Goods allocation planning method and device, equipment, storage medium and program product
By analyzing the cargo information data in the warehouse, building constraints and decomposing the cargo space planning model, the problem of low cargo space planning efficiency in complex warehousing scheduling is solved, and more efficient cargo space planning and cargo transportation is achieved.
Patent Information
- Application Number
- CN202411213108.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2025-02-07
AI Technical Summary
The prior art is difficult to obtain the optimal solution in complex warehousing scheduling problems, resulting in low efficiency in cargo space planning.
By obtaining the information data of warehouse goods, analyzing and processing obtains the feature vector and similarity matrix, constructing constraints, and using the decomposition algorithm to decompose the cargo space planning model into main problems and sub-problems, and optimizing the solution to obtain the shortest time cargo space planning results.
It improves the efficiency of cargo space planning, can solve complex scheduling problems more quickly, and optimizes the operating order and time of goods.
Smart Images
Figure CN119398654A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of warehousing logistics technology, and in particular to a cargo location planning method, device, equipment, storage medium and program product. Background Art
[0002] In recent years, with the growing development of business-to-consumer (B2C) e-commerce business, the number of emerging automated warehouses has increased. It is of great significance to develop efficient automated warehouse scheduling technology to meet the strict timeliness requirements of B2C business.
[0003] At present, the existing strategies for warehouse scheduling are mainly aimed at dealing with simple warehousing problems, but it is difficult to obtain the optimal solution for complex scheduling problems, which will cause the problem of low efficiency in cargo space planning.
[0004] Therefore, how to improve the efficiency of cargo space planning has become an urgent problem to be solved. Summary of the invention
[0005] The embodiments of the present application provide a cargo space planning method, apparatus, device, storage medium and program product, which can improve the efficiency of collaborative cargo space planning.
[0006] In a first aspect, an embodiment of the present application provides a cargo space planning method, the method comprising:
[0007] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0008] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0009] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0010] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0011] In one of the embodiments, based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo space planning result, including: based on the second constraint condition, the second objective function is optimized and solved to obtain a feasible solution for the cargo space planning model; based on the feasible solution, a cut is added to the first objective function, and an optimal solution of the first objective function is obtained through iteration; the optimal solution is used as the cargo space planning result; the optimal solution includes a time parameter matrix for transferring target goods into the target cargo space and a time parameter matrix for transferring target goods out of the target cargo space.
[0012] In one embodiment, the cargo space planning model is:
[0013]
[0014] Among them, c n+1 It represents the total accumulated time before the n+1th cargo is transferred.
[0015] In one embodiment, the first objective function is:
[0016] ;
[0017] Among them, c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring goods x to location y;
[0018] The second objective function is:
[0019] ;
[0020] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
[0021] In one embodiment, the first constraint is:
[0022] Ax+By=b;
[0023] Among them, A represents the similarity matrix of goods; B represents the characteristic vector of goods; x represents goods; y represents the cargo location; b represents the time constraint parameter;
[0024] The second constraint is:
[0025] ;
[0026] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; J represents the total number of cargo locations.
[0027] In one of the embodiments, the method further includes: based on the cargo space planning result, using a wireless sensor network to control the handling equipment to carry the target cargo.
[0028] In a second aspect, the present application provides a cargo space planning device, the device comprising:
[0029] The acquisition and processing module is used to acquire the cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0030] A construction module, used to construct a first constraint condition based on the feature vector and the similarity matrix, and to construct a second constraint condition based on the similarity matrix;
[0031] A decomposition module is used to decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem by using a decomposition algorithm; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0032] The processing module is used to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0033] In a third aspect, the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:
[0034] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0035] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0036] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0037] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:
[0039] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0040] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0041] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0042] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0043] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:
[0044] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0045] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0046] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0047] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0048] In the above-mentioned cargo location planning method, device, equipment, storage medium and program product, the computer equipment can obtain the cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the characteristic vector and similarity matrix corresponding to the cargo; based on the characteristic vector and the similarity matrix, the first constraint condition is constructed, and based on the similarity matrix, the second constraint condition is constructed; using the decomposition algorithm, the pre-constructed cargo location planning model is decomposed into the first objective function corresponding to the main problem and the second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo; based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain the cargo location planning result with the shortest time for the target cargo to be transported to the target cargo location. Using this method, the computer equipment can decompose the pre-constructed cargo location planning model into the first objective function corresponding to the main problem and the second objective function corresponding to the sub-problem, and optimize and solve the first objective function based on the second objective function. Compared with the method of directly solving the cargo location planning model for complex scheduling problems, the solution efficiency can be improved, thereby improving the efficiency of cargo location planning. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0050] Figure 1 This is a schematic diagram of an application scenario of a cargo space planning method provided in an embodiment of the present application;
[0051] Figure 2 This is a flowchart of a cargo space planning method provided by an embodiment of the present application;
[0052] Figure 3 This is a flow chart of another method for cargo space planning provided by an embodiment of the present application;
[0053] Figure 4 It is a schematic diagram of a collaborative operation process provided by an embodiment of the present application;
[0054] Figure 5 This is a schematic diagram of the structure of a cargo space planning device provided in an embodiment of the present application;
[0055] Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0057] The following introduces the application scenarios of the cargo space planning method provided in the embodiments of the present application.
