Goods distribution center data processing method, device and equipment

Abstract

The invention provides a goods distribution center data processing method, device and equipment. The method comprises the following steps: obtaining goods distribution center data of a plurality of periods, wherein the goods distribution center data of each period comprises demand data, supply network data and cost data of distributors; taking the goods distribution center data of the plurality of periods as input of a pre-constructed target model, solving decision variables in the target model through a prepared optimization mode, and obtaining goods distribution center site selection data and supply matching relation data of the plurality of periods; the target model is constructed based on a mixed integer programming algorithm framework; the target function of the target model comprises a newly built warehouse and rental cost thereof, a warehouse closing and rental cost thereof, a warehouse expanding and rental cost thereof, a transportation cost, a warehouse explosion penalty term and a supply matching relation change penalty term. According to the scheme, the sorting center site selection data and the supply matching relation data of multiple periods can be automatically solved, and the accuracy and the efficiency are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and equipment for data processing in a distribution center. Background Art

[0002] A distribution center is also called a distribution center or a distribution center. It is a key node in the logistics system. It is a place where activities such as cargo classification, distribution and temporary storage are centralized. Its main purpose is to effectively integrate cargo from different suppliers or production bases, and then sort and distribute them according to different destinations, customer needs or transportation routes.

[0003] The location selection and supply matching relationship of distribution centers over multiple cycles refers to the decisions on opening, expanding, and closing warehouses of distribution centers, as well as the supply matching relationship between distribution centers and distributors within an overall planning cycle of several years.

[0004] In the related art, the expert review method is usually used to determine the location data of the distribution center and the supply matching relationship data for multiple periods based on manual experience. However, this method has poor accuracy and low efficiency. Summary of the invention

[0005] In view of the above problems, the present application aims to provide a distribution center data processing method, device and equipment to improve the accuracy and efficiency of solving distribution center site selection data and supply matching relationship data for multiple cycles.

[0006] In a first aspect, the present application provides a method for processing data of a distribution center, the method comprising:

[0007] Acquire distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of distributors;

[0008] The distribution center data of multiple periods is used as the input of the pre-built target model, and the decision variables in the target model are solved by the prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple periods;

[0009] Among them, the target model is constructed based on the mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closing and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty and supply matching relationship change penalty; the constraint conditions of the target model include at least one of the following: new warehouse constraints, warehouse closing and expansion constraints, supply matching relationship constraints, storage capacity occupancy constraints and supply matching relationship change constraints.

[0010] In a possible implementation, the storage capacity occupancy constraint is determined based on the floor space required by the distributor for goods, the warehouse opening area, the warehouse closing area, the warehouse expansion area, and the warehouse explosion area.

[0011] In a possible implementation, the warehouse explosion penalty item is determined based on the warehouse explosion area and the warehouse explosion penalty coefficient of the distribution center.

[0012] In a possible implementation, the supply matching relationship change constraint is determined based on a change indication coefficient of a distribution center and distribution centers to which adjacent period distributors belong.

[0013] In a possible implementation, the supply matching relationship change penalty item is determined based on a change penalty coefficient and a change indication coefficient.

[0014] In a possible implementation, the target model includes a single-period model and a multi-period model. The data of the distribution centers of multiple periods are used as the input of the pre-built target model, and the decision variables in the target model are solved by a prepared optimization method to obtain the location data of the distribution centers of multiple periods and the supply matching relationship data, including:

[0015] If it is determined that the distributor's demand data in multiple periods meets the first preset data requirement, then selecting the period corresponding to the demand data that meets the second preset data requirement as the target period;

[0016] Based on the demand data of the target period, the lower limit of the number of positions to be opened is determined; and based on the number of periods in multiple periods, the upper limit of the number of positions to be opened is determined;

[0017] Select any opening quantity between the lower opening quantity limit and the upper opening quantity limit, and update the distribution center data of the target period;

[0018] The updated distribution center data of the target period is used as the input of the single-period model, and the decision variables in the single-period model are solved by the prepared optimization method to obtain the candidate warehouse information corresponding to the selected opening quantity;

[0019] The distribution center data of multiple periods are updated based on the information of each alternative warehouse; the updated distribution center data of multiple periods are used as the input of the multi-period model, and the decision variables in the multi-period model are solved through the prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple periods.

