Combination optimization method, device, computer equipment and storage medium for asset outbound delivery

Abstract

The embodiment of the present invention discloses a combined optimization method, device, computer equipment and storage medium for asset outbound delivery. The method includes: obtaining order requirement parameters, and reading asset data that meets the order requirements in a database according to the order requirement parameters; obtaining the overlapping relationship between the asset data, and integrating the overlapping relationship to obtain an asset list; inputting the asset list into a preset integer programming model, and solving it according to the preset constraints and preset objective function of the integer programming model to determine the target assets that need to be outbound. By applying the integer programming model, only the asset data is input to automatically solve the appropriate target assets, and the optimal solution can be obtained at the second level, which greatly improves the speed of obtaining the results, can effectively improve the efficiency of asset matching, and realize the rapid utilization of assets. At the same time, it has a unified measurement standard, and for multiple similar matching results, the optimal solution can also be obtained according to the measurement standard, so the solution result has a higher quality.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of asset outbound delivery, and in particular, to a combined optimization method, device, computer equipment, and storage medium for asset outbound delivery. Background Art

[0002] In the logistics industry, as consumers' requirements for same-city delivery efficiency continue to increase, "last mile" delivery has gradually become the focus of the industry, and has spawned a variety of diversified services, such as express cabinets, smart micro-warehouses, parcel lockers, government cabinets and other smart terminal products to meet the customized needs of multiple scenarios. The various cabinets included in the above-mentioned smart terminal products need to go through the asset outbound process before they are transported to the corresponding service points. Asset outbound is mainly based on business needs, matching suitable assets from different warehouse assets, and deciding the placement of each asset, and then placing the assets at the service point based on the decision. Since the process of matching assets involves multiple business rules, the huge number of warehouses and assets, as well as complex business rules, poses a huge challenge to the asset outbound problem.

[0003] The traditional method of removing assets from the warehouse is mainly to gradually screen a large number of assets based on business rules to obtain assets that meet the business rules. This process involves a lot of logical judgments, and the matching is time-consuming and cannot respond to daily business requests in a timely manner. At the same time, when there are multiple similar matching results, the selection can only rely on manual experience and lack of unified measurement standards, resulting in low quality of matching results. Summary of the invention

[0004] The embodiments of the present invention provide a combined optimization method, device, computer equipment and storage medium for asset outbound matching to improve the efficiency of asset outbound matching and improve the quality of matching results.

[0005] In a first aspect, an embodiment of the present invention provides a combined optimization method for asset outbound delivery, the method comprising:

[0006] Obtaining order requirement parameters, and reading asset data that meets the order requirements from a database according to the order requirement parameters;

[0007] Obtaining the overlapping relationships between the asset data, and integrating the overlapping relationships to obtain an asset list;

[0008] The asset list is input into a preset integer programming model, and is solved according to preset constraints and a preset objective function of the integer programming model to determine the target assets that need to be shipped out.

[0009] Optionally, after solving the preset constraints and the preset objective function of the integer programming model, the method further includes:

[0010] Determining the placement location of each of the target assets;

[0011] Correspondingly, the decision variables of the integer programming model are:

[0012]

[0013] Among them, a DM It means that the Mth asset is placed in the Dth position and is a 0-1 variable. D represents the number of the target asset and M represents the number of assets that meet the order requirements.

[0014] Optionally, the target asset includes an intelligent cabinet, and the intelligent cabinet includes a main cabinet and a sub-cabinet;

[0015] The preset constraints include: one or more of main cabinet placement rules, continuous placement rules, matching brand restrictions, attribute placement rules, warehouse source restrictions, and custom function restrictions.

[0016] Optionally, the preset objective function includes one or more of: warehouse priority, maximizing the number of assets with the same supplier brand, minimizing the number of warehouse sources, maximizing the number of assets with awnings, minimizing asset transportation distance, and minimizing pattern differences.

