Traditional Chinese medicine decoction piece delivery review method, device and equipment based on picking path optimization

Abstract

The application discloses a traditional Chinese medicine decoction piece warehouse-out review method, device and equipment based on a picking path optimization, comprising the following steps: calculating the distance between each element in a traditional Chinese medicine decoction piece warehouse; establishing a shortest picking path model in the warehouse, and solving the shortest picking path model by using a genetic algorithm; distributing the picking sequence of a plurality of picking orders in each picking set by using a greedy algorithm and a dynamic adjustment strategy; and comparing the scanned product information with the pre-stored product information to complete the warehouse-out review. The application can realize the effective splicing of the starting points of the picking orders by dynamically adjusting the starting points and the shortest picking paths, thereby shortening the picking time and improving the operation efficiency. The problems of low efficiency and easy errors in manual review can be avoided by setting a code scanning device at a review point to scan the product information and automatically comparing the product information through a warehouse management terminal.

Description

Technical Field

[0001] This invention belongs to the field of data management technology, specifically relating to a method, apparatus, and equipment for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization. Background Technology

[0002] With the booming development of e-commerce, online shopping orders have become the mainstream business model across various industries. For example, in the field of traditional Chinese medicine (TCM) decoction pieces, warehouses, as crucial transit points for these products, handle a series of operations including receiving, storing, managing, picking, and dispatching goods. Therefore, optimizing warehouse operations is of great significance for improving warehouse efficiency.

[0003] The current common warehouse operation process for shipping Chinese herbal medicine pieces is generally as follows: receiving customer orders --- warehouse issues picking lists --- picking --- verification --- packing. Currently, most warehouses are multi-zone warehouses, and picking route planning for multi-zone warehouses plays a crucial role in improving outbound efficiency. Current picking routes mainly include improved and hybrid algorithms based on intelligent algorithms such as ant colony algorithms and simulated annealing. However, these methods cannot effectively connect picking routes when the warehouse has multiple verification points, resulting in poor overall picking route planning performance. Furthermore, the current method for product verification typically involves assigning dedicated personnel to manually verify the products at each verification point, leading to low work efficiency and a high risk of errors. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and equipment for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization, in order to solve the technical problems of poor overall picking path planning effect, low efficiency of manual verification, and easy error in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The first aspect provides a method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking route optimization, including:

[0007] Calculate the distances between each element in the Chinese herbal medicine decoction piece warehouse. The elements include shelves and verification points. The distances between each element include the distance between shelves, the distance between shelves and verification points, and the distance between verification points. Each Chinese herbal medicine decoction piece product is attached with a product label. Each verification point is equipped with a barcode scanning device. The barcode scanning device is used to scan the product label and send the obtained product information to the warehouse management terminal.

[0008] Based on the distance between each element, with path constraints and container constraints as constraints, and with the objective function of minimizing the distance for each picking order to complete picking, a shortest picking path model in the warehouse is established, and a genetic algorithm is used to solve the shortest picking path model. In the shortest picking path model, the starting point and the ending point of the path are both any one of multiple verification points.

[0009] Combining the shortest path picking model, a greedy algorithm and a dynamic adjustment strategy are used to allocate the picking order of multiple picking orders in each picking set, where each picking set corresponds to one picking person.

[0010] Obtain the verification point information corresponding to the destination of each picking list, and use the barcode scanner at the verification point to scan the Chinese herbal medicine product label of each picking list so that the warehouse management terminal can compare the scanned product information with the pre-stored product information and complete the outbound verification after the comparison is successful.

[0011] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0012] The formula for calculating the distance between containers located in the same column is as follows:

[0013] D(i1,i2)=|C(i1,2)-C(i2,2)|+2d; (1)

[0014] Where D(i1,i2) represents the distance between container i1 and container i2, C(i1,2) represents the longitudinal offset coordinate of container i1, C(i2,2) represents the longitudinal offset coordinate of container i2, A represents the A-th column of a shelf, and d represents the offset amount when the picking personnel walk around the shelf obstacle zigzag.

[0015] The formula for calculating the distance between containers located in the same row but different columns is as follows:

[0016]

[0017] Where C(i1,1) represents the lateral offset coordinate of container i1, C(i2,1) represents the lateral offset coordinate of container i2, w1 represents the width of each container, and b i1 and b i2 These represent the container numbers corresponding to container i1 and container i2 in the shelf, respectively, and A represents the number of containers on each shelf;

[0018] The formula for calculating the distance between containers located in different rows and columns is as follows:

[0019] D(i1,i2)=|C(i1,1)-C(i2,1)|+|C(i1,2)-C(i2,2)|+2d(3).

[0020] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0021] The distance between the container and the checkpoint is calculated using the following formula:

[0022]

[0023] Where C(i,1) and C(i,2) represent the lateral and longitudinal offset coordinates of container i, respectively, and C(j,1) and C(j,2) represent the lateral and longitudinal offset coordinates of container j, respectively.

[0024] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0025] Calculate the distance between checkpoints located on the same side of the shelf, using the following formula:

[0026]

[0027] Where, x j1 and x j2 Let y represent the x-coordinates of verification point j1 and verification point j2, respectively. j1 and y j2 Let l1 and l2 represent the ordinates of the verification points j1 and j2, respectively, and let w2 represent the length and width of each verification point, respectively.

