Warehouse site selection method, device and equipment and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to a warehouse site selection method, device and equipment and a storage medium. Basic input data and model configuration parameters of warehouse site selection are obtained, and multi-scene and multi-constraint limiting conditions are applied to warehouse site selection; the demand point address set and the alternative warehouse address set are determined through the preset address processing model, more alternative warehouses are expanded and the diversity of the supply chain is improved on the premise that the user demand is met, the site selection instance file is constructed according to the demand point address set, the alternative warehouse address set and the model configuration parameters, and the site selection efficiency is improved. And finally, by running and calculating the site selection instance file, a warehouse site selection result is obtained, so that the reasonability of warehouse site selection is improved, the technical problem of relatively high supply chain cost caused by an unreasonable warehouse site selection mode in the prior art is avoided, and the cost of the supply chain is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a warehouse site selection method, device, equipment and storage medium. Background Art

[0002] With the development of the logistics industry, in order to achieve cost reduction and efficiency improvement, reasonable warehouse site selection has become the primary solution to reduce supply chain costs. In traditional warehouse site selection methods, warehouse addresses are generally determined based on manual experience, without considering the impact of factors such as the coverage area, the number of warehouses built, and the cost of building warehouses, resulting in a low effective coverage rate, which in turn leads to higher costs for the entire supply chain.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a warehouse site selection method, device, equipment and storage medium, aiming to solve the technical problem that the warehouse site selection method in the prior art is unreasonable, resulting in high supply chain costs.

[0005] To achieve the above object, the present invention provides a warehouse site selection method, which comprises the following steps:

[0006] Obtain basic input data and model configuration parameters for warehouse location selection;

[0007] Determine a set of demand point addresses and a set of candidate warehouse addresses through a preset address processing model based on the model configuration parameters and the basic input data;

[0008] Constructing a site selection instance file according to the demand point address set, the candidate warehouse address set and the model configuration parameters;

[0009] The corresponding warehouse site selection result is determined according to the site selection instance file.

[0010] Optionally, determining the demand point address set and the candidate warehouse address set by a preset address processing model based on the model configuration parameters and the basic input data includes:

[0011] Respectively performing reliability checks on the model configuration parameters and the basic input data;

[0012] When the reliability checks of the model configuration parameters and the basic input data are both passed, parsing the demand point names in the basic input data to obtain multiple demand point addresses;

[0013] Expanding the address of the demand point according to the model configuration parameters to obtain multiple candidate warehouse addresses;

[0014] The adaptability of each demand point address and each candidate warehouse address is judged respectively to obtain a demand point address set and a candidate warehouse address set.

[0015] Optionally, before respectively judging the compatibility of each demand point address and each candidate warehouse address to obtain a demand point address set and a candidate warehouse address set, the method further includes:

[0016] According to the preset computing resources, the order of magnitude detection is performed on the address set of the demand point and the address set of the candidate warehouse address to obtain the order of magnitude detection result;

[0017] The demand point address set and / or the candidate warehouse address set are point aggregated according to the order of magnitude detection result to obtain the demand point address set and the candidate warehouse address set that meet the preset calculation efficiency conditions.

[0018] Optionally, performing point aggregation on the demand point address set and / or the candidate warehouse address set according to the order of magnitude detection result includes:

[0019] When the magnitude of the demand point address set is greater than a preset first threshold, point aggregation is performed on each demand point address in the demand point address set according to a preset demand point aggregation rule, wherein the preset demand point aggregation rule includes at least one of the following: at least one aggregated demand point is reserved in an area where there is demand, there is no cross-coverage between the aggregated demand points, the aggregated demand point is located at the weighted center of the demand point address set, the number of aggregated demand points is less than or equal to a preset configuration threshold, and the total demand of the aggregated demand points is less than a preset demand threshold;

[0020] When the order of magnitude of the set of alternative warehouse addresses is greater than a preset second threshold, point aggregation is performed on each alternative warehouse address in the set of alternative warehouse addresses according to a pre-equipment warehouse selection aggregation rule, and the pre-equipment warehouse selection aggregation rule includes at least one of the following requirements: there is at least one aggregated alternative warehouse in each demand area, the number of alternative warehouses in each demand area is positively correlated with the cargo weight ratio and the address weight ratio, the concentration of aggregated alternative warehouses is greater than a preset concentration threshold, the aggregated alternative warehouses are at the weighted center of the aggregated demand points, the number of aggregated alternative warehouses is less than or equal to a preset configuration threshold, and the set of alternative warehouse addresses satisfies a radius coverage constraint requirement, wherein the preset first threshold is greater than the preset second threshold.

[0021] Optionally, determining a corresponding warehouse site selection result according to the site selection instance file includes:

[0022] Constructing a mixed integer programming model according to the basic input information and model configuration parameters;

[0023] When the current computing power resources of the preset algorithm solving engine are sufficient, the computing power resources corresponding to the site selection instance file are allocated through the preset algorithm solving engine, and the warehouse site selection result corresponding to the site selection instance file is calculated through the mixed integer programming model based on the computing power resources;

[0024] or,

[0025] When the current computing resources of the preset algorithm solving engine are insufficient, the computing resources corresponding to the site selection instance file are allocated according to the submission time of the site selection instance file.

[0026] Optionally, the obtaining of basic input data and model configuration parameters for warehouse site selection includes:

[0027] Upon receiving the warehouse location selection instruction, updating the stored data in the preset database by at least one of data import, associating with the local database, and associating with external data;

[0028] Edit model configuration parameters according to the warehouse location selection instruction, wherein the model configuration parameters include: business scenario, calculation target, calculation constraint and address requirement;

[0029] The basic input data in the updated preset database is read according to the warehouse location selection instruction.

