A warehouse storage location allocation method and device, computer equipment and storage medium

Abstract

This invention provides a warehouse storage location allocation method, apparatus, computer equipment, and storage medium, belonging to the field of material storage. The method includes: acquiring historical order data; calculating the turnover rate coefficient of each material and the demand correlation between any two materials; determining two materials as a pair requiring distance constraints only when the demand correlation of two materials is higher than a first preset threshold and both turnover rate coefficients are lower than a second preset threshold; constructing a storage location allocation optimization model with the objective of minimizing the total travel distance of loading equipment; for each pair of materials requiring distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable; and solving the optimization model to obtain the storage location allocation scheme for each material. This selective constraint mechanism effectively avoids fast-moving materials being placed far from entrances and exits due to correlation, significantly shortening the travel path.

Description

Technical Field

[0001] This invention belongs to the field of material storage, and specifically relates to a warehouse storage location allocation method, device, computer equipment, and storage medium. Background Technology

[0002] In warehouse management, especially in steel and wire rod yards, the rationality of storage location allocation directly affects the travel path length of loading equipment and the efficiency of outbound operations. Materials with high turnover rates should be prioritized for storage near entrances and exits to shorten the paths for frequent retrieval, while materials with strong demand correlations should be stored as close as possible to reduce the extra detour distance when retrieving multiple materials in a single outbound task. How to balance these two factors is a key research issue in the field of storage location optimization.

[0003] In existing technologies, some scholars have proposed multi-objective optimization methods that combine material turnover rate with demand correlation. For example, Li Mingkun et al. disclosed a storage location allocation model in "Optimization Strategy and Algorithm for Storage Location Allocation Based on Material Turnover Rate and Demand Correlation" (Operations Research and Management, Vol. 27, No. 9, 2018). This model first calculates the turnover rate coefficient COI (cube index, the smaller the value, the faster the turnover) for each material, and calculates the demand correlation r between any two materials. Based on this, an objective function is constructed. This objective function aims to bring materials with high turnover rates as close as possible to the entrance and exit. It introduces two intermediate variables to control the distance between material pairs under different correlation thresholds: when the correlation between two materials is greater than or equal to the high threshold, their distance should not exceed the first intermediate variable; when the correlation is between the low and high thresholds, their distance should not exceed the second intermediate variable. This model attempts to make materials with high correlation move closer to each other in space through the constraints of the two intermediate variables.

[0004] However, this model imposes distance constraints on all material pairs that reach a certain relevance threshold. For cases where demand relevance is high and both are slow-flowing materials, bringing them closer together helps reduce the picking path in a single outbound task, as these two types of materials are already placed far from the entrance / exit, and moving them closer does not significantly increase the main path length. But for cases where demand relevance is high but at least one of them is a fast-flowing material, forcing them to be close together may cause the fast-flowing material to deviate from its optimal position near the entrance / exit. Each entry / exit of a fast-flowing material generates a walking path, and the increased path cost of moving it away from the entrance / exit may far outweigh the cost savings from moving it closer due to relevance. The existing model does not distinguish between these differences, which may lead to situations where fast-flowing materials are placed further away to accommodate relevance, thus increasing the overall walking distance. Summary of the Invention

[0005] To address the problem in existing technologies where only distance constraints are applied during material stacking without distinguishing the material's own turnover characteristics, thus increasing the distance for material entry and exit, this invention provides a warehouse storage location allocation method, apparatus, computer equipment, and storage medium.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A warehouse storage location allocation method, the method comprising: Obtain historical order data, calculate the inventory turnover rate coefficient for each material, and the demand correlation between any two materials; The two materials are identified as a pair of materials that require distance constraints only when the demand correlation between the two materials is higher than the first preset threshold and the turnover coefficients of both are lower than the second preset threshold. A storage location allocation optimization model is constructed based on the location decision variables, turnover rate coefficient, and distance decision variables of the materials. The objective is to minimize the total travel distance of the loading equipment. For each pair of materials with distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable. The location decision variables are used to determine the storage location of the materials. The optimization model is solved to obtain the storage location allocation scheme for each material.