[0017] Optionally, after inputting the asset list into a preset integer programming model, the method further includes:

[0018] Convert the input asset list into a feature matrix and a feature vector;

[0019] The feature matrix includes: one or more of a supplier brand matrix, a warehouse source matrix, and a product model matrix; the feature vector includes: one or more of a cabinet attribute vector, a canopy vector, an asset distance vector, and a pattern vector;

[0020] The supplier brand matrix is ​​B M*P , where P represents the number of supplier brands, B M*P Used to indicate the vendor brand to which each asset belongs;

[0021] The warehouse source matrix is ​​V M*Q , where Q represents the number of warehouses, V M*Q Used to indicate the warehouse to which each asset belongs;

[0022] The product model matrix is ​​S M*W , where W represents the number of product models, S M*W A product model number used to represent each asset;

[0023] The cabinet attribute vector is T M, where T M The type of cabinet used to represent each asset;

[0024] The canopy vector is R M , where R M Used to indicate whether each asset has a canopy;

[0025] The asset distance vector is Dt M , where Dt M Used to indicate the transportation distance of each asset from its warehouse;

[0026] The pattern vector is DE M , where DE M Used to indicate the type of pattern for each asset.

[0027] Optionally, the main cabinet placement rule is:

[0028]

[0029]

[0030] The continuous placement rule is: Supplier brand placement matrix C D*P =A D*M *B M*P The non-zero values ​​in each column need to appear continuously;

[0031] The mutual brand restriction is: The supplier brand placement matrix C D*P The total number of columns that are not 0 does not exceed the preset maximum number of brands;

[0032] The attribute placement rule is: place the cabinet type in vector E D =A D*M *T M Set as preset attribute placement rule vector;

[0033] The warehouse source is limited to: warehouse placement matrix G D*Q =A D*M *V M*Q The total number of columns that are not 0 does not exceed the preset maximum number of warehouses;

[0034] The custom function restriction includes: restricting the supplier brand, cabinet attributes, warehouse source, product model, awning, asset distance or pattern of the target asset at any of the placement positions according to a preset custom matrix or a preset custom vector.

[0035] Optionally, the warehouse priority is: the assets to be shipped out are determined according to the preset priorities of each warehouse, and when the assets of the warehouse with the previous priority are insufficient, the warehouse with the next priority is prioritized for shipment;

[0036] The maximum number of brand assets of the same supplier is: Supplier brand placement matrix C D*P =A D*M *B M*P Maximize the sum of the columns where the main cabinet supplier brands are located;

[0037] The minimum number of warehouse sources is: Minimize the warehouse placement matrix G D*Q =A D*M *V M*Q The sum of the columns that are not zero in ;

[0038] The maximum number of assets with awnings is: Minimize the awning placement vector F D =A D*M *R M With preset awning vector The difference between

[0039] The minimum asset transportation distance is: Minimize the asset distance placement vector X D =A D*M *Dt M The sum of

[0040] The minimization of the pattern difference is: minimizing the pattern placement vector Z D =A D*M *DE M With preset pattern vector The difference between.

[0041] In a second aspect, an embodiment of the present invention further provides a combined optimization device for asset outbound delivery, the device comprising:

[0042] An asset data reading module is used to obtain order requirement parameters and read asset data that meets the order requirements from the database according to the order requirement parameters;

[0043] An asset list integration module, used to obtain the overlapping relationship between the asset data, and integrate the overlapping relationship to obtain an asset list;

[0044] The target asset determination module is used to input the asset list into a preset integer programming model, and solve the integer programming model according to the preset constraints and preset objective function to determine the target assets that need to be shipped out.

[0045] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0046] one or more processors;

[0047] A memory for storing one or more programs;

[0048] When the one or more programs are executed by the one or more processors, the one or more processors implement the combined optimization method for asset removal provided by any embodiment of the present invention.

[0049] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the combined optimization method for asset outbound delivery provided by any embodiment of the present invention.

[0050] The embodiment of the present invention provides a combined optimization method for asset delivery. First, order requirement parameters are obtained, and asset data that meets the order requirements is read in the database according to the order requirement parameters. Then, the overlapping relationship between the asset data is obtained, and an asset list is obtained according to the overlapping relationship. Then, the asset list is input into a preset integer programming model, and then the preset constraint conditions and preset objective function of the integer programming model are solved to determine the target assets that need to be delivered for this order. The combined optimization method for asset delivery provided by the embodiment of the present invention, by applying the integer programming model, only needs to input the asset data to automatically solve for the appropriate target asset, and the optimal solution can be obtained within seconds, so that the speed of obtaining the result is greatly improved, which can effectively improve the asset matching efficiency, realize the rapid utilization of assets, thereby improving the operation and maintenance efficiency, and reducing the operation and maintenance cost. At the same time, it has a unified measurement standard, and the optimal solution can also be obtained for multiple similar matching results according to the measurement standard, so the solution result has higher quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of a combined optimization method for asset outbound delivery provided in Embodiment 1 of the present invention;