[0030] Optionally, the warehouse site selection method further includes:

[0031] Storing the warehouse site selection results in a preset database;

[0032] When receiving a warehouse location analysis instruction, calling a plurality of warehouse location results from the preset database according to the warehouse location analysis instruction;

[0033] The site selection results of each warehouse are displayed and a visual comparative analysis is performed.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also proposes a warehouse site selection device, the warehouse site selection device comprising:

[0035] The acquisition module is used to obtain the basic input data and model configuration parameters for warehouse location selection;

[0036] A determination module, configured to determine a set of demand point addresses and a set of candidate warehouse addresses through a preset address processing model based on the model configuration parameters and the basic input data;

[0037] A construction module, used to construct a site selection instance file according to the demand point address set, the candidate warehouse address set and the model configuration parameters;

[0038] The calculation module is used to determine the corresponding warehouse site selection result according to the site selection instance file.

[0039] In addition, to achieve the above-mentioned objectives, the present invention also proposes a warehouse site selection device, which includes: a memory, a processor, and a warehouse site selection program stored on the memory and executable on the processor, and the warehouse site selection program is configured to implement the steps of the warehouse site selection method described above.

[0040] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a warehouse site selection program is stored, and when the warehouse site selection program is executed by a processor, the steps of the warehouse site selection method described above are implemented.

[0041] The present invention discloses a warehouse site selection method, which comprises: obtaining basic input data and model configuration parameters for warehouse site selection; determining a demand point address set and an alternative warehouse address set through a preset address processing model based on the model configuration parameters and the basic input data; constructing a site selection instance file according to the demand point address set, the alternative warehouse address set and the model configuration parameters; and determining a corresponding warehouse site selection result according to the site selection instance file. Compared with the prior art, the present invention imposes multi-scenario and multi-constraint restrictions on warehouse site selection by obtaining basic input data and model configuration parameters for warehouse site selection, and then determines the demand point address set and the alternative warehouse address set through a preset address processing model, so as to achieve expansion of more alternative warehouses and improve the diversity of the supply chain under the premise of meeting user needs, and then constructs a site selection instance file according to the demand point address set, the alternative warehouse address set and the model configuration parameters, and finally obtains the warehouse site selection result by running and calculating the site selection instance file, thereby improving the rationality of warehouse site selection, avoiding the technical problem of unreasonable warehouse site selection in the prior art, resulting in high supply chain cost, and reducing the cost of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural schematic diagram of a warehouse site selection device in a hardware operating environment involved in an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the process of the first embodiment of the warehouse site selection method of the present invention;

[0044] Figure 3 This is a schematic diagram of an example calculation flow of an embodiment of a warehouse site selection method of the present invention;

[0045] Figure 4 This is a schematic diagram of the flow chart of the second embodiment of the warehouse site selection method of the present invention;

[0046] Figure 5A schematic diagram of the address processing flow of an embodiment of a warehouse site selection method of the present invention;

[0047] Figure 6 The local address distribution map of demand points after aggregation according to the warehouse site selection method of the first embodiment of the present invention;

[0048] Figure 7 A schematic diagram of a partial address distribution diagram of candidate warehouses according to an embodiment of a warehouse site selection method of the present invention;

[0049] Figure 8 This is a schematic diagram of the flow chart of the third embodiment of the warehouse site selection method of the present invention;

[0050] Fig. 9 This is a schematic diagram showing the comparison of example results of an embodiment of a warehouse site selection method according to the present invention;

[0051] Fig.10 This is a structural block diagram of the first embodiment of the warehouse site selection device of the present invention.

[0052] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0054] Reference Figure 1 , Figure 1 A schematic diagram of the warehouse site selection equipment structure of the hardware operating environment involved in the embodiment of the present invention.

[0055] like Figure 1 As shown, the warehouse site selection device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0056] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation of the warehouse site selection equipment, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0057] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a warehouse site selection program.

[0058] exist Figure 1 In the warehouse site selection device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the warehouse site selection device of the present invention can be set in the warehouse site selection device, and the warehouse site selection device calls the warehouse site selection program stored in the memory 1005 through the processor 1001, and executes the warehouse site selection method provided by the embodiment of the present invention.

[0059] The embodiment of the present invention provides a warehouse site selection method, referring to Figure 2 , Figure 2 It is a flow chart of a first embodiment of a warehouse site selection method of the present invention.

[0060] In this embodiment, the warehouse site selection method includes the following steps:

[0061] Step S10: Obtain basic input data and model configuration parameters for warehouse site selection.

[0062] It should be noted that the executor of the method of this embodiment can be a device with functions such as data processing, program running and network communication, such as a server or a control computer, etc., or other devices that can achieve the same or similar functions. This embodiment does not impose specific restrictions on this. In this embodiment and subsequent embodiments, the server will be used as an example for explanation.

[0063] It is understandable that in traditional warehouse site selection plans, the warehouse site selection results will continue to affect the supply and transportation costs at the warehousing end, the distribution costs at the outbound end, the distribution fulfillment time at the outbound end, and the total inventory of goods. The traditional site selection problem mainly solves the P-Median problem of minimizing the total transportation distance and the P-Center problem of minimizing the maximum transportation distance. It lacks multi-objective and multi-priority considerations for key indicators with inconsistent dimensions such as cost, transportation distance, and timeliness. At the same time, traditional warehouse site selection plans fail to take into account a variety of practical requirements such as the warehouse coverage area, the maximum fulfillment radius of the warehouse, the maximum number of warehouses, and the maximum warehouse area, which further leads to an increase in supply chain costs.

[0064] In the existing warehouse site selection methods, specific warehouse site selection scenarios are usually planned based on heuristic methods. For example, the range of alternative warehouses is defined based on certain manual experience, and sites are selected according to some specific business requirements. The above site selection schemes lack warehouse site selection planning methods that support multiple scenarios, multiple objectives, and multiple constraints. It is impossible to more comprehensively analyze the impact of the number of warehouse sites on multiple core indicators, and the coverage and versatility are not strong.

[0065] In order to solve the above problems, this embodiment obtains model configuration parameters and establishes an address processing model with multiple scenarios and multiple constraints, so as to select a set of demand point addresses and a set of alternative warehouse addresses according to the basic input data entered by the user, thereby improving the coverage of the supply chain. Finally, by constructing a site selection instance file and determining the corresponding warehouse site selection results, the rationality of warehouse site selection is improved.

