouse capacity constraints according to an embodiment of this application.

[0142] likeFigure 4 As shown, the online warehouse allocation device 10 considering order sequence and warehouse capacity constraints comprises an acquisition module 100, a calculation module 200 and a determination module 300.

[0143] Specifically, the acquisition module 100 is configured to acquire the warehouse capacity occupied by each order on average.

[0144] The calculation module 200 is configured to acquire a current order that needs to be responded to at present, and calculate a revised profit of each warehouse responding to the order based on the current order, and determine a warehouse with the maximum revised profit.

[0145] The determination module 300 is configured to revise a shadow price of the warehouse capacity according to a warehouse allocation result obtained by the warehouse with the maximum revised profit, until all orders are revised in a preset observation period, and determine a final warehouse allocation result.

[0146] Optionally, in an embodiment of the present application, the acquisition module 100 comprises a constraint unit, a modeling unit and a calculation unit.

[0147] The constraint unit is configured to establish a target function of a global optimization model of the warehouse allocation problem and a constraint condition of the global optimization model of the warehouse allocation problem.

[0148] The modeling unit is configured to solve the global optimization model of the warehouse allocation problem based on the target function and the constraint condition, and obtain a warehouse capacity consumed in the whole observation period.

[0149] The calculation unit is configured to calculate the warehouse capacity occupied by each order on average according to the warehouse capacity consumed in the preset observation period.

[0150] Optionally, in an embodiment of the present application, the target function is as follows:

[0151]

[0152] wherein, are decision variables, represents whether the product k in the current order i is responded to by the warehouse s, represents whether there is a product in the current order i shipped from the warehouse s, is the quantity of the product k required by the order i, r (s ,k) is the gross profit of the product k in the warehouse s, o s is the operating cost of the warehouse s.

[0153] Optionally, in an embodiment of the present application, the constraint condition of the global optimization model of the warehouse allocation problem comprises at least one of a product category shipping constraint, a warehouse capacity constraint and a warehouse capacity constraint, wherein,

[0154] the product category shipping constraint is:

[0155]

[0156] The warehouse capacity constraint is:

[0157]

[0158] where v real(s,k) represents the inventory of commodity k in warehouse s;

[0159] The decision variable association constraint is:

[0160]

[0161]

[0162] Optionally, in an embodiment of the present application, the correction formula of the shadow price is:

[0163]

[0164] where, is the transpose of the kth row of w i , and is the transpose of the kth row of d, and a is the learning rate.

[0165] It should be noted that the foregoing explanation and description of the online warehouse allocation method embodiment considering the order sequence and warehouse capacity constraints also applies to the online warehouse allocation device embodiment considering the order sequence and warehouse capacity constraints, which will not be described here.

[0166] The online warehouse allocation device considering the order sequence and warehouse capacity constraints according to the embodiment of the present application can obtain the average warehouse capacity occupied by each order and the current order to be responded, and calculate the corrected profit of each warehouse responding to the order, determine the warehouse with the maximum corrected profit, and then obtain the shadow price of the warehouse capacity corrected by the warehouse allocation result, until all orders in the examination period are corrected, and the final warehouse allocation result is determined, so that the time sequence characteristics of the order, the global operation capacity of the store, the global inventory constraint of the warehouse, and the randomness of the future order can be considered in the real-time warehouse allocation problem, the multi-objective optimization problem of the business is solved, and the warehouse allocation result in the whole examination period is close to the global optimum, the solving efficiency is improved, and the application prospect is wide. Therefore, the technical problem that the global operation capacity of the store, the global inventory constraint of the warehouse, and the randomness of the future order are ignored in the related art, resulting in that the calculation result cannot meet the global demand and the algorithm solving efficiency is low is solved.

[0167] Figure 5 The structure schematic diagram of the electronic device provided by the embodiment of the present application is shown. The electronic device can include:

[0168] The memory 501, the processor 502 and the computer program stored in the memory 501 and executable on the processor 502.

[0169] The processor 502 implements the online warehouse allocation method considering order sequence and warehouse capacity constraints provided in the above embodiments when executing the program.

[0170] Further, the electronic device further comprises:

[0171] The communication interface 503 is used for communication between the memory 501 and the processor 502.

[0172] The memory 501 is used for storing the computer program executable on the processor 502.

[0173] The memory 501 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0174] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 5 In the figure, only one thick line is used to represent, but it does not mean that there is only one bus or one type of bus.

[0175] Optionally, in specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can complete communication between each other through an internal interface.