[0052] Figure 2 A schematic diagram of the structure of a combined optimization device for asset outbound delivery provided in the second embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0055] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0056] Embodiment 1

[0057] Figure 1 This is a flow chart of the combined optimization method for asset outbound delivery provided in the first embodiment of the present invention. This embodiment is applicable to the case where assets are outbound according to order requirements. The method can be executed by the combined optimization device for asset outbound delivery provided in the embodiment of the present invention. The device can be implemented in hardware and / or software and can generally be integrated into a computer device. Figure 1 As shown, the specific steps include:

[0058] S11. Obtain order requirement parameters, and read asset data that meets the order requirements from a database according to the order requirement parameters.

[0059] Specifically, the asset outbound process for each order will correspond to an order requirement, so that the assets that can be outbound can be determined according to the order requirements. For example, the outbound process of the smart cabinet is used as an example for explanation. The order requirements may include order requirement parameters such as city name, asset type name, indoor and outdoor information, and supplier code. Of course, the method provided in this embodiment is not limited to the outbound process of smart cabinets, but can also be applied to other types of assets, and the corresponding order requirement parameters can be determined according to each order. After obtaining the order requirement parameters, the assets that meet the order requirements can be first screened out in the database according to the order requirement parameters, and the asset data of these assets can be read from the database at the same time, where the asset data can include the warehouse name, warehouse code, cabinet type, product code, product name, pattern type, and number of awnings to which the asset belongs, etc.

[0060] S12: Obtain the overlapping relationships among the asset data, and integrate the overlapping relationships to obtain an asset list.

[0061] Specifically, different assets may have a mutual matching relationship, such as asset A can be used with asset B, but asset A cannot be used with asset C, etc., then the asset data can be integrated according to the mutual matching relationship between the asset data to obtain an asset list as an input parameter for the subsequent model. For example, taking the outbound process of the smart cabinet as an example, the mutual matching relationship between the asset data can be obtained through the hardware interface, which can be the mutual matching relationship between the asset product models, so as to select the sub-cabinet that can be matched with the main cabinet, thereby integrating the asset list.

[0062] S13, inputting the asset list into a preset integer programming model, and solving the model according to preset constraints and preset objective function of the integer programming model to determine the target assets that need to be shipped out.

[0063] Specifically, the combinatorial optimization method is a type of optimization method that finds the optimal solution in a finite set of feasible solutions. It is an important branch of operations research. This method arranges the decision object according to a certain constraint condition. When a specific arrangement that meets this constraint condition exists, the optimal solution of this specific arrangement under a certain optimization criterion is sought. Integer programming is a form of combinatorial programming. This embodiment uses the integer programming method to select the target asset with the highest adaptability from the assets that meet the order requirements. In addition, the number of target assets required can also be determined from the acquired order requirement parameters. Exemplarily, the outbound process of the intelligent cabinet is used as an example to illustrate. Assuming that the total number of assets in the warehouse is N, the number of assets that meet the order requirements is M, and the number of target assets is D, which may include a main cabinet and D-1 sub-cabinets, then based on the integer programming method, the D assets with the highest adaptability can be selected from the M assets according to the order requirements and business requirements.

[0064] After obtaining the asset list, the asset list can be input into a preset integer programming model, and solved according to the preset constraints and preset objective function of the integer programming model to determine the target assets that need to be shipped out, wherein the preset constraints and preset objective functions can be preset according to order requirements and business requirements, and the solution process can be solved using the CP_SAT open source solver of the ortools platform. Of course, this embodiment is not limited to the use of any valid integer programming solver.

[0065] Optionally, after solving the preset constraints and the preset objective function of the integer programming model, the method further includes: determining the placement position of each target asset; accordingly, the decision variables of the integer programming model are:

[0066]

[0067] Among them, aDM Indicates that the Mth asset is placed at the Dth position, and is a 0-1 variable, D represents the number of the target assets, and M represents the number of assets that meet the order requirements. Specifically, in addition to confirming the target assets to be shipped out, a general order also needs to confirm the matching method between the target assets, that is, the required placement position of each target asset on the final product. This can be achieved by establishing the above decision variable A D*M To carry out subsequent calculations, the target asset and its location can be automatically solved directly through the integer programming model. D*M It includes both the feature of the asset and the feature of the location where the asset is placed, so it is easy to select D suitable target assets from M assets and place them in their respective appropriate locations.

