Warehouse material allocation optimization method and device

Abstract

The invention relates to the technical field of warehouse logistics, and discloses a warehouse material allocation optimization method, which comprises the following steps: constructing a warehouse allocation model, and storing allocation data by using a one-dimensional array; the goods allocation data are processed based on a goods allocation optimization algorithm, a goods allocation optimization result is generated, and the goods allocation optimization algorithm ranks goods stored on goods allocations according to the number of turns of warehouse-in and warehouse-out tasks, the perfection degree of goods attributes, the time value of goods production dates, whether failure time is close or not and the length of warehouse-in time; and the goods allocation is optimized based on the sorting result. By applying the method, the method can be suitable for warehouse material warehouse-in and warehouse-out scenes, the problems that when high-frequency warehouse-in and warehouse-out materials are warehoused, due to the fact that high-quality goods locations are occupied by other materials, the high-frequency warehouse-in and warehouse-out materials have no proper positions, and the warehouse-in and warehouse-out efficiency is affected are solved, and the purpose of improving the warehouse-in and warehouse-out efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of warehousing and logistics technology, and in particular to a method and device for optimizing warehouse material storage locations. Background Art

[0002] In recent years, with the continuous development of the warehousing industry and the continuous advancement of information technology, the comprehensive management of logistics warehouses has also been continuously improved. Among them, some warehouses with relatively good conditions use large-scale automated stereoscopic warehouses with a relatively high degree of automation, and are equipped with some simple cargo optimization software. However, in warehouse management, there is still a lot of room for improvement in warehouse cargo optimization software, which needs to be upgraded and improved.

[0003] In order to adapt to the rapidly developing economic environment in my country, the existing warehouse management of warehousing and outbound operations must be faster and more efficient than before. Before carrying out warehousing and outbound operations, in order to improve work efficiency, we must ensure that the items in the warehouse are placed reasonably. Based on this, the concept of cargo space optimization was proposed. The essence of cargo space optimization is to redistribute the items in the warehouse, reclassify the items according to the actual use of the items and the warehouse, and store them reasonably according to the rules. In this way, the space utilization rate of the warehouse and the work efficiency of warehousing and outbound operations can be improved during operation, so as to achieve the purpose of reducing warehouse operation costs.

[0004] Most of the existing problems related to storage optimization are controlled before the warehousing and outbound operations, and the purpose of improving work efficiency is achieved by optimizing the placement of items in the warehouse. It solves the problems of low warehouse space utilization, unstable shelves, easy congestion during warehousing and outbound operations, long distances for warehousing and outbound operations, and long warehousing and outbound operations. However, no corresponding solutions are given for the layout of the storage space after the materials are put on the shelves and before they are shipped out.

[0005] In view of this, how to provide a method for optimizing the storage space of warehouse materials to optimize the storage space after the materials are put on the shelves and before they are shipped out, so as to utilize the idle time period after the materials are put on the shelves and before they are shipped, to sort out the goods stored in the storage space, supplement and improve the existing goods layout, and improve the shipping efficiency, has become a technical problem that urgently needs to be solved. Summary of the invention

[0006] The embodiments of the present application provide a warehouse material storage location optimization method, a warehouse material storage location optimization device, an electronic device and a computer storage medium, which are used to solve the current problems of long storage operation distance and time, low warehouse space utilization, and low storage materials occupying high-quality storage locations.

[0007] In a first aspect of an embodiment of the present application, a method for optimizing warehouse material storage space is provided, comprising:

[0008] Construct a warehouse cargo location model and use a one-dimensional array to store cargo location data, wherein the warehouse cargo location model uses the number of cargo location rows as the horizontal coordinate and the number of cargo location columns as the vertical coordinate; the structure of the cargo location data carries the cargo location index, cargo location sequence number, cargo location objective function value, inbound and outbound task rounds, and whether there is a material mark on the cargo location;

[0009] The cargo location data is processed based on a cargo location optimization algorithm to generate a material cargo location optimization result, wherein the cargo location optimization algorithm sorts the materials stored on the cargo location according to the number of warehousing and outbound task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of warehousing time, and optimizes the material cargo location based on the sorting result.