[0028] Calculate the distance between checkpoints located on different sides of the shelf using the following formula:

[0029]

[0030] In one possible design, based on the distances between elements, using path constraints and container constraints as constraints, and taking the minimization of the picking distance for each picking order as the objective function, a shortest picking path model within the warehouse is established, including:

[0031] Based on the distances between each element, the path constraints are constructed as follows:

[0032]

[0033] Where i and j represent any container and any checkpoint, respectively;

[0034] Based on the distances between each element, the container constraints are constructed as follows:

[0035]

[0036] Where N represents the number of containers in a picking list, 0 represents the starting checkpoint, N+1 represents the ending checkpoint, and 1,2,...,N represent the number of containers passed between the starting checkpoint and the ending checkpoint.

[0037] Using the minimum distance for completing each picking order as the objective function, a shortest path model for picking within the warehouse is established as follows:

[0038]

[0039] Where D(i,j) represents the distance between any container and any checkpoint, x ij This indicates the path taken during the picking process.

[0040] In one possible design, a genetic algorithm is used to solve the shortest path model for picking, including:

[0041] A multi-level encoding method based on a genetic algorithm is used to solve the shortest path model for picking.

[0042] In one possible design, combining the shortest picking path model, a greedy algorithm and a dynamic adjustment strategy are used to allocate the picking order of multiple picking orders in each picking set, including:

[0043] Get the number of picking orders, the set of picking orders, and the information of picking personnel in the current period; assign a picking order subset to each picking personnel; and initialize each picking order subset.

[0044] Based on the shortest path picking model, the termination checkpoint of the current picking order in the picking order subset is selected as the inspiration checkpoint for the next picking order, and the path of the next picking order is replanned based on the dynamic adjustment strategy.

[0045] In one possible design, product information includes a combination of one or more pieces of information: product ID, manufacturer, product name, specifications, place of origin, batch number, production date, and expiration date.

[0046] The second aspect provides a traditional Chinese medicine decoction pieces outbound verification device based on picking route optimization, including:

[0047] The distance calculation module is used to calculate the distance between each element in the Chinese herbal medicine warehouse. The elements include shelves and verification points. The distance between each element includes the distance between shelves, the distance between shelves and verification points, and the distance between verification points. Each Chinese herbal medicine product is attached with a product label, and each verification point is equipped with a barcode scanning device. The barcode scanning device is used to scan the product label and send the obtained product information to the warehouse management terminal.

[0048] The model building module is used to establish a shortest picking path model in the warehouse based on the distance between each element, with path constraints and container constraints as constraints, and with the objective function of minimizing the distance for each picking order to complete picking. The genetic algorithm is used to solve the shortest picking path model. The starting point and ending point of the shortest picking path model are both any one of multiple verification points.

[0049] The picking dynamic adjustment module is used to combine the picking shortest path model with a greedy algorithm and dynamic adjustment strategy to allocate the picking order of multiple picking orders in each picking set. Each picking set corresponds to one picking person.

[0050] The information verification module is used to obtain the verification point information corresponding to the end point of each picking list. It uses the barcode scanner at the verification point to scan the Chinese herbal medicine product label of each picking list so that the warehouse management terminal can compare the scanned product information with the pre-stored product information and complete the outbound verification after the comparison is successful.

[0051] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0052] The formula for calculating the distance between containers located in the same column is as follows:

[0053] D(i1,i2)=|C(i1,2)-C(i2,2)|+2d; (1)

[0054] Where D(i1,i2) represents the distance between container i1 and container i2, C(i1,2) represents the longitudinal offset coordinate of container i1, C(i2,2) represents the longitudinal offset coordinate of container i2, A represents the A-th column of a shelf, and d represents the offset amount when the picking personnel walk around the shelf obstacle zigzag.

[0055] The formula for calculating the distance between containers located in the same row but different columns is as follows:

[0056]

[0057] Where C(i1,1) represents the lateral offset coordinate of container i1, C(i2,1) represents the lateral offset coordinate of container i2, w1 represents the width of each container, and b i1 and b i2 These represent the container numbers corresponding to container i1 and container i2 in the shelf, respectively, and A represents the number of containers on each shelf;

[0058] The formula for calculating the distance between containers located in different rows and columns is as follows:

[0059] D(i1,i2)=|C(i1,1)-C(i2,1)|+|C(i1,2)-C(i2,2)|+2d (3).

[0060] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0061] The distance between the container and the checkpoint is calculated using the following formula:

[0062]

[0063] Where C(i,1) and C(i,2) represent the lateral and longitudinal offset coordinates of container i, respectively, and C(j,1) and C(j,2) represent the lateral and longitudinal offset coordinates of container j, respectively.

[0064] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0065] Calculate the distance between checkpoints located on the same side of the shelf, using the following formula:

[0066]

[0067] Where, x j1 and x j2 Let y represent the x-coordinates of verification point j1 and verification point j2, respectively. j1 and y j2 Let l1 and l2 represent the ordinates of the verification points j1 and j2, respectively, and let w2 represent the length and width of each verification point, respectively.