[0066] Furthermore, the acquisition of basic input data and model configuration parameters for warehouse site selection includes:

[0067] Upon receiving the warehouse location selection instruction, updating the stored data in the preset database by at least one of data import, associating with the local database, and associating with external data;

[0068] Edit model configuration parameters according to the warehouse location selection instruction, wherein the model configuration parameters include: business scenario, calculation target, calculation constraint and address requirement;

[0069] The basic input data in the updated preset database is read according to the warehouse location selection instruction.

[0070] In this embodiment, the basic input data includes but is not limited to the commodity demand distribution related to warehouse site selection, basic address information, warehouse selection extended address set, distance matrix information, commodity information, and cost-revenue parameter information. Among them, the basic address information corresponds to the address information of the demand point of commodity demand, and the warehouse selection extended address set is a supplement to the alternative warehouse address. The combination of the two forms a complete and usable alternative warehouse address set for the instance.

[0071] The basic input data may be obtained by reading data stored in a preset database. The source of the data stored in the preset database may be through data import, connection to a local database or an external database, and online editing of a system page, etc. This embodiment does not impose any specific restrictions on this.

[0072] The model configuration parameters include business scenarios, calculation targets, calculation constraints and address requirements, among which the business scenarios are divided into two scenarios: fixed number of warehouses and coverage requirements. One of the business scenarios of a calculation instance needs to be selected from the two; the calculation targets are divided into optimal weighted transportation distance, maximum coverage, minimum total cost and maximum total profit, etc., and the calculation targets support multiple selections. Different calculation targets determine the priority of multiple targets according to the selection order; the calculation constraints are divided into the address must be a warehouse, the address must not be a warehouse, the number range of selected warehouses, the upper limit of the distribution radius, the radius coverage requirement, the regional jurisdiction restriction, the upper limit of the warehouse area and the minimum purchase batch, etc.; the address requirements are divided into whether to expand the internal logistics park, whether to expand the external logistics park, the custom extended address and the address aggregation rules, etc. This embodiment does not make specific restrictions on this. The above model configuration parameters are all for adapting to the warehouse site selection under different scenarios, different requirements and different constraints, and on the premise of satisfying the user's supply chain configuration, more warehouse site selection possibilities are realized, thereby improving the diversity of warehouse site selection.

[0073] Step S20: Determine a demand point address set and an alternative warehouse address set through a preset address processing model based on the model configuration parameters and the basic input data.

[0074] It should be noted that the preset address processing model mainly parses and expands the address information in the basic input data according to the model configuration parameters, so as to obtain a set of demand point addresses and a set of alternative warehouse addresses, so as to facilitate warehouse site selection according to the constraints of warehouse site selection and improve the coverage of warehouses.

[0075] In addition, the address processing model can also be used to evaluate whether the number of addresses is too large. If there are too many addresses, the address set can be aggregated to obtain the demand point set and the alternative warehouse set.

[0076] In the specific implementation, the preset address processing model of this embodiment has built-in national city four-level addresses, national logistics park addresses and users' existing warehouse addresses. By introducing the above-mentioned extended warehouse selection address information and superimposing the input data demand point address, an available address for warehouse site selection is constructed; the system uses a clustering algorithm to construct a set of high-quality alternative warehouses based on address information, address attributes, and configuration parameters of aggregation rules. There is no need to manually circle the set of alternative warehouses, thereby reducing the cost of warehouse site selection.

[0077] Step S30: constructing a site selection instance file according to the demand point address set, the candidate warehouse address set and the model configuration parameters.

[0078] It should be understood that a computing instance is constructed based on the model configuration parameters, the computing scenarios, target relationships, and constraints required by the computing instance are opened, and the variables and constants required by the computing instance algorithm are constructed to obtain a site selection instance file, so that the site selection instance file can be solved subsequently through the constructed mixed integer programming model to obtain the optimal warehouse site selection plan under the current constraints, where the computing instance file records computing instance information such as the aggregated demand point set, alternative warehouse set, computing scenarios, computing targets, and computing constraint requirements.

[0079] Step S40: Determine the corresponding warehouse site selection result according to the site selection instance file.

[0080] In the specific implementation, refer to Figure 3 ,After building the site selection instance file according to the set of demand point addresses, the set of alternative warehouse addresses and ,model configuration parameters, the mixed integer programming expressions of the ,corresponding model objectives and constraints are generated by calculating the ,instance file and the solver calculation file is obtained.

[0081] Take the "fixed number of warehouses" scenario, the calculation target of "maximum coverage within 200KM of the first target, maximum weighted transport distance of the second target", and the calculation requirement of "fixed 8 warehouses, warehouse A is required" as an example to illustrate. The calculation formula for calculating "maximum coverage within 200KM of the first target" is:

[0082]

[0083] Among them, w k represents the importance weight of the coverage radius k, that is, the importance weight within 200KM, dist k Denotes d with a radius less than k i,j Set, d i,j is a continuous variable, indicating the proportion of demand point j covered by alternative warehouse i, k indicates the set of coverage radii with the largest target coverage, and M1 is the calculation result under the constraint condition of maximum coverage within 200KM of the first target.

[0084] The calculation formula for "maximum coverage within 200KM of the first target and minimum weighted transport distance" is:

[0085]

[0086] Among them, d i,j is a continuous variable, indicating the proportion of demand at demand point j covered by alternative warehouse i, v j represents the demand quantity of demand point j, dis i,jrepresents the distance from warehouse i to demand point j, I represents the set of alternative warehouses, J represents the set of aggregated demand points, and M2 is the calculation result under the constraints of the first goal of maximizing coverage within 200KM and the second goal of maximizing weighted transportation distance.

[0087] When determining the calculation constraints, each demand point must be covered:

[0088]

[0089] Among them, d i,j It is a continuous variable, indicating the proportion of demand of demand point j covered by alternative warehouse i.