[0058] See also Figure 1 , Figure 1 Schematic diagram of an application scenario of a cargo space planning method provided in an embodiment of the present application. Figure 1 As shown, the computer device 101 ( Figure 1 In the figure, the computer device 101 is drawn as an example of a terminal device) and a database server 102, wherein data can be transmitted between the computer device 101 and the database server 102 via a network.
[0059] The computer device 101 can obtain the cargo information data of the cargo in the warehouse from the database server 102, and analyze and process the cargo information data to obtain the characteristic vector and similarity matrix corresponding to the cargo; construct the first constraint condition based on the characteristic vector and the similarity matrix, and construct the second constraint condition based on the similarity matrix; use the decomposition algorithm to decompose the pre-constructed cargo location planning model into the first objective function corresponding to the main problem and the second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo; based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain the cargo location planning result with the shortest time for the target cargo to be transported to the target cargo location. Using this method, the computer device 101 can decompose the pre-constructed cargo location planning model into the first objective function corresponding to the main problem and the second objective function corresponding to the sub-problem, and optimize and solve the first objective function based on the second objective function. Compared with the method of directly solving the cargo location planning model for complex scheduling problems, the solution efficiency can be improved, thereby improving the efficiency of cargo location planning.
[0060] Optionally, the computer device 101 may be a terminal device or a server. The terminal device mentioned here may include but is not limited to: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart watch, a smart TV, a smart car terminal, etc. The server mentioned here may be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers.
[0061] See also Figure 2 , Figure 22 is a flow chart of a method for cargo space planning provided in an embodiment of the present application. The method can be executed by a computer device (for example, the computer device 201 described above). Figure 2 As shown, the cargo space planning method may include but is not limited to the following steps:
[0062] S201. Obtain cargo information data of cargo in the warehouse, and analyze and process the cargo information data to obtain feature vectors and similarity matrices corresponding to the cargo.
[0063] Optionally, the cargo information data may include, but is not limited to, the cargo's weight, volume, value, shelf life, customer demand, cargo space capacity, and transportation cost.
[0064] In an optional implementation, the computer device can use a collaborative computing method to analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo. Collaborative computing is a data processing technology based on a multi-agent system that can use multiple distributed computing nodes to collaboratively complete complex data tasks, such as data collection, analysis, and mining.
[0065] S202: construct a first constraint condition based on the feature vector and the similarity matrix, and construct a second constraint condition based on the similarity matrix.
[0066] S203: Decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem by using a decomposition algorithm.
[0067] Among them, the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods.
[0068] Optionally, the decomposition algorithm may be a Benders decomposition algorithm, which is an optimization solution technique based on duality theory, which can decompose a complex optimization problem into a main problem and one or more sub-problems, and ultimately achieve the purpose of accurately solving the original problem through iterative solution and interaction between the main problem and the sub-problems.
[0069] S204. Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0070] In an optional implementation, after step S204, the computer device may also control the transport device to transport the target goods using the wireless sensor network based on the cargo location planning result. Optionally, the transport device is, for example, an automated guided vehicle (AGV).
[0071] In the embodiment of the present application, the computer device can obtain the cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the characteristic vector and similarity matrix corresponding to the cargo; construct the first constraint condition based on the characteristic vector and the similarity matrix, and construct the second constraint condition based on the similarity matrix; use the decomposition algorithm to decompose the pre-constructed cargo location planning model into the first objective function corresponding to the main problem and the second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo; based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain the cargo location planning result with the shortest time for the target cargo to be transported to the target cargo location. Using this method, the computer device can decompose the pre-constructed cargo location planning model into the first objective function corresponding to the main problem and the second objective function corresponding to the sub-problem, and optimize and solve the first objective function based on the second objective function. Compared with the method of directly solving the cargo location planning model for complex scheduling problems, the solution efficiency can be improved, thereby improving the efficiency of cargo location planning.