[0068] Calculate the distance between checkpoints located on different sides of the shelf using the following formula:

[0069]

[0070] In one possible design, based on the distances between elements, using path constraints and container constraints as constraints, and taking the minimization of the picking distance for each picking order as the objective function, a shortest picking path model within the warehouse is established, including:

[0071] Based on the distances between each element, the path constraints are constructed as follows:

[0072]

[0073] Where i and j represent any container and any checkpoint, respectively;

[0074] Based on the distances between each element, the container constraints are constructed as follows:

[0075]

[0076] Where N represents the number of containers in a picking list, 0 represents the starting checkpoint, N+1 represents the ending checkpoint, and 1,2,...,N represent the number of containers passed between the starting checkpoint and the ending checkpoint.

[0077] Using the minimum distance for completing each picking order as the objective function, a shortest path model for picking within the warehouse is established as follows:

[0078]

[0079] Where D(i,j) represents the distance between any container and any checkpoint, x ij This indicates the path taken during the picking process.

[0080] In one possible design, a genetic algorithm is used to solve the shortest path model for picking, including:

[0081] A multi-level encoding method based on a genetic algorithm is used to solve the shortest path model for picking.

[0082] In one possible design, combining the shortest picking path model, a greedy algorithm and a dynamic adjustment strategy are used to allocate the picking order of multiple picking orders in each picking set, including:

[0083] Get the number of picking orders, the set of picking orders, and the information of picking personnel in the current period; assign a picking order subset to each picking personnel; and initialize each picking order subset.

[0084] Based on the shortest path picking model, the termination checkpoint of the current picking order in the picking order subset is selected as the inspiration checkpoint for the next picking order, and the path of the next picking order is replanned based on the dynamic adjustment strategy.

[0085] In one possible design, product information includes a combination of one or more pieces of information: product ID, manufacturer, product name, specifications, place of origin, batch number, production date, and expiration date.

[0086] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization as described in any possible design of the first aspect.

[0087] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the outbound verification method for traditional Chinese medicine decoction pieces based on picking path optimization as described in any possible design of the first aspect.

[0088] Fifthly, the present invention provides a computer program product containing instructions that, when the instructions are executed on a computer, cause the computer to perform the method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization as described in any possible design of the first aspect.

[0089] The advantages of this invention compared to the prior art are as follows:

[0090] This invention overcomes the difficulty of traversal and search caused by the uncertainty of start and end points when multiple checkpoints are set in existing warehouses. By using any checkpoint as the starting or ending point of the shortest picking path model, and by allocating the picking order of multiple picking orders in each picking set based on a greedy algorithm and dynamic adjustment strategy, this invention achieves effective splicing of the starting points of each picking order, thereby shortening picking time and improving operational efficiency. By setting up barcode scanning devices at checkpoints to scan product information and automatically comparing product information through the warehouse management terminal, the problems of low efficiency and easy error in manual verification can be avoided. Attached Figure Description

[0091] Figure 1 This is a flowchart of the outbound verification method for traditional Chinese medicine decoction pieces based on picking path optimization in the embodiments of this application. Detailed Implementation

[0092] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0093] Example

[0094] To address the shortcomings of existing technologies in overall picking path planning, as well as the low efficiency and error-prone nature of manual verification, this application provides a method for outbound verification of traditional Chinese medicine decoction pieces based on picking path optimization. This method uses any verification point as the starting or ending point of the shortest picking path model and allocates the picking order of multiple picking orders in each picking set based on a greedy algorithm and dynamic adjustment strategy. This overcomes the difficulty of traversal and search caused by uncertain start and end points in existing warehouses with multiple verification points. By dynamically adjusting the starting point and the shortest picking path, the starting points of each picking order can be effectively spliced, thereby shortening picking time and improving operational efficiency. Furthermore, by setting up barcode scanners at verification points to scan product information and automatically comparing product information through a warehouse management terminal, the low efficiency and error-prone nature of manual verification can be avoided.

[0095] The following will provide a detailed description of the method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization provided in the embodiments of this application.

[0096] It should be noted that the method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization provided in this application can be applied to any terminal device using an operating system. The operating system includes, but is not limited to, Windows, Mac, Linux, Chrome OS, UNIX, iOS, and Android systems, etc., and is not limited here. The terminal device includes, but is not limited to, iPads, personal mobile computers, industrial computers, and personal computers, etc., and is not limited here. For ease of description, unless otherwise specified, the embodiments in this application are described using an industrial computer as the execution subject. It is understood that the execution subject does not constitute a limitation on the embodiments of this application, and other types of terminal devices may be used as the execution subject in other embodiments.

[0097] like Figure 1 The diagram shown is a flowchart of a method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking route optimization, provided in an embodiment of this application. This method includes, but is not limited to, steps S1 to S4.

[0098] Step S1. Calculate the distance between each element in the Chinese herbal medicine warehouse. The elements include shelves and verification points. The distance between each element includes the distance between shelves, the distance between shelves and verification points, and the distance between verification points. Each Chinese herbal medicine product is attached with a product label. Each verification point is equipped with a barcode scanner. The barcode scanner is used to scan the product label and send the obtained product information to the warehouse management terminal.

