A yard stacking position optimization method and system for mixed loading of steel

Abstract

The present application relates to a kind of steel mixed loading demand-oriented yard stacking position optimization method, comprising the following steps: step S1: based on the order data of logistics park in a certain period of time, the frequency of the delivery of steel grade is analyzed, and the stacking position number of logistics park is combined to determine the stacking position demand number and stacking mode of class I steel grade and class II steel grade;Step S2: based on the order data of iron and steel logistics park in a certain period of time, the mixed loading combination and mixed loading frequency between class I steel grade are analyzed;Step S3: construct mixed stacking optimization model, including the mathematical model of first stage and second stage, the mathematical model of first stage will allow mixed stacking class I steel to be combined according to the rule, and the mathematical model of second stage will be combined according to the rule The aggregation of first stage is distributed to specific yard with class II steel;Step S4: all steel is distributed to the specific stacking position of specific yard, and according to the delivery demand of each steel from high to low, the steel with large demand is placed close to the position of goods taking.The present application effectively improves logistics efficiency.

Description

Technical Field

[0001] This invention relates to the field of steel logistics park layout optimization, specifically to a method and system for optimizing storage yard locations to meet the needs of mixed steel loading. Background Technology

[0002] The significant increase in my country's steel trading volume has placed higher demands on the efficiency of steel logistics. However, due to the lag in the current design of steel mill warehouse management, it is difficult to meet the high throughput requirements of steel logistics. For orders containing multiple steel products, the current logistics scheduling mechanism requires customers to retrieve the target steel from different storage yards, resulting in a large amount of unexpected waiting time and significantly reducing logistics efficiency. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a yard stacking optimization method for steel mixed loading needs, which allocates stacking spaces to steel types with different outbound frequencies and mixed loading needs, so as to reduce the number of times vehicles need to move around to pick up goods in a limited space and effectively improve logistics efficiency.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for optimizing stockpile locations in a storage yard to meet the needs of mixed steel loading includes the following steps:

[0006] Step S1: Based on the order data of the steel logistics park within a certain period of time, analyze the outbound frequency of steel types, and combine the number of stackable positions in the logistics park to determine the number of stackable positions required for Class I steel types and Class II steel types and the stacking method.

[0007] Step S2: Based on order data from the steel logistics park over a certain period, analyze the mixed loading combinations and frequency of Class I steel grades, and use support Q... ij To quantify and characterize;

[0008] Step S3: Construct a hybrid stacking optimization model, including a first-stage and a second-stage mathematical model. The first-stage mathematical model will allow hybrid Class I steel to be clustered and combined according to rules. The second-stage mathematical model will allocate the clustered combinations obtained in the first stage and Class II steel to specific stockyards.

[0009] Step S4: Allocate all steel grades to specific stacking locations in a specific storage yard, sorting them from highest to lowest according to the outbound demand for each steel grade, and placing the steel grades with the highest demand near the picking location.

[0010] Furthermore, if the outbound demand for a steel grade is less than a tons, it is classified as Class I steel grade, and a straight-line stacking method is adopted, and it is allowed to be mixed with other steel grades; if the outbound demand for a steel grade is greater than or equal to a tons, it is classified as Class II steel grade, and the prescribed stacking method is adopted.

[0011] Furthermore, the support Q ij = Frequency of mixed loading of steel type i and steel type j / Total number of orders; Q ij The larger the value, the stronger the correlation between different steel grades in the mixed loading.

[0012] Furthermore, the mathematical model for the first stage is as follows:

[0013] 1) Decision variables:

[0014]

[0015]

[0016] 2) Objective function and constraints:

[0017]

[0018] Among them, i d j d This represents two distinct types of steel, and i d j d ∈I d I d H represents the set of steel grades that allow mixed stacking; H is the set of clustered combinations, and Q is the set of steel grades that allow mixed stacking. idjd For steel i d With j d The degree of correlation between mixed packaging;

[0019] The objective function (1) reduces the number of times vehicles need to pick up goods at different locations by maximizing the mixed loading correlation of the same cluster combination, and simplifies the modeling difficulty of the second stage.

[0020]

[0021] Constraint (2) defines that steel of the same type in class I can only belong to one cluster combination.

[0022]

[0023] Where, γ max C represents the maximum stacking size in a linear stacking configuration. i Steel grade i pending inventory quantity;

[0024] Constraint (3) defines that the total number of clustered steel inventory shall not exceed the maximum storage capacity of the straight-line layout.

[0025]

[0026] Where 'a' represents the maximum permissible length difference of steel grades within the same cluster combination;

[0027] Constraint (4) defines that the length difference of steel grades within the same combination should be within a certain range to ensure the structural stability of mixed stacking.

