Storage resource library point layout optimization method and device

Abstract

The invention discloses a storage resource library point layout optimization method and apparatus. The method comprises the steps of obtaining a demand point position information set; performing first processing on the demand point position information to obtain first result information; performing second processing on the demand point position information to obtain second result information; and carrying out fusion processing on the first result information and the second result information to obtain library point layout optimization result information. According to the method, on the basis of the target task information and the storage resource quantity scale optimization result, optimization processing of the storage resource layout is realized, the problem that the storage resources are too dispersed is solved, and the comprehensive benefits of the storage resources are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the warehouse technical field, and particularly relates to a warehouse resource library point layout optimization method and device. BACKGROUND

[0002] At present, warehouse resources are matched with material support, and the warehouse area and the inventory volume are expanded, but there is a structural contradiction that warehouse resource libraries are too scattered. The present application is based on target task information and based on warehouse resource quantity scale optimization results, realizes rapid processing of warehouse resource layout optimization, improves the efficiency of warehouse resource layout optimization processing, and solves the problem of scattered warehouse resource library layout. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a warehouse resource library point layout optimization method and device. The present application is based on target task information and based on warehouse resource quantity scale optimization results, realizes rapid processing of warehouse resource layout optimization, improves the efficiency of warehouse resource layout optimization processing, and solves the problem of scattered warehouse resource library layout.

[0004] To solve the above technical problems, a warehouse resource library point layout optimization method is disclosed in the first aspect of the present application, and the method comprises:

[0005] S1, obtaining a demand point position information set;

[0006] S2, using a first optimization model to perform first processing on the demand point position information to obtain first result information;

[0007] S3, performing second processing on the demand point position information to obtain second result information;

[0008] S4, performing fusion processing on the first result information and the second result information to obtain library point layout optimization result information.

[0009] As an optional implementation, in the first aspect of the present application, the first processing on the demand point position information using the first optimization model to obtain the first result information comprises:

[0010] S21, determining first optimization target information;

[0011] S22, performing analysis processing on the demand point position information to obtain first library point type information and a first library point position information set;

[0012] S23, constructing a first optimization model based on the first optimization target information and the first library point type information;

[0013] S24, processing the first warehouse location information set by using the first optimization model to obtain first result information.

[0014] As an optional implementation, in the first aspect of the embodiment of the present application, the first optimization model expression is:

[0015]

[0016] q i ≥0;

[0017]

[0018] wherein i represents the index of the guarantee target; j represents the index of the first warehouse; M represents the target area position set; L represents the first warehouse location information set; q i represents the demand of the ith target area; c represents the unit material transportation rate; d ij . represents the actual distance from the ith user or ad target area to the jth first warehouse candidate point; p represents the first warehouse quantity scale optimization result; x j represents the decision variable of the jth first warehouse candidate point; y ij . represents the guarantee decision variable of the ith user or target area by the jth first warehouse candidate point.

[0019] As an optional implementation, in the first aspect of the embodiment of the present application, the processing of the first warehouse location information set by using the first optimization model to obtain first result information comprises:

[0020] S2401, performing analysis processing on the first warehouse location information set to obtain the first warehouse quantity M and all first warehouse location information;

[0021] S2402, setting the optimization number n=M and the replacement number m=1;

[0022] S2403, processing the first warehouse location information set by using the first optimization model to obtain first result information and guarantee relationship information;

[0023] S2404, obtaining the mth first warehouse location information, deleting the mth first warehouse location information from the first warehouse location information set to obtain second warehouse location information, obtaining the second warehouse location information element number Q, and increasing the replacement number m by 1;

[0024] S2405, processing the second warehouse location information by using the first optimization model to obtain second result information;

[0025] S2406, replacing any second depot position information of the second depot position information set with the nth first depot position information to obtain a third depot position information set;

[0026] S2407, processing the third depot position information set by using the first optimization model to obtain third result information;

[0027] S2408, judging whether the third result information is less than the second result information to obtain a first judgment result;

[0028] When the first judgment result is yes, S2410 is executed; otherwise, S2409 is executed;

[0029] S2409, judging whether the replacement number m is less than the second depot position information element number Q to obtain a replacement number judgment result;

[0030] When the replacement number judgment result is yes, the second depot position information is used to replace the first depot position information set to obtain an updated first depot position information set, and S2404 is executed;

[0031] When the replacement number judgment result is no, S2410 is executed;

[0032] S2410, reducing the optimization number n by 1, and judging whether the optimization number n is equal to the first depot number scale optimization result to obtain an optimization number judgment result;

[0033] When the optimization number judgment result is yes, S2411 is executed;

[0034] When the replacement number judgment result is no, the third depot position information set is used to replace the second depot position information to obtain updated second depot position information, and S2404 is executed;

[0035] S2411, performing fusion processing on the third result information and the guarantee relationship information to obtain first result information.