[0020] In a possible implementation, if the following conditions are met, it is determined that the demand data of the distributor in multiple cycles meets the first preset data requirement:

[0021] The difference between the demand data of the distributors in the Nth period and the first period is greater than the preset threshold;

[0022] Wherein, N is greater than or equal to 2, and N is less than or equal to the number of cycles.

[0023] In a possible implementation, the demand data includes: demand quantity, and the total area corresponding to the demand quantity; the supply network data includes at least: established warehouse data, alternative warehouse data, distributor location data, and distance data between the distributor and its corresponding distribution center; the cost data includes at least: warehouse opening cost, warehouse closing cost, warehouse expansion cost, transportation cost, and rate data.

[0024] In a second aspect, the present application provides a data processing device for a distribution center, the device comprising:

[0025] An acquisition unit is used to acquire the distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of the distributor;

[0026] A processing unit is used to use the distribution center data of multiple periods as the input of the pre-built target model, solve the decision variables in the target model through a prepared optimization method, and obtain the distribution center location data and supply matching relationship data of multiple periods;

[0027] Among them, the target model is constructed based on the mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closing and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty and supply matching relationship change penalty; the constraint conditions of the target model include at least one of the following: new warehouse constraints, warehouse closing and expansion constraints, supply matching relationship constraints, storage capacity occupancy constraints and supply matching relationship change constraints.

[0028] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0029] The memory stores computer-executable instructions;

[0030] The processor executes the computer-executable instructions stored in the memory to implement the method in any possible implementation of the first aspect above.

[0031] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method in any possible implementation of the above-mentioned first aspect.

[0032] The present application provides a distribution center data processing method, device and equipment, the method includes: obtaining distribution center data of multiple periods, wherein the distribution center data of each period includes: distributor demand data, supply network data and cost data; using the distribution center data of multiple periods as the input of a pre-constructed target model, solving the decision variables in the target model through a prepared optimization method, and obtaining the distribution center location data and supply matching relationship data of multiple periods; wherein the target model is constructed based on a mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closure and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty item and supply matching relationship change penalty item; the constraint conditions of the target model include at least one of the following: new warehouse constraint, warehouse closure and warehouse expansion constraint, supply matching relationship constraint, storage capacity occupancy constraint and supply matching relationship change constraint. This scheme uses a target model constructed based on a mixed integer programming algorithm framework to process distribution center data of multiple periods, which can realize the automatic solution of distribution center location data and supply matching relationship data of multiple periods, and improves accuracy and efficiency. In addition, the target model also includes warehouse explosion penalty items, supply matching relationship change penalty items, storage capacity occupancy constraints and supply matching relationship change constraints, which can make the distribution center location data and supply matching relationship data obtained in multiple cycles more in line with actual business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] Figure 1 A flow chart of a data processing method for a distribution center provided in an embodiment of the present application;

[0035] Figure 2 A flowchart of another method for processing data in a distribution center provided in an embodiment of the present application;

[0036] Figure 3 A structural diagram of a data processing device for a distribution center provided in an embodiment of the present application;

[0037] Figure 4 A hardware structure diagram of an electronic device provided in an embodiment of the present application.

[0038] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments.

[0039] Description of reference numerals:

[0040] 300-data processing device of the distribution center; 301-acquisition unit; 302-processing unit;

[0041] 401 - processor; 402 - memory; 403 - communication interface; 404 - communication bus. DETAILED DESCRIPTION

[0042] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0043] In the embodiments of the present application, words such as "first" and "second" are used to distinguish the same or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference.

[0044] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0045] The location and supply matching relationship of distribution centers in multiple cycles refers to the decision on the opening, expansion and withdrawal of distribution centers within the overall planning cycle of several years, as well as the decision on the supply matching relationship between distribution centers and distributors. The location and supply matching relationship analysis of distribution centers in multiple cycles is used to optimize the construction cost, inventory cost and transportation cost in the future. For the supply network of external supply of goods, the freight difference from the upstream source of goods to each distribution center is very small. The layout and location of each distribution center mainly considers the transportation method, freight and distance from its physical location to each distributor, the demand of each distributor, and the storage capacity and turnover rate of the distribution center. There are some key and difficult points in this type of problem. First, the location and storage capacity of each distribution center. First, the location and storage capacity of the existing distribution centers in the current distribution center network should be considered as the initial state. Secondly, the new distribution center should screen the alternative addresses in advance, as well as the objective storage capacity that can be rented. Finally, consider three types of feasible decisions: opening, expanding and withdrawing. Opening warehouses should be targeted at newly built distribution centers, while warehouse expansion and closure should be targeted at distribution centers that have been built or are expected to be built. Second, the calculation of the demand of each distributor and the turnover rate of the distribution center. Generally speaking, the matching relationship between distributors and distribution centers is a one-to-one match. The demand of distributors needs to be converted into the volume of turnover in the distribution center to determine the storage capacity occupied by the distributor corresponding to the distribution center. Third, the freight calculation from each distribution center to each distributor. The timeliness of the categories required by each distributor is different, and the required transportation methods are also different, and the corresponding rates are also different. At the same time, the transportation distance between the distribution center and the distributor will also be calculated with the rate to obtain the final freight, which is an important component that needs to be optimized.