[0028] Furthermore, the mathematical model for the second stage is as follows:

[0029] 1) Decision variables:

[0030]

[0031] 2) Objective function and constraints:

[0032]

[0033] Where V represents the collection of storage yards within the logistics park, N represents the cluster combinations to be laid out and Class II steel grades; S nm The degree of correlation between mixed loading of steel grades m and n;

[0034] Objective function (5) aims to increase the throughput of the logistics park by maximizing the inter-packing correlation of steel types in the same yard, thereby reducing the number of times vehicles need to move between yards to pick up goods.

[0035]

[0036] Among them, I W A collection of steel grades that can only be stacked individually; λ max C represents the maximum stacking size in a tic-tac-toe stacking configuration. n For the quantity of goods to be stored for combination or steel grade n; y nv The number of stacks occupied by combination or steel type n in stockyard v;

[0037] Constraint (6) Definition: If steel n is a type II steel, that is, belongs to set H, its stacking position requirement is determined by its outbound demand and the maximum load of the grid stacking method.

[0038]

[0039] Constraint (7) defines that each cluster combination can only be assigned to a certain stack location in a storage yard;

[0040]

[0041] Constraint (8) defines that steel grades with a stacking demand greater than 1 are distributed across multiple stockyards to balance the workload of each stockyard.

[0042]

[0043] Among them, D V The number of stackable numbers within yard v

[0044] Constraint (9) defines that the number of stacks of steel allocated to a stockpile is no greater than the number of stacks that can be placed in the stockpile.

[0045]

[0046]

[0047] Constraint (10) defines the degree of inter-packing correlation between clustered combinations and Class II steel grades, and constraint (11) defines the degree of inter-packing correlation between clustered combinations.

[0048] A yard stacking optimization system for steel mixed loading needs includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the yard stacking optimization method for steel mixed loading needs as described above.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] 1. This invention combines the outbound demand of each steel type with the actual stacking method and maximum load of each stacking location to determine the required number of stacking locations for each steel type.

[0051] 2. This invention addresses orders requiring mixed loading and pickup by proposing an optimization model that integrates steel grade mixing and specific yard stacking locations. It comprehensively considers the number of times mixed loading vehicles relocate under the pickup order and the operational intensity of cranes in each cross-regional area, effectively improving the efficiency of mixed loading and pickup in logistics. Attached Figure Description

[0052] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] Please refer to Figure 1 This invention provides a method for optimizing stockpile locations in a storage yard to meet the needs of mixed steel loading, comprising the following steps:

[0055] Step 1: Obtain order data from the steel logistics park, analyze the outbound frequency of steel types, and combine it with the number of stackable units in the logistics park. If the outbound demand for a steel type is less than a tons (referred to as Class I steel type), a straight-line stacking method is adopted, and it is allowed to be mixed with other steel types. Proceed to Step 2. If the outbound demand for a steel type is greater than or equal to a tons (referred to as Class II steel type), a grid-shaped single steel type stacking method is adopted. Proceed to Step 4.

[0056] In this embodiment, steel grades with high outbound volumes are generally stacked using a grid-like method. This method allows only one type of steel to be stacked per stack, with a maximum capacity of approximately 1500 tons. Steel grades with low outbound volumes are generally stacked using a straight-line method, which allows for mixed stacking, but the maximum capacity is approximately 400 tons. Based on one month's order data from this logistics park (1806 orders), the 131 types of steel awaiting outbound shipment are divided into two categories according to outbound demand: Category I steel (87 types with low outbound volumes, allowing mixed stacking) and Category II steel (44 types with high outbound volumes, requiring individual stacking).

[0057] Step 2: Based on the order data of the steel logistics park during this period, analyze the mixed loading combination and frequency of Class I steel, and use "support" to quantify and characterize it.

[0058] Step 3: In the first stage, the 87 types of steel that are allowed to be mixed are stacked together, with a combined load of 400 tons and a length difference of no more than 3000 mm between the steel pieces to ensure structural stability of the stack. The mathematical model for the first stage is solved using the GUROBI solver to obtain the optimal combination solution. Type I steel is divided into 46 groups, some examples of which are shown in Table 1. After grouping, step 4 is executed.

[0059] Table 1

[0060]

[0061]

[0062] Step 4: Allocate the clustered combinations and Class II steel grades from the first phase to specific storage yards. The goal is to maximize the inter-yard loading correlation of steel grades within the same storage yard, minimizing the number of times vehicles need to move between yards to pick up goods. The 46 clustered combinations awaiting allocation each require 1 storage location. For Class II steel grades, I... 127 The required number of stack positions is 3, I6, I 25 I 42 The required number of stacking positions is 2, while other steel types only require one stacking position. In the example, the logistics park has five storage yards, each with one pickup location. After solving the mathematical model of the second stage using the GUROBI solver, the storage yard allocation results are shown in Table 2. Then, step 5 is executed.