[0036] As an optional implementation, in the first aspect of the embodiment of the application, the second processing of the demand point position information to obtain second result information comprises:

[0037] S31, determining second optimization target information;

[0038] S32, performing analysis processing on the demand point position information to obtain second depot type information and second depot position information;

[0039] S33, constructing a second optimization model based on the second optimization target information and the second depot type information;

[0040] S34, processing the second warehouse point position information by using the second optimization model to obtain first result information.

[0041] As an optional implementation, in the first aspect of the embodiment of the application, the second optimization model expression is:

[0042]

[0043] 0≤y ij ≤1, x j ∈{0,1};

[0044] Wherein, H represents the position set of the second warehouse candidate point; A(j) represents the position set of the target support region that can be covered by the jth second warehouse candidate point; B(i) represents the position set of the second warehouse candidate point that can cover the ith target support region; d i . represents the material demand of the ith target region; C j represents the material throughput capacity of the jth second warehouse candidate point; x j represents the selected variable of the jth second warehouse candidate point; y ij represents the proportion of the material quantity of the ith target support region guaranteed by the jth second warehouse candidate point to the total demand of the material.

[0045] As an optional implementation, in the first aspect of the embodiment of the application, the processing of the second warehouse point position information by using the second optimization model to obtain second result information comprises:

[0046] S341, parameter variable setting is performed on the second optimization model to obtain a third optimization model;

[0047] S342, processing the second warehouse point position information by using the third optimization model to obtain second result information.

[0048] The second aspect of the embodiment of the application discloses a warehouse resource warehouse point layout optimization device, and the device comprises:

[0049] An acquisition module is configured to acquire a demand point position information set;

[0050] A first processing module is configured to perform first processing on the demand point position information by using a first optimization model to obtain first result information;

[0051] Second, second processing is performed on the demand point position information to obtain second result information;

[0052] The data fusion processing module fuses the first result information and the second result information to obtain warehouse point layout optimization result information.

[0053] The third aspect of the present application discloses another warehouse resource warehouse point layout optimization device, and the device comprises:

[0054] The memory stores executable program codes.

[0055] The processor is coupled with the memory.

[0056] The processor calls the executable program codes stored in the memory to execute part or all steps of the warehouse resource warehouse point layout optimization method disclosed in the first aspect of the present application.

[0057] The fourth aspect of the present application discloses a computer readable storage medium which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all steps of the warehouse resource warehouse point layout optimization method disclosed in the first aspect of the present application.

[0058] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0059] In the embodiment of the present application, the target task information is used as the basis, and the warehouse resource quantity scale optimization result is used as the basis to realize the rapid processing of the warehouse resource layout optimization, improve the efficiency of the warehouse resource layout optimization processing, and solve the problem of too scattered warehouse resources. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0061] Figure 1 is a scene schematic diagram of the warehouse resource management system provided by the embodiment of the present application;

[0062] Figure 2 is a flowchart of the warehouse resource warehouse point layout optimization method disclosed by the embodiment of the present application;

[0063] Figure 3 is a structure schematic diagram of the warehouse resource warehouse point layout optimization device disclosed by the embodiment of the present application;

[0064] Figure 4 is a structure schematic diagram of another warehouse resource warehouse point layout optimization device disclosed by the embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the persons skilled in the art better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0066] The terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, rather than to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or apparatus.

[0067] In this document, reference to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to one of skill in the art, embodiments described herein can be combined with other embodiments.

[0068] In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for purposes of explanation, specific details are set forth. It is apparent to those skilled in the art that the present application can be practiced without using these specific details. In other instances, well-known structures and processes are not elaborated in order not to obscure the description of the application with unnecessary details. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded with the widest scope consistent with the principles and features disclosed.