[0046] In the current classic multi-cycle distribution center site selection and supply matching relationship analysis process, the most common method is the expert review method. The site selection area is first divided in the form of artificial division, and then the construction priority and construction time are discussed by category, and the final decision recommendation is made through expert review. This type of method focuses on manual experience, but this type of method often has problems such as incomplete consideration and inaccurate calculation. As the scale of the problem increases, the calculation time and difficulty will also increase significantly, and once changes occur, the adjustment difficulty will also be very high. In addition, within the multi-cycle time range, the establishment of different distribution centers affects and restricts each other. Relying solely on human judgment is inefficient and the solution is not optimal.

[0047] In order to solve the above technical problems, the embodiment of the present application provides a distribution center data processing method, device and equipment, which uses a target model built based on a mixed integer programming algorithm framework to process the distribution center data of multiple periods, and can realize the automatic solution of the distribution center location data and supply matching relationship data of multiple periods, thereby improving the accuracy and efficiency. In addition, the target model also includes warehouse explosion penalty items, supply matching relationship change penalty items, storage capacity occupancy constraints and supply matching relationship change constraints, which can make the distribution center location data and supply matching relationship data obtained for multiple periods more in line with actual business needs.

[0048] Figure 1 A flow chart of a distribution center data processing method provided in an embodiment of the present application. This embodiment can be applicable to multi-cycle distribution center site selection and supply matching relationship analysis scenarios. For example, the general distributor of a certain brand of automobiles agrees with distributors across the country to provide goods to each distributor, and the goods may include a certain brand of automobiles and related spare parts. The general distributor can cooperate with multiple distribution centers across the country to transport the goods from the general distributor to each distribution center, and each distribution center will store the goods and deliver the goods to the corresponding distributors when needed. The general distributor pays fees to each distribution center. The method can be executed by a distribution center data processing device, which can be implemented by software and / or hardware, and is specifically configured in an electronic device. Figure 1 As shown, the method includes:

[0049] Step 101, obtaining distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of distributors.

[0050] The length of a cycle can be set according to actual needs, for example, a cycle can be 1 year. M cycles of distribution center data can be obtained. M can be set according to actual needs, for example, 5 years of distribution center data can be obtained. The current year can be taken as the first cycle, and the second, third, fourth, and fifth years can be taken as the second, third, fourth, and fifth cycles respectively.

[0051] Among them, the distributor's demand data refers to the data related to the quantity, volume, etc. of the goods required by the distributor in the current cycle.

[0052] Among them, supply network data refers to data related to the location of distribution centers and distributors in the current cycle.

[0053] Among them, cost data refers to the costs incurred by the distribution center in the process of storing and distributing goods in the current cycle.

[0054] In one implementable method, the demand data includes: demand quantity, and the total area corresponding to the demand quantity; the supply network data includes at least: established warehouse data, alternative warehouse data, distributor location data, and distance data between the distributor and its corresponding distribution center; the cost data includes at least: warehouse opening cost, warehouse closing cost, warehouse expansion cost, transportation cost, and rate data.

[0055] Among them, demand refers to the demand of distributors for the required goods.

[0056] The total area is calculated based on the volume of goods required by distributors.

[0057] The data of established warehouses include the location and area of ​​established warehouses. Warehouse refers to the warehouse of the distribution center.

[0058] Among them, the alternative warehouse data includes the location and area of ​​the alternative warehouse.

[0059] Among them, warehouse opening includes newly built warehouses and newly rented warehouses.

[0060] Among them, warehouse expansion refers to expanding the area of ​​an established warehouse.