[0007] Optionally, the construction of the storage location allocation optimization model based on the location decision variables of the goods, the corresponding turnover rate coefficient, and the distance decision variables includes: The material allocation location is determined based on location decision variables. Using the turnover rate coefficient as a weight, the distance from the allocated storage location to the entrance / exit is converted into a travel cost. A storage location allocation optimization model is constructed based on the weighted sum of the travel cost and distance decision variables, with the following formula: ; in, The total number of lanes, I The total number of storage locations in each alleyway. This represents the total number of material types. Let be the distance from the i-th storage location in lane h to the entrance / exit. The location decision variable is either 0 or 1, taking the value of 1 when material k is assigned to the i-th storage location in aisle h, and 0 otherwise. Let k be the turnover rate coefficient of material k; For distance decision variables, it represents the maximum permissible distance between all material pairs that are determined to require distance constraints; and For the preset weighting coefficients, satisfy ; For each pair of materials that requires distance constraints ( If materials Assigned to the alley The When there is one storage location, the location decision variable =1, material Assigned to the alley The When there is one storage location, the location decision variable =1, then the distance between these two storage locations The following constraints must be met: ; in, Indicates the location of the goods ( ) and cargo location ( The distance between them.

[0008] Optionally, before constructing the optimization model, cluster analysis is used to reduce the solution size of the model, including: The demand correlation between any two materials is transformed into individual attributes, and the sharing degree of each material is calculated. The sharing degree is used to characterize the degree of demand correlation between a material and other materials. Based on the COI coefficient and sharing degree of each material, the priority of each material is calculated, and the materials are clustered and grouped according to the priority, merging materials with similar priorities into the same group. A simplified storage location allocation optimization model is constructed and solved using a group as the unit to obtain the storage location allocation scheme for each group. Then, the storage location of each material is determined based on the group allocation results.

[0009] Optionally, the step of converting the demand correlation between any two materials into individual attributes and calculating the sharing degree of each material includes: The sharing degree of each material is calculated using a sharing function. The calculation formula is as follows: ; in, For materials The degree of sharing This represents the distance between the two materials in the demand correlation space. This represents the total number of material types. Shared functions Defined as: ; The pre-defined niche radius, These are preset shape parameters used to control the rate at which the correlation between two materials decreases with distance. This sharing function only applies when the correlation distance between the two materials is less than [a certain value]. When the time is positive, it is used to filter out strongly correlated material pairs that have practical significance, and quantify the degree of correlation as the sharing degree of each material.

[0010] Optionally, the step of calculating the priority of each material based on its COI coefficient and sharing degree, and clustering the materials based on their priority to merge materials with similar priorities into the same group includes: The priority of each material is calculated by comprehensively considering the need for materials with high turnover rates to be located near entrances and exits, and the need for materials to be stored together due to strong correlation with other materials. The formula is as follows: ; in, and Preset weighting coefficients are used to balance the importance of turnover rate and sharing factor in clustering. Let k be the turnover rate coefficient of material k. The degree of sharing of material k; Materials are hierarchically clustered based on priority, and materials with similar priorities are merged into the same group as the basic unit for subsequent storage allocation.

[0011] Optionally, after merging the populations, the fitness of each population is calculated to determine the priority of each population relative to the entry and exit points, using the following formula: ; in, Let be the average COI coefficient of all materials in group q. The average demand relevance of all materials in group q. and Preset weighting coefficients are used to balance the impact of the average turnover rate of the population and internal correlation on the allocation of storage spaces; Determine the fitness level of different ethnic groups. The higher the fitness level of an ethnic group, the closer its overall storage location is to the entrance and exit.

[0012] Optionally, the turnover rate coefficient is used to measure the ratio of the material's outbound frequency to the occupied storage space area, and the formula is: ; The demand relevance is used to measure the probability that two materials are simultaneously demanded in the same order, and the formula is: ; in, Let k be a 0-1 row vector representing the occurrences of material k in each order. This represents the total number of times material k appears within the sampling period, used to characterize its turnover frequency; This represents the storage area or number of storage locations required for material k, used to characterize its space occupancy. is the turnover rate coefficient of material k. The larger the value of this coefficient, the higher the frequency of material k leaving the warehouse per unit storage area, and it should be preferentially allocated to storage locations closer to the entrance and exit. This indicates the number of times materials k1 and k2 appear simultaneously in the same order; The demand correlation between materials k1 and k2 is calculated by subtracting the number of times they appear together from the sum of their individual occurrences in the formula. This is used to eliminate interference from the high frequency of a single material and to reflect the true strength of the relationship between the two. The larger the demand correlation value, the more likely the two materials are to be shipped out together.