[0176] The processor 502 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0177] The embodiment also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the online warehouse reservation method considering order sequence and warehouse capacity constraints as above.

[0178] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0179] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0180] Any process or method descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for performing a step of a preferred implementation of the application, and that the various embodiments of the application can include different implementations of the code modules, segments, or portions of code and that the software components include one or more code modules, segments, or portions of code. The software components can also include the code modules stored in any sequence, stored in an object-oriented manner, or otherwise stored in a compressed, interpreted, or encrypted format. The software components can be executed by a hardware device, such as a microprocessor, or by an application-specific integrated circuit (ASIC), or by a combination of the two.

[0181] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of them. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0182] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0183] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiments is included.

[0184] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0185] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. An online warehousing method considering order sequence and warehouse capacity constraints, characterized in that, The method comprises the following steps: obtaining the average warehouse capacity occupied by each order; obtaining a current order that needs to be responded to, and calculating the modified profit of each warehouse in responding to the order based on the current order, and determining a warehouse with the maximum modified profit; and modifying the shadow price of the warehouse capacity according to the warehousing result obtained by the warehouse with the maximum modified profit until the modification of all orders in a preset observation period is completed, and determining a final warehousing result; wherein the obtaining of the average warehouse capacity occupied by each order comprises: establishing a target function of a global optimization model of the warehousing problem and a constraint condition of the global optimization model of the warehousing problem; solving the global optimization model of the warehousing problem based on the target function and the constraint condition to obtain the warehouse capacity consumed in the whole observation period; calculating the average warehouse capacity occupied by each order based on the warehouse capacity consumed in the preset observation period; wherein the target function is: ， in, , All are decision variables. This indicates the current order. i Goods in k Is it from the warehouse? s In response, This refers to the order currently being discussed. i Are there any goods in the warehouse? s Shipment, For the order i The product in question k Quantity, For the warehouse s The goods described in k Gross profit, For the warehouse s Operating costs; wherein the constraint condition of the global optimization model of the warehousing problem comprises a category shipment constraint, a warehouse capacity constraint and a decision variable correlation constraint, wherein the category shipment constraint is: ， the warehouse capacity constraint is: ， wherein, is the inventory of the goods in the warehouse s k ;​ the decision variable correlation constraint is: ， 。 2. The method of claim 1, wherein, the modified formula of the shadow price is: ， wherein, is the transpose of the first row of k , is d the transpose of the first row of k , is the learning rate.

3. An online warehousing device considering order sequence and warehouse capacity constraints, characterized in that, comprises: an obtaining module configured to obtain the average warehouse capacity occupied by each order; a calculating module configured to obtain a current order that needs to be responded to, and calculate the modified profit of each warehouse in responding to the order based on the current order, and determine a warehouse with the maximum modified profit; a determining module configured to modify the shadow price of the warehouse capacity according to the warehousing result obtained by the warehouse with the maximum modified profit until the modification of all orders in a preset observation period is completed, and determine a final warehousing result; wherein the obtaining module comprises: a constraint unit configured to establish a target function of a global optimization model of the warehousing problem and a constraint condition of the global optimization model of the warehousing problem; a modeling unit configured to solve the global optimization model of the warehousing problem based on the target function and the constraint condition to obtain the warehouse capacity consumed in the whole observation period; and a calculating unit configured to calculate the average warehouse capacity occupied by each order based on the warehouse capacity consumed in the preset observation period; wherein the target function is: ， in, , All are decision variables. This indicates the current order. i Goods in k Is it from the warehouse? s In response, This refers to the order currently being discussed. i Are there any goods in the warehouse? s Shipment, For the order i The product in question k Quantity, For the warehouse s The goods described in k Gross profit, For the warehouse s Operating costs; wherein the constraint condition of the global optimization model of the warehousing problem comprises a category shipment constraint, a warehouse capacity constraint and a decision variable correlation constraint, wherein the category shipment constraint is: ， the warehouse capacity constraint is: ， wherein, is the inventory of the goods in the warehouse s k ;​ the decision variable correlation constraint is: ， 。 4. The apparatus of claim 3, wherein, the modified formula of the shadow price is: ， wherein, is the transpose of the first row of k is the transpose of the first row of d is k the transpose of the first row of is the learning rate.

5. An electronic device, comprising: comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the online warehousing method considering order sequence and warehouse capacity constraints as claimed in claim 1 or 2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the online warehousing method considering order sequence and warehouse capacity constraints as claimed in claim 1 or 2.

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