[0061] During implementation, these distribution center data are used to solve the distribution center location selection data and supply matching relationship data for multiple periods, which is conducive to ensuring the accuracy of the distribution center location selection data and supply matching relationship data for multiple periods.

[0062] Step 102, using the distribution center data of multiple periods as the input of a pre-built target model, solving the decision variables in the target model through a prepared optimization method, and obtaining the distribution center location data and supply matching relationship data of multiple periods; wherein the target model is constructed based on a mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closure and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty item and supply matching relationship change penalty item; the constraint conditions of the target model include at least one of the following: new warehouse constraint, warehouse closure and expansion constraint, supply matching relationship constraint, storage capacity occupancy constraint and supply matching relationship change constraint.

[0063] The prepared optimization method refers to an optimization method prepared in advance. This solution does not limit the optimization method. For example, the optimization method can be a solver or a heuristic method.

[0064] Among them, the distribution center site selection data includes the location and area of ​​the distribution center.

[0065] The supply matching relationship refers to the corresponding relationship between the distribution of goods between distributors and distribution centers. Usually, the same distribution center can distribute goods to multiple distributors, and a distributor can only obtain goods from one distribution center.

[0066] Among them, the change in supply matching relationship means that the goods of distributor A were originally distributed by distribution center B, but are changed to be distributed by distribution center C.

[0067] In the related technology, warehouse overflow is usually not allowed in the distribution center. However, for some special scenarios, warehouse overflow is allowed to a certain extent, which can save costs and facilitate management and scheduling. For example, the distribution center A only needs 10 square meters of space to store the goods shipped from the general distributor. If warehouse overflow is not allowed, the general distributor needs to store the goods in the distribution center B. The cost of going to the distribution center B is much higher than the cost of a 10 square meter warehouse overflow in the distribution center A.

[0068] The warehouse explosion penalty item and storage capacity occupancy constraint work together to allow a certain limit of warehouse explosion in the distribution center.

[0069] In related technologies, changes in supply matching relationships are usually not allowed. However, for some special scenarios, changes in supply matching relationships are allowed to a certain extent, which will increase profits. For example, changes in supply matching relationships reduce transportation costs; changes in supply matching relationships reduce warehouse expansion costs.

[0070] The supply matching relationship change penalty item and the supply matching relationship change constraint work together to allow the distribution center to change the supply matching relationship within a certain limit.

[0071] The distribution center data processing method provided in the above embodiment uses a target model built based on a mixed integer programming algorithm framework to process the distribution center data of multiple periods, which can realize the automatic solution of the distribution center location data and supply matching relationship data of multiple periods, thereby improving accuracy and efficiency. In addition, the target model also includes warehouse explosion penalty items, supply matching relationship change penalty items, storage capacity occupancy constraints, and supply matching relationship change constraints, which can make the distribution center location data and supply matching relationship data obtained for multiple periods more in line with actual business needs.

[0072] Figure 2 A flow chart of another method for processing data in a distribution center provided in an embodiment of the present application. Figure 1 Based on the illustrated embodiment, improvements are made to the data processing method of the distribution center.

[0073] like Figure 2 As shown, a data processing method for a distribution center may include the following steps:

[0074] Step 201, obtaining distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of distributors.

[0075] Specifically, the principle and implementation of step 201 are similar to those of step 101 and will not be described in detail.

[0076] Step 202: If it is determined that the demand data of the distributor in multiple cycles meets the first preset data requirement, then the cycle corresponding to the demand data meeting the second preset data requirement is selected as the target cycle.

[0077] Optionally, the first preset data requirement may be that the distribution center data in multiple cycles has an increasing trend that changes over time.

[0078] In one achievable manner, if the following conditions are met, it is determined that the distributor's demand data in multiple cycles meets the preset data requirements: the difference between the distributor's demand data in the Nth cycle and the first cycle is greater than a preset threshold; wherein N is greater than or equal to 2, and N is less than or equal to the number of cycles.

[0079] The set first preset data requirements meet the actual business situation, so that the distribution center location data and supply matching relationship data of multiple periods obtained based on the first preset data requirements are more in line with actual business needs.

[0080] In implementation, if there are demand data corresponding to multiple cycles, and the difference between the demand data corresponding to the first cycle is greater than the preset threshold, then these demand data can be sorted according to size, and any demand data can be selected from them, and the cycle corresponding to the demand data is used as the target cycle. For example, the largest demand data can be selected, and the cycle corresponding to the largest demand data can be used as the target cycle.