[0010] In a second aspect of an embodiment of the present application, a device for optimizing the storage location of warehouse materials is provided, comprising:

[0011] A construction module is configured to construct a warehouse cargo location model and use a one-dimensional array to store cargo location data, wherein the warehouse cargo location model uses the number of cargo location rows as the horizontal coordinate and the number of cargo location columns as the vertical coordinate; the structure of the cargo location data carries the cargo location index, cargo location sequence number, cargo location objective function value, inbound and outbound task rounds, and whether there is a material mark on the cargo location;

[0012] The optimization module is configured to process the cargo location data based on the cargo location optimization algorithm to generate a material cargo location optimization result, wherein the cargo location optimization algorithm sorts the materials stored on the cargo location according to the number of inbound and outbound task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of the warehousing time, and optimizes the material cargo location based on the sorting result.

[0013] According to a third aspect of an embodiment of the present application, a computing device is provided, including:

[0014] Memory and processor;

[0015] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned warehouse material storage location optimization method are implemented.

[0016] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned warehouse material storage location optimization method are implemented.

[0017] The present application provides a method for optimizing warehouse material storage locations, comprising: first, constructing a warehouse storage location model, and using a one-dimensional array to store storage location data, wherein the warehouse storage location model uses the number of storage location rows as the horizontal coordinate and the number of storage location columns as the vertical coordinate; the structure of the storage location data carries a storage location index ID, a storage location sequence number, a storage location objective function value, a storage in and out task round, and whether a material identifier exists on the storage location; then, based on a storage location optimization algorithm, the storage location data is processed to generate a material storage location optimization result, wherein the storage location optimization algorithm sorts the materials stored on the storage location according to the number of storage in and out task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of the storage time, and optimizes the material storage location based on the sorting result.

[0018] The warehouse material storage location optimization method provided in the embodiment of the present application can be applied to the warehouse material entry and exit scenarios. It can improve the problem that when high-frequency entry and exit materials are entered into the warehouse, the high-quality storage locations are occupied by other materials, resulting in no suitable location for the high-frequency entry and exit materials, which affects the entry and exit efficiency, so as to achieve the purpose of improving the entry and exit efficiency.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0021] Figure 1 A flowchart of a method for optimizing the storage space of warehouse materials provided in an embodiment of the present application;

[0022] Figure 2 A schematic diagram of a warehouse material storage location optimization method provided in an embodiment of the present application;

[0023] Figure 3 A schematic diagram of the main algorithm flow of a warehouse material storage location optimization method provided in an embodiment of the present application;

[0024] Figure 4 A schematic diagram of a process of decomposing and finding reverse order pairs of a warehouse material storage location optimization method provided in an embodiment of the present application;

[0025] Figure 5A schematic diagram of a combined flow chart of a warehouse material storage location optimization method provided in an embodiment of the present application;

[0026] Figure 6 A schematic diagram of a flow chart of searching for reverse order pairs in a method for optimizing the storage location of warehouse materials provided in an embodiment of the present application;

[0027] Figure 7 A schematic diagram of the structure of a warehouse material storage location optimization device provided in an embodiment of the present application. ;

[0028] Figure 8 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0030] See also Figure 1 , Figure 1 A flowchart of a warehouse material storage location optimization method provided in an embodiment of the present application. Figure 1 As shown, the specific steps include the following steps.

[0031] It should be noted that the embodiment of the present application improves a warehouse material location optimization method, which combines the actual business scenarios on the theoretical basis of the traditional merge sort algorithm, comprehensively considers time and material location, so that the algorithm can optimize materials and goods and improve warehousing efficiency.

[0032] Step S102: construct a warehouse cargo location model and use a one-dimensional array to store cargo location data, wherein the warehouse cargo location model uses the number of cargo location rows as the horizontal coordinate and the number of cargo location columns as the vertical coordinate; the structure of the cargo location data carries the cargo location index, cargo location sequence number, cargo location objective function value, warehousing task rounds and whether there is a material identification on the cargo location.

[0033] In practical applications, a warehouse location model is constructed, with the location of the location as the horizontal and vertical coordinates, the number of shelf rows as the horizontal coordinate, and the number of location columns as the vertical coordinate. The model is sorted from left to right and from bottom to top, and mathematical modeling is performed. One-dimensional arrays are used to store data. For details, see Figure 2 , Figure 2 A schematic diagram of a warehouse material storage location optimization method provided in an embodiment of the present application is shown in FIG. Figure 2As shown in the figure, the numbers are the serial numbers of the cargo locations, the two bottom grids are the in-and-out storage area, the number of cargo locations is 12, and the coordinates of cargo location 3 are [1,1]. It is not difficult to see from the figure that the larger the serial number of the cargo location, the longer the distance to the in-and-out storage area, and vice versa.