[0013] A warehouse storage location allocation device, the device comprising: The acquisition module is used to acquire historical order data, calculate the inventory turnover rate coefficient of each material, and the demand correlation between any two materials; The judgment module is used to determine the two materials as a material pair that needs to be subject to distance constraints only when the demand correlation between the two materials is higher than a first preset threshold and the turnover rate coefficients of both are lower than a second preset threshold. The module is used to construct a storage location allocation optimization model based on the location decision variables, turnover rate coefficient, and distance decision variables of the materials, with the objective of minimizing the total travel distance of the loading equipment. For each pair of materials with distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable; the location decision variable is used to determine the storage location of the material. The optimization module is used to solve the optimization model and obtain the storage location allocation scheme for each material.

[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a warehouse location allocation method.

[0015] A computer device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a warehouse storage location allocation method.

[0016] The warehouse storage location allocation method provided by this invention has the following beneficial effects: This invention first identifies material combinations that truly require constraints on their relative positions. Distance restrictions are only imposed when the demand correlation between two materials is high and their turnover coefficients are both low. This selective constraint mechanism effectively prevents fast-flowing materials from being placed far from the entrance / exit due to correlation, ensuring that high-turnover materials always preferentially occupy prime storage areas near the entrance / exit, thereby significantly shortening the travel path of loading equipment when performing high-frequency outbound tasks. For slow-flowing materials that are already stored far away and have strong demand correlation, the constraints of this invention bring them closer together. This reduces additional detour distances when retrieving multiple related materials in a single outbound task. Furthermore, since the inbound / outbound frequency of such materials is low, the increased path cost due to bringing them closer is negligible, thus minimizing the overall travel distance. On the other hand, this invention significantly simplifies the number of constraints in the optimization model, eliminates a large number of redundant material pair distance constraints in the existing model, significantly reduces the model solution scale, effectively improves computational efficiency, and can quickly adapt to the complex scenarios of diverse materials and dynamically changing orders in actual warehousing environments. It provides warehouse managers with an efficient basis for storage location allocation decisions, ultimately increasing warehouse throughput and shortening outbound queuing time. Attached Figure Description

[0017] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are 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.

[0018] Figure 1 This is a schematic flowchart of a warehouse storage location allocation method provided by the present invention according to an exemplary embodiment.

[0019] Figure 2 This is a general layout plan of a freight yard according to an exemplary embodiment of the present invention.

[0020] Figure 3 This is a plan view provided by the present invention according to an exemplary embodiment.

[0021] Figure 4 This is a block diagram of a warehouse storage location allocation device provided by the present invention according to an exemplary embodiment. Detailed Implementation

[0022] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0023] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] First, this invention provides a warehouse storage location allocation method, specifically as follows: Figure 1 As shown, it includes the following steps: S101. Obtain historical order data, calculate the turnover rate coefficient of each material, and the demand correlation between any two materials.

[0025] The turnover rate coefficient is used to measure the ratio of the frequency of material outflow to the storage area occupied, and the formula is: ; in, Let k be a 0-1 row vector representing the occurrences of material k in each order. This represents the total number of times material k appears within the sampling period, used to characterize its turnover frequency; This represents the storage area or number of storage locations required for material k, used to characterize its space occupancy. is the turnover rate coefficient of material k. The larger the value of this coefficient, the higher the frequency of material k leaving the warehouse per unit storage area, and it should be preferentially allocated to storage locations closer to the entrance and exit.

[0026] The demand relevance is used to measure the probability that two materials are simultaneously demanded in the same order, and the formula is: ; in, This indicates the number of times materials k1 and k2 appear simultaneously in the same order; The demand correlation between materials k1 and k2 is calculated by subtracting the number of times they appear together from the sum of their individual occurrences in the formula. This is used to eliminate interference from the high frequency of a single material and to reflect the true strength of the relationship between the two. The larger the demand correlation value, the more likely the two materials are to be shipped out together.

[0027] S102. Only when the demand correlation between the two materials is higher than the first preset threshold and the turnover coefficients of both are lower than the second preset threshold, the two materials are determined to be a material pair that requires distance constraints.

[0028] The first preset threshold is manually assigned based on historical experience, or, under computational conditions, determined by trial calculations between 0.1 and 1 based on the target travel distance of the handling equipment within the warehouse to determine the optimal first preset threshold. The second preset threshold is set based on the COI value distribution of all materials. A COI value below the second preset threshold indicates that the material is a slow-flowing material and should be stored in an area far from the entrance / exit; a COI value above the second preset threshold indicates that the material is a fast-flowing material and should be stored in an area near the entrance / exit.