[0090] The calculation formula for selecting the number of warehouses must be equal to 8 is:

[0091]

[0092] Alternative warehouse A must be selected:

[0093] x i =1,i∈A

[0094] Among them, x i It is a 0-1 variable, indicating whether the candidate bin i is selected.

[0095] Relationship constraints between alternative warehouses and demand points:

[0096]

[0097] Among them, d i,j It is a continuous variable, indicating the proportion of demand of demand point j covered by alternative warehouse i.

[0098] Priority constraints when calculating the second target:

[0099]

[0100] Among them, w k represents the importance weight of coverage radius k, where k represents the coverage radius set with the largest target coverage, that is, the importance weight within the range of 200KM, dist k Denotes d with a radius less than k i,j Set, d i,j It is a continuous variable, indicating the proportion of demand point j covered by alternative warehouse i. M1 is the calculation result under the constraint condition of maximum coverage within 200KM of the first target.

[0101] Further, the determining the corresponding warehouse site selection result according to the site selection instance file includes:

[0102] Constructing a mixed integer programming model according to the basic input information and model configuration parameters;

[0103] When the current computing power resources of the preset algorithm solving engine are sufficient, the computing power resources corresponding to the site selection instance file are allocated through the preset algorithm solving engine, and the warehouse site selection result corresponding to the site selection instance file is calculated through the mixed integer programming model based on the computing power resources;

[0104] or,

[0105] When the current computing resources of the preset algorithm solving engine are insufficient, the computing resources corresponding to the site selection instance file are allocated according to the submission time of the site selection instance file.

[0106] In a specific implementation, a certain amount of computing power resources are allocated to solve the site selection instance file through the mixed integer programming model to obtain a warehouse site selection result; if computing power resources are tight, the computing tasks are queued in the order of submission time, wherein the model parameters of the mixed integer programming model can be constructed by the basic input information and model configuration parameters mentioned above or obtained.

[0107] This embodiment obtains basic input data and model configuration parameters for warehouse site selection, imposes multi-scenario and multi-constraint restrictions on warehouse site selection, and then determines the demand point address set and the alternative warehouse address set through a preset address processing model, so as to expand more alternative warehouses and improve the diversity of the supply chain while meeting user needs. Then, a site selection instance file is constructed according to the demand point address set, the alternative warehouse address set and the model configuration parameters. Finally, the warehouse site selection result is obtained by running and calculating the site selection instance file, thereby improving the rationality of warehouse site selection, avoiding the technical problem of unreasonable warehouse site selection methods in the prior art, which leads to high supply chain costs, and reducing the cost of the supply chain.

[0108] refer to Figure 4 , Figure 4 This is a flow chart of a second embodiment of a warehouse site selection method according to the present invention.

[0109] Based on the above first embodiment, in this embodiment, step S20 includes:

[0110] Step S201: performing reliability verification on the model configuration parameters and the basic input data respectively.

[0111] It should be noted that reliability verification of basic input data and model configuration parameters refers to coupling verification of basic input data and model configuration parameters, analyzing the reliability of data and configuration parameters, and ensuring that data and configuration will not lead to logical contradictions, such as: judging whether required fields are empty, the legality of field formats, the rationality of field values, and the rationality of constraint logic (the number of required bins must not be greater than the fixed number of bins). The above validity checks are mainly used to ensure that input data and model configuration parameters will not cause large deviations to the results, and thus will not affect the rationality of subsequent site selection results.

[0112] Step S202: When the reliability checks of the model configuration parameters and the basic input data are passed, the demand point names in the basic input data are parsed to obtain multiple demand point addresses.

[0113] Step S203: Expand the address of the demand point according to the model configuration parameters to obtain multiple candidate warehouse addresses.

[0114] In the specific implementation, refer to Figure 5 , Figure 5 This is a schematic diagram of the address processing module process. Parsing the demand point names in the basic input data refers to performing address parsing on all input address names to obtain the standard province / city / district / town address name information corresponding to the address and obtain its longitude and latitude information; address expansion of the demand point address refers to expanding the address according to the address requirements of the parameter configuration platform, and the expanded address is only used as an available address for the alternative warehouse. For example: address expansion is performed through the national four-level city addresses, national logistics park addresses and the user's existing warehouse addresses to obtain multiple alternative warehouse addresses. This embodiment does not make specific restrictions on this.

[0115] Furthermore, before respectively judging the compatibility of each demand point address and each candidate warehouse address to obtain a demand point address set and a candidate warehouse address set, the method further includes:

[0116] According to the preset computing resources, the order of magnitude detection is performed on the address set of the demand point and the address set of the candidate warehouse address to obtain the order of magnitude detection result;

[0117] The demand point address set and / or the candidate warehouse address set are point aggregated according to the order of magnitude detection result to obtain the demand point address set and the candidate warehouse address set that meet the preset calculation efficiency conditions.

[0118] In specific implementation, in warehouse site selection problems, the number of demand points is usually tens of thousands, and the geographical distribution of different demands is different. It is possible that the number of demand points is greater than the actual number of warehouses needed to be built. By grouping the numerous demand points, overlapping coverage can be avoided.

[0119] It is understandable that, combined with the server resource situation, the actual number of demand points is 5,000, and the number of alternative warehouses is 450. However, the number of demand points in the demand point address set processed by the above-mentioned preset address processing model is 20,000, and the number of alternative warehouses in the alternative warehouse address set is 600. At this time, in order to reduce the cost of the supply chain and increase the coverage rate of each warehouse in the supply chain, the demand point addresses can be aggregated and / or the alternative warehouse addresses can be aggregated.