[0069] It should be noted that the method of the present application is executed in a computer device, and the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It can be understood that if the size, quantity, position, and the like are mentioned in subsequent embodiments, they all exist in the form of corresponding data for processing by the computer device, and specific details are not described here.

[0070] It should be noted that the artificial intelligence related technologies involved in the present application are briefly described. Artificial intelligence (AI) is the use of digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology of computer science, which attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0071] Artificial intelligence technology is a comprehensive discipline, involving a wide range of fields, both hardware and software technologies. Artificial intelligence basic technologies generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0072] Computer vision (CV) is a science that studies how to make machines "see". Further, it refers to using cameras and computers to replace human eyes to identify and measure targets, and further to process graphics, so that the computer processing becomes more suitable for human eye observation or image transmission to instrument detection. As a scientific discipline, computer vision researches related theories and technologies, trying to establish artificial intelligence systems that can obtain information from images or multidimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, etc. It also includes common face recognition, fingerprint recognition and other biometric identification technologies.

[0073] Single modal information is only one type of data, such as text, image, audio, video, electromagnetic signal, etc. Multi-modal information is data information including at least two single modal information. Further, multi-modal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved in tasks.

[0074] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. The model usually needs to be trained on a large-scale dataset and requires a large amount of computing resources for optimization and adjustment. Large models are usually used to solve complex natural language processing, computer vision, and speech recognition tasks. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiments of the present application, the large model can be a large language model such as ChatGPT, BERT, XLNet, Zhibu model, Claude, Moonshot AI model, ChatGLM model, Qianyitong question model, MiniMax model, Xinghuo model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunding model, vivoLM model, DeepSeek model, and Wenxin Yanyan, etc. The embodiments of the present application are not limited.

[0075] The embodiments of the present application provide a warehouse resource library point layout optimization method and device, computer equipment and computer readable storage medium, which are described in detail below.

[0076] Please refer to Figure 1 , Figure 1 The warehouse resource management system provided by the embodiments of the present application is a scene schematic diagram, which can include a computer equipment 100, and the computer equipment 100 is integrated with a warehouse resource library point layout optimization device, such as Figure 1 computer equipment.

[0077] In the embodiments of the present application, the computer equipment 100 can be a standalone server, or a server network or server cluster composed of servers. For example, the computer equipment 100 described in the embodiments of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0078] It can be understood that the computer equipment 100 used in the embodiments of the present application can be a device that includes receiving and transmitting hardware, i.e., a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device can include a cellular or other communication device with a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer equipment 100 can be a desktop terminal or a mobile terminal, and the computer equipment 100 can also be one of a mobile phone, a tablet computer, a notebook computer, etc.

[0079] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the data processing system can also include one or more other services, which are not limited here.

[0080] In addition, if Figure 1 As shown, the warehouse resource management system may further include a memory 200 for storing warehouse resource data, such as Google Maps data, security point location information, warehouse location information, obstacle location information, and stored material information.

[0081] It should be noted that Figure 1 The scenario diagram of the warehouse resource management system shown is only an example. The warehouse resource management system and scenario described in the embodiment of this application are intended to more clearly illustrate the technical solution of the embodiment of this application, and do not constitute a limitation on the technical solution provided by the embodiment of this application. Ordinary technicians in this field can know that with the evolution of the warehouse resource management system and the emergence of new business scenarios, the technical solution provided by the embodiment of this application is also applicable to similar technical problems.

[0082] This invention discloses a method and device for optimizing the layout of warehouse resource points. Based on target mission information, this method utilizes a warehouse resource point layout optimization model to optimize the quantity and scale of warehouse resources, balancing military and economic benefits. These are described in detail below.

[0083] Example 1

[0084] See also Figure 2 , Figure 2 This is a flow chart of a method for optimizing the layout of warehouse resource points disclosed in an embodiment of the present invention. Figure 2 The warehouse resource storage point layout optimization method described is applied to a warehouse resource management system, such as a local server or cloud server for the warehouse resource management system, and the embodiment of the present invention does not limit it. Figure 1 As shown, the warehouse resource storage point layout optimization method may include the following operations:

[0085] S1, obtain the demand point location information set;

[0086] It should be noted that the demand point location information includes the first warehouse quantity scale optimization information, the first warehouse location information, the second warehouse quantity scale optimization information, the second warehouse location information and the target area location information;

[0087] S2, performing first processing on the demand point position information by using a first optimization model to obtain first result information;

[0088] S3, performing second processing on the demand point position information to obtain second result information;

[0089] S4, performing fusion processing on the first result information and the second result information to obtain warehouse point layout optimization result information.