[0081] Optionally, if it is determined that the distributor's demand data in multiple cycles does not meet the first preset data requirements, the distribution center data of multiple cycles can be used as the input of the multi-cycle model, and the decision variables in the multi-cycle model can be solved by a prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple cycles.

[0082] Step 203, based on the demand data of the target period, determine the lower limit of the number of positions to be opened; and based on the period numbers of multiple periods, determine the upper limit of the number of positions to be opened.

[0083] Specifically, the minimum number of warehouse openings that meets the warehouse area requirement of the demand data of the target period may be used as the lower limit of the number of warehouse openings.

[0084] Specifically, the number of open positions corresponding to each period can be used as the upper limit of the number of open positions.

[0085] Step 204, select any opening quantity between the lower opening quantity limit and the upper opening quantity limit, and update the distribution center data of the target period.

[0086] Specifically, the candidate warehouse data in the distribution center data may also include the allowed quantity of the candidate warehouse. The selected open warehouse quantity may be used as the allowed quantity of the candidate warehouse.

[0087] Furthermore, in the process of solving the single-period model, the allowed number of alternative bins can be used as a constraint.

[0088] Step 205, using the updated distribution center data of the target period as the input of the single-period model, solving the decision variables in the single-period model through the prepared optimization method, and obtaining the candidate warehouse information corresponding to the selected opening quantity; wherein the target model includes a single-period model and a multi-period model; the target model is constructed based on the mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closure and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty and supply matching relationship change penalty; the constraint conditions of the target model include at least one of the following: new warehouse constraint, warehouse closure and expansion constraint, supply matching relationship constraint, storage capacity occupancy constraint and supply matching relationship change constraint.

[0089] The candidate warehouse data at least includes the location and area of ​​the candidate warehouse. The candidate warehouse is used for opening a warehouse.

[0090] In practice, the periodic parameters in the multi-period model are constantized to obtain a single-period model. The multi-period model has the same objective function and constraint terms as the target model.

[0091] In one achievable manner, the warehouse explosion penalty item is determined based on the warehouse explosion area and the warehouse explosion penalty coefficient of the distribution center.

[0092] Specifically, the liquidation penalty item can be easily determined by using the liquidation area and liquidation penalty coefficient.

[0093] In one implementable manner, the supply matching relationship change penalty item is determined based on a change penalty coefficient and a change indication coefficient.

[0094] Specifically, the change penalty coefficient and the change indication coefficient can be used to conveniently determine the change penalty item of the supply matching relationship.

[0095] The objective function formula of the target model is as follows:

[0096]

[0097] Where i∈I={1,2,…,I} represents the set of all dealers; j∈J e ={1,2,...,J e} represents the set of all opened positions; j∈J n ={1,2,...,J n} represents the set of all candidate warehouses; j∈J={1,2,…,J} represents the set of all warehouse addresses; m∈M={m 1 , m 2 , ...} represents the set of all optional warehouse area sizes; p∈P={p 1 , p 2 ,...} represents the set of all warehouse expandable areas; l∈L={1,2,3} represents the set of three modes of transportation, corresponding to full-truck-load (FTL), less-than-truck-load (LTL), and air transport (AIR); t∈T={1,2,...,T} represents the set of all cycles; k∈T k ={1,2,...,T 1} represents the set of all cycles that allow warehouse changes; T 1 Less than or equal to T; T k ∈T; F m represents the construction cost of each warehouse; T represents the number of cycles; α represents the rental cost of the warehouse per unit area per cycle; If warehouse j is opened in period k and the area is m, it is 1, otherwise it is 0; f represents the closing fee of each warehouse; m′ jt represents the known area of ​​warehouse j in period t, and there is a one-to-one correspondence between warehouses and distribution centers; Indicates that if warehouse j is closed in period k, it is 1, otherwise it is 0; It represents the total billable weight of the goods that the i dealer delivers from the j warehouse in period t using the l transportation method; represents the rate matrix of unit distance and unit weight from warehouse j to dealer i using mode l of transportation; s ij represents the distance matrix from distributor i to warehouse j; x ijt It means that if the i dealer chooses the j warehouse for transportation in the t period, it is 1, otherwise it is 0; e represents the expansion cost of each warehouse; Indicates that if warehouse j is expanded in period k, it is 1, otherwise it is 0; It means that the area of ​​the built warehouse j exceeds the upper limit in period t, that is, the warehouse explosion area; represents the liquidation penalty coefficient; γ represents the change penalty coefficient; c ijt It means that if dealer i chooses warehouse j in period t but changes the corresponding warehouse in period t+1, it is 1, otherwise it is 0, which is the change indication coefficient.