[0120] Furthermore, performing point aggregation on the demand point address set and / or the candidate warehouse address set according to the order of magnitude detection result includes:

[0121] When the magnitude of the demand point address set is greater than a preset first threshold, point aggregation is performed on each demand point address in the demand point address set according to a preset demand point aggregation rule, wherein the preset demand point aggregation rule includes at least one of the following: at least one aggregated demand point is reserved in an area where there is demand, there is no cross-coverage between the aggregated demand points, the aggregated demand point is located at the weighted center of the demand point address set, the number of aggregated demand points is less than or equal to a preset configuration threshold, and the total demand of the aggregated demand points is less than a preset demand threshold;

[0122] When the order of magnitude of the set of alternative warehouse addresses is greater than a preset second threshold, point aggregation is performed on each alternative warehouse address in the set of alternative warehouse addresses according to a pre-equipment warehouse selection aggregation rule, and the pre-equipment warehouse selection aggregation rule includes at least one of the following requirements: there is at least one aggregated alternative warehouse in each demand area, the number of alternative warehouses in each demand area is positively correlated with the cargo weight ratio and the address weight ratio, the concentration of aggregated alternative warehouses is greater than a preset concentration threshold, the aggregated alternative warehouses are at the weighted center of the aggregated demand points, the number of aggregated alternative warehouses is less than or equal to a preset configuration threshold, and the set of alternative warehouse addresses satisfies a radius coverage constraint requirement, wherein the preset first threshold is greater than the preset second threshold.

[0123] In the specific implementation, refer to Figure 6 and Figure 7 , Figure 6 is the local address distribution map of the aggregated demand points. Figure 7It is the local address distribution map of the alternative warehouses after aggregation, where the aggregation requirements of demand points are as follows: a. At least one demand point is guaranteed to be retained for each county address with demand; b. There cannot be any cross-coverage relationship between the aggregated demand points; c. The aggregated demand points are located at the weighted centroid of the set; d. The total demand of the aggregated demand points shall not exceed the configured threshold; e. The total number of aggregated demand points shall not exceed the configured threshold as much as possible; The specific requirements for the aggregation of alternative warehouses are as follows: a. At least one alternative warehouse is guaranteed to exist in each area; b. The number of regional alternative warehouses is positively correlated with the cargo weight ratio and the address weight ratio; c. The concentration of the alternative warehouse set in the area cannot be lower than the configured threshold; d. The alternative warehouse is selected at the weighted centroid of the coverage range; e. The total number of alternative warehouses shall not exceed the configured threshold as much as possible; f. The result of the alternative warehouse set must meet the radius coverage rate constraint requirements.

[0124] Step S204: Perform compatibility judgment on each demand point address and each candidate warehouse address respectively to obtain a demand point address set and a candidate warehouse address set.

[0125] In a specific implementation, the compatibility judgment of each demand point address and each alternative warehouse address refers to judging whether the total number of demand point addresses and alternative warehouse addresses meets the server computing requirements based on the allocated computing resources.

[0126] This embodiment uses the address processing model to perform reliability verification on the basic input data and model configuration parameters to improve the logical rationality of the data, and then parses, expands and aggregates the basic input data to obtain a set of demand point addresses and a set of alternative warehouse addresses, so that the subsequent warehouse site selection coverage rate is higher and adapts to more scenarios.

[0127] refer to Figure 8 , Figure 8 It is a flow chart of a third embodiment of a warehouse site selection method of the present invention.

[0128] Based on the first embodiment, in this embodiment, after step S40, the following steps are further included:

[0129] Step S50: storing the warehouse site selection result in a preset database.

[0130] Step S60: When a warehouse site selection analysis instruction is received, multiple warehouse site selection results are called from the preset database according to the warehouse site selection analysis instruction.

[0131] Step S70: Display the site selection results of each warehouse and conduct visual comparative analysis.

[0132] It should be noted that after obtaining the warehouse site selection results, the warehouse site selection results and site selection instance files can be returned to the preset repository for storage, so that subsequent users can make horizontal comparisons of multiple computing instances and analyze the changing trends of the core indicators of the warehouse site selection results under different conditions. The system weighs the advantages and disadvantages of multiple options and recommends the warehouse address with the best comprehensive indicators.

[0133] refer to Fig. 9 By comparing the two example solutions in terms of the demand proportion of 0-100KM, the demand proportion of 100-300KM, the demand proportion of 300-500KM, the demand proportion of more than 500KM and the number of warehouses in the solution, different warehouse construction solutions can be selected according to user needs to meet user needs.

[0134] This embodiment stores the warehouse site selection results in a preset database; when receiving a warehouse site selection analysis instruction, calls multiple warehouse site selection results from the preset database according to the warehouse site selection analysis instruction; displays each warehouse site selection result and performs a visual comparative analysis, so that users can choose a more suitable warehouse site selection plan according to their needs, thereby improving the user experience.

[0135] In addition, an embodiment of the present invention further proposes a storage medium, on which a warehouse site selection program is stored. When the warehouse site selection program is executed by a processor, the steps of the warehouse site selection method described above are implemented.

[0136] Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0137] Reference Fig.10 , Fig.10 This is a structural block diagram of the first embodiment of the warehouse site selection device of the present invention.

[0138] like Fig.10 As shown, the warehouse site selection device proposed in the embodiment of the present invention includes:

[0139] The acquisition module 10 is used to obtain basic input data and model configuration parameters for warehouse site selection.

[0140] It is understandable that in traditional warehouse site selection plans, the warehouse site selection results will continue to affect the supply and transportation costs at the warehousing end, the distribution costs at the outbound end, the distribution fulfillment time at the outbound end, and the total inventory of goods. The traditional site selection problem mainly solves the P-Median problem of minimizing the total transportation distance and the P-Center problem of minimizing the maximum transportation distance. It lacks multi-objective and multi-priority considerations for key indicators with inconsistent dimensions such as cost, transportation distance, and timeliness. At the same time, traditional warehouse site selection plans fail to take into account a variety of practical requirements such as the warehouse coverage area, the maximum fulfillment radius of the warehouse, the maximum number of warehouses, and the maximum warehouse area, which further leads to an increase in supply chain costs.

[0141] In the existing warehouse site selection methods, specific warehouse site selection scenarios are usually planned based on heuristic methods. For example, the range of alternative warehouses is defined based on certain manual experience, and sites are selected according to some specific business requirements. The above site selection schemes lack warehouse site selection planning methods that support multiple scenarios, multiple objectives, and multiple constraints. It is impossible to more comprehensively analyze the impact of the number of warehouse sites on multiple core indicators, and the coverage and versatility are not strong.

