An optimization method for prefabricated molds of assembled residential wall panels

Abstract

The present invention discloses a method for optimizing prefabricated molds for prefabricated residential wall panels, comprising the following steps: S1, scenario description to determine information about the prefabricated wall panel production modules for prefabricated residential buildings; S2, mold combination calculation, using a recursive function to calculate all mold combinations; S3, establishing an integer linear programming model; and S4, solving the integer linear programming model. This method generates the wall panel prefabricated mold combination optimization results. With the goal of minimizing mold platform batches, the present invention establishes a mold combination optimization model. The optimal mold combination production batch plan can be calculated using a mathematical programming method. A recursive method can be used to efficiently exhaustively enumerate mold combinations, and the wall panel direction can be adaptively adjusted to further save mold platform space, ultimately improving wall panel prefabrication production efficiency.

Description

Technical Field

[0001] The present invention relates to the field of optimization of prefabricated molds for assembled residential wall panels. More specifically, the present invention relates to a method for optimizing prefabricated molds for assembled residential wall panels. Background Art

[0002] Prefabricated housing shifts much of the on-site work involved in traditional construction methods to the factory, where components are processed and manufactured before being transported to the site for assembly. This eco-friendly approach reduces costs and improves efficiency. During the prefabrication process, molds are arranged on a formwork platform. Mold sizes and quantities vary, so the combination of molds from different batches can affect the efficiency of the formwork platform. Improper combinations can increase the number of mold batches and labor hours, leading to decreased production efficiency. Therefore, optimizing the combination of molds from different batches is crucial to the efficiency of component prefabrication. Summary of the Invention

[0003] In order to achieve these purposes and other advantages according to the present invention, a preferred embodiment of the present invention provides a method for optimizing a prefabricated mold for prefabricated wall panels of an assembled residential building, comprising the following steps:

[0004] S1. Scene Description

[0005] Determine information on prefabricated wall panel production modules for prefabricated housing;

[0006] The prefabricated wall panel production module includes a mold platform and a mold. A plurality of the molds are spaced apart and arranged in a straight line on the mold platform. The mold platform has a length a and a width b. The left side of all the mold platforms as a whole is at a distance of not less than d1 from the edge of the mold. The right side of the mold platform as a whole is at a distance of not less than d3 from the edge of the mold. The distance between the upper and lower sides of the mold platform as a whole and the mold is not less than d4. The distance between two adjacent molds is not less than d2.

[0007] Wall panels are all rectangular. The length, height, and quantity of a batch of wall panels required for production are represented by arrays e, f, and g, respectively. e = [e1 e2… e i …], f=[f1 f2 … f i …], g=[g1 g2 … g i …], where e i and f i represents the length and height of the i-th wall panel, and f i All satisfy f i ≤b-2d4,g i represents the number of type i wall panels;

[0008] S2. Calculation of mold combination

[0009] First, for alli <e i For the wall panel <b-2d4>, swap the e and f in its dimensions. Then, merge the wall panels with the same length and height of the wall panel, and add up the number of merged wall panels together to form the final e, f, and g; i and f i For the wall panels with the same length and height of the wall panel, merge them, add up the number of merged wall panels together, and form the final e, f, and g;

[0010] Secondly, calculate all the combination pairs of the molds through a recursive function, that is, all possible combination pairs of different molds placed on the mold table and meeting the spacing requirements between the mold table and the molds, and represent them through the matrix Z. z mn represents the number of the m-th wall panel mold in the n-th combination;

[0011] S3. Establish an integer linear programming model

[0012] Each mold combination pair corresponds to a precast batch. The decision variable x is the number of times each combination pair in Z is used, x = [x1 x2... x i …], where x i is the number of times the i-th cutting combination in Z is used; the constraint condition is that the number of each wall panel corresponding to x is equal to the production demand quantity of its corresponding wall panel, and establish an integer linear programming model;

[0013] S4. Solve the integer linear programming model

[0014] Use the genetic algorithm to solve the integer linear programming model, and the optimal solution x of the integer linear programming can be obtained, that is, the optimization result of the wall panel precast mold combination.

[0015] Preferably, in the above S2, calculating all the combination pairs of the molds through a recursive function specifically includes the following steps:

[0016] S1. Calculate the initial combination matrix Z0 = [0 0... 0], and the length of the matrix is len(e), where len(e) is the number of elements in e;

[0017] S2. Establish a recursive function Z, and the function input parameters are a, d1, d2, d3, e. The function calculation steps are as follows:

[0018] S21. Calculate (a - d1 - d3 + d2) / (e i + d2), and round down to get h = [h1 h2... h i …], where h i = [(a - d1 - d3 + d2) / (e i + d2)], and "[]" represents rounding down;

[0019] S22. Determine whether len(e) is 1. If it is 1, then Z = [0 1…h1] and output Z. If not, proceed to the next step.