[0034] It should be noted that the data structure of cargo F is shown in Table 1.

[0035] Table 1 Cargo location data structure

[0036] id number value epoch state

[0037] Among them, id is the cargo location index; number is the cargo location sequence number; value is the calculated objective function value of the cargo location, which is used to comprehensively compare the ranking of the cargo locations; epoch is the round of the in-and-out tasks; state is the identifier of whether there are materials on the cargo location.

[0038] In the embodiment of the present application, there are two methods for calculating the rounds of inbound and outbound tasks.

[0039] The first calculation method is: set the initial in-and-out task round corresponding to the first entry of goods to zero, and use the batch of out-of-warehouse operations or in-warehouse operations performed after the goods are entered as the counting standard to determine the value corresponding to the initial in-and-out task round, wherein each time an out-of-warehouse operation or in-warehouse operation is performed after the goods are entered, the corresponding in-and-out task round increases by one.

[0040] The second calculation method is: based on a preset time slice, determine the round of in-and-out tasks, wherein the time slice is a preset fixed duration, and each time a fixed duration passes, the corresponding round of in-and-out tasks increases by one.

[0041] In actual applications, epoch is set to 0 when the goods are put on the shelf for the first time. After each round of outbound or inbound tasks, epoch needs to be incremented by 1 to determine the length of time the materials stay in the storage location after being stored. In addition to using the batches of inbound and outbound tasks as the counting standard, this task round can also allow users to set the time slice by themselves. The time slice is a fixed duration. For example, if the time slice is 1h, we can increment the round epoch by 1 after one hour of the task. The principle is the same as the configuration method of the task round, and it can be flexibly configured.

[0042] In the embodiment of the present application, the method for calculating the cargo location objective function value includes:

[0043] The cargo information corresponding to the cargo location is processed by the objective function to generate the cargo location objective function value, and the incoming cargo is sorted based on the cargo location objective function value, wherein the objective function includes:

[0044] Value=w1*v1+w2*v2+w3*v3+w4*v4+w5*epoch;

[0045]

[0046] Where value is the objective function value of the calculated cargo space; w k >=0(k=1,2,3,...,m) is the weight coefficient, which is determined according to the actual influencing factors and can be manually set and adjusted; v 1 is the value of the material attribute; v 2 is the value of the material production date; 3 is the value of failure time; v 4 is the value of the storage time; epoch is the round of storage and outbound tasks.

[0047] It should be noted that when calculating the value, it is necessary to consider the material attributes, material production date, expiration time, warehousing time, warehousing and outbound task rounds, etc., and use the cargo information on each storage location as input to generate the storage location objective function value.

[0048] The objective function set in the embodiment of the present application is calculated using the linear weighted sum method.

[0049] Specifically, the material attribute includes normal materials and abnormal materials. When the material attribute belongs to normal materials, the value of the assigned material attribute is 0. When the material attribute belongs to abnormal materials, the value of the assigned material attribute is 1.

[0050] It should be noted that the normal materials here are good goods, and the abnormal materials here are bad goods.

[0051] Specifically, the calculation method of the material production date includes:

[0052] Read the production dates corresponding to all goods stored in the cargo location, convert the format of the production date into the target format, perform a first normalization process, obtain a target production date, and use the target production date as the material production date, wherein the first normalization process includes:

[0053] v 2 =(T[i]-Min) / (Max-Min);

[0054] Among them, v 2 It is the value obtained after normalization of the production date and time values ​​of the materials stored in the storage location numbered i; T[i] is the time value corresponding to the format conversion of the production date of the materials on the storage location numbered i; Max is the time value corresponding to the format conversion of the latest production date of all storage locations; Min is the time value corresponding to the format conversion of the earliest production date of all storage locations.

[0055] In actual applications, when calculating the value of the F array, the production date of the stored materials is read, the date type is converted into a computable value of the long type through a computer program, normalized, and assigned to v 2 , normalization can make the data dimensionless and improve data comparability. The time value of the material production date of all the materials stored in the storage locations calculated by the design is the array T, and the v obtained for each storage location after normalization 2 .