[0142] In order to solve the above problems, this embodiment obtains model configuration parameters and establishes an address processing model with multiple scenarios and multiple constraints, so as to select a set of demand point addresses and a set of alternative warehouse addresses according to the basic input data entered by the user, thereby improving the coverage of the supply chain. Finally, by constructing a site selection instance file and determining the corresponding warehouse site selection results, the rationality of warehouse site selection is improved.

[0143] Furthermore, the acquisition of basic input data and model configuration parameters for warehouse site selection includes:

[0144] Upon receiving the warehouse location selection instruction, updating the stored data in the preset database by at least one of data import, associating with the local database, and associating with external data;

[0145] Edit model configuration parameters according to the warehouse location selection instruction, wherein the model configuration parameters include: business scenario, calculation target, calculation constraint and address requirement;

[0146] The basic input data in the updated preset database is read according to the warehouse location selection instruction.

[0147] In this embodiment, the basic input data includes but is not limited to the commodity demand distribution related to warehouse site selection, basic address information, warehouse selection extended address set, distance matrix information, commodity information, and cost-revenue parameter information. Among them, the basic address information corresponds to the address information of the demand point of commodity demand, and the warehouse selection extended address set is a supplement to the alternative warehouse address. The combination of the two forms a complete and usable alternative warehouse address set for the instance.

[0148] The basic input data may be obtained by reading data stored in a preset database. The source of the data stored in the preset database may be through data import, connection to a local database or an external database, and online editing of a system page, etc. This embodiment does not impose any specific restrictions on this.

[0149] The model configuration parameters include business scenarios, calculation targets, calculation constraints and address requirements, among which the business scenarios are divided into two scenarios: fixed number of warehouses and coverage requirements. One of the business scenarios of a calculation instance needs to be selected from the two; the calculation targets are divided into optimal weighted transportation distance, maximum coverage, minimum total cost and maximum total profit, etc., and the calculation targets support multiple selections. Different calculation targets determine the priority of multiple targets according to the selection order; the calculation constraints are divided into the address must be a warehouse, the address must not be a warehouse, the number range of selected warehouses, the upper limit of the distribution radius, the radius coverage requirement, the regional jurisdiction restriction, the upper limit of the warehouse area and the minimum purchase batch, etc.; the address requirements are divided into whether to expand the internal logistics park, whether to expand the external logistics park, the custom extended address and the address aggregation rules, etc. This embodiment does not make specific restrictions on this. The above model configuration parameters are all for adapting to the warehouse site selection under different scenarios, different requirements and different constraints, and on the premise of satisfying the user's supply chain configuration, more warehouse site selection possibilities are realized, thereby improving the diversity of warehouse site selection.

[0150] The determination module 20 is used to determine the demand point address set and the candidate warehouse address set through a preset address processing model based on the model configuration parameters and the basic input data.

[0151] It should be noted that the preset address processing model mainly parses and expands the address information in the basic input data according to the model configuration parameters, so as to obtain a set of demand point addresses and a set of alternative warehouse addresses, so as to facilitate warehouse site selection according to the constraints of warehouse site selection and improve the coverage of warehouses.

[0152] In addition, the address processing model can also be used to evaluate whether the number of addresses is too large. If there are too many addresses, the address set can be aggregated to obtain the demand point set and the alternative warehouse set.

[0153] In the specific implementation, the preset address processing model of this embodiment has built-in national city four-level addresses, national logistics park addresses and users' existing warehouse addresses. By introducing the above-mentioned extended warehouse selection address information and superimposing the input data demand point address, an available address for warehouse site selection is constructed; the system uses a clustering algorithm to construct a set of high-quality alternative warehouses based on address information, address attributes, and configuration parameters of aggregation rules. There is no need to manually circle the set of alternative warehouses, thereby reducing the cost of warehouse site selection.

[0154] The construction module 30 is used to construct a site selection instance file according to the demand point address set, the candidate warehouse address set and the model configuration parameters.

[0155] It should be understood that a computing instance is constructed based on the model configuration parameters, the computing scenarios, target relationships, and constraints required by the computing instance are opened, and the variables and constants required by the computing instance algorithm are constructed to obtain a site selection instance file, so that the site selection instance file can be solved subsequently through the constructed mixed integer programming model to obtain the optimal warehouse site selection plan under the current constraints, where the computing instance file records computing instance information such as the aggregated demand point set, alternative warehouse set, computing scenarios, computing targets, and computing constraint requirements.

[0156] The calculation module 40 is used to determine the corresponding warehouse site selection result according to the site selection instance file.

[0157] In the specific implementation, refer to Figure 3 ,After building the site selection instance file according to the set of demand point addresses, the set of alternative warehouse addresses and ,model configuration parameters, the mixed integer programming expressions of the ,corresponding model objectives and constraints are generated by calculating the ,instance file and the solver calculation file is obtained.

[0158] Take the "fixed number of warehouses" scenario, the calculation target of "maximum coverage within 200KM of the first target, maximum weighted transport distance of the second target", and the calculation requirement of "fixed 8 warehouses, warehouse A is required" as an example to illustrate. The calculation formula for calculating "maximum coverage within 200KM of the first target" is:

[0159]

[0160] Among them, w k represents the importance weight of the coverage radius k, that is, the importance weight within 200KM, dist k Denotes d with a radius less than k i,j Set, d i,j is a continuous variable, indicating the proportion of demand point j covered by alternative warehouse i, k indicates the set of coverage radii with the largest target coverage, and M1 is the calculation result under the constraint condition of maximum coverage within 200KM of the first target.

[0161] The calculation formula for "maximum coverage within 200KM of the first target and minimum weighted transport distance" is:

[0162]

[0163] Among them, d i,j is a continuous variable, indicating the proportion of demand at demand point j covered by alternative warehouse i, v j represents the demand quantity of demand point j, dis i,jrepresents the distance from warehouse i to demand point j, I represents the set of alternative warehouses, J represents the set of aggregated demand points, and M2 is the calculation result under the constraints of the first goal of maximizing coverage within 200KM and the second goal of maximizing weighted transportation distance.