[0029] S103. Based on the location decision variables, turnover rate coefficients, and distance decision variables of the materials, construct a storage location allocation optimization model with the goal of minimizing the total travel distance of the loading equipment. For each pair of materials with distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable.

[0030] The location decision variable is used to determine the storage location of the material.

[0031] In this step, an optimization model is constructed with the objective of minimizing the total travel distance of the loading equipment.

[0032] For example, the material allocation storage location is determined based on location decision variables. Using the turnover rate coefficient as a weight, the distance from the allocated storage location to the entrance / exit is converted into a travel cost. Based on the weighted sum of the travel cost and distance decision variables, a storage location allocation optimization model is constructed, with the following formula: ; in, Let I be the total number of lanes, and let I be the total number of storage locations in each lane. This represents the total number of material types. Let be the distance from the i-th storage location in lane h to the entrance / exit. The location decision variable is either 0 or 1, taking the value of 1 when material k is assigned to the i-th storage location in aisle h, and 0 otherwise. Let k be the turnover rate coefficient of material k; For distance decision variables, it represents the maximum permissible distance between all material pairs that are determined to require distance constraints; and For the preset weighting coefficients, satisfy ; For each pair of materials that requires distance constraints ( If materials Assigned to the alley The When there is one storage location, the location decision variable =1, material Assigned to the alley The When there is one storage location, the location decision variable =1, then the distance between these two storage locations The following constraints must be met: ; in, Indicates the location of the goods ( ) and cargo location ( The distance between them.

[0033] In one embodiment, cluster analysis of material types can be performed to reduce the model solution size and simplify the model solution difficulty.

[0034] For example, firstly, the demand correlation between any two materials is transformed into individual attributes, and the sharing degree of each material is calculated. The sharing degree is used to characterize the degree of demand correlation between a material and other materials. Based on the material's COI coefficient and sharing degree, the priority of each material is calculated, and the materials are clustered based on the priority. Materials with similar priorities are merged into the same group. Using the group as the unit, a simplified storage location allocation optimization model is constructed and solved to obtain the storage location allocation scheme for each group. Then, the storage location of each material is determined based on the group allocation results.

[0035] For example, firstly, the sharing degree of each material is calculated using a sharing function, and the calculation formula is as follows: ; in, For materials The degree of sharing This represents the distance between the two materials in the demand correlation space. This represents the total number of material types. Shared functions Defined as: ; The pre-defined niche radius, These are preset shape parameters used to control the rate at which the correlation between two materials decreases with distance. This sharing function only applies when the correlation distance between the two materials is less than [a certain value]. When the time is positive, it is used to filter out strongly correlated material pairs that have practical significance, and quantify the degree of correlation as the sharing degree of each material.

[0036] Secondly, the priority of each material is calculated by comprehensively considering the need for materials with high turnover rates to be located near entrances and exits, and the need for materials to be stored together due to strong correlation with other materials. The formula is as follows: ; in, and Preset weighting coefficients are used to balance the importance of turnover rate and sharing factor in clustering. Let k be the turnover rate coefficient of material k. Let k be the sharing degree of material k; based on priority, hierarchical clustering is performed on materials, and materials with similar priorities are merged into the same group as the basic unit for subsequent storage location allocation.

[0037] Finally, after merging the populations, the fitness of each population is calculated to determine the priority of each population relative to the entry and exit points, using the following formula: ; in, Let be the average COI coefficient of all materials in group q. The average demand relevance of all materials in group q. and Preset weighting coefficients are used to balance the impact of average turnover rate and internal correlation of the population on storage allocation; the fitness of different populations is determined, and the population with higher fitness has its overall storage space closer to the entrance and exit.

[0038] S104. Solve the optimization model to obtain the storage location allocation scheme for each material.

[0039] Using the above method, the material combinations that truly require constraint on their relative positions are first identified. That is, distance restrictions are only imposed when the demand correlation between two materials is high and their turnover coefficients are both low. This selective constraint mechanism effectively avoids situations where fast-flowing materials are placed far from the entrance / exit due to correlation, ensuring that high-turnover materials always preferentially occupy prime storage areas near the entrance / exit, thereby significantly shortening the travel path of loading equipment when performing high-frequency outbound tasks. For slow-flowing materials that are already stored far away and have strong demand correlation, the constraint conditions of this invention bring them closer together. This reduces the extra detour distance when retrieving multiple related materials in a single outbound task. Furthermore, since the inbound / outbound frequency of such materials is low, the increased path cost due to bringing them closer is negligible, thus minimizing the overall travel distance. On the other hand, this invention significantly simplifies the number of constraints in the optimization model, eliminates a large number of redundant material pair distance constraints in the existing model, significantly reduces the model solution scale, effectively improves computational efficiency, and can quickly adapt to the complex scenarios of diverse materials and dynamically changing orders in actual warehousing environments. It provides warehouse managers with an efficient basis for storage location allocation decisions, ultimately increasing warehouse throughput and shortening outbound queuing time.