[0099] It should be noted that the method described in this application embodiment is mainly applied to large-scale, multi-zone traditional Chinese medicine (TCM) decoction piece storage warehouses. For example, a TCM decoction piece warehouse consists of multiple parallel horizontal and vertical aisles, each with the same width. Specifically, the warehouse has multiple sets of shelves, each shelf with multiple display cases. Simultaneously, the warehouse has multiple verification points distributed on the same or different sides of the shelves. The specific layout can be adjusted according to actual application needs and is not limited here. A barcode scanning device, such as an RFID reader, is installed at each verification point. The corresponding product label can be an RFID tag. By installing barcode scanning devices at verification points, product information can be automatically verified, avoiding errors caused by manual verification. The product information includes, but is not limited to, product ID, manufacturer, product name, specifications, place of origin, batch number, production date, and expiration date.

[0100] Preferably, since there is an offset between the coordinates of the display cabinet and the coordinates of the picker when picking items from the cabinet, to avoid further complex calculations of the coordinate offset, this embodiment of the application constructs an offset matrix for each display cabinet coordinate before calculating the distance between the display cabinet element and other elements. This converts the value of the display cabinet coordinate to the position of the center point of the adjacent aisle of the display cabinet, i.e., the external offset of the picker's position when retrieving items. This achieves consistency between the display cabinet coordinates and the picker's position offset. For example, when two closely adjacent shelves are located in the same row, the formula for calculating the coordinate offset between the left and right shelves is:

[0101]

[0102] Where C(i,1) represents the lateral offset coordinate of container i, C(i,2) represents the longitudinal offset coordinate of container i, w1 represents the width of each container, l1 represents the length of each container, and lr represents the longitudinal aisle length.

[0103] Similarly, the formula for calculating the coordinate offset of each verification point can be obtained as follows:

[0104]

[0105] Where C(j,1) represents the horizontal offset coordinate of the verification point j, C(j,2) represents the vertical offset coordinate of the verification point j, w2 represents the width of each verification point, and l2 represents the length of each verification point.

[0106] Based on the above-disclosed content, by calculating the coordinate offset of the shelf and the coordinate offset of each verification point, it is possible to unify the coordinates of the container and the position offset of the picker. By unifying the calculation of the distance between each element under the same coordinate system, complex calculations are avoided.

[0107] Preferably, in one possible design of step S1, calculating the distance between each element in the Chinese herbal medicine decoction piece warehouse includes:

[0108] The formula for calculating the distance between containers located in the same column is as follows:

[0109] D(i1,i2)=|C(i1,2)-C(i2,2)|+2d; (1)

[0110] Where D(i1,i2) represents the distance between container i1 and container i2, C(i1,2) represents the longitudinal offset coordinate of container i1, C(i2,2) represents the longitudinal offset coordinate of container i2, A represents the A-th column of a shelf, and d represents the offset amount when the picking personnel walk around the shelf obstacle zigzag.

[0111] The formula for calculating the distance between containers located in the same row but different columns is as follows:

[0112]

[0113] Where C(i1,1) represents the lateral offset coordinate of container i1, C(i2,1) represents the lateral offset coordinate of container i2, w1 represents the width of each container, and b i1 and b i2 These represent the container numbers corresponding to container i1 and container i2 in the shelf, respectively, and A represents the number of containers on each shelf;

[0114] The formula for calculating the distance between containers located in different rows and columns is as follows:

[0115] D(i1,i2)=|C(i1,1)-C(i2,1)|+|C(i1,2)-C(i2,2)|+2d(3).

[0116] Preferably, in one possible design of step S1, calculating the distance between each element in the Chinese herbal medicine decoction piece warehouse includes:

[0117] The distance between the container and the checkpoint is calculated using the following formula:

[0118]

[0119] Where C(i,1) and C(i,2) represent the lateral and longitudinal offset coordinates of container i, respectively, and C(j,1) and C(j,2) represent the lateral and longitudinal offset coordinates of container j, respectively.

[0120] Preferably, in one possible design of step S1, calculating the distance between each element in the Chinese herbal medicine decoction piece warehouse includes:

[0121] Calculate the distance between checkpoints located on the same side of the shelf, using the following formula:

[0122]

[0123] Where, x j1 and x j2 Let y represent the x-coordinates of verification point j1 and verification point j2, respectively. j1 and y j2 Let l1 and l2 represent the ordinates of the verification points j1 and j2, respectively, and let w2 represent the length and width of each verification point, respectively.

[0124] Calculate the distance between checkpoints located on different sides of the shelf using the following formula:

[0125]

[0126] Step S2. Based on the distance between each element, with path constraints and container constraints as constraints, and with the objective function of minimizing the distance for each picking order to complete picking, establish a shortest picking path model in the warehouse, and use a genetic algorithm to solve the shortest picking path model. In the shortest picking path model, the starting point and the ending point of the path are both any one of multiple verification points.