[0098] Among them, the first item in the above formula represents the cost of building a new warehouse and its rental cost; the second item represents the cost of closing a warehouse and its rental cost; the third item represents the cost of expanding a warehouse and its rental cost; the fourth item represents the transportation cost; the fifth item represents the penalty for warehouse explosion; and the sixth item represents the penalty for changes in the supply matching relationship.

[0099] In the above formula, the decision variables include: x ijt , c ijt ,

[0100] In one achievable manner, the storage capacity occupancy constraint is determined based on the floor space required by the distributor for goods, the warehouse opening area, the warehouse closing area, the warehouse expansion area, and the warehouse explosion area.

[0101] Specifically, the storage capacity occupancy constraint can be conveniently determined in this way.

[0102] In one achievable manner, the supply matching relationship change constraint is determined based on the change indication coefficient of the distribution center and the distribution center to which the adjacent period distributors belong.

[0103] Specifically, supply matching relationship change constraints can be conveniently determined in this way.

[0104] Specifically, the formula for creating a new warehouse constraint is as follows. For explanations of the relevant parameters in this formula, please refer to the objective function formula above.

[0105]

[0106] Specifically, the formula for closing and expanding positions is as follows. For explanations of the relevant parameters in this formula, please refer to the above objective function formula.

[0107]

[0108] Specifically, the formula for the supply matching relationship constraint is as follows. For explanations of the relevant parameters in this formula, please refer to the above objective function formula.

[0109]

[0110] Specifically, the formula for storage capacity occupancy constraint is as follows.

[0111]

[0112] Among them, dm it It represents the total area required by the dealer i in period t; β represents the coefficient for converting the sum of the goods area into the total area; trm represents the turnover rate based on the amount; for the explanation of other parameters in this formula, please refer to the above objective function formula.

[0113] Among them, β∑ i∈I dmit x ijt / trm represents the floor space required by the distributor for the goods;

[0114] in, Indicates the opening area;

[0115] in, Indicates the closed warehouse area;

[0116] in, Indicates the warehouse expansion area;

[0117] in, Indicates the margin call area.

[0118] Where, trm =

[0119] Specifically, the formula for the supply matching relationship change constraint is as follows.

[0120]

[0121] Among them, x ij(t+1) It means that if the i dealer chooses the j warehouse for transportation in the t+1 period, it is 1, otherwise it is 0. If it is 1, it means that the distribution center of the i dealer is the distribution center corresponding to the j warehouse. For the explanation of other parameters in this formula, please refer to the above objective function formula.

[0122] Step 206, based on the information of each alternative warehouse, the distribution center data of multiple periods are updated; the updated distribution center data of multiple periods are used as the input of the multi-period model, and the decision variables in the multi-period model are solved by the prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple periods.

[0123] In implementation, the collection of information of each candidate warehouse may be used as candidate warehouse data in the updated distribution center data of multiple cycles.

[0124] Specifically, this solution is based on a single-period model and captures a portion of high-quality alternative warehouse information that has price advantages or position building advantages, avoiding traversing ultra-large-scale alternative warehouse data sets in multi-period dimensions, and greatly improving the solution efficiency without losing too much accuracy.

[0125] Figure 3 This is a schematic diagram of the structure of a data processing device for a distribution center provided in an embodiment of the present application. The device may be in the form of software and / or hardware. Figure 3 As shown, a distribution center data processing device 300 includes: an acquisition unit 301 and a processing unit 302.

[0126] The acquisition unit 301 is used to acquire the distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of the distributor;

[0127] The processing unit 302 is used to use the distribution center data of multiple periods as the input of the pre-built target model, solve the decision variables in the target model through the prepared optimization method, and obtain the distribution center location data and supply matching relationship data of multiple periods;

[0128] Among them, the target model is constructed based on the mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closing and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty and supply matching relationship change penalty; the constraint conditions of the target model include at least one of the following: new warehouse constraint, warehouse closing and expansion constraint, supply matching relationship constraint, storage capacity occupancy constraint and supply matching relationship change constraint.

[0129] In one achievable manner, the storage capacity occupancy constraint is determined based on the floor space required by the distributor for goods, the warehouse opening area, the warehouse closing area, the warehouse expansion area, and the warehouse explosion area.