[0164] When determining the calculation constraints, each demand point must be covered:

[0165]

[0166] Among them, d i,j It is a continuous variable, indicating the proportion of demand of demand point j covered by alternative warehouse i.

[0167] The calculation formula for selecting the number of warehouses must be equal to 8 is:

[0168]

[0169] Alternative warehouse A must be selected:

[0170] x i =1,i∈A

[0171] Among them, x i It is a 0-1 variable, indicating whether the candidate bin i is selected.

[0172] Relationship constraints between alternative warehouses and demand points:

[0173]

[0174] Among them, d i,j It is a continuous variable, indicating the proportion of demand of demand point j covered by alternative warehouse i.

[0175] Priority constraints when calculating the second target:

[0176]

[0177] Among them, w k represents the importance weight of coverage radius k, where k represents the coverage radius set with the largest target coverage, that is, the importance weight within the range of 200KM, dist k Denotes d with a radius less than k i,j Set, d j,j It is a continuous variable, indicating the proportion of demand point j covered by alternative warehouse i. M1 is the calculation result under the constraint condition of maximum coverage within 200KM of the first target.

[0178] Further, the determining the corresponding warehouse site selection result according to the site selection instance file includes:

[0179] Constructing a mixed integer programming model according to the basic input information and model configuration parameters;

[0180] When the current computing power resources of the preset algorithm solving engine are sufficient, the computing power resources corresponding to the site selection instance file are allocated through the preset algorithm solving engine, and the warehouse site selection result corresponding to the site selection instance file is calculated through the mixed integer programming model based on the computing power resources;

[0181] or,

[0182] When the current computing resources of the preset algorithm solving engine are insufficient, the computing resources corresponding to the site selection instance file are allocated according to the submission time of the site selection instance file.

[0183] In a specific implementation, a certain amount of computing power resources are allocated to solve the site selection instance file through the mixed integer programming model to obtain a warehouse site selection result; if computing power resources are tight, the computing tasks are queued in the order of submission time, wherein the model parameters of the mixed integer programming model can be constructed by the basic input information and model configuration parameters mentioned above or obtained.

[0184] In one embodiment, the determination module 20 is further used to perform reliability verification on the model configuration parameters and the basic input data respectively; when the reliability verification of the model configuration parameters and the basic input data are passed, the demand point name in the basic input data is parsed to obtain multiple demand point addresses; the demand point address is expanded based on the model configuration parameters to obtain multiple alternative warehouse addresses; and the adaptability of each demand point address and each alternative warehouse address is judged respectively to obtain a demand point address set and an alternative warehouse address set.

[0185] In one embodiment, the determination module 20 is further used to perform order of magnitude detection on the demand point address set and the alternative warehouse address set according to preset computing power resources to obtain order of magnitude detection results; and perform point aggregation on the demand point address set and / or the alternative warehouse address set according to the order of magnitude detection results to obtain the demand point address set and the alternative warehouse address set that meet preset computing efficiency conditions.

[0186] In one embodiment, the determination module 20 is further used to aggregate the demand point addresses in the demand point address set according to a preset demand point aggregation rule when the order of magnitude of the demand point address set is greater than a preset first threshold, wherein the preset demand point aggregation rule includes at least one of the following: retaining at least one aggregated demand point in the area where there is demand, no cross-overlapping between the aggregated demand points, the aggregated demand point being at the weighted center of the demand point address set, the number of aggregated demand points being less than or equal to a preset configuration threshold, and the total demand of the aggregated demand points being less than a preset demand threshold; when the order of magnitude of the candidate warehouse address set is greater than When the second threshold is preset, each alternative warehouse address in the alternative warehouse address set is point aggregated according to the pre-equipment warehouse selection aggregation rule, and the pre-equipment warehouse selection aggregation rule includes at least one of the following requirements: there is at least one aggregated alternative warehouse in each demand area, the number of alternative warehouses in each demand area is positively correlated with the cargo weight ratio and the address weight ratio, the concentration of aggregated alternative warehouses is greater than the preset concentration threshold, the aggregated alternative warehouses are at the weighted center of the aggregated demand point, the number of aggregated alternative warehouses is less than or equal to the preset configuration threshold, and the alternative warehouse address set meets the radius coverage constraint requirement, wherein the preset first threshold is greater than the preset second threshold.

[0187] In one embodiment, the calculation module 40 is also used to construct a mixed integer programming model based on the basic input information and model configuration parameters; when the current computing power resources of the preset algorithm solving engine are sufficient, the computing power resources corresponding to the site selection instance file are allocated through the preset algorithm solving engine, and the warehouse site selection result corresponding to the site selection instance file is calculated based on the computing power resources through the mixed integer programming model; or, when the current computing power resources of the preset algorithm solving engine are insufficient, the computing power resources corresponding to the site selection instance file are allocated according to the submission time of the site selection instance file.

[0188] In one embodiment, the acquisition module 10 is also used to update the stored data in the preset database through at least one of data import, associating with the local database, and associating with external data when receiving the warehouse site selection instruction; edit the model configuration parameters according to the warehouse site selection instruction, and the model configuration parameters include: business scenarios, calculation goals, calculation constraints, and address requirements; read the basic input data in the updated preset database according to the warehouse site selection instruction.

[0189] In one embodiment, the calculation module 40 is also used to store the warehouse site selection results in a preset database; when receiving a warehouse site selection analysis instruction, call multiple warehouse site selection results from the preset database according to the warehouse site selection analysis instruction; display each warehouse site selection result, and perform a visual comparative analysis.