[0127] It's important to note that traditional single-exit and single-checkpoint picking route optimization often involves fixed start and end points. To address this, we've modeled warehouse picking routes based on TSP (Tracking Strategy Programming) and solved the model using algorithms such as simulated annealing, ant colony optimization, and tabu search, achieving relatively ideal picking routes. However, this method is unsuitable for multi-checkpoint route optimization, as the start and end points are often not fixed. For example, assuming a warehouse has n checkpoints, performing shortest path traversal on all checkpoints results in a complexity of O(n log n) for checkpoint selection alone. 2 Furthermore, the computational efficiency of the intelligent optimization algorithm itself decreases sharply as the number of checkpoints increases. Therefore, this application introduces the concept of replacing checkpoints, transforming the multi-checkpoint path planning problem with unfixed start and end points into a picking path optimization problem with known start and end points. Based on this, a reasonable allocation strategy for multiple picking orders is provided to minimize the working time of picking personnel.

[0128] Based on the above, this application embodiment solves the above problems by introducing fixed checkpoints. Specifically, if there are N containers Hi = (i = 1, ..., N) in the picking list, and the starting point and ending point can be arbitrarily selected from all checkpoints, then in order to obtain the shortest path, by introducing fixed checkpoints, the path between the fixed checkpoints and each container passed in between is minimized. This can solve the problem that path optimization with multiple checkpoints often involves unfixed starting and ending points.

[0129] In one possible design of step S2, based on the distance between each element, using path constraints and container constraints as constraints, and taking the minimum distance for each picking order to complete the picking as the objective function, a shortest picking path model within the warehouse is established, including:

[0130] Based on the distances between each element, the path constraints are constructed as follows:

[0131]

[0132] Where i and j represent any container and any checkpoint, respectively;

[0133] Based on the distances between each element, the container constraints are constructed as follows:

[0134]

[0135] Where N represents the number of containers in a picking list, 0 represents the starting checkpoint, N+1 represents the ending checkpoint, and 1,2,...,N represent the number of containers passed between the starting checkpoint and the ending checkpoint.

[0136] Using the minimum distance for completing each picking order as the objective function, a shortest path model for picking within the warehouse is established as follows:

[0137]

[0138] Where D(i,j) represents the distance between any container and any checkpoint, x ij This indicates the path taken during the picking process.

[0139] Based on the above disclosure, this application embodiment transforms the calculation of multi-core point path optimization into a picking path optimization problem with known start and end points. This problem is NP-hard, and the genetic algorithm is used to optimize it based on the idea of ​​survival of the fittest in population genetic operations. When solving more complex combinatorial optimization problems, it can usually optimize the results better than conventional optimization algorithms. Therefore, this application embodiment uses a genetic algorithm to solve the path planning strategy. The algorithm principle of the genetic algorithm is an existing algorithm principle, which will not be described in detail here.

[0140] In one possible design of step S2, a genetic algorithm is used to solve the shortest path model for picking, including:

[0141] A multi-level encoding method based on a genetic algorithm is used to solve the shortest path model for picking.

[0142] Specifically, the encoding format of the multi-layer coding is X = [X1, X2, X3], where X1 represents a 1xN matrix and N represents the priority of each student; X2 represents an MxN matrix and M represents the number of picking paths; X3 represents an Nx(M-1) matrix, which is the scaling factor for each path.

[0143] Step S3. Combining the shortest path picking model, a greedy algorithm and a dynamic adjustment strategy are used to allocate the picking order of multiple picking orders in each picking set. Each picking set corresponds to one picking person.

[0144] In one possible design of step S3, combining the shortest picking path model, a greedy algorithm and a dynamic adjustment strategy are used to allocate the picking order of multiple picking orders in each picking set, including:

[0145] Get the number of picking orders, the set of picking orders, and the information of picking personnel in the current period; assign a picking order subset to each picking personnel; and initialize each picking order subset.

[0146] Based on the shortest path picking model, the termination checkpoint of the current picking order in the picking order subset is selected as the inspiration checkpoint for the next picking order, and the path of the next picking order is replanned based on the dynamic adjustment strategy.

[0147] Based on the aforementioned publicly available information, the picking order path optimization strategy can obtain the optimal picking path for each picking order. To minimize the walking distance for each picker, picking orders with the same start and end points should be prioritized for concatenation. Considering that the constructed graph structure is an undirected weighted graph, the direction of the shortest path or the choice of endpoints for each picking order can be adjusted to make the picking orders as closely connected as possible. That is, after completing a picking order and reaching the checkpoint, this checkpoint can be immediately used as the starting point for the next picking order, thereby minimizing the overall picking path for the picker.

[0148] Step S4. Obtain the verification point information corresponding to the end point of each picking list, and use the barcode scanner at the verification point to scan the Chinese herbal medicine product label of each picking list so that the warehouse management terminal can compare the scanned product information with the pre-stored product information and complete the outbound verification after the comparison is successful.

[0149] Specifically, during the production of Chinese herbal medicine slices, all suppliers are required to include a QR code on the product labels containing information such as manufacturer, product name, specifications, place of origin, batch number, production date, and expiration date or shelf life (if applicable). Upon arrival of goods, the QR code on the product label is scanned, and the corresponding information is pre-stored in the warehouse management terminal. When a product enters the verification point, the barcode on the picking list or picking box is automatically scanned onto the display using a barcode scanner. Then, the QR code on the product label is scanned one by one, and the computer automatically matches and verifies the information. Once the comparison is successful, the outbound verification is completed.