[0130] In one achievable manner, the warehouse explosion penalty item is determined based on the warehouse explosion area and the warehouse explosion penalty coefficient of the distribution center.

[0131] In one achievable manner, the supply matching relationship change constraint is determined based on the change indication coefficient of the distribution center and the distribution center to which the adjacent period distributors belong.

[0132] In one implementable manner, the supply matching relationship change penalty item is determined based on a change penalty coefficient and a change indication coefficient.

[0133] The processing unit 302 is specifically used for the target model including a single-period model and a multi-period model. If it is determined that the demand data of the distributor in multiple periods meets the first preset data requirement, the period corresponding to the demand data meeting the second preset data requirement is selected as the target period;

[0134] Based on the demand data of the target period, the lower limit of the number of positions to be opened is determined; and based on the number of periods in multiple periods, the upper limit of the number of positions to be opened is determined;

[0135] Select any opening quantity between the lower opening quantity limit and the upper opening quantity limit, and update the distribution center data of the target period;

[0136] The updated distribution center data of the target period is used as the input of the single-period model, and the decision variables in the single-period model are solved by the prepared optimization method to obtain the candidate warehouse information corresponding to the selected opening quantity;

[0137] The distribution center data of multiple periods are updated based on the information of each alternative warehouse; the updated distribution center data of multiple periods are used as the input of the multi-period model, and the decision variables in the multi-period model are solved through the prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple periods.

[0138] In an achievable manner, if the following conditions are met, it is determined that the demand data of the distributor in multiple cycles meets the first preset data requirement:

[0139] The difference between the demand data of the distributors in the Nth period and the first period is greater than the preset threshold;

[0140] Wherein, N is greater than or equal to 2, and N is less than or equal to the number of cycles.

[0141] In one implementable method, the demand data includes: demand quantity, and the total area corresponding to the demand quantity; the supply network data includes at least: established warehouse data, alternative warehouse data, distributor location data, and distance data between the distributor and its corresponding distribution center; the cost data includes at least: warehouse opening cost, warehouse closing cost, warehouse expansion cost, transportation cost, and rate data.

[0142] The data processing device for the distribution center provided in the embodiment of the present application has the same implementation principle and technical effects as those in the aforementioned data processing method embodiment for the distribution center. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned data processing method embodiment for the distribution center.

[0143] Figure 4 A hardware structure diagram of an electronic device provided in an embodiment of the present application. This embodiment provides an electronic device, comprising: at least one processor 401, and a memory 402 in communication with the at least one processor 401; the memory 402 stores computer-executable instructions; the processor 401 executes the computer-executable instructions stored in the memory 402, so as to implement the distribution center data processing method described in any of the above embodiments.

[0144] Figure 4 The electronic device shown also includes a communication interface 403 and a communication bus 404, wherein the processor 401, the memory 402 and the communication interface 403 are connected to each other via the communication bus 404. The communication bus 404 can be divided into an address bus, a data bus, a control bus, etc. Figure 4 In the figure, only one thick line is used to represent the communication bus 404, but it does not mean that there is only one communication bus 404 or one type of communication bus 404. The processor 401 may also be called a controller, and there is no limitation on the name.

[0145] In the embodiment of the present application, the memory 402 stores instructions that can be executed by at least one processor 401. The at least one processor 401 can execute the distribution center data processing method discussed above by executing the instructions stored in the memory 402. The processor 401 can implement Figure 4 The functions of each module in the device shown.

[0146] Among them, the processor 401 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 402 and calling the data stored in the memory 402, the various functions of the device and process data, the device can be monitored as a whole.

[0147] In one possible design, the processor 401 may include one or more processing units, and the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 may be implemented on the same chip or on separate chips.

[0148] Processor 401 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the distribution center data processing method disclosed in the embodiments of the present application can be directly embodied as a hardware processor to be executed, or a combination of hardware and software modules in the processor can be executed.

[0149] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 402 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 402 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0150] By programming the processor 401, the code corresponding to the data processing method for the distribution center described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 or Figure 2 The steps of the distribution center data processing method of the embodiment shown are as follows: How to design and program the processor 401 is a technology well known to those skilled in the art and will not be described in detail here.

[0151] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by the processor, they are used to implement the distribution center data processing method described in any of the above embodiments. Therefore, they will not be described in detail here. In addition, the description of the beneficial effects of using the same method will not be repeated. For technical details not disclosed in the computer storage medium embodiment involved in the present invention, please refer to the description of the method embodiment of the present invention.