[0072] See also Figure 3 , Figure 3 is a flow chart of another method for cargo space planning provided by an embodiment of the present application. Figure 2 Compared with the storage location planning method shown in Figure 3 The method also describes how the computer device optimizes and solves the first objective function based on the first constraint condition, the second constraint condition and the second objective function to obtain the cargo space planning result. Figure 3 As shown, the cargo space planning method may include but is not limited to the following steps:
[0073] S301. Obtain cargo information data of cargo in the warehouse, and analyze and process the cargo information data to obtain feature vectors and similarity matrices corresponding to the cargo.
[0074] In an optional implementation, the relevant description of step S301 can refer to the description of the aforementioned step S201, which will not be repeated here.
[0075] S302: construct a first constraint condition based on the feature vector and the similarity matrix, and construct a second constraint condition based on the similarity matrix.
[0076] In an optional implementation, the computer device may construct a first constraint condition as shown in the following formula (1) based on the feature vector and the similarity matrix.
[0077] Ax+By=b (1)
[0078] In formula (1), A represents the similarity matrix of goods; B represents the characteristic vector of goods; x represents goods; y represents the cargo location; b represents the time constraint parameter;
[0079] In an optional implementation, the computer device may construct a second constraint condition as shown in the following formula (2) based on the similarity matrix.
[0080] (2)
[0081] In formula (2), It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; J represents the total number of cargo locations.
[0082] S303: Using a decomposition algorithm, decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem.
[0083] Among them, the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods.
[0084] In an optional implementation, the cargo space planning model pre-built by the computer device may be as shown in the following formula (3).
[0085] (3)
[0086] In formula (3), c n+1 It represents the total accumulated time before the n+1th cargo transfer, that is, the total accumulated time until the nth cargo transfer is completed.
[0087] Optionally, the constraint conditions of formula (3) can be the following formulas (4) to (9).
[0088] (4)
[0089] (5)
[0090] (6)
[0091] (7)
[0092] (8)
[0093] (9)
[0094] In formulas (4) to (9), x ijrepresents the goods that are shipped out of the i-th warehouse and transferred to the j-th cargo location; I represents the total amount of goods; J represents the total amount of cargo locations; k represents the number of handling equipment in the same time period; y ij =1 means that the jth storage location to which the i-th outbound goods are transferred can store at most one piece of goods; m represents the total number of storage locations of a certain type; cj represents the time when the goods are outbound; ci represents the time when the goods are put into storage; M represents the time when the handling equipment handles the goods (i.e., the transportation time); aij represents the working time of the handling equipment.
[0095] In an optional implementation, the computer device uses a decomposition algorithm to decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem, and may use a Benders decomposition algorithm to decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem. The first objective function may be the following formula (10).
[0096] (10)
[0097] In formula (10), c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring goods x to location y.
[0098] Optionally, the constraints corresponding to formula (10) may include the above formula (1) and the following formulas (11) and (12).
[0099] x ≥ 0 (11)
[0100] y ≥ 0 and y is an integer (12)
[0101] In summary, the first objective function corresponding to the main problem is: ; The constraints corresponding to the first objective function are: Ax+By=b; x≥0; y≥0 and y is an integer.
[0102] Among them, x is a continuous variable and y is a discrete variable. Since the decision variables in the first objective function corresponding to the main problem include continuous variables x and discrete variables y, and these two types of variables are connected by the constraint condition Ax+By=b, which increases the difficulty of solving the problem, the computer device can separate the discrete variable y from the continuous variable x in the first objective function. Since the discrete variable y is a more complex variable than the continuous variable x, if the value of the discrete variable y can be determined in advance, then the model of the continuous variable x is often easier to solve. Therefore, the computer device can transform the sub-problem into the second objective function shown in the following formula (13).
[0103] (13)
[0104] In formula (13), It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
[0105] Optionally, the constraint condition of formula (13) may be as shown in the above formula (2).
[0106] To sum up, the second objective function corresponding to the subproblem is: ; The constraints corresponding to the second objective function are: .