[0040] In this invention, based on the above method, a possible implementation is also provided.

[0041] First, establish the model, with the objective function: f = min(β1 l hi * X hik ) / COI k + β2*y1); Constraints: ; ,k∈1,2,3,…,K; X h1ik1 *X h2ik2 *d h1h2ij ≤y1,h1,h2∈1,2,3,…,H, i,j∈1,2,3,…,I k1,k2∈ 1, 2, 3,…,K r k1k2 ≥ U 1； X hik ∈ {0, 1}; y1∈[0,+∞]; The demand correlation r between material k1 and material k2 k1k2 : ; COI coefficient of material k: ; The above parameter settings are as follows: K: Material number, k = 1, 2, 3, ..., K, k1, k2 ∈ K; O pk The 0-1 parameter indicates whether order p contains material k. If order p contains material k, then 0... pk =1, otherwise O pk =0;

[0042] ∏k: A 1-row, P-column 0-1 matrix representing the occurrence of material k in each order, ∏k = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1 ... 1k O 2k , ..., O Pk ); r k1k2 Demand correlation between material k1 and material k2; S k The number of storage locations required for material k; COI k COI coefficient of material k; xl hi The x-coordinate of the i-th storage location in lane h; yl hi The ordinate of the i-th storage location in lane h; l hi : The distance of the i-th storage location from the I / O in the lane h; d h1i，h2j The distance between any two storage locations in the warehouse; β1, β2: target weights, β1 + β2 = 1; U1: Correlation parameter with values ​​between [0, 1].

[0043] Decision variables in the objective function: X hik :0-1 decision variable, when material k is placed at position i in aisle h, X hik =1; y1: indicates that the pairwise distance between all materials with a correlation greater than or equal to U1 should be less than or equal to y1.

[0044] The model is then solved. The algorithm steps are as follows:

[0045] Step 1: Transform the demand correlation between any two materials into individual attributes of the materials, i.e., the sharing degree of the materials.

[0046] ① Calculate the Euclidean distance od between material k1 and material k2. k1k2 Using (1-r) k1k2 The value of ) is used as the Euclidean distance od between material k1 and material k2. k1k2 .

[0047] ② Calculate the degree of closeness of the relationship between materials using shared functions.

[0048] ; σ share The niche radius is a set value. α: constant, a set value.

[0049] Each material has a function value of 1 relative to itself, meaning that each material is identical to itself. When od k1k2 ≥σ share At that time, sh(od) odk1k2 )=0 indicates that the Euclidean distance between the two materials is greater than the set value σ. share When the correlation between two materials is 0, that is, when the demand correlation r between the two materials is 0... k1k2 Less than (1-σ) share When the two materials are considered to have no demand correlation, it is assumed that there is no demand correlation between them.

[0050] ③ Calculate the sharing degree.

[0051] k2 = k2 + 1. If k2 ≤ K (K is the number of material types), move to 1; otherwise, calculate the sharing degree c of material k1. k1 That is, the sum of the function values ​​shared by k1 and other materials. k1 This reflects the degree of demand correlation between material k1 and other materials.

[0052] c k1 The description is: .

[0053] Step 2: Group and cluster the materials according to their COI coefficient and sharing degree.

[0054] First, calculate the priority (prio(k)) of each material: ; Then, cluster analysis is performed based on the priority of the materials, prio(k), to divide the materials into q groups (q=1, 2, 3, ...Q and Q≤K).

[0055] ① Treat each material as a group, i.e., the number of material types K = the number of groups Q. Let k = K, calculate the priority difference between each group, and obtain a k*k matrix.

[0056] ② Merge the groups corresponding to the minimum values ​​of the off-diagonal elements in the k*k matrix into a single group. If multiple groups have a priority difference equal to the minimum value, randomly select one group to merge.

[0057] ③k=k-1. If K is odd, the classification ends when k=(K+1) / 2; if K is even, the classification ends when k=K / 2; otherwise, the average priority of the generated new group is calculated as the priority of the new group, and the priority difference between each group is calculated to obtain a k*k matrix, and then the process returns to the previous step.