[0190] This embodiment obtains basic input data and model configuration parameters for warehouse site selection, imposes multi-scenario and multi-constraint restrictions on warehouse site selection, and then determines the demand point address set and the alternative warehouse address set through a preset address processing model, so as to expand more alternative warehouses and improve the diversity of the supply chain while meeting user needs. Then, a site selection instance file is constructed according to the demand point address set, the alternative warehouse address set and the model configuration parameters. Finally, the warehouse site selection result is obtained by running and calculating the site selection instance file, thereby improving the rationality of warehouse site selection, avoiding the technical problem of unreasonable warehouse site selection methods in the prior art, which leads to high supply chain costs, and reducing the cost of the supply chain.

[0191] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0192] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0193] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0194] In addition, for technical details that are not described in detail in this embodiment, reference can be made to the warehouse site selection method provided in any embodiment of the present invention, and will not be repeated here.

[0195] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0196] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0197] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0198] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A warehouse site selection method, characterized in that: The warehouse site selection method includes: Obtain basic input data and model configuration parameters for warehouse location selection; Determine a set of demand point addresses and a set of candidate warehouse addresses through a preset address processing model based on the model configuration parameters and the basic input data; Constructing a site selection instance file according to the demand point address set, the candidate warehouse address set and the model configuration parameters; The corresponding warehouse site selection result is determined according to the site selection instance file.

2. The warehouse site selection method according to claim 1, characterized in that: The determining of the demand point address set and the candidate warehouse address set by a preset address processing model based on the model configuration parameters and the basic input data includes: Respectively performing reliability checks on the model configuration parameters and the basic input data; When the reliability checks of the model configuration parameters and the basic input data are both passed, parsing the demand point names in the basic input data to obtain multiple demand point addresses; Expanding the address of the demand point according to the model configuration parameters to obtain multiple candidate warehouse addresses; The adaptability of each demand point address and each candidate warehouse address is judged respectively to obtain a demand point address set and a candidate warehouse address set.

3. The warehouse site selection method according to claim 2, characterized in that: Before respectively judging the compatibility of each demand point address and each candidate warehouse address to obtain a demand point address set and a candidate warehouse address set, the method further includes: According to the preset computing resources, the order of magnitude detection is performed on the address set of the demand point and the address set of the candidate warehouse address to obtain the order of magnitude detection result; The demand point address set and / or the candidate warehouse address set are point aggregated according to the order of magnitude detection result to obtain the demand point address set and the candidate warehouse address set that meet the preset calculation efficiency conditions.

4. The warehouse site selection method according to claim 3, characterized in that: The performing point aggregation on the demand point address set and / or the candidate warehouse address set according to the order of magnitude detection result includes: When the magnitude of the demand point address set is greater than a preset first threshold, point aggregation is performed on each demand point address in the demand point address set according to a preset demand point aggregation rule, wherein the preset demand point aggregation rule includes at least one of the following: at least one aggregated demand point is reserved in an area where there is demand, there is no cross-coverage between the aggregated demand points, the aggregated demand point is located at the weighted center of the demand point address set, the number of aggregated demand points is less than or equal to a preset configuration threshold, and the total demand of the aggregated demand points is less than a preset demand threshold; When the order of magnitude of the set of alternative warehouse addresses is greater than a preset second threshold, point aggregation is performed on each alternative warehouse address in the set of alternative warehouse addresses according to a pre-equipment warehouse selection aggregation rule, and the pre-equipment warehouse selection aggregation rule includes at least one of the following requirements: there is at least one aggregated alternative warehouse in each demand area, the number of alternative warehouses in each demand area is positively correlated with the cargo weight ratio and the address weight ratio, the concentration of aggregated alternative warehouses is greater than a preset concentration threshold, the aggregated alternative warehouses are at the weighted center of the aggregated demand points, the number of aggregated alternative warehouses is less than or equal to a preset configuration threshold, and the set of alternative warehouse addresses satisfies a radius coverage constraint requirement, wherein the preset first threshold is greater than the preset second threshold.

5. The warehouse site selection method according to any one of claims 1 to 4, characterized in that: The determining the corresponding warehouse site selection result according to the site selection example file includes: Constructing a mixed integer programming model according to the basic input information and model configuration parameters; When the current computing power resources of the preset algorithm solving engine are sufficient, the computing power resources corresponding to the site selection instance file are allocated through the preset algorithm solving engine, and the warehouse site selection result corresponding to the site selection instance file is calculated through the mixed integer programming model based on the computing power resources; or, When the current computing resources of the preset algorithm solving engine are insufficient, the computing resources corresponding to the site selection instance file are allocated according to the submission time of the site selection instance file.

6. The warehouse site selection method according to any one of claims 1 to 4, characterized in that: The basic input data and model configuration parameters for warehouse location selection are obtained, including: Upon receiving the warehouse location selection instruction, updating the stored data in the preset database by at least one of data import, associating with the local database, and associating with external data; Edit model configuration parameters according to the warehouse location selection instruction, wherein the model configuration parameters include: business scenario, calculation target, calculation constraint and address requirement; The basic input data in the updated preset database is read according to the warehouse location selection instruction.

7. The warehouse site selection method according to any one of claims 1 to 4, characterized in that: The warehouse site selection method further includes: Storing the warehouse site selection results in a preset database; When receiving a warehouse location analysis instruction, calling a plurality of warehouse location results from the preset database according to the warehouse location analysis instruction; The site selection results of each warehouse are displayed and a visual comparative analysis is performed.

8. A warehouse site selection device, characterized in that: The warehouse site selection device comprises: The acquisition module is used to obtain the basic input data and model configuration parameters for warehouse location selection; A determination module, configured to determine a set of demand point addresses and a set of candidate warehouse addresses through a preset address processing model based on the model configuration parameters and the basic input data; A construction module, used to construct a site selection instance file according to the demand point address set, the candidate warehouse address set and the model configuration parameters; The calculation module is used to determine the corresponding warehouse site selection result according to the site selection instance file.

9. A warehouse site selection device, characterized in that: The warehouse site selection device includes: a memory, a processor, and a warehouse site selection program stored in the memory and executable on the processor, wherein the warehouse site selection program is configured to implement the warehouse site selection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a warehouse site selection program, and when the warehouse site selection program is executed by the processor, the warehouse site selection method according to any one of claims 1 to 7 is implemented.

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