[0107] S304: Optimize and solve the second objective function based on the second constraint condition to obtain a feasible solution for the cargo space planning model.
[0108] S305 . Add cuts to the first objective function based on the feasible solution, and obtain the optimal solution of the first objective function through iteration.
[0109] Among them, the computer device adds cuts to the first objective function corresponding to the main problem based on the feasible solution, so that each added cut will cut a part of the feasible domain of the first objective function corresponding to the main problem, so that through iteration, the optimal solution of the first objective function can be obtained.
[0110] S306. The optimal solution is used as the cargo location planning result; the optimal solution includes a time parameter matrix for transferring target cargo into the target cargo location and a time parameter matrix for transferring target cargo out of the target cargo location.
[0111] In an optional implementation, after step S306, the computer device may also control the transport equipment to transport the target goods using the wireless sensor network based on the cargo location planning result.
[0112] Optionally, when the computer device controls the handling device to carry the target goods, the following method can be used: Figure 4 See the workflow shown in the figure. Figure 4 , Figure 4 This is a schematic diagram of a collaborative operation process provided by an embodiment of the present application. Figure 4 As shown, the computer device can also control the stacker to release from the current cargo position first, start resetting in advance, and transport to the next operation position when the current operation is warehousing; when the current operation is warehousing operation, the handling equipment (such as AGV) is controlled to release from the current cargo position first, start resetting in advance, and transport to the next operation position. Therefore, the completion time of the horizontal handling task of AGV, the vertical stacking task of the stacker, and the current outbound or inbound operation is not synchronized. In the mode of collaborative operation of AGV and stacker, by scheduling the operation sequence of the two types of equipment, the time for transportation and resetting and the waiting time between equipment can be reduced, thereby effectively improving the efficiency of warehousing in and out of the automated warehouse.
[0113] In an embodiment of the present application, a computer device can obtain cargo information data of cargo in a warehouse, and analyze and process the cargo information data to obtain a characteristic vector and a similarity matrix corresponding to the cargo; construct a first constraint condition based on the characteristic vector and the similarity matrix, and construct a second constraint condition based on the similarity matrix; use a decomposition algorithm to decompose a pre-constructed cargo location planning model into a first objective function corresponding to a main problem and a second objective function corresponding to a sub-problem; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo; optimize and solve the second objective function based on the second constraint condition to obtain a feasible solution for the cargo location planning model; add cuts to the first objective function based on the feasible solution, and obtain the optimal solution of the first objective function through iteration; use the optimal solution as a cargo location planning result; the optimal solution includes a time parameter matrix for the target cargo to be transferred into the target cargo location and a time parameter matrix for the target cargo to be transferred out of the target cargo location. By adopting this method, the computer equipment can decompose the pre-constructed cargo space planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem, and optimize and solve the first objective function based on the second objective function. Compared with the method of directly solving the cargo space planning model for complex scheduling problems, the solution efficiency can be improved, thereby improving the efficiency of cargo space planning.
[0114] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0115] Based on the same inventive concept, the embodiment of the present application also provides a cargo location planning device for implementing the cargo location planning method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more cargo location planning device embodiments provided below can refer to the limitations of the cargo location planning method above, and will not be repeated here.
[0116] See also Figure 5 , Figure 5 Schematic diagram of a storage location planning device provided in an embodiment of the present application. Figure 5 As shown, the cargo space planning device may include but is not limited to:
[0117] The acquisition and processing module 501 is used to acquire the cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0118] A construction module 502, configured to construct a first constraint condition based on the feature vector and the similarity matrix, and to construct a second constraint condition based on the similarity matrix;
[0119] The decomposition module 503 is used to decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem by using a decomposition algorithm; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo;
[0120] The processing module 504 is used to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0121] In one embodiment, when the processing module 504 is used to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function to obtain the cargo location planning result, it is specifically used to: optimize and solve the second objective function based on the second constraint condition to obtain a feasible solution for the cargo location planning model; add cuts to the first objective function based on the feasible solution, and obtain the optimal solution of the first objective function through iteration; use the optimal solution as the cargo location planning result; the optimal solution includes a time parameter matrix for the target cargo to be transferred into the target cargo location and a time parameter matrix for the target cargo to be transferred out of the target cargo location.
[0122] In one embodiment, the cargo space planning model is:
[0123]
[0124] Among them, c n+1 It represents the total accumulated time before the n+1th cargo is transferred.