[0058] Step 3: Allocate storage slots to each group.

[0059] First, calculate the average COI coefficient (ACOI) for all materials in each group. q and average correlation AR q The fitness of each population can be obtained through the formula. q Then, based on the fitness of each population, use the fit algorithm. q Assign storage locations to populations with higher fitness (fit(k)) so that they are closer to the entrance / exit I / O.

[0060] ; Decision variable: z hiq :0-1 decision variable, when group q occupies storage position i in lane h, z hiq =1.

[0061] Example 1: A steel industrial park (warehouse) covers an area of ​​133 mu (approximately 8.5 hectares), with 6 outdoor storage yards totaling approximately 35,000 square meters and 4 indoor storage warehouses totaling approximately 13,000 square meters. The total storage capacity is 300,000 tons, with an annual storage capacity of 150,000-200,000 tons of steel. The outdoor storage yards have 11 gantry cranes. See details... Figure 2 Site plan and Figure 3 Floor plan.

[0062] There are nine main categories of steel products stored, among which rebar, industrial wire rod, coiled rebar, and high-strength wire rod are suitable for outdoor storage. All rebar is stored in yards 1-5. Steel products are stacked individually, with each type stored separately. Each warehouse location holds steel products that entered the warehouse at the same time (batch). Therefore, the shipping principle follows the first-in, first-out (FIFO) principle, selecting the appropriate warehouse location for shipment. There is no internal transfer of goods within the yard or rearrangement of goods within the same warehouse location. There are two stacking rules for rebar: For warehouses with high inflow and outflow volumes, a "grid" pattern is used, accommodating up to 1000-1500 tons of rebar, with a stack height not exceeding 5 meters. The footprint of the "grid" pattern is determined by the length of the rebar; for example, a 12-meter rebar stack would have a footprint of 12 meters x 12 meters, with a 0.8-meter gap between stacks. For warehouses with relatively low inflow and outflow volumes, a "straight line" pattern is used, accommodating 200-400 tons of rebar per stack. For safety reasons, the stack height generally does not exceed 2 meters. The footprint of the "straight line" pattern is calculated by multiplying the width of the bottom stack by the length of the rebar, with a 0.8-meter gap between stacks. Each piece of rebar has an end face diameter of approximately 0.25 meters, and when laid flat, it can be calculated as 0.35 meters wide and 0.2 meters high. Generally, warehouses with high single-item throughput can only stack steel produced in the current month together. If monthly production of a single item is low, the steel stacked in the same warehouse generally does not exceed two months' worth.

[0063] Optimize the existing storage locations (stacking) in the 1-5 freight yards to reduce the movement of loading (outbound) vehicles in the freight yards and improve loading and unloading efficiency.

[0064] Example 2: Historical order data for steel products from the sampling yard; The order sampling time was selected as October 20**, and the total number of outbound order data was P=6212.

[0065] The COI flow coefficients for various specifications of steel in the sample are calculated, as shown in Table 1 below.

[0066] K: Material number, the 37 types of steel are numbered 1, 2, 3, ... 37 in sequence.

[0067] Sk: The required storage space for materials, uniformly represented by the area occupied by the materials.

[0068] COI: Flow coefficient It is a record of the occurrences of k within the sampling time. It is the number of times k appears during the sampling period. The COI of k is the number of times k appears during the sampling period divided by the area occupied by k.

[0069] Table 1. COI Flow Coefficients for Various Specifications of Steel in the Sample Calculate the correlation coefficients r1r2 between steel of various specifications.

[0070] r1r2: Correlation coefficient, This represents the number of times k1 and k2 appear together in a single order. r1r2 is the number of times k1 and k2 appear together divided by the number of times k1 appears plus the number of times k2 appears minus the number of times they appear together.

[0071] Finally, the sharing degree of various steel materials is calculated using the sharing function; the Euclidean distance od between material k1 and material k2 is also calculated. k1k2 =(1-r k1k2 σshare: niche radius, with no strong correlation between different steel specifications, set to 0.95, α set to 10.

[0072] sh(od odk1k2 ): Correlation function. And calculate ck based on it.

[0073] Materials are clustered into groups based on their COI coefficient and sharing degree. Each specification of steel is considered as a group, and the priority difference (prio(k)) between groups is calculated to obtain the corresponding matrix. Groups are then merged and grouped according to the priority difference in the matrix.