[0063] Table 2

[0064]

[0065]

[0066] Step 5: Assign all steel grades to specific stacking locations within a designated storage yard. Using the storage yard as a unit, sort the steel grades according to their outbound demand from highest to lowest, e.g., storage yard V1: {I 127 ,I6,I42 I 13 I 12 I 22 I 17 I 40 I 16 I2, I9, I 60 ,h1,h 38 h 23 h 43 h 12 h 42 h 41}. Steel grade I with high demand 127 Place it in the position closest to the pickup location to reduce the travel distance of the crane to pick up the goods, and further reduce the waiting time of the pickup vehicle.

[0067] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be included in the scope of the present invention.

Claims

1. A method for optimizing a stacking position of a yard for mixed loading of steel materials, characterized in that, The method comprises the following steps: Step S1: based on the order data of the steel logistics park within a certain period of time, analyze the frequency of the steel grade, and combine the available stacking position number of the logistics park to determine the stacking position demand number and stacking mode of the class I steel grade and the class II steel grade; Step S2: Based on the order data of the steel logistics park within a certain period of time, the mixed loading combination and mixed loading frequency among the I-type steel grades are analyzed, and the support Q ij is used to quantify the representation; Step S3: constructing a mixed stacking optimization model, including the mathematical model of the first stage and the second stage, the mathematical model of the first stage combines the class I steel grade allowed to be mixed and stacked according to the rules to form a group, and the mathematical model of the second stage distributes the group obtained in the first stage and the class II steel grade to a specific yard; Step S4: distributing all steel grades to specific stacking positions in the specific yard, and sequentially sorting the steel grades according to the descending order of the delivery demand, and placing the steel grades with large demand close to the delivery position; The mathematical model of the first stage is specifically: 1) decision variable: 2) objective function and constraint condition: wherein, i d , j d represent two types of steel materials which are different from each other, and i d , j d ∈I d , I d is a set of steel types allowed to be mixed and stacked; H is a set of aggregated combinations, is a mixed loading correlation degree of steel i d and j d . The objective function (1) reduces the number of vehicle transfer delivery by maximizing the mixed loading correlation degree of the same group, and simplifies the modeling difficulty of the second stage; The constraint condition (2) defines that the same class of steel in the class I can only belong to one group; wherein γ max is the maximum stacking amount of the one-letter stacking mode; C i steel grade i to be stored in the warehouse amount; The constraint condition (3) defines that the total number of steel in the group is not greater than the maximum capacity of the one-dimensional type; Wherein a is the maximum allowed length difference of the steel in the same group; The constraint condition (4) defines that the length difference of the steel in the same group should be within a certain range to ensure the structural stability of the mixed stacking; The mathematical model of the second stage is specifically: 1) decision variable: 2) objective function and constraint condition: Wherein, V is the yard collection in the logistics park, N is the combination of the layout of the group and the II steel type; S nm The mixed loading correlation degree between steel types m and n; The objective function (5) reduces the number of vehicle transfer delivery by maximizing the mixed loading correlation degree of the same yard, and improves the logistics park throughput where I W is the set of steel grades that can only be stacked individually; λ max is the maximum stacking amount for the cross-shaped stacking method; C n is the amount of the combination or steel grade n to be stored; y nv is the number of stacks occupied by the combination or steel grade n in the yard v; The constraint condition (6) defines that if the steel n is the class II steel, it belongs to the set H, and the stacking position demand number is determined by the delivery demand and the maximum load of the cross-shaped stacking method; The constraint condition (7) defines that each group can only be distributed to a stacking position in a yard; The constraint condition (8) defines that the steel with a stacking position demand number greater than 1 is distributed in multiple yards to balance the work load of each yard; wherein D V is the number of stackable positions within the yard v The constraint condition (9) defines that the number of stacking positions allocated to a yard is not greater than the number of available stacking positions in the yard The constraint condition (10) defines the mixed loading correlation degree of the group and the class II steel, and the constraint condition (11) defines the mixed loading correlation degree between the groups.

2. The method according to claim 1, wherein, If the delivery demand of the steel is less than a ton, it is the class I steel, adopts the one-dimensional stacking mode, and is allowed to be mixed and stacked with the remaining steels; if the delivery demand of the steel is greater than or equal to a ton, it is the class II steel, and adopts the specified stacking mode.

3. The method of claim 1, wherein, The support degree Q ij = the mixed loading frequency of steel grade i and steel grade j / total number of orders; Q ij The greater, the stronger the mixed loading correlation between steel grades.

4. A system for implementing the method for optimizing the stacking position of the yard according to any one of claims 1-3, characterized in that, The computer program product comprises a processor, a memory, and a computer program stored in the memory, and when the processor executes the computer program, the steps in the yard stacking optimization method for the mixed loading demand of the steel are specifically executed.

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