[0090] It can be seen that, by implementing the warehouse resource warehouse point layout optimization method described in the embodiments of the present application, the first optimization model and the second optimization model are used to process the warehouse resource quantity scale optimization result based on the target task information, the rapid processing of the warehouse resource layout optimization is realized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0091] In an optional embodiment, in the step S2, the first processing on the demand point position information by using the first optimization model to obtain the first result information includes:

[0092] S21, determining first optimization target information;

[0093] It should be noted that, in the present embodiment, the first optimization target information indicates that the shortest transportation distance is taken as the objective function;

[0094] S22, performing analysis processing on the demand point position information to obtain first warehouse point type information and a first warehouse point position information set;

[0095] S23, constructing a first optimization model based on the first optimization target information and the first warehouse point type information; it should be noted that the first warehouse point type information indicates that the first warehouse point position information is the position information of a temporary warehouse;

[0096] It should be noted that the first optimization model is an optimization model for a temporary warehouse;

[0097] S24, processing the first warehouse point position information set by using the first optimization model to obtain the first result information.

[0098] It can be seen that, by implementing the warehouse resource warehouse point layout optimization method described in the embodiments of the present application, the first optimization model is used to perform the first processing on the demand point position information to obtain the first result information, the optimization processing of the first warehouse resource warehouse point layout is realized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0099] In another optional embodiment, in the step S2, the first optimization model expression is:

[0100]

[0101] q i ≥0;

[0102]

[0103] wherein, i represents the index of the guarantee target; j represents the index of the first warehouse; M represents the target area position set; L represents the first warehouse position information set; q i represents the demand of the ith target area; c represents the unit material transportation rate; d ij represents the actual distance from the ith user or ad target area to the jth first warehouse candidate point; p represents the first warehouse quantity scale optimization result; x j represents the decision variable of the jth first warehouse candidate point; y ij represents the guarantee decision variable of the ith user or target area by the jth first warehouse candidate point;

[0104] It should be noted that the x j =1 represents that the jth first warehouse candidate point is selected;

[0105] It should be noted that the x j =0 represents that the jth first warehouse candidate point is not selected;

[0106] It should be noted that the y ij =1 represents that the ith user or target area is guaranteed by the jth first warehouse candidate point;

[0107] It should be noted that the y ij =0 represents that the ith user or target area is not guaranteed by the jth first warehouse candidate point;

[0108] It should be noted that in the embodiment, the minimum material transportation cost is taken as the objective function, which meets the requirement of considering traffic factors in the first warehouse site selection;

[0109] It should be noted that the guarantees that the demand of the same type of material of each user or target area can only be guaranteed by one first warehouse, which meets the actual situation of material guarantee;

[0110] It should be noted that the y ij ≤x j , j∈L guarantees that the unselected first warehouse candidate point cannot provide material guarantee service for the target area or user, and the purpose of selecting the first warehouse candidate point is achieved;

[0111] It should be noted that the Σ j∈L x j = p ensures that the number of selected first warehouses is equal to the preset economic reasonable number value, and the optimization result of the number of first warehouses is inherited;

[0112] It should be noted that the y ij , x j ∈{0,1}, j∈L indicates that the first optimization model is a 0-1 integer linear programming model, and the 'near and convenient' guarantee and guaranteed relationship between the first warehouse and the user and the target area can be established;

[0113] It can be seen that the warehouse resource point layout optimization method described in the embodiment of the application is implemented, the first optimization model is constructed, and support is provided for subsequent data processing, the optimization processing of the first warehouse resource point layout is realized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0114] In another optional embodiment, in the step S22, the demand point position information is analyzed to obtain the first warehouse point type information and the first warehouse point position information set, including:

[0115] S221, obtaining the demand point position information;

[0116] S222, traversing the demand point position information to obtain all the demand point position information;

[0117] S223, obtaining the data type information of any demand point position information to obtain the data type data;

[0118] S224, judging whether the data type data is equal to the preset data type value to obtain a data type judgment result;

[0119] S225, when the data type judgment result is yes, obtaining the first warehouse point position information;

[0120] When the data type judgment result is no, S223 is executed;

[0121] S226, all the first warehouse point position information is combined in order to obtain the first warehouse point position information set;

[0122] The data type data is the first warehouse point type information.