[0068] Optionally, the target asset includes an intelligent cabinet, and the intelligent cabinet includes a main cabinet and a sub-cabinet; the preset constraint conditions include: one or more of main cabinet placement rules, continuous placement rules, overlapping brand restrictions, attribute placement rules, warehouse source restrictions, and custom function restrictions. Specifically, the method provided in this embodiment can be specifically applied to intelligent cabinets, wherein the main cabinet placement rules may include: only one main cabinet can appear in the target asset, and the main cabinet must be placed in the main cabinet position; the continuous placement rules may include: assets belonging to the same supplier brand must be placed continuously; overlapping brand restrictions may include: the number of overlapping asset supplier brands does not exceed the maximum number of brands; attribute placement rules may include: a certain type of cabinet must be placed in a specified position (such as the outermost side), the number of a certain type of cabinet cannot exceed a specified number (such as 1), etc.; warehouse source restrictions may include: the warehouse source site selection of the target asset does not exceed the maximum number of warehouses. In addition, custom function restrictions can also be added, such as customizing the supplier brand, cabinet attributes, warehouse source, product model, canopy, asset distance and pattern of the target asset at a certain placement position.

[0069] Optionally, the preset objective function includes one or more sub-goals of: warehouse priority, maximizing the number of brand assets from the same supplier, minimizing the number of warehouse sources, maximizing the number of assets with awnings, minimizing asset transportation distance, and minimizing pattern differences. Among them, for warehouse priority, taking three warehouses I, J, and K as an example, if the warehouse priority is I>J>K, warehouse I will be prioritized for shipment. If warehouse I has insufficient assets, warehouse J will be prioritized for shipment. If warehouse J has insufficient assets, warehouse K will be prioritized for shipment. In the preset objective function, by setting weights for the assets of the three warehouses, that is, I weight>J weight>K weight, the warehouse priority can be met. On the basis of the above preset objective function, the weight value of each sub-goal can be adjusted according to the preset priorities of each sub-goal.

[0070] On the basis of the above embodiment of the present invention, it is also possible to generate a feature matrix and a feature vector by extracting relevant data, so as to facilitate the subsequent setting of constraints and objective functions and model solving. Optionally, after the asset list is input into a preset integer programming model, it also includes: converting the input asset list into a feature matrix and a feature vector; the feature matrix includes: one or more of a supplier brand matrix, a warehouse source matrix and a product model matrix; the feature vector includes: one or more of a cabinet attribute vector, a canopy vector, an asset distance vector and a pattern vector; the supplier brand matrix is ​​B M*P , where P represents the number of supplier brands, B M*P Used to indicate the supplier brand to which each asset belongs; the warehouse source matrix is ​​V M*Q , where Q represents the number of warehouses, V M*Q It is used to indicate the warehouse to which each asset belongs; the product model matrix is ​​S M*W , where W represents the number of product models, S M*W It is used to represent the product model of each asset; the cabinet attribute vector is T M , where T M It is used to represent the cabinet type of each asset; the awning vector is R M , where R M It is used to indicate whether each asset has a canopy; the asset distance vector is Dt M , where Dt M It is used to represent the transportation distance of each asset from its warehouse; the pattern vector is DE M , where DE M Used to represent the pattern type of each asset. Specifically, since business requirements may require consideration of multiple factors such as supplier brand, cabinet type, pattern type, warehouse source, distance, and awning, data related to the above factors can be extracted from the model input parameters, that is, the asset list, to generate feature matrices and feature vectors, so as to facilitate the subsequent setting of constraints and objective functions and model solving.

[0071] Further optionally, the main cabinet placement rule is:

[0072]

[0073]