[0150] Based on the above disclosure, this application embodiment overcomes the problem of difficult traversal and search caused by the uncertainty of the start and end points when multiple checkpoints are set in the existing warehouse. By using any checkpoint as the starting point or end point of the shortest picking path model, and by allocating the picking order of multiple picking orders in each picking set based on a greedy algorithm and a dynamic adjustment strategy, the starting points of each picking order can be effectively spliced, thereby shortening the picking time and improving the efficiency of operation. By setting up barcode scanning devices at the checkpoints to scan product information and automatically comparing product information through the warehouse management terminal, the problems of low efficiency and easy error of manual check can be avoided.

[0151] The second aspect provides a traditional Chinese medicine decoction pieces outbound verification device based on picking route optimization, including:

[0152] The distance calculation module is used to calculate the distance between each element in the Chinese herbal medicine warehouse. The elements include shelves and verification points. The distance between each element includes the distance between shelves, the distance between shelves and verification points, and the distance between verification points. Each Chinese herbal medicine product is attached with a product label, and each verification point is equipped with a barcode scanning device. The barcode scanning device is used to scan the product label and send the obtained product information to the warehouse management terminal.

[0153] The model building module is used to establish a shortest picking path model in the warehouse based on the distance between each element, with path constraints and container constraints as constraints, and with the objective function of minimizing the distance for each picking order to complete picking. The genetic algorithm is used to solve the shortest picking path model. The starting point and ending point of the shortest picking path model are both any one of multiple verification points.

[0154] The picking dynamic adjustment module is used to combine the picking shortest path model with a greedy algorithm and dynamic adjustment strategy to allocate the picking order of multiple picking orders in each picking set. Each picking set corresponds to one picking person.

[0155] The information verification module is used to obtain the verification point information corresponding to the end point of each picking list. It uses the barcode scanner at the verification point to scan the Chinese herbal medicine product label of each picking list so that the warehouse management terminal can compare the scanned product information with the pre-stored product information and complete the outbound verification after the comparison is successful.

[0156] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0157] The formula for calculating the distance between containers located in the same column is as follows:

[0158] D(i1,i2)=|C(i1,2)-C(i2,2)|+2d; (1)

[0159] Where D(i1,i2) represents the distance between container i1 and container i2, C(i1,2) represents the longitudinal offset coordinate of container i1, C(i2,2) represents the longitudinal offset coordinate of container i2, A represents the A-th column of a shelf, and d represents the offset amount when the picking personnel walk around the shelf obstacle zigzag.

[0160] The formula for calculating the distance between containers located in the same row but different columns is as follows:

[0161]

[0162] Where C(i1,1) represents the lateral offset coordinate of container i1, C(i2,1) represents the lateral offset coordinate of container i2, w1 represents the width of each container, and b i1 and b i2 These represent the container numbers corresponding to container i1 and container i2 in the shelf, respectively, and A represents the number of containers on each shelf;

[0163] The formula for calculating the distance between containers located in different rows and columns is as follows:

[0164] D(i1,i2)=|C(i1,1)-C(i2,1)|+|C(i1,2)-C(i2,2)|+2d(3).

[0165] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0166] The distance between the container and the checkpoint is calculated using the following formula:

[0167]

[0168] Where C(i,1) and C(i,2) represent the lateral and longitudinal offset coordinates of container i, respectively, and C(j,1) and C(j,2) represent the lateral and longitudinal offset coordinates of container j, respectively.

[0169] In one possible design, calculating the distances between elements in a warehouse of prepared Chinese medicinal herbs includes:

[0170] Calculate the distance between checkpoints located on the same side of the shelf, using the following formula:

[0171]

[0172] Where, x j1 and x j2 Let y represent the x-coordinates of verification point j1 and verification point j2, respectively. j1 and y j2 Let l1 and l2 represent the ordinates of the verification points j1 and j2, respectively, and let w2 represent the length and width of each verification point, respectively.

[0173] Calculate the distance between checkpoints located on different sides of the shelf using the following formula:

[0174]

[0175] In one possible design, based on the distances between elements, using path constraints and container constraints as constraints, and taking the minimization of the picking distance for each picking order as the objective function, a shortest picking path model within the warehouse is established, including:

[0176] Based on the distances between each element, the path constraints are constructed as follows:

[0177]

[0178] Where i and j represent any container and any checkpoint, respectively;

[0179] Based on the distances between each element, the container constraints are constructed as follows:

[0180]

[0181] Where N represents the number of containers in a picking list, 0 represents the starting checkpoint, N+1 represents the ending checkpoint, and 1,2,...,N represent the number of containers passed between the starting checkpoint and the ending checkpoint.

[0182] Using the minimum distance for completing each picking order as the objective function, a shortest path model for picking within the warehouse is established as follows:

[0183]

[0184] Where D(i,j) represents the distance between any container and any checkpoint, x ij This indicates the path taken during the picking process.

[0185] In one possible design, a genetic algorithm is used to solve the shortest path model for picking, including:

[0186] A multi-level encoding method based on a genetic algorithm is used to solve the shortest path model for picking.