[0056] Specifically, the method for calculating the value of the failure time includes:

[0057] Obtain the material expiration dates corresponding to all goods stored in the cargo space and determine the expired materials;

[0058] The current date corresponding to the expired material is read, and a second normalization process is performed based on the material expiration date and the current date to obtain a value of the target expiration time, wherein the second normalization process includes:

[0059] v 3 =(T[i]-Min) / (Max-Min);

[0060] Among them, v 3 For the materials stored in the storage location numbered i, the normalized value is obtained by calculating the difference in days between the material expiration date and the current date; T[i] is the difference in days between the calculated material expiration date and the second generated date in the storage location numbered i; Max is the maximum value of the difference in days among all storage locations; Min is the minimum value of the difference in days among all storage locations.

[0061] In actual applications, when calculating the value of the F array, the expiration time is calculated based on the difference between the expiration date of the material and the current date, the number of days between the two is calculated, normalized, and assigned to v 3 The difference between the expiration date of all the materials stored in the storage locations calculated and the current date is the array T. After normalization, the v obtained for each storage location is 3 .

[0062] Specifically, the method for calculating the value of the warehousing time includes: obtaining the material warehousing time corresponding to all goods stored in the cargo location, and performing a third normalization process based on the material warehousing time and the current date to obtain the value of the target warehousing time.

[0063] In actual applications, when calculating the value of the F array, the entry time is calculated based on the difference between the current date and the material entry date, the number of days between the two is calculated, normalized, and assigned to v 4 .

[0064] Step S104: Processing the cargo location data based on the cargo location optimization algorithm to generate a material cargo location optimization result, wherein the cargo location optimization algorithm sorts the materials stored on the cargo location according to the number of warehousing and outbound task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of warehousing time, and optimizes the material cargo location based on the sorting result.

[0065] In the embodiment of this application, the main algorithm process is shown in Figure 3 , Figure 3 The main flow diagram of the algorithm of a warehouse material storage location optimization method provided in the embodiment of the present application is as follows. Figure 3 As shown, the specific steps include the following steps.

[0066] Step S301: Initialize the storage location array F, variable l is 0, and r is the maximum serial number of the storage location minus 1.

[0067] Specifically, the initialization operation is to enter the warehouse, and the task round of the cargo location is set to 0, and the state is set to false, which means that the goods are not stored. After entering the warehouse, the epoch field of the cargo location where the goods are stored is automatically increased to 1, and the state is set to true. The reverse order pair list list is defined as the final result, and the array subscript list numlist for storing the reverse order pairs is defined at the same time.

[0068] When carrying out warehousing and outbound business, after each round of tasks is completed, the epoch of all the cargo locations F[i] storing the goods increases by 1.

[0069] Define the variable l as 0 and r as the largest serial number in the storage location array F minus 1.

[0070] Step S302: Calculate the intermediate value m=(l+r) / 2 according to the input l and r.

[0071] Step S303: Obtain two intervals [l,m], [m+1,r] from array F.

[0072] Specifically, start merge sort, calculate the middle value m = (l + r) / 2 between l and r, and then divide the array into two intervals [l, m] and [m + 1, r].

[0073] Step S304: merge and sort the interval [l,m] and search for reverse order pairs.

[0074] Step S305: merge and sort the interval [m+1, r] to find the reverse order pair.

[0075] It should be noted that step S304 and step S305 use the two ends of the interval as new l and r input values ​​to enter the decomposition and reverse order pair process for solution, such as step S304 is l=l, r=m; step S305 is l=m+1, r=r, and this process is iterated recursively until the interval can no longer be divided.

[0076] Step S306: merge the two intervals, obtain the reverse order pairs therein, and swap their positions.

[0077] In addition, the present application embodiment provides a process for decomposing and finding the reverse order pair, see Figure 4 , Figure 4 A schematic diagram of the process of decomposing and finding reverse order pairs of a warehouse material storage location optimization method provided in an embodiment of the present application. Figure 4 As shown, the specific steps include the following steps.

[0078] Step S401: Initialize the storage location array F, variables l, r, which are the input values ​​of the previous step;

[0079] Step S402: Calculate the intermediate value m=(l+r) / 2 according to the input l and r;

[0080] Step S403: Obtain two intervals [l,m], [m+1,r] from array F;

[0081] Step S404: merge and sort the interval [l,m] and find the reverse order pair;

[0082] Step S405: merge and sort the interval [m+1, r] to find the reverse order pair;

[0083] Step S406: merge the two intervals, obtain the reverse order pairs therein, and swap their positions.