[0074] The continuous placement rule is: Supplier brand placement matrix C D*P =A D*M *B M*P The values ​​that are not 0 in each column need to appear continuously; the mutual brand restriction is: the supplier brand placement matrix CD*P The total number of columns that are not 0 in the vector E does not exceed the preset maximum number of brands; the attribute placement rule is: place the cabinet type in the vector E D =A D*M *T M Set as the preset attribute placement rule vector; the warehouse source restriction is: warehouse placement matrix G D*Q =A D*M *V M*Q The total number of columns that are not 0 in the formula does not exceed the preset maximum number of warehouses; the custom function restriction includes: limiting the supplier brand, cabinet attribute, warehouse source, product model, canopy, asset distance or pattern of the target asset at any placement position according to the preset custom matrix or preset custom vector. Specifically, after obtaining the feature matrix and feature vector, each preset constraint condition can be set by operating between the feature matrices, where the supplier brand placement matrix C D*P The asset supplier brand at each placement position can be represented. For the continuous placement rule, the supplier brand placement matrix C D*P Each column represents the placement of a brand. Assets belonging to the same supplier brand need to be placed consecutively, so the supplier brand placement matrix C D*P The values ​​that are not 0 in each column of need to appear continuously. Regarding the restriction on matching brands, different supplier brands can match each other, but the number of matches can be limited. Therefore, the supplier brand placement matrix C D*P The total number of columns that are not 0 in cannot exceed the preset maximum number of brands. D =A D*M *T M Indicates the type of cabinet at each placement position. For the placement rules of different types of cabinets, we can place the vector E for the cabinet type D The value of is constrained to achieve this, specifically, the cabinet type can be placed in the vector E D Set as the preset attribute placement rule vector. For warehouse source restrictions, the warehouse placement matrix G D*Q It can represent the warehouse to which the asset at each placement location belongs. The number of warehouse source locations can be limited, so the warehouse placement matrix G D*Q The total number of columns that are not 0 in the matrix cannot exceed the preset maximum number of warehouses. For custom function restrictions, taking supplier brands as an example, you can use the preset custom matrix The supplier brand of the asset at each placement location is user-defined, and the supplier brand placement matrix can be used to To meet the user's customized supplier brand, other customized functions can be obtained similarly.

[0075] Furthermore, the generated characteristic matrix and characteristic vector can be used to construct the preset objective function, so as to facilitate the calculation and execution of the computer and improve the accuracy and efficiency of the outbound delivery results. Optionally, the warehouse priority is: the assets that need to be outbound are determined according to the preset priority of each warehouse, and when the assets of the warehouse with the previous priority are insufficient, the warehouse with the next priority is given priority to outbound delivery; the maximization of the number of assets of the same supplier brand is: the supplier brand placement matrix C D*P =A D*M *B M*P In the formula, the sum of the columns of the main cabinet supplier brands is maximized; the minimum number of warehouse sources is: Minimize the warehouse placement matrix G D*Q =A D*M *V M*Q The sum of the columns that are not 0 in the ; the maximization of the number of assets with awnings is: minimize the awning placement vector F D =A D*M *R M With preset awning vector The difference between the two; the minimization of asset transportation distance is: Minimization of asset distance placement vector X D =A D*M *Dt M The sum of the pattern difference is minimized as follows: minimize the pattern placement vector Z D =A D*M *DE M With preset pattern vector The difference between the two. Among them, the canopy placement vector F D It can indicate whether the asset at each placement location has a canopy. is the preset awning vector, then maximizing the number of assets with awnings is to minimize F D and The difference between the asset distance placement vector X D The distance of the warehouse described by each asset at each placement location can be expressed as: D The sum of the pattern placement vector Z D The type of pattern that can be used to represent the asset at each placement position. is the preset pattern vector, then minimizing the pattern difference is to minimize Z D and The difference between.

[0076] The technical solution provided by the above embodiment of the present invention first obtains the order requirement parameters, and reads the asset data that meets the order requirements in the database according to the order requirement parameters, then obtains the mutual relationship between the asset data, and integrates the mutual relationship to obtain an asset list, and then inputs the asset list into a preset integer programming model, thereby solving according to the preset constraints and preset objective function of the integer programming model to determine the target assets that need to be shipped out of the warehouse for this order. By applying the integer programming model, only the asset data needs to be input to automatically solve the appropriate target assets, and the optimal solution can be obtained within seconds, which greatly improves the speed of obtaining results, can effectively improve the asset matching efficiency, and realize the rapid use of assets, thereby improving operation and maintenance efficiency and reducing operation and maintenance costs. At the same time, it has a unified measurement standard, and for multiple similar matching results, the optimal solution can also be obtained according to the measurement standard, so the solution result has a higher quality.

[0077] Embodiment 2

[0078] Figure 2 This is a schematic diagram of the structure of the combined optimization device for asset outbound provided in the second embodiment of the present invention. The device can be implemented by hardware and / or software, and can generally be integrated into a computer device to execute the combined optimization method for asset outbound provided in any embodiment of the present invention. Figure 2 As shown, the device comprises:

[0079] The asset data reading module 21 is used to obtain order requirement parameters and read asset data that meets the order requirements from the database according to the order requirement parameters;

[0080] An asset list integration module 22 is used to obtain the overlapping relationship between the asset data and integrate the overlapping relationship to obtain an asset list;

[0081] The target asset determination module 23 is used to input the asset list into a preset integer programming model, and solve the integer programming model according to the preset constraints and preset objective function to determine the target assets that need to be shipped out.