[0020] S23, remove the first element of array e and recursively call function Z, Z t =Z(a d1 d2 d3 e);

[0021] S24. Set a variable k. The initial value of k is 0. Each time the loop is repeated, k increases by 1. When k is greater than h1, the loop is exited. In each loop, the matrix Z is established. k , matrix Z k Z t Add a row above, all elements in this row are k;

[0022] S25. Combine matrices Z0, Z1, ... into Z;

[0023] S26. Calculate the length L and the number of molds c of each column combination in Z, delete the columns where L + d2 (c-1) + d1 + d3 > a, and output Z.

[0024] S3. Finally, the combined matrix Z is obtained.

[0025] Preferably, in S3, the integer linear programming model is as follows:

[0026]

[0027] Preferably, in S4, the parameter configuration of the genetic algorithm is as follows: constraint_eq represents the objective function, n_dim represents the dimension of the objective function, that is, the dimension of the decision variable x, precision represents the accuracy of the variable value, and since all elements in x are non-negative integers, precision is 1; size_pop is the population size, which is 200, max_iter is the maximum number of iterations, which is 1200, prob_mut is the mutation probability, which is 0.02, and lb is the minimum value of each independent variable, which is 0;

[0028] The constraints are expressed as

[0029] The present invention includes at least the following beneficial effects: the present invention takes the minimum mold platform batch as the goal, establishes a mold matching combination optimization model, and can calculate the optimal mold combination production batch plan through mathematical programming methods. The recursive method can achieve efficient exhaustive enumeration of mold combinations, and can adaptively adjust the direction of wall panels to further save mold platform space, ultimately achieving improved wall panel prefabrication production efficiency.

[0030] Other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the layout of the prefabricated wall panel production module in the present invention.

[0032] Figure 2 Schematic diagram of step S2 in the present invention. DETAILED DESCRIPTION

[0033] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0034] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0035] Those skilled in the art should understand that, in the disclosure of the present invention, the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the above terms should not be understood as limiting the present invention.

[0036] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0037] like Figure 1 As shown, a preferred embodiment of the present invention provides a method for optimizing a prefabricated mold for assembled residential wall panels, comprising the following steps:

[0038] S1. Scene Description

[0039] Determine information on prefabricated wall panel production modules for prefabricated housing;

[0040] Among them, the precast wall panel production module includes a casting bed and molds. A plurality of the molds are arranged in a row at intervals, namely Mold 1, Mold 2, Mold 3... and are located on the casting bed in a straight line. The length of the casting bed is a, and the width is b. As a whole casting bed, the distance between its left side and the edge of the mold is not less than d1, the distance between the right side of the whole casting bed and the edge of the mold is not less than d3, the distances between the upper and lower sides of the whole casting bed and the mold are not less than d4, and the distance between adjacent two molds is not less than d2;

[0041] The wall panels are all rectangular. The lengths, heights and quantities required for the production of a batch of wall panels are represented by arrays e, f, g respectively, e = [e1 e2… e i …], f = [f1 f2 … f i …], g = [g1 g2 … g i …], where, e i and f i represent the length and height of the i-th type of wall panel, and f i all satisfy f i ≤b - 2d4, g i represents the quantity of the i-th type of wall panel;

[0042] S2. Calculation of mold matching combinations

[0043] First, for all wall panels that satisfy f i <e i <b - 2d4, swap e i and f i in their dimensions. Then, merge the wall panels with the same length and height, and add up the quantities of the merged wall panels and put them together to form the final e, f, g;

[0044] Secondly, calculate all the matching combinations of the molds through a recursive function, that is, all possible matching combinations of different molds placed on the casting bed and meeting the spacing requirements between the casting bed and the molds, and represent them by a matrix Z, z mn represents the number of the m-th wall panel mold in the n-th combination;

[0045] S3. Establish an integer linear programming model

[0046] Each mold matching combination corresponds to a precast batch. The decision variable x is the number of times each column matching combination in Z is used, x = [x1 x2…x i …], where, x i is the number of times the i-th column cutting combination in Z is used; the constraint condition is that the quantities of the respective wall panels corresponding to x are equal to the production requirement quantities of their corresponding wall panels, and an integer linear programming model is established;

[0047] S4. Solve the integer linear programming model

[0048] By solving the integer linear programming model based on genetic algorithm, the optimal solution x of the integer linear programming can be obtained, which is the optimization result of the wall panel prefabrication mold combination.

[0049] In another technical solution, in S2, all the matching combinations of the molds are calculated by a recursive function, such as Figure 2 As shown, the specific steps include:

[0050] S1. Calculate the initial combination matrix Z0 = [0 0…0], where the matrix length is len(e), where len(e) is the number of elements in e.