[0084] In addition, the present application embodiment provides a merging process, see Figure 5 , Figure 5 A schematic diagram of a combined flow chart of a warehouse material storage location optimization method provided in an embodiment of the present application. Figure 5 As shown, the specific steps include the following steps.

[0085] Step S501: Calculate the sizes of the sub-arrays n1 and n2 to be merged, and load them into lists list, numlist.

[0086] Specifically, calculate the sizes of the two merged sub-arrays n1 and n2, where n1=m-l+1 and n2=rm.

[0087] Step S502: Create two temporary sub-arrays L and R, and copy values ​​from the array range in F to L and R.

[0088] Specifically, create two temporary sub-arrays L and R to store the two sub-arrays to be merged, and copy the data into them. That is to say, L is the value of the F array in the interval [l, m], and R is the value of the F array in the interval [m + 1, r].

[0089] Step S503: Initialize the variables i = 0, j = 0, k = 1.

[0090] Specifically, merge the temporary arrays, and initialize the variables i = 0, j = 0, k = l.

[0091] Step S504: Determine whether i < n1 and j < n2. If so, execute Step S505; if not, execute Step S509.

[0092] Step S505: If so, determine whether L[i] <= R[j]. If so, execute Step S506; if not, execute Step S507.

[0093] Step S506: Let F[K] = L[i], i = i + 1.

[0094] Specifically, if so, determine the size of L[i] and R[j]. If L[i] <= R[j], then F[k] = L[i], i = i + 1, k = k + 1; otherwise, it proves that these two elements are an inversion pair, which will be stored in the list list as an inversion pair array, and their subscript numbers will be stored in numlist. Let F[k] = R[j], j = j + 1, k = k + 1, and return to Step S504.

[0095] Step S507: Prove it is an inversion pair, store [L[i], R[j]] in list, store [l + 1, m + 1 + j] in numlist, and let F[k] = R[j], j = j + 1.

[0096] Step S508: k = k + 1.

[0097] Step S509: Determine whether i < n1? If so, execute Step S5010; if not, execute Step S5012.

[0098] Step S5010: F[k] = L[i], pair L[i] with the elements in R one by one and put them in list, pair the serial number l + 1 with the numbers from m + 1 to r one by one and store them in numlist.

[0099] Step S5011: Let i = i + 1, k = k + 1, and repeat Steps S509 to S5011 until i is not less than n1.

[0100] Specifically, if i < n1, it proves that there are still remaining values in array L. The loop assigns values to F until all the remaining values are assigned. At the same time, the remaining values in array L and the elements in the entire array R form reverse pairs, which are paired one by one, stored in the list list, and their subscript numbers are stored in numlist.

[0101] Step S5012: Determine whether j < n2? If yes, execute step S5013; if no, execute step S5015.

[0102] Step S5013: Let F[k] = R[j].

[0103] Step S5014: Let j = j + 1, k = k + 1, and repeat steps S5012 to S5014 until j is not less than n2.

[0104] Specifically, if j < n2, it proves that there are still remaining values in array R. The loop assigns values to F until all the remaining values are assigned.

[0105] Step S5015: End the process.

[0106] After ending the main process, the reverse pair list list and the list numlist of the corresponding reverse pair numbers in the original array F are obtained. After getting these two lists, several reverse pairs with the largest difference are selected from list for swapping.

[0107] Exemplarily, refer to Figure 6 , Figure 6 is a schematic diagram of the process of sorting and finding reverse pairs for a method of optimizing the storage location of warehouse materials provided by an embodiment of the present application.

[0108] Such as Figure 6 shown, the initial array is [6, 1, 4, 3, 9, 4], and the specific meaning is that the values of the objective function value value corresponding to the storage locations 0 - 5 before swapping are 6, 1, 4, 3, 9, 4.