[0187] In one possible design, combining the shortest picking path model, a greedy algorithm and a dynamic adjustment strategy are used to allocate the picking order of multiple picking orders in each picking set, including:

[0188] Get the number of picking orders, the set of picking orders, and the information of picking personnel in the current period; assign a picking order subset to each picking personnel; and initialize each picking order subset.

[0189] Based on the shortest path picking model, the termination checkpoint of the current picking order in the picking order subset is selected as the inspiration checkpoint for the next picking order, and the path of the next picking order is replanned based on the dynamic adjustment strategy.

[0190] In one possible design, product information includes a combination of one or more pieces of information: product ID, manufacturer, product name, specifications, place of origin, batch number, production date, and expiration date.

[0191] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the method described in the first aspect or any possible design of the first aspect, and will not be repeated here.

[0192] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization as described in any possible design of the first aspect.

[0193] Specifically, the memory may include, but is not limited to, Random-Access Memory (RAM), Read-Only Memory (ROM), Flash Memory, First-In-First-Out (FIFO) Memory, and / or First-In-Last-Out (FILO) Memory, etc.; the processor may not be limited to the STM32F105 series microprocessor; the transceiver may be, but is not limited to, a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a GPRS (General Packet Radio Service) wireless transceiver, and / or a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) wireless transceiver, etc. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0194] The working process, working details and technical effects of the aforementioned computer device provided in the third aspect of this embodiment can be found in the method described in the first aspect or any possible design of the first aspect, and will not be repeated here.

[0195] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the outbound verification method for traditional Chinese medicine decoction pieces based on picking path optimization as described in any possible design of the first aspect.

[0196] The computer-readable storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives and / or memory sticks, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0197] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the method described in the first aspect or any possible design of the first aspect, and will not be repeated here.

[0198] Fifthly, the present invention provides a computer program product containing instructions that, when the instructions are executed on a computer, cause the computer to perform the method for verifying the outbound shipment of traditional Chinese medicine decoction pieces based on picking path optimization as described in any possible design of the first aspect.

[0199] The working process, working details and technical effects of the aforementioned computer program product containing instructions provided in the fifth aspect of this embodiment can be found in the method described in the first aspect or any possible design of the first aspect, and will not be repeated here.

[0200] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A traditional Chinese medicine decoction piece warehouse-outbound review method based on order-picking path optimization, characterized in that, The method comprises the following steps: The distance between each element in the traditional Chinese medicine piece warehouse is calculated, wherein the elements include cabinets and review points, and the distance between each element includes the distance between cabinets, the distance between cabinets and review points, and the distance between review points. The distance between cabinets in the same column is calculated, and the calculation formula is as follows: D(i1,i2) = |C(i1,2)-C(i2,2)|+2d; Wherein D(i1,i2) represents the distance between cabinet i1 and cabinet i2, C(i1,2) represents the longitudinal offset coordinate of cabinet i1, C(i2,2) represents the longitudinal offset coordinate of cabinet i2, and d represents the offset amount when the picker walks around the obstacle line of the shelf; The distance between cabinets in the same row and different columns is calculated, and the calculation formula is as follows: wherein C(i1,1) represents the lateral offset coordinate of container i1, C(i2,1) represents the lateral offset coordinate of container i2, w1 represents the width of each container, b i1 and b i2 respectively represent the corresponding bin number of container i1 and container i2 in the rack, and A represents the number of containers of each rack. The distance between cabinets in different rows and different columns is calculated, and the calculation formula is as follows: D(i1,i2) = |C(i1,1)-C(i2,1)|+|C(i1,2)-C(i2,2)|+2d; The distance between the cabinet and the review point is calculated, and the calculation formula is as follows: Wherein C(i,1) and C(i,2) represent the horizontal offset coordinate and the longitudinal offset coordinate of cabinet i respectively, and C(j,1) and C(j,2) represent the horizontal offset coordinate and the longitudinal offset coordinate of cabinet j respectively; According to the distance between each element, the shortest path model for picking in the warehouse is established by taking the path constraint and the cabinet constraint as the constraint conditions and taking the minimum distance of each picking order for completing picking as the objective function, and the genetic algorithm is used to solve the shortest path model for picking, wherein the path starting point and the path ending point of the shortest path model for picking are any one of the multiple review points; Combined with the shortest path model for picking, the greedy algorithm and the dynamic adjustment strategy are used to allocate the picking sequence of the multiple picking orders in each picking set, wherein each picking set corresponds to a picker; The review point information corresponding to the path ending point of each picking order is obtained, and the product label of the traditional Chinese medicine piece product of each picking order is scanned by using the code scanning device of the review point, so that the warehouse management terminal compares the scanned product information with the pre-stored product information, and completes the outbound review after the comparison is passed.