[0074] To mitigate the impact of demand relevance, we set β1=0.1 and β2=0.9. Each material is considered a group, and the number of material types K equals the number of groups (37). Let k=K, calculate the priority difference between groups, and obtain a 37th-order matrix. The groups corresponding to the minimum values ​​of the off-diagonal elements in the matrix are merged into a single group.

[0075] When k=k-1, proceed to the next merging step. The average priority of the new group generated after each merge is used as the priority of that new group. The priority difference between each group is recalculated to obtain a new k-1 order matrix for group merging. This process continues until k=(K+1) / 2=19, at which point the classification ends.

[0076] Storage allocation is based on material groups. The average COI (CoI) coefficient (ACOI) of all materials in each group is used. q and average correlation AR q The fitness of each population is obtained. q Based on population fitness q Allocate storage locations so that populations with higher fitness (fit(k)) are closer to the entrance / exit I / O.

[0077] Based on the aforementioned steps, storage locations closest to the entrance / exit should be prioritized for storing S. k *fit q For large groups, the order of arrangement within the group is not considered because the picking, shipping and loading methods are the same.

[0078] The steel stored in the yard includes nine main categories: rebar, industrial wire rod, coiled rebar, and high-strength wire, each with various grades. Their dimensions and lengths differ, resulting in varying floor space requirements for stacking and different stacking methods. To illustrate the model calculation results, storage location numbers are standardized, dividing each yard into two rows, A and B. Starting from the end of the yard closest to the main road, each meter is numbered using the following format: Yard Number - Row A or Row B - Start Number - End Number. For example, the goods occupying meters 1-9 of row A in yard 3 are 20*9 specification HRB400 rebar, with storage location numbers 3-A-1-9. The final storage location allocation for yard 3 can be shown in Table 2.

[0079] Table 2 Storage Location Allocation Table Secondly, the present invention also provides a warehouse storage location allocation device, such as... Figure 4 As shown, it includes: The acquisition module 201 is used to acquire historical order data, calculate the turnover rate coefficient of each material, and the demand correlation between any two materials.

[0080] The judgment module 202 is used to determine the two materials as a material pair that needs to be subject to distance constraints only when the demand correlation between the two materials is higher than a first preset threshold and the turnover rate coefficients of both are lower than a second preset threshold.

[0081] The construction module 203 is used to construct a storage location allocation optimization model based on the location decision variables, turnover rate coefficients and distance decision variables of the materials, with the goal of minimizing the total travel distance of the loading equipment. For each pair of materials with distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable; the location decision variable is used to determine the storage location of the material.

[0082] The optimization module 204 is used to solve the optimization model and obtain the storage location allocation scheme for each material.

[0083] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The steps of a warehouse storage location allocation method are provided.

[0084] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for various operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above-mentioned functions. Figure 1 The steps of a warehouse storage location allocation method are provided.

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A warehouse storage location allocation method, characterized in that, The method includes: Obtain historical order data, calculate the inventory turnover rate coefficient for each material, and the demand correlation between any two materials; The two materials are identified as a pair of materials that require distance constraints only when the demand correlation between the two materials is higher than the first preset threshold and the turnover coefficients of both are lower than the second preset threshold. A storage location allocation optimization model is constructed based on the location decision variables, turnover rate coefficient, and distance decision variables of the materials. The objective is to minimize the total travel distance of the loading equipment. For each pair of materials with distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable. The location decision variables are used to determine the storage location of the materials. The optimization model is solved to obtain the storage location allocation scheme for each material.

2. The method according to claim 1, characterized in that, The construction of the storage location allocation optimization model based on the location decision variables of goods, the corresponding turnover rate coefficients, and the distance decision variables includes: The material allocation location is determined based on location decision variables. Using the turnover rate coefficient as a weight, the distance from the allocated storage location to the entrance / exit is converted into a travel cost. A storage location allocation optimization model is constructed based on the weighted sum of the travel cost and distance decision variables, with the following formula: ； in, The total number of lanes, I The total number of storage locations in each alleyway. This represents the total number of material types. Let be the distance from the i-th storage location in lane h to the entrance / exit. The location decision variable is either 0 or 1, taking the value of 1 when material k is assigned to the i-th storage location in aisle h, and 0 otherwise. Let k be the turnover rate coefficient of material k; For distance decision variables, it represents the maximum permissible distance between all material pairs that are determined to require distance constraints; and For the preset weighting coefficients, satisfy ; For each pair of materials that requires distance constraints ( If materials Assigned to the alley The When there is one storage location, the location decision variable =1, material Assigned to the alley The When there is one storage location, the location decision variable =1, then the distance between these two storage locations The following constraints must be met: ； in, Indicates the location of the goods ( ) and cargo location ( The distance between them.