[0051] S2. Create a recursive function Z with the following input parameters: a, d1, d2, d3, and e. The function calculation steps are as follows:

[0052] S21, calculate (a-d1-d3+d2) / (e i +d2), and round down to get h=[h1 h2…h i …], where h i =[(a-d1-d3+d2) / (e i +d2)], “[]” means round down;

[0053] S22. Determine whether len(e) is 1. If it is 1, then Z = [0 1…h1] and output Z. If not, proceed to the next step.

[0054] S23, remove the first element of array e and recursively call function Z, Z t =Z(a d1 d2 d3 e);

[0055] S24. Set a variable k. The initial value of k is 0. Each time the loop is repeated, k increases by 1. When k is greater than h1, the loop is exited. In each loop, the matrix Z is established. k , matrix Z k Z t Add a row above, all elements in this row are k;

[0056] S25. Combine matrices Z0, Z1, ... into Z;

[0057] S26. Calculate the length L and the number of molds c of each column combination in Z, delete the columns where L + d2 (c-1) + d1 + d3 > a, and output Z.

[0058] S3. Finally, the combined matrix Z is obtained.

[0059] In another technical solution, in S3, the integer linear programming model is as follows:

[0060]

[0061]

[0062] Preferably, in S4, the parameter configuration of the genetic algorithm is as follows: constraint_eq represents the objective function, n_dim represents the dimension of the objective function, that is, the dimension of the decision variable x, precision represents the precision of the variable value. Since the elements in x are all non-negative integers, precision takes 1; size_pop is the population size, taking 200, max_iter is the maximum number of iterations, taking 1200, prob_mut is the mutation probability, taking 0.02, lb is the minimum value of each independent variable, taking 0;

[0063] The constraint condition is expressed as

[0064] Implementation cases

[0065] 1) Case scenario

[0066] There is a batch of wall panels to be precast in a precast component factory of a prefabricated residential project. The requirements for the wall panels are as follows: Wall panel 1 has a length and height of 7660 mm × 3180 mm, with a demand quantity of 2 pieces; Wall panel 2 has a length and height of 3360 mm × 3180 mm, with a demand quantity of 7 pieces; Wall panel 3 has a length and height of 5520 mm × 3180 mm, with a demand quantity of 4 pieces. The length of the precast table is 12000 mm, the width is 4000 mm. The distance between the left side of the table and the mold is not less than 400 mm, the distance between the right side of the table and the mold is not less than 300 mm, the distances between the upper and lower sides of the table and the mold are not less than 300 mm, and the distance between the molds is not less than 500 mm.

[0067] According to the scenario, it can be known that a = 12000, b = 4000, d1 = 400, d2 = 500, d3 = 300, d4 = 300, e

[0068] = [7660 3360 5520], f = [3180 3180 3180], g = [2 7 4], where f i all satisfy f i ≤ b - 2d4.

[0069] 2) Calculation of mold combination

[0070] First, wall panel 2 satisfies f i < e2 < b - 2d4, so e2 and f2 are swapped, and e, f, g are updated to e

[0071] = [7660 3180 5520], f = [3180 3360 3180], g = [2 7 4]. Calculate the mold combination according to the following steps:

[0072] S1. Calculate (a-d1-d3+d2) / (e i + d2) and round down to get h = [1 3 1].

[0073] S2, len(e) = 3, remove the first element of array e, then e = [31805520], recursively call function Z, Z t =Z(ad1 d2 d3 e), the recursive call steps are as follows:

[0074] S21, calculate (a-d1-d3+d2) / (e i +d2) and round down to get h = [3 1].

[0075] S22, len(e) = 2, remove the first element of array e, then e =

[5520] , recursively call function Z, Z t =Z(ad1d2d3e), the recursive call steps are as follows:

[0076] S221, calculate (a-d1-d3+d2) / (e i +d2) and round down to get h = [1].

[0077] S222, len(e) = 1, then Z = [0 1], output Z = [0 1].

[0078] S23, h1=3, set a variable k, k takes the initial value of 0, each loop k increases by 1 until k=h1, the first loop k=0, establish the matrix Z0, The second cycle k=1, build matrix Z1, The third cycle k=2, build matrix Z2, The fourth cycle k=3, build matrix Z3, Then exit the loop and merge Z0, Z1, Z2, and Z3 into

[0079] S24. Calculate the length L and the number of molds c of each column combination in Z, delete the columns where L+d2(c-1)+d1+d3>a, and get Output Z.

[0080] S3, h1=1, then create 2 matrices, Merge Z0 and Z1 into

[0081] S4. Delete the columns where L+d2(c-1)+d1+d3>a, and delete the columns with all zeros.

[0082] get Output Z.

[0083] 3) Establish an integer linear programming model

[0084] Then the decision variable is x = [x1 x2 … x6], and the integer linear programming model is as follows:

[0085]

[0086]

[0087] 4) Solve the integer linear programming model

[0088] Genetic algorithm is used to