[0082] The technical solution provided by the embodiment of the present invention first obtains the order requirement parameters, and reads the asset data that meets the order requirements in the database according to the order requirement parameters, then obtains the mutual relationship between the asset data, and integrates the mutual relationship to obtain an asset list, and then inputs the asset list into a preset integer programming model, thereby solving according to the preset constraints and preset objective functions of the integer programming model to determine the target assets that need to be shipped out of the warehouse for this order. By applying the integer programming model, only the asset data needs to be input to automatically solve for the appropriate target assets, and the optimal solution can be obtained within seconds, which greatly improves the speed of obtaining results, can effectively improve the efficiency of asset matching, and realize the rapid use of assets, thereby improving operation and maintenance efficiency and reducing operation and maintenance costs. At the same time, it has a unified measurement standard, and for multiple similar matching results, the optimal solution can also be obtained according to the measurement standard, so the solution result has a higher quality.

[0083] Based on the technical solution provided in the above embodiment, optionally, the combined optimization device for asset outbound delivery further includes:

[0084] A placement location determination module, configured to determine the placement location of each of the target assets after solving the preset constraints and the preset objective function according to the integer programming model;

[0085] Correspondingly, the decision variables of the integer programming model are:

[0086]

[0087] Among them, a DM It means that the Mth asset is placed in the Dth position and is a 0-1 variable. D represents the number of the target asset and M represents the number of assets that meet the order requirements.

[0088] On the basis of the above technical solution, optionally, the target asset includes an intelligent cabinet, and the intelligent cabinet includes a main cabinet and a sub-cabinet;

[0089] The preset constraints include: one or more of main cabinet placement rules, continuous placement rules, matching brand restrictions, attribute placement rules, warehouse source restrictions, and custom function restrictions.

[0090] Based on the above technical solution, optionally, the preset objective function includes one or more of: warehouse priority, maximizing the number of assets of the same supplier brand, minimizing the number of warehouse sources, maximizing the number of assets with awnings, minimizing asset transportation distance, and minimizing pattern differences.

[0091] On the basis of the above technical solution, optionally, the combined optimization device for asset outbound delivery further includes:

[0092] A matrix-vector conversion module, used for converting the inputted asset list into a feature matrix and a feature vector after the asset list is inputted into a preset integer programming model;

[0093] The feature matrix includes: one or more of a supplier brand matrix, a warehouse source matrix, and a product model matrix; the feature vector includes: one or more of a cabinet attribute vector, a canopy vector, an asset distance vector, and a pattern vector;

[0094] The supplier brand matrix is ​​B M*P , where P represents the number of supplier brands, B M*P Used to indicate the vendor brand to which each asset belongs;

[0095] The warehouse source matrix is ​​V M*Q , where Q represents the number of warehouses, V M*Q Used to indicate the warehouse to which each asset belongs;

[0096] The product model matrix is ​​S M*W , where W represents the number of product models, S M*W A product model number used to represent each asset;

[0097] The cabinet attribute vector is T M , where T M The type of cabinet used to represent each asset;

[0098] The canopy vector is R M , where R M Used to indicate whether each asset has a canopy;

[0099] The asset distance vector is Dt M , where Dt M Used to indicate the transportation distance of each asset from its warehouse;

[0100] The pattern vector is DE M , where DE M Used to indicate the type of pattern for each asset.

[0101] On the basis of the above technical solution, optionally, the main cabinet placement rule is:

[0102]

[0103]

[0104] The continuous placement rule is: Supplier brand placement matrix C D*P =A D*M *B M*PThe non-zero values ​​in each column need to appear continuously;

[0105] The mutual brand restriction is: The supplier brand placement matrix C D*P The total number of columns that are not 0 does not exceed the preset maximum number of brands;

[0106] The attribute placement rule is: place the cabinet type in vector E D =A D*M *T M Set as preset attribute placement rule vector;

[0107] The warehouse source is limited to: warehouse placement matrix G D*Q =A D*M *V M*Q The total number of columns that are not 0 does not exceed the preset maximum number of warehouses;

[0108] The custom function restriction includes: restricting the supplier brand, cabinet attributes, warehouse source, product model, awning, asset distance or pattern of the target asset at any of the placement positions according to a preset custom matrix or a preset custom vector.