[0125] In one embodiment, the first objective function is:
[0126] ;
[0127] Among them, c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring goods x to location y;
[0128] The second objective function is:
[0129] ;
[0130] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
[0131] In one embodiment, the first constraint is:
[0132] Ax+By=b;
[0133] Among them, A represents the similarity matrix of goods; B represents the characteristic vector of goods; x represents goods; y represents the cargo location; b represents the time constraint parameter;
[0134] The second constraint is:
[0135] ;
[0136] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; J represents the total number of cargo locations.
[0137] In one embodiment, the processing module 504 is further used to: based on the cargo location planning result, control the transport equipment to transport the target cargo using the wireless sensor network.
[0138] Each module in the above-mentioned cargo space planning device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of the processor in the terminal device in the form of hardware, or can be stored in the memory in the terminal device in the form of software, so that the processor can call and execute the corresponding operations of each module.
[0139] In an exemplary embodiment, the present application provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a cargo location planning method is implemented.
[0140] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0141] In an exemplary embodiment, the present application provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:
[0142] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0143] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0144] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0145] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0146] In one embodiment, the processor executes a computer program to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function, and when obtaining the cargo location planning result, the following steps are specifically implemented: optimizing and solving the second objective function based on the second constraint condition to obtain a feasible solution for the cargo location planning model; adding cuts to the first objective function based on the feasible solution, and obtaining the optimal solution of the first objective function through iteration; using the optimal solution as the cargo location planning result; the optimal solution includes a time parameter matrix for the target cargo to be transferred into the target cargo location and a time parameter matrix for the target cargo to be transferred out of the target cargo location.
[0147] In one embodiment, the cargo space planning model is:
[0148]
[0149] Among them, c n+1 It represents the total accumulated time before the n+1th cargo is transferred.
[0150] In one embodiment, the first objective function is:
[0151] ;
[0152] Among them, c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring goods x to location y;
[0153] The second objective function is:
[0154] ;
[0155] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
[0156] In one embodiment, the first constraint is:
[0157] Ax+By=b;
[0158] Among them, A represents the similarity matrix of goods; B represents the characteristic vector of goods; x represents goods; y represents the cargo location; b represents the time constraint parameter;
[0159] The second constraint is:
[0160] ;
[0161] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; J represents the total number of cargo locations.
[0162] In one embodiment, when the processor executes the computer program, the following steps are further implemented: based on the cargo location planning result, using the wireless sensor network to control the handling equipment to carry the target cargo.
[0163] In an exemplary embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:
[0164] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0165] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0166] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0167] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0168] In one embodiment, the computer program is executed by a processor to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function, and when obtaining the cargo location planning result, the following steps are specifically implemented: optimizing and solving the second objective function based on the second constraint condition to obtain a feasible solution for the cargo location planning model; adding cuts to the first objective function based on the feasible solution, and obtaining the optimal solution of the first objective function through iteration; using the optimal solution as the cargo location planning result; the optimal solution includes a time parameter matrix for transferring target cargo into the target cargo location and a time parameter matrix for transferring target cargo out of the target cargo location.
[0169] In one embodiment, the cargo space planning model is:
[0170]
[0171] Among them, c n+1 It represents the total accumulated time before the n+1th cargo is transferred.
[0172] In one embodiment, the first objective function is:
[0173] ;
[0174] Among them, c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring goods x to location y;
[0175] The second objective function is:
[0176] ;
[0177] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
[0178] In one embodiment, the first constraint is:
[0179] Ax+By=b;
[0180] Among them, A represents the similarity matrix of goods; B represents the characteristic vector of goods; x represents goods; y represents the cargo location; b represents the time constraint parameter;
[0181] The second constraint is:
[0182] ;
[0183] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; J represents the total number of cargo locations.
[0184] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the cargo location planning result, using the wireless sensor network to control the handling equipment to carry the target cargo.
[0185] In an exemplary embodiment, the present application provides a computer program product, including a computer program, which implements the following steps when executed by a processor:
[0186] Obtain cargo information data of the cargo in the warehouse, and analyze and process the cargo information data to obtain the feature vector and similarity matrix corresponding to the cargo;
[0187] constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix;
[0188] By using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem; the first objective function is used to solve the operation sequence for the goods, and the second objective function is used to solve the operation time for the goods;
[0189] Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
[0190] In one embodiment, the computer program is executed by a processor to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function, and when obtaining the cargo location planning result, the following steps are specifically implemented: optimizing and solving the second objective function based on the second constraint condition to obtain a feasible solution for the cargo location planning model; adding cuts to the first objective function based on the feasible solution, and obtaining the optimal solution of the first objective function through iteration; using the optimal solution as the cargo location planning result; the optimal solution includes a time parameter matrix for transferring target cargo into the target cargo location and a time parameter matrix for transferring target cargo out of the target cargo location.