3. The method according to claim 1, characterized in that, Before constructing the optimization model, cluster analysis was used to reduce the solution size of the model, including: The demand correlation between any two materials is transformed into individual attributes, and the sharing degree of each material is calculated. The sharing degree is used to characterize the degree of demand correlation between a material and other materials. Based on the COI coefficient and sharing degree of each material, the priority of each material is calculated, and the materials are clustered and grouped according to the priority, merging materials with similar priorities into the same group. A simplified storage location allocation optimization model is constructed and solved using a group as the unit to obtain the storage location allocation scheme for each group. Then, the storage location of each material is determined based on the group allocation results.

4. The method according to claim 3, characterized in that, The process of converting the demand correlation between any two materials into individual attributes and calculating the sharing degree of each material includes: The sharing degree of each material is calculated using a sharing function. The calculation formula is as follows: ； in, For materials The degree of sharing This represents the distance between the two materials in the demand correlation space. This represents the total number of material types. Shared functions Defined as: ； The pre-defined niche radius, These are preset shape parameters used to control the rate at which the correlation between two materials decreases with distance. This sharing function only applies when the correlation distance between the two materials is less than [a certain value]. When the time is positive, it is used to filter out strongly correlated material pairs that have practical significance, and quantify the degree of correlation as the sharing degree of each material.

5. The method according to claim 3, characterized in that, The step of calculating the priority of each material based on its COI coefficient and sharing degree, and clustering the materials based on their priority to merge materials with similar priorities into the same group includes: The priority of each material is calculated by comprehensively considering the need for materials with high turnover rates to be located near entrances and exits, and the need for materials to be stored together due to strong correlation with other materials. The formula is as follows: ； in, and Preset weighting coefficients are used to balance the importance of turnover rate and sharing factor in clustering. Let k be the turnover rate coefficient of material k. The degree of sharing of material k; Materials are hierarchically clustered based on priority, and materials with similar priorities are merged into the same group as the basic unit for subsequent storage allocation.

6. The method according to claim 3, characterized in that, After merging the populations, the fitness of each population is calculated to determine the priority of each population relative to the entry and exit points. The formula is as follows: ； in, Let be the average COI coefficient of all materials in group q. The average demand relevance of all materials in group q. and Preset weighting coefficients are used to balance the impact of the average turnover rate of the population and internal correlation on the allocation of storage spaces; Determine the fitness level of different ethnic groups. The higher the fitness level of an ethnic group, the closer its overall storage location is to the entrance and exit.

7. The method according to claim 1, characterized in that, The turnover rate coefficient is used to measure the ratio of the frequency of material outflow to the storage area occupied, and the formula is: ； The demand relevance is used to measure the probability that two materials are simultaneously demanded in the same order, and the formula is: ； in, Let k be a 0-1 row vector representing the occurrences of material k in each order. This represents the total number of times material k appears within the sampling period, used to characterize its turnover frequency; This represents the storage area or number of storage locations required for material k, used to characterize its space occupancy. is the turnover rate coefficient of material k. The larger the value of this coefficient, the higher the frequency of material k leaving the warehouse per unit storage area, and it should be preferentially allocated to storage locations closer to the entrance and exit. This indicates the number of times materials k1 and k2 appear simultaneously in the same order; The demand correlation between materials k1 and k2 is calculated by subtracting the number of times they appear together from the sum of their individual occurrences in the formula. This is used to eliminate interference from the high frequency of a single material and to reflect the true strength of the relationship between the two. The larger the demand correlation value, the more likely the two materials are to be shipped out together.

8. A warehouse storage location allocation device, characterized in that, The device includes: The acquisition module is used to acquire historical order data, calculate the inventory turnover rate coefficient of each material, and the demand correlation between any two materials; The judgment module is used to determine the two materials as a material pair that needs to be subject to distance constraints only when the demand correlation between the two materials is higher than a first preset threshold and the turnover rate coefficients of both are lower than a second preset threshold. The module is used to construct a storage location allocation optimization model based on the location decision variables, turnover rate coefficient, and distance decision variables of the materials, with the objective of minimizing the total travel distance of the loading equipment. For each pair of materials with distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable; the location decision variable is used to determine the storage location of the material. The optimization module is used to solve the optimization model and obtain the storage location allocation scheme for each material.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 7.

10. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 7.

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