[0109] On the basis of the above technical solution, optionally, the warehouse priority is: the assets that need to be shipped out are determined according to the preset priority of each warehouse, and when the assets of the warehouse with the previous priority are insufficient, the warehouse with the next priority is prioritized for shipment;

[0110] The maximum number of brand assets of the same supplier is: Supplier brand placement matrix C D*P =A D*M *B M*P Maximize the sum of the columns where the main cabinet supplier brands are located;

[0111] The minimum number of warehouse sources is: Minimize the warehouse placement matrix G D*Q =A D*M *V M*Q The sum of the columns that are not zero in ;

[0112] The maximum number of assets with awnings is: Minimize the awning placement vector F D =A D*M *R M With preset awning vector The difference between

[0113] The minimum asset transportation distance is: Minimize the asset distance placement vector X D =A D*M *Dt M The sum of

[0114] The minimization of the pattern difference is: minimizing the pattern placement vector ZD =A D*M *DE M With preset pattern vector The difference between.

[0115] The combined optimization device for asset outbound delivery provided in the embodiment of the present invention can execute the combined optimization method for asset outbound delivery provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0116] It is worth noting that in the above-mentioned embodiment of the combined optimization device for asset outbound delivery, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0117] Embodiment 3

[0118] Figure 3 The schematic diagram of the structure of the computer device provided for the third embodiment of the present invention shows a block diagram of an exemplary computer device suitable for implementing the implementation mode of the present invention. Figure 3 The computer device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3 As shown, the computer device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the computer device can be one or more. Figure 3 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the computer device can be connected through a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0119] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the combined optimization method for asset outbound in the embodiment of the present invention (for example, the asset data reading module 21, the asset list integration module 22 and the target asset determination module 23 in the combined optimization device for asset outbound). The processor 31 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 32, that is, realizes the combined optimization method for asset outbound.

[0120] The memory 32 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 32 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 32 may further include a memory remotely arranged relative to the processor 31, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0121] The input device 33 may be used to obtain order requirement parameters, and generate key signal input related to user settings and function control of the computer device, etc. The output device 34 may include a display screen and other devices, which may be used to display the determined target assets and their placement locations, etc. to the user.

[0122] Embodiment 4

[0123] Embodiment 4 of the present invention further provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to execute a combined optimization method for asset outbound when executed by a computer processor, the method comprising:

[0124] Obtaining order requirement parameters, and reading asset data that meets the order requirements from a database according to the order requirement parameters;

[0125] Obtaining the overlapping relationships between the asset data, and integrating the overlapping relationships to obtain an asset list;

[0126] The asset list is input into a preset integer programming model, and is solved according to preset constraints and a preset objective function of the integer programming model to determine the target assets that need to be shipped out.

[0127] The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system, which is connected to the computer system via a network (such as the Internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that may be executed by one or more processors.

[0128] Of course, the computer executable instructions of a storage medium containing computer executable instructions provided in an embodiment of the present invention are not limited to the method operations described above, but can also execute related operations in the combined optimization method for asset outbound delivery provided in any embodiment of the present invention.

[0129] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0130] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0131] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented 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, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0132] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A combined optimization method for asset outbound delivery, characterized in that: include: Obtaining order requirement parameters, and reading asset data that meets the order requirements from a database according to the order requirement parameters; Obtaining the overlapping relationships between the asset data, and integrating the overlapping relationships to obtain an asset list; Input the asset list into a preset integer programming model, and solve the model according to preset constraints and preset objective function to determine the target assets that need to be shipped out; After solving the preset constraints and the preset objective function of the integer programming model, the method further includes: Determine the placement position of each of the target assets; the placement position of the target assets is the required placement position of each of the target assets on the final product determined according to the matching mode between the target assets; Correspondingly, the decision variables of the integer programming model are: Among them, a DM It means that the Mth asset is placed in the Dth position and is a 0-1 variable. D represents the number of the target asset and M represents the number of assets that meet the order requirements.

2. The combined optimization method for asset outbound delivery according to claim 1 is characterized in that: The target asset includes an intelligent cabinet, and the intelligent cabinet includes a main cabinet and a sub-cabinet; The preset constraints include: one or more of main cabinet placement rules, continuous placement rules, matching brand restrictions, attribute placement rules, warehouse source restrictions, and custom function restrictions.