[0152] In some possible implementations, various aspects of the distribution center data processing method provided in the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the distribution center data processing method according to various exemplary embodiments of the present application described above in this specification.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A data processing method for a distribution center, characterized in that: The method comprises: Acquire distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of distributors; The distribution center data of multiple periods is used as the input of the pre-built target model, and the decision variables in the target model are solved by the prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple periods; Among them, the target model is constructed based on the mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closing and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty and supply matching relationship change penalty; the constraint conditions of the target model include at least one of the following: new warehouse constraints, warehouse closing and expansion constraints, supply matching relationship constraints, storage capacity occupancy constraints and supply matching relationship change constraints.

2. The method according to claim 1, characterized in that The storage capacity occupancy constraint is determined based on the floor space required by the distributor for the goods, the warehouse opening area, the warehouse closing area, the warehouse expansion area and the warehouse explosion area.

3. The method according to claim 1, characterized in that The warehouse explosion penalty item is determined based on the warehouse explosion area and the warehouse explosion penalty coefficient of the distribution center.

4. The method according to claim 1, characterized in that The supply matching relationship change constraint is determined based on the change indication coefficient of the distribution center and the distribution center to which the adjacent period distributors belong.

5. The method according to claim 1, characterized in that The supply matching relationship change penalty item is determined based on a change penalty coefficient and a change indication coefficient.

6. The method according to claim 1, characterized in that The target model includes a single-period model and a multi-period model. The data of the distribution centers of multiple periods are used as the input of the pre-built target model, and the decision variables in the target model are solved by a prepared optimization method to obtain the location data of the distribution centers of multiple periods and the supply matching relationship data, including: If it is determined that the distributor's demand data in multiple periods meets the first preset data requirement, then selecting the period corresponding to the demand data that meets the second preset data requirement as the target period; Based on the demand data of the target period, the lower limit of the number of positions to be opened is determined; and based on the number of periods in multiple periods, the upper limit of the number of positions to be opened is determined; Select any opening quantity between the lower opening quantity limit and the upper opening quantity limit, and update the distribution center data of the target period; The updated distribution center data of the target period is used as the input of the single-period model, and the decision variables in the single-period model are solved by the prepared optimization method to obtain the candidate warehouse information corresponding to the selected opening quantity; The distribution center data of multiple periods are updated based on the information of each alternative warehouse; the updated distribution center data of multiple periods are used as the input of the multi-period model, and the decision variables in the multi-period model are solved through the prepared optimization method to obtain the distribution center location data and supply matching relationship data of multiple periods.

7. The method according to claim 6, characterized in that If the following conditions are met, it is determined that the demand data of the distributor in multiple cycles meets the first preset data requirement: The difference between the demand data of the distributors in the Nth period and the first period is greater than the preset threshold; Wherein, N is greater than or equal to 2, and N is less than or equal to the number of cycles.

8. The method according to claim 1, characterized in that The demand data includes: demand quantity and the total area corresponding to the demand quantity; the supply network data includes at least: established warehouse data, alternative warehouse data, distributor location data, and distance data between the distributor and its corresponding distribution center; the cost data includes at least: warehouse opening cost, warehouse closing cost, warehouse expansion cost, transportation cost, and rate data.

9. A data processing device for a distribution center, characterized in that: The device comprises: An acquisition unit is used to acquire the distribution center data of multiple periods, wherein the distribution center data of each period includes: demand data, supply network data and cost data of the distributor; A processing unit is used to use the distribution center data of multiple periods as the input of the pre-built target model, solve the decision variables in the target model through a prepared optimization method, and obtain the distribution center location data and supply matching relationship data of multiple periods; Among them, the target model is constructed based on the mixed integer programming algorithm framework; the objective function of the target model includes: new warehouse construction and its rental cost, warehouse closing and its rental cost, warehouse expansion and its rental cost, transportation cost, warehouse explosion penalty and supply matching relationship change penalty; the constraint conditions of the target model include at least one of the following: new warehouse constraints, warehouse closing and expansion constraints, supply matching relationship constraints, storage capacity occupancy constraints and supply matching relationship change constraints.

10. An electronic device, characterized in that: comprising a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the distribution center data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the distribution center data processing method according to any one of claims 1 to 8 is implemented.

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