[0123] It can be seen that by implementing the warehouse resource storage point layout optimization method described in the embodiment of the present invention, the demand point location information is analyzed and processed to obtain the first storage point location information set, which provides support for subsequent data processing, realizes the optimization processing of the first warehouse resource storage point layout, and improves the efficiency of the warehouse resource layout optimization processing.

[0124] In another optional embodiment, in step S24, the first optimization model is used to process the first inventory location information set to obtain first result information, including:

[0125] S2401: parse the first storage point location information set to obtain the first storage point quantity M and all first storage point location information;

[0126] S2402, set the number of optimizations n = M and the number of permutations m = 1;

[0127] S2403: Process the first inventory location information set using the first optimization model to obtain first result information and guarantee relationship information;

[0128] S2404: Obtain the mth piece of the first stock point location information, delete the mth piece of the first stock point location information from the first stock point location information set, obtain the second stock point location information, obtain the number Q of elements in the second stock point location information, and increase the replacement quantity m by 1;

[0129] S2405: Process the second storage point location information using the first optimization model to obtain second result information;

[0130] S2406: Replace any second storage point location information of the second storage point location information with the nth first storage point location information to obtain a third storage point location information set;

[0131] S2407: Process the third inventory point location information set using the first optimization model to obtain third result information;

[0132] S2408, determining whether the third result information is less than the second result information, and obtaining a first determination result;

[0133] When the first judgment result is yes, execute S2410; otherwise, execute S2409;

[0134] S2409, determining whether the replacement quantity m is less than the number Q of the second storage point location information elements, and obtaining a replacement quantity determination result;

[0135] When the replacement quantity judgment result is yes, the first warehouse point position information set is replaced by the second warehouse point position information, to obtain an updated first warehouse point position information set, and S2404 is executed;

[0136] When the replacement quantity judgment result is no, S2410 is executed;

[0137] S2410, the optimization number n is reduced by 1, and whether the optimization number n is equal to the first warehouse point quantity scale optimization result is judged, to obtain an optimization number judgment result;

[0138] When the optimization number judgment result is yes, S2411 is executed;

[0139] When the replacement quantity judgment result is no, the second warehouse point position information is replaced by the third warehouse point position information set, to obtain an updated second warehouse point position information, and S2404 is executed;

[0140] S2411, the third result information and the guarantee relationship information are fused, to obtain first result information.

[0141] It can be seen that the warehouse resource warehouse point layout optimization method described in the embodiment of the application is used to process the first warehouse point position information set by using the first optimization model, to obtain first result information, so that the optimization processing of the first warehouse resource warehouse point layout is realized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0142] In another optional embodiment, the step S3 described above, the second processing of the demand point position information to obtain second result information includes:

[0143] S31, second optimization target information is determined;

[0144] It should be noted that in the embodiment, the second optimization target information indicates that the minimum quantity is the objective function;

[0145] S32, the demand point position information is parsed to obtain second warehouse point type information and second warehouse point position information;

[0146] It should be noted that the second warehouse point type information indicates that the second warehouse point position information is the position information of a guarantee base;

[0147] It should be noted that the second optimization model is an optimization model for a guarantee base;

[0148] S33, a second optimization model is constructed based on the second optimization target information and the second warehouse point type information;

[0149] S34, processing the second warehouse location information by using the second optimization model to obtain first result information.

[0150] It can be seen that the warehouse resource location layout optimization method described in the embodiment of the application is used to perform second processing on the demand point location information to obtain second result information, so that the second warehouse resource location layout is optimized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0151] In another optional embodiment, the step S32 of analyzing and processing the demand point location information to obtain the second warehouse location type information and the second warehouse location information includes:

[0152] S321, obtaining the demand point location information;

[0153] S322, traversing the demand point location information to obtain all the demand point location information;

[0154] S323, obtaining the data type information of any demand point location information to obtain data type data;

[0155] S324, judging whether the data type data is equal to a preset data type value to obtain a data type judgment result;

[0156] S325, when the data type judgment result is yes, performing S323;

[0157] When the data type judgment result is no, the second warehouse location information is obtained.