[0109] A number M can be customized as the number of selected reverse pairs, and the largest M reverse pairs are selected. For example, M = 2, and Figure 4Take the array as an example. In the reverse pair list, select the two reverse pairs with the largest difference. The two pairs with the largest difference in {[6,1],[6,4],[9,4],[4,3],[6,3],[6,4]} are [6,1],[9,4]. After selection, check the corresponding sequence subscripts from numlist, which are [0,1] and [4,5] respectively. Issue an exchange instruction and call the WCS system to exchange the cargo locations and the materials on the cargo locations. After the exchange, the task round epoch and value of these cargo locations are updated through the program at the same time. The process ends here. After the process is completed, the positions of materials on cargo positions 0 and 6 are exchanged, and the objective function values ​​become 1 and 6 respectively. The materials on cargo positions 4 and 5 are exchanged, and the objective function values ​​become 4 and 6 respectively. After this exchange, it is ensured that the objective function value of materials stored in the back cargo positions is larger, and these materials are not easily moved, while the objective function value of the cargo positions at the front positions is smaller, which is suitable for storing materials that are frequently in and out of the warehouse, have intact material properties, have an early production date, are close to the expiration time, and have a short warehousing time. At this point, the design goal has been achieved.

[0110] In addition, the embodiments of the present application take into account the problem that after a period of time after the materials are put into storage, some storage spaces are occupied for a long time and the utilization rate of materials is low, and targeted improvements are made. After the process designed by this application, it can be applicable to the warehouse material entry and exit scenarios.

[0111] The embodiments of the present application aim to improve the problem that when high-frequency in-and-out materials enter the warehouse, the high-quality storage spaces are occupied by other materials, resulting in no suitable space for the high-frequency in-and-out materials, which affects the in-and-out efficiency, so as to achieve the purpose of improving the in-and-out efficiency.

[0112] The embodiments of the present application have certain scalability and are convenient for subsequent improvement. For example, after using the algorithm in this article to calculate the material storage locations to be exchanged, it is possible to comprehensively consider whether adjustments and storage location optimization are needed based on material attributes, material production date, expiration time, storage time, and other factors.

[0113] The warehouse material storage location optimization method provided in the embodiment of the present application can be applied to the warehouse material entry and exit scenarios. It can improve the problem that when high-frequency entry and exit materials are entered into the warehouse, the high-quality storage locations are occupied by other materials, resulting in no suitable location for the high-frequency entry and exit materials, which affects the entry and exit efficiency, so as to achieve the purpose of improving the entry and exit efficiency.

[0114] Corresponding to the above method embodiment, this specification also provides an embodiment of a warehouse material storage location optimization device, Figure 7 This is a schematic diagram of the structure of a warehouse material storage location optimization device provided in an embodiment of the present application. Figure 7 As shown, the device comprises:

[0115] The construction module 702 is configured to construct a warehouse cargo location model and use a one-dimensional array to store cargo location data, wherein the warehouse cargo location model uses the number of cargo location rows as the horizontal coordinate and the number of cargo location columns as the vertical coordinate; the structure of the cargo location data carries the cargo location index, cargo location sequence number, cargo location objective function value, inbound and outbound task round, and whether there is a material mark on the cargo location;

[0116] The optimization module 704 is configured to process the cargo location data based on the cargo location optimization algorithm to generate a material cargo location optimization result, wherein the cargo location optimization algorithm sorts the materials stored on the cargo location according to the number of warehousing and outbound task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of warehousing time, and optimizes the material cargo location based on the sorting result.

[0117] In an optional embodiment, the optimization module 704 is further configured to:

[0118] The initial in-and-out task round corresponding to the first entry of goods is set to zero, and the batch of out-or-out operations or in-or-out operations performed after the goods are entered is used as the counting standard to determine the value corresponding to the initial in-and-out task round, wherein each time an out-or-out operation or in-or-out operation is performed after the goods are entered, the corresponding in-and-out task round increases by one.

[0119] In an optional embodiment, the optimization module 704 is further configured to:

[0120] Based on a preset time slice, the round of inbound and outbound tasks is determined, wherein the time slice is a preset fixed duration, and each time a fixed duration passes, the corresponding round of inbound and outbound tasks increases by one.

[0121] In an optional embodiment, the optimization module 704 is further configured to:

[0122] The cargo information corresponding to the cargo location is processed by the objective function to generate the cargo location objective function value, and the incoming cargo is sorted based on the cargo location objective function value, wherein the objective function includes:

[0123] Value=w1*v1+w2*v2+w3*v3+w4*v4+w5*epoch;

[0124]

[0125] Where value is the objective function value of the calculated cargo space; w k >=0(k=1,2,3,...,m) is the weight coefficient, which is determined according to the actual influencing factors; v 1is the value of the material attribute; v 2 is the value of the material production date; 3 is the value of failure time; v 4 is the value of the storage time; epoch is the round of storage and outbound tasks.