2. The Chinese medicine decoction piece delivery review method based on order picking path optimization according to claim 1, characterized in that, The distance between each element in the traditional Chinese medicine piece warehouse is calculated, including: The distance between the review points on the same side of the shelf is calculated, and the calculation formula is as follows: wherein x j1 and x j2 represent the horizontal coordinates of the complex check point j 1 and the complex check point j 2, respectively, y j1 and y j2 represent the vertical coordinates of the complex check point j 1 and the complex check point j 2, respectively, and l2 and w2 represent the length and width of each complex check point, respectively. The distance between the review points on different sides of the shelf is calculated, and the calculation formula is as follows:

3. The Chinese medicine decoction piece delivery review method based on order picking path optimization according to claim 1, characterized in that, According to the distance between each element, the shortest path model for picking in the warehouse is established by taking the path constraint and the cabinet constraint as the constraint conditions and taking the minimum distance of each picking order for completing picking as the objective function, including: According to the distance between each element, the path constraint condition is constructed as follows: Wherein i and j represent any one cabinet and any one review point respectively. According to the distance between each element, the container constraint condition is constructed as follows: Wherein, N represents the number of containers contained in a picking order, 0 represents the starting review point, N+1 represents the terminal review point, 1, 2, …, N represent the containers passed between the starting review point and the terminal review point; The shortest path model for picking in the warehouse is established with the minimum distance of completing picking of each picking order as the objective function, as follows: where D(i,j) represents the distance between any one container and any one checking point, x ij represents the path of picking.

4. The Chinese medicine decoction piece warehouse-out checking method based on order-picking path optimization according to claim 1, characterized in that, The genetic algorithm is used to solve the shortest path model for picking, including: The genetic algorithm is used to solve the shortest path model for picking in the multi-layer coding mode.

5. The Chinese medicine decoction piece warehouse-out checking method based on order-picking path optimization according to claim 1, characterized in that, Combined with the shortest path model for picking, the greedy algorithm and the dynamic adjustment strategy are used to allocate the picking sequence of multiple picking orders in each picking set, including: The information of the number of picking orders, the picking order set and the picking personnel in the current period is acquired, the picking order subsets are respectively allocated to each picking personnel, and the picking order subsets are initialized; According to the shortest path model for picking, the terminal review point of the current picking order in the picking order subset is selected as the starting review point of the next picking order, and the path of the next picking order is re-planned based on the dynamic adjustment strategy.

6. The Chinese medicine decoction piece delivery review method based on order picking path optimization according to claim 1, characterized in that, The product information includes one or more combinations of information: product ID, manufacturer, product name, specification, origin, batch number, production date and shelf life information.

7. A Chinese herbal medicine decoction piece warehouse-out checking device based on order picking path optimization, characterized in that, It includes: The distance calculation module is used to calculate the distance between each element in the traditional Chinese medicine decoction piece warehouse, wherein the elements include containers and review points, the distance between each element includes the distance between containers, the distance between containers and review points, and the distance between review points, each traditional Chinese medicine decoction piece product is attached with a product label, and each review point is provided with a code scanning device, the code scanning device is used to scan the product label and send the acquired product information to the warehouse management terminal; the distance between each element in the traditional Chinese medicine decoction piece warehouse is calculated, including: The distance between containers located in the same column is calculated, and the calculation formula is as follows: D(i1,i2)=|C(i1,2)-C(i2,2)|+2d; Wherein, D(i1,i2) represents the distance between container i1 and container i2, C(i1,2) represents the longitudinal offset coordinate of container i1, C(i2,2) represents the longitudinal offset coordinate of container i2, and d represents the offset amount when the picking personnel walks around the obstacle line of the shelf; The distance between containers located in the same row and different columns is calculated, and the calculation formula is as follows: wherein C(i1,1) represents the lateral offset coordinate of container i1, C(i2,1) represents the lateral offset coordinate of container i2, w1 represents the width of each container, b i1 and b i2 respectively represent the corresponding bin number of container i1 and container i2 in the rack, and A represents the number of containers of each rack. The distance between containers located in different rows and different columns is calculated, and the calculation formula is as follows: D(i1,i2)=|C(i1,1)-C(i2,1)|+|C(i1,2)-C(i2,2)|+2d; The distance between containers and review points is calculated, and the calculation formula is as follows: Wherein, C(i,1) and C(i,2) represent the horizontal offset coordinate and the longitudinal offset coordinate of container i respectively, and C(j,1) and C(j,2) represent the horizontal offset coordinate and the longitudinal offset coordinate of container j respectively; The model establishing module is configured to establish a shortest path model for picking in the warehouse according to distances between elements, with path constraints and container constraints as constraint conditions and with a distance for completing picking of each picking order as an objective function, and to solve the shortest path model for picking by using a genetic algorithm, wherein the path start point and the path end point of the shortest path model for picking are each any one of multiple review points. The picking dynamic adjustment module is configured to allocate picking sequences of the multiple picking orders in each picking set by using a greedy algorithm and a dynamic adjustment strategy in combination with the shortest path model for picking, wherein each picking set corresponds to one picking personnel. The information review module is configured to acquire review point information corresponding to the path end point of each picking order, to scan labels of the traditional Chinese medicine decoction pieces by using a code scanning device of the review point, and to compare scanned product information with pre-stored product information by using the warehouse management terminal, and to complete warehouse-out review after the comparison is passed.

8. A computer device, comprising: The device comprises a memory, a processor and a transceiver connected in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to receive and send messages, and the processor is configured to read the computer program and execute the method for traditional Chinese medicine decoction piece warehouse-out review based on picking path optimization according to any one of claims 1-6.

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