[0158] S326, combining all the second warehouse location information in a sequence to obtain the second warehouse location information;

[0159] The data type data is the second warehouse location type information.

[0160] It can be seen that the warehouse resource location layout optimization method described in the embodiment of the application is used to perform second processing on the demand point location information to obtain second result information, so that the second warehouse resource location layout is optimized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0161] In another optional embodiment, the step S3 of the second optimization model expression is:

[0162]

[0163] 0≤y ij ≤1, x j ∈{0, 1};

[0164] wherein H represents a position set of the second warehouse candidate point; A(j) represents a position set of the target support region that can be covered by the jth second warehouse candidate point; B(i) represents a position set of the second warehouse candidate point that can cover the ith target support region; d i represents the material demand of the ith target region; C j represents the material throughput capacity of the jth second warehouse candidate point; x j represents the selected variable of the jth second warehouse candidate point; y ij represents the proportion of the material quantity of the ith target support region supported by the jth second warehouse candidate point to the total material demand of the ith target support region;

[0165] It should be noted that the x j = 1 represents that the jth second warehouse candidate point is selected;

[0166] It should be noted that the x j = 0 represents that the jth second warehouse candidate point is not selected;

[0167] It should be noted that in the embodiment, the quantity scale is the smallest as the objective function of the second optimization model, which meets the overall requirement of the second warehouse of the warehouse resource intensification construction and the quantity optimization target of the second warehouse;

[0168] It should be noted that the ensures that the material support demand of each target region can be completely met;

[0169] It should be noted that the i∈A(j) d i y ij ≤ C j x j ensures that the material demand of the target region covered by the second warehouse can be met under the condition that the material throughput capacity and support timeliness of the second warehouse are certain;

[0170] It should be noted that the 0 ≤ y ij ≤ 1, x j ∈ {0, 1} allows the selected second warehouse to provide partial material support service for the target region, and the second warehouse that is not selected cannot provide material support service for the target region, which not only meets the characteristics of the material support, but also achieves the purpose of selecting the second warehouse candidate point;

[0171] It can be seen that the warehouse resource warehouse point layout optimization method described in the embodiment of the application is implemented, the second optimization model is constructed to provide support for subsequent data processing, the optimization processing of the second warehouse resource warehouse point layout is realized, and the efficiency of the warehouse resource layout optimization processing is improved.

[0172] In another optional embodiment, in the step S34, the processing of the second depot location information by using the second optimization model to obtain the second result information comprises:

[0173] S341, setting a parameter variable of the second optimization model to obtain a third optimization model;

[0174] S342, processing the second depot location information by using the third optimization model to obtain the second result information;

[0175] It should be noted that the processing can be solving the third optimization model by using a Lagrange relaxation heuristic algorithm or a greedy heuristic algorithm, which is not limited in the embodiment.

[0176] It can be seen that the warehouse resource depot layout optimization method described in the embodiment of the application processes the demand point location information to obtain the second result information, realizes the optimization processing of the second warehouse resource depot layout, and improves the efficiency of the warehouse resource layout optimization processing.

[0177] In another optional embodiment, in the step S341, the setting of the parameter variable of the second optimization model to obtain the third optimization model comprises:

[0178] S3411, obtaining task target information;

[0179] S3412, obtaining a target function and a constraint condition according to the task information;

[0180] It should be noted that the target function is the minimum number of the second warehouse.

[0181] It should be noted that the constraint condition comprises:

[0182] (1) the position and demand of each target support region are known;

[0183] (2) each target support region can be supported by multiple second warehouses;

[0184] (3) the support capacity of the second warehouse is limited by the material throughput capacity thereof;

[0185] (4) the coverage range of the second warehouse is restricted by the material support time efficiency thereof;

[0186] (5) the transportation capacity within the support time efficiency range can meet the material transportation demand;

[0187] S3413, setting a parameter variable of the second optimization model by using the target function and the constraint condition to obtain a third optimization model;

[0188] It should be noted that the third optimization model represents the improved second optimization model with the constraint condition setting.

[0189] It can be seen that the warehouse resource depot layout optimization method described in the embodiment of the application sets the parameter variables of the second optimization model to obtain a third optimization model, which provides support for subsequent data processing, so as to realize the optimization processing of the second warehouse resource depot layout and improve the efficiency of the warehouse resource layout optimization processing.