[0191] In one embodiment, the cargo space planning model is:
[0192]
[0193] Among them, c n+1 It represents the total accumulated time before the n+1th cargo is transferred.
[0194] In one embodiment, the first objective function is:
[0195] ;
[0196] Among them, c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring goods x to location y;
[0197] The second objective function is:
[0198] ;
[0199] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
[0200] In one embodiment, the first constraint is:
[0201] Ax+By=b;
[0202] Among them, A represents the similarity matrix of goods; B represents the characteristic vector of goods; x represents goods; y represents the cargo location; b represents the time constraint parameter;
[0203] The second constraint is:
[0204] ;
[0205] in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; J represents the total number of cargo locations.
[0206] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the cargo location planning result, using the wireless sensor network to control the handling equipment to carry the target cargo.
[0207] It should be noted that the data involved in this application (including but not limited to cargo information data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0208] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.
[0209] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0210] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A cargo space planning method, characterized in that: The method comprises: Acquire cargo information data of cargo in the warehouse, and analyze and process the cargo information data to obtain feature vectors and similarity matrices corresponding to the cargo; constructing a first constraint condition based on the feature vector and the similarity matrix, and constructing a second constraint condition based on the similarity matrix; Using a decomposition algorithm, the pre-built cargo location planning model is decomposed into a first objective function corresponding to a main problem and a second objective function corresponding to a sub-problem; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo; Based on the first constraint condition, the second constraint condition and the second objective function, the first objective function is optimized and solved to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
2. The method according to claim 1, characterized in that The optimizing and solving the first objective function based on the first constraint condition, the second constraint condition and the second objective function to obtain a cargo location planning result includes: Optimizing and solving the second objective function based on the second constraint condition to obtain a feasible solution for the cargo space planning model; Adding a cut to the first objective function based on the feasible solution, and obtaining an optimal solution of the first objective function through iteration; The optimal solution is used as a cargo location planning result; the optimal solution includes a time parameter matrix for transferring the target cargo into the target cargo location and a time parameter matrix for transferring the target cargo out of the target cargo location.
3. The method according to claim 1 or 2, characterized in that: The cargo space planning model is: Among them, c n+1 It represents the total accumulated time before the n+1th cargo is transferred.
4. The method according to claim 1 or 2, characterized in that: The first objective function is: Among them, c T It represents the time parameter matrix for transferring goods x to storage location y; f T It represents the time parameter matrix for transferring the cargo x out of the cargo location y; The second objective function is: ； in, It represents the limit; B represents the characteristic vector of the cargo location; b represents the time constraint parameter; I represents the total quantity of the cargo.
5. The method according to claim 1 or 2, characterized in that: The first constraint condition is: Ax+By=b; Among them, A represents the similarity matrix of the goods; B represents the feature vector of the goods; x represents the goods; y represents the cargo location; b represents the time constraint parameter; The second constraint is: ； in, It represents the limit; B represents the characteristic vector of the cargo space; b represents the time constraint parameter; J represents the total number of cargo spaces.
6. The method according to claim 1 or 2, characterized in that: The method further comprises: Based on the cargo location planning result, a wireless sensor network is used to control a transport device to transport the target cargo.
7. A cargo space planning device, characterized in that: The device comprises: An acquisition and processing module is used to acquire cargo information data of cargo in the warehouse, and analyze and process the cargo information data to obtain a feature vector and a similarity matrix corresponding to the cargo; A construction module, configured to construct a first constraint condition based on the feature vector and the similarity matrix, and to construct a second constraint condition based on the similarity matrix; A decomposition module, used to decompose the pre-built cargo location planning model into a first objective function corresponding to the main problem and a second objective function corresponding to the sub-problem by using a decomposition algorithm; the first objective function is used to solve the operation sequence for the cargo, and the second objective function is used to solve the operation time for the cargo; The processing module is used to optimize and solve the first objective function based on the first constraint condition, the second constraint condition and the second objective function to obtain a cargo location planning result with the shortest time for transporting the target cargo to the target cargo location.
8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.