3. The combined optimization method for asset outbound delivery according to claim 2 is characterized in that: The preset objective function includes one or more of: warehouse priority, maximizing the number of assets with the same supplier brand, minimizing the number of warehouse sources, maximizing the number of assets with awnings, minimizing asset transportation distance, and minimizing pattern differences.

4. The combined optimization method for asset outbound delivery according to claim 3 is characterized in that: After the asset list is input into the preset integer programming model, the method further includes: Convert the input asset list into a feature matrix and a feature vector; The feature matrix includes: one or more of a supplier brand matrix, a warehouse source matrix, and a product model matrix; the feature vector includes: one or more of a cabinet attribute vector, a canopy vector, an asset distance vector, and a pattern vector; The supplier brand matrix is ​​B M*P , where P represents the number of supplier brands, B M*P Used to indicate the vendor brand to which each asset belongs; The warehouse source matrix is ​​V M*Q , where Q represents the number of warehouses, V M*Q Used to indicate the warehouse to which each asset belongs; The product model matrix is ​​S M*W , where W represents the number of product models, S M*W A product model number used to represent each asset; The cabinet attribute vector is T M , where T M The type of cabinet used to represent each asset; The canopy vector is R M , where R M Used to indicate whether each asset has a canopy; The asset distance vector is Dt M , where Dt M Used to indicate the transportation distance of each asset from its warehouse; The pattern vector is DE M , where DE M Used to indicate the type of pattern for each asset.

5. The combined optimization method for asset outbound delivery according to claim 4 is characterized in that: The main cabinet placement rules are: The continuous placement rule is: Supplier brand placement matrix C D*P =A D*M *B M*P The non-zero values ​​in each column need to appear continuously; The mutual brand restriction is: The supplier brand placement matrix C D*P The total number of columns that are not 0 does not exceed the preset maximum number of brands; The attribute placement rule is: place the cabinet type in vector E D =A D*M *T M Set as preset attribute placement rule vector; The warehouse source is limited to: warehouse placement matrix G D*Q =A D*M *V M*Q The total number of columns that are not 0 does not exceed the preset maximum number of warehouses; The custom function restriction includes: restricting the supplier brand, cabinet attributes, warehouse source, product model, awning, asset distance or pattern of the target asset at any of the placement positions according to a preset custom matrix or a preset custom vector.

6. The combined optimization method for asset outbound delivery according to claim 4 is characterized in that: The warehouse priority is: the assets that need to be shipped out are determined according to the preset priority of each warehouse. When the assets of the previous priority warehouse are insufficient, the next priority warehouse is prioritized for shipment. The maximum number of brand assets of the same supplier is: Supplier brand placement matrix C D*P =A D*M *B M*P Maximize the sum of the columns where the main cabinet supplier brands are located; The minimum number of warehouse sources is: Minimize the warehouse placement matrix G D*Q =A D*M *V M*Q The sum of the columns that are not zero in ; The maximum number of assets with awnings is: Minimize the awning placement vector F D =A D*M *R M With preset awning vector The difference between The minimum asset transportation distance is: Minimize the asset distance placement vector X D =A D*M *Dt M The sum of The minimization of the pattern difference is: minimizing the pattern placement vector Z D =A D*M *DE M With preset pattern vector The difference between.

7. A combined optimization device for asset outbound delivery, characterized in that: include: An asset data reading module is used to obtain order requirement parameters and read asset data that meets the order requirements from the database according to the order requirement parameters; An asset list integration module, used to obtain the overlapping relationship between the asset data, and integrate the overlapping relationship to obtain an asset list; A target asset determination module is used to input the asset list into a preset integer programming model, and solve the model according to preset constraints and preset objective functions to determine the target assets that need to be shipped out; The combined optimization device for asset outbound also includes: A placement position determination module, used to determine the placement position of each target asset after solving the preset constraint conditions and the preset objective function according to the integer programming model; the placement position of the target asset is the required placement position of each target asset on the final product determined according to the matching mode between the target assets; Correspondingly, the decision variables of the integer programming model are: Among them, a DM It means that the Mth asset is placed in the Dth position and is a 0-1 variable. D represents the number of the target asset and M represents the number of assets that meet the order requirements.

8. A computer device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the combined optimization method for asset removal as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the combined optimization method for asset removal as described in any one of claims 1 to 6 is implemented.

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