[0190] In another optional embodiment, in the step S4, the fusion processing of the first result information and the second result information to obtain the depot layout optimization result information includes:

[0191] S41, traversing the first result information to obtain all the first result information;

[0192] S42, analyzing any first result information to obtain first depot number information and first depot position information;

[0193] S43, traversing the second result information to obtain all the second result information;

[0194] S44, analyzing any second result information to obtain second depot number information and second depot position information;

[0195] S45, based on the first depot number information and the second depot number information, performing the deduplication processing on the first depot position information and the second depot position information to obtain the depot layout optimization result information.

[0196] It can be seen that the warehouse resource depot layout optimization method described in the embodiment of the application performs the fusion processing of the first result information and the second result information to obtain the depot layout optimization result information, realizes the rapid processing of the warehouse resource layout optimization, improves the efficiency of the warehouse resource layout optimization processing, and solves the problem of the over-dispersed warehouse resources.

[0197] Embodiment Two

[0198] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a warehouse resource depot layout optimization device disclosed in the embodiment of the application. Wherein, Figure 3 The device described can be applied to a warehouse resource management system, such as a local server or a cloud server of a warehouse resource management system, and the embodiment of the application is not limited. As shown in Figure 3 The device can include:

[0199] A second aspect of an embodiment of the present invention discloses a device for optimizing the layout of warehouse resource storage points, the device comprising:

[0200] An acquisition module 101 is used to acquire a demand point location information set;

[0201] A first processing module 102 is configured to perform a first processing on the demand point location information using a first optimization model to obtain first result information;

[0202] A second processing module 103 is configured to perform a second processing on the demand point location information to obtain second result information;

[0203] The data fusion processing module 104 performs fusion processing on the first result information and the second result information to obtain warehouse layout optimization result information.

[0204] It can be seen that implementation Figure 3 The described storage resource storage point layout optimization device uses a storage resource storage point layout optimization model to process the storage resource information set to obtain storage resource quantity information and storage resource scale information, thereby achieving optimized processing of storage resource quantity and scale, and balancing the relationship between the military and economic benefits of storage resources.

[0205] Example 3

[0206] See also Figure 4 , Figure 4 This is a structural diagram of another storage resource point layout optimization device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a warehouse resource management system, such as a local server or cloud server for managing a warehouse resource management system, and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0207] A memory 201 storing executable program code;

[0208] a processor 202 coupled to the memory 201;

[0209] The processor 202 calls the executable program code stored in the memory 201 to execute the steps of the warehouse resource storage point layout optimization method described in the first embodiment.

[0210] Example 4

[0211] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the warehouse resource storage point layout optimization method described in the first embodiment.

[0212] Example 5

[0213] The embodiment of the present application discloses a computer program product, which comprises a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to make a computer execute the steps in the warehouse resource depot layout optimization method described in embodiment one.

[0214] The device embodiments described above are only schematic, wherein the modules illustrated as separate components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0215] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, it can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software product can be stored in a computer readable storage medium, and the storage medium includes a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a programmable read-only memory (Programmable Read-only Memory, PROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, EPROM), a one-time programmable read-only memory (One-time Programmable Read-Only Memory, OTPROM), an electrically erasable programmable read-only memory (Electrically-Erasable Programmable Read-Only Memory, EEPROM), a compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0216] It should be finally pointed out that: the warehouse resource depot layout optimization method and device disclosed by the embodiment of the application are only the preferred embodiment of the application, and are used to illustrate the technical solutions of the application, but not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for optimizing the layout of warehouse resource points, characterized in that: The method comprises: S1, obtain the demand point location information set; S2, performing a first processing on the demand point location information to obtain first result information; S3, performing a second process on the demand point location information to obtain second result information; S4: Fusing the first result information and the second result information to obtain warehouse layout optimization result information.

2. The method for optimizing the layout of storage resource points according to claim 1, characterized in that: The performing a first processing on the demand point location information to obtain first result information includes: S21, determining first optimization target information; S22, parsing the demand point location information to obtain first storage point type information and a first storage point location information set; S23: constructing a first optimization model based on the first optimization target information and the first storage point type information; S24: Using the first optimization model, process the first inventory location information set to obtain first result information.