[0126] In an optional embodiment, the optimization module 704 is further configured to:

[0127] The material attributes include normal materials and abnormal materials. When the material attributes belong to normal materials, the value of the assigned material attributes is 0. When the material attributes belong to abnormal materials, the value of the assigned material attributes is 1.

[0128] In an optional embodiment, the optimization module 704 is further configured to:

[0129] Read the production dates corresponding to all goods stored in the cargo location, convert the format of the production date into the target format, perform a first normalization process, obtain a target production date, and use the target production date as the material production date, wherein the first normalization process includes:

[0130] v 2 =(T[i]-Min) / (Max-Min);

[0131] Among them, v 2 It is the value obtained after normalization of the production date and time values ​​of the materials stored in the storage location numbered i; T[i] is the time value corresponding to the format conversion of the production date of the materials on the storage location numbered i; Max is the time value corresponding to the format conversion of the latest production date of all storage locations; Min is the time value corresponding to the format conversion of the earliest production date of all storage locations.

[0132] In an optional embodiment, the optimization module 704 is further configured to:

[0133] Obtain the material expiration dates corresponding to all goods stored in the cargo space and determine the expired materials;

[0134] The current date corresponding to the expired material is read, and a second normalization process is performed based on the material expiration date and the current date to obtain a value of the target expiration time, wherein the second normalization process includes:

[0135] v 3 =(T[i]-Min) / (Max-Min);

[0136] Among them, v 3For the materials stored in the storage location numbered i, the normalized value is obtained by calculating the difference in days between the material expiration date and the current date; T[i] is the difference in days between the calculated material expiration date and the second generated date in the storage location numbered i; Max is the maximum value of the difference in days among all storage locations; Min is the minimum value of the difference in days among all storage locations.

[0137] In an optional embodiment, the optimization module 704 is further configured to:

[0138] The material entry time corresponding to all the goods stored in the cargo location is obtained, and the value of the target entry time is obtained after performing a third normalization process based on the material entry time and the current date.

[0139] The warehouse material storage location optimization device provided in the embodiment of the present application can be applied to the warehouse material entry and exit scenarios. It can improve the problem that when high-frequency entry and exit materials are entered into the warehouse, the high-quality storage locations are occupied by other materials, resulting in no suitable location for the high-frequency entry and exit materials, which affects the entry and exit efficiency, so as to achieve the purpose of improving the entry and exit efficiency.

[0140] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the warehouse material cargo location optimization device, since it is basically similar to the warehouse material cargo location optimization method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the warehouse material cargo location optimization method embodiment.

[0141] Figure 8 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 800 include but are not limited to a memory 810 and a processor 820. The processor 820 is connected to the memory 810 via a bus 830, and a database 850 is used to store data.

[0142] The computing device 800 also includes an access device 840, which enables the computing device 800 to communicate via one or more networks 860. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 840 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0143] In one embodiment of the present specification, the above components of the computing device 800 and Figure 8 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 8 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0144] The computing device 800 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 800 may also be a mobile or stationary server.

[0145] The processor 820 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned warehouse material storage location optimization method.

[0146] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the warehouse material storage location optimization method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the warehouse material storage location optimization method embodiment.

[0147] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned warehouse material storage location optimization method.

[0148] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the warehouse material storage location optimization method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the warehouse material storage location optimization method embodiment.

[0149] An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to execute the steps of the above-mentioned warehouse material storage location optimization method.

[0150] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the warehouse material storage location optimization method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the warehouse material storage location optimization method embodiment.

[0151] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0152] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0153] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.

[0154] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for optimizing the storage space of warehouse materials, characterized in that: include: Construct a warehouse cargo location model and use a one-dimensional array to store cargo location data, wherein the warehouse cargo location model uses the number of cargo location rows as the horizontal coordinate and the number of cargo location columns as the vertical coordinate; the structure of the cargo location data carries the cargo location index, cargo location sequence number, cargo location objective function value, inbound and outbound task rounds, and whether there is a material mark on the cargo location; The cargo location data is processed based on a cargo location optimization algorithm to generate a material cargo location optimization result, wherein the cargo location optimization algorithm sorts the materials stored on the cargo location according to the number of warehousing and outbound task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of warehousing time, and optimizes the material cargo location based on the sorting result.