3. The method for optimizing the layout of storage resource points according to claim 2, characterized in that: The first optimization model expression is: q i ≥0； Where i represents the index of the security target; j represents the index of the first warehouse; M represents the target area location set; L represents the first warehouse location information set; q i represents the demand of the i-th target area; c represents the unit material transportation rate; d ij· represents the actual distance between the i-th user or ad target area and the j-th first storage point candidate point; p represents the optimization result of the number and scale of the first storage point; x j represents the decision variable of the jth first storage point candidate; y ij· The guarantee decision variable that represents the material guarantee implemented by the jth first warehouse candidate point for the i-th user or target area.

4. The method for optimizing the layout of storage resource points according to claim 2, characterized in that: The first optimization model is used to process the first storage point location information set to obtain first result information, including: S2401: parse the first storage point location information set to obtain the first storage point quantity M and all first storage point location information; S2402, set the number of optimizations n = M and the number of permutations m = 1; S2403: Process the first inventory location information set using the first optimization model to obtain first result information and guarantee relationship information; S2404: Obtain the mth piece of the first stock point location information, delete the mth piece of the first stock point location information from the first stock point location information set, obtain the second stock point location information, obtain the number Q of elements in the second stock point location information, and increase the replacement quantity m by 1; S2405: Process the second storage point location information using the first optimization model to obtain second result information; S2406: Replace any second storage point location information of the second storage point location information with the nth first storage point location information to obtain a third storage point location information set; S2407: Process the third inventory point location information set using the first optimization model to obtain third result information; S2408, determining whether the third result information is less than the second result information, and obtaining a first determination result; When the first judgment result is yes, execute S2410; otherwise, execute S2409; S2409, determining whether the replacement quantity m is less than the number Q of the second storage point location information elements, and obtaining a replacement quantity determination result; When the result of the replacement quantity judgment is yes, the first stock point location information set is replaced with the second stock point location information to obtain an updated first stock point location information set, and S2404 is executed; When the result of the replacement quantity determination is no, executing S2410; S2410, subtract 1 from the optimized number n, determine whether the optimized number n is equal to the first inventory point quantity scale optimization result, and obtain the optimized number determination result; When the result of the optimization number judgment is yes, execute S2411; When the result of the replacement quantity determination is no, the second storage point location information is replaced with the third storage point location information set to obtain updated second storage point location information, and S2404 is executed; S2411: Fusing the third result information with the guarantee relationship information to obtain first result information.

5. The method for optimizing the layout of storage resource points according to claim 1, characterized in that: The second processing of the demand point location information to obtain second result information includes: S31, determining second optimization target information; S32, parsing the demand point location information to obtain second storage point type information and second storage point location information; S33: constructing a second optimization model based on the second optimization target information and the second storage point type information; S34: Use the second optimization model to process the second inventory point location information to obtain second result information.

6. The method for optimizing the layout of storage resource points according to claim 5, characterized in that: The second optimization model expression is: 0≤y ij ≤1,x j ∈{0,1}; Where H represents the location set of the second warehouse candidate points; A(j) represents the location set of the target support area that can be covered by the j-th second warehouse candidate point; B(i) represents the location set of the second warehouse candidate points that can cover the i-th target support area; d i· represents the material demand in the i-th target area; C j represents the material throughput capacity of the jth candidate second warehouse; x j represents the selected variable of the jth second warehouse candidate point; y ij It represents the ratio of the quantity of materials guaranteed by the jth second warehouse candidate point to the total material demand of the i-th target guarantee area.

7. The method for optimizing the layout of storage resource points according to claim 6, characterized in that: The second optimization model is used to process the second storage point location information to obtain second result information, including: S341, setting parameter variables for the second optimization model to obtain a third optimization model; S342: Use the third optimization model to process the second inventory point location information to obtain second result information.

8. A storage resource point layout optimization device, characterized in that: The device comprises: An acquisition module is used to obtain a demand point location information set; A first processing module is configured to perform a first processing on the demand point location information using a first optimization model to obtain first result information; Second, performing a second process on the demand point location information to obtain second result information; The data fusion processing module performs fusion processing on the first result information and the second result information to obtain warehouse layout optimization result information.

9. A storage resource point layout optimization device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the warehouse resource storage point layout optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program for electronic data exchange, wherein: The computer program enables a computer to execute the steps in the warehouse resource storage point layout optimization method as described in any one of claims 1 to 7.

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