2. The method according to claim 1, characterized in that The method for calculating the round of inbound and outbound tasks includes: The initial in-and-out task round corresponding to the first entry of goods is set to zero, and the batch of out-or-out operations or in-or-out operations performed after the goods are entered is used as the counting standard to determine the value corresponding to the initial in-and-out task round, wherein each time an out-or-out operation or in-or-out operation is performed after the goods are entered, the corresponding in-and-out task round increases by one.

3. The method according to claim 2, characterized in that The method for calculating the rounds of inbound and outbound tasks also includes: Based on a preset time slice, the round of inbound and outbound tasks is determined, wherein the time slice is a preset fixed duration, and each time a fixed duration passes, the corresponding round of inbound and outbound tasks increases by one.

4. The method according to claim 1, characterized in that: The method for calculating the cargo location objective function value includes: The cargo information corresponding to the cargo location is processed by the objective function to generate the cargo location objective function value, and the incoming cargo is sorted based on the cargo location objective function value, wherein the objective function includes: Value=w1*v1+w2*v2+w3*v3+w4*v4+w5*epoch; Where value is the objective function value of the calculated cargo space; w k >=0(k=1,2,3,...,m) is the weight coefficient, which is determined according to the actual influencing factors; v1 is the value of the material attribute; v2 is the value of the material production date; v3 is the value of the expiration time; v4 is the value of the warehousing time; epoch is the round of the warehousing and out-of-warehouse tasks.

5. The method according to claim 4, characterized in that The material attributes include normal materials and abnormal materials. When the material attributes belong to normal materials, the value of the assigned material attributes is 0. When the material attributes belong to abnormal materials, the value of the assigned material attributes is 1.

6. The method according to claim 4, characterized in that The calculation method of the production date of the materials includes: Read the production dates corresponding to all goods stored in the cargo location, convert the format of the production date into the target format, perform a first normalization process, obtain a target production date, and use the target production date as the material production date, wherein the first normalization process includes: v2 = (T[i] - Min) / (Max - Min); Among them, v2 is the value obtained after the normalization of the production date and time of the materials stored in the storage location with storage location index i; T[i] is the time value corresponding to the format conversion of the production date of the materials on the storage location numbered i; Max is the time value corresponding to the format conversion of the latest production date of all storage locations; Min is the time value corresponding to the format conversion of the earliest production date of all storage locations.

7. The method according to claim 4, characterized in that The method for calculating the value of the failure time includes: Obtain the material expiration dates corresponding to all goods stored in the cargo space and determine the expired materials; The current date corresponding to the expired material is read, and a second normalization process is performed based on the material expiration date and the current date to obtain a value of the target expiration time, wherein the second normalization process includes: v3 = (T[i] - Min) / (Max - Min); Where v3 is the value obtained by normalizing the difference between the expiration date and the current date of the materials stored in the storage location with storage location index i; T[i] is the difference between the expiration date and the second generated date of the materials calculated in the storage location numbered i; Max is the maximum value of the difference in days among all storage locations; Min is the minimum value of the difference in days among all storage locations.

8. The method according to claim 4, characterized in that The calculation method of the value of the storage time includes: The material entry time corresponding to all the goods stored in the cargo location is obtained, and the value of the target entry time is obtained after performing a third normalization process based on the material entry time and the current date.

9. A warehouse material location optimization device, characterized in that: include: A construction module is configured to construct a warehouse cargo location model and use a one-dimensional array to store cargo location data, wherein the warehouse cargo location model uses the number of cargo location rows as the horizontal coordinate and the number of cargo location columns as the vertical coordinate; the structure of the cargo location data carries the cargo location index, cargo location sequence number, cargo location objective function value, inbound and outbound task rounds, and whether there is a material mark on the cargo location; The optimization module is configured to process the cargo location data based on the cargo location optimization algorithm to generate a material cargo location optimization result, wherein the cargo location optimization algorithm sorts the materials stored on the cargo location according to the number of inbound and outbound task rounds, the degree of perfection of material attributes, the time value of the material production date, whether the expiration time is approaching, and the length of the warehousing time, and optimizes the material cargo location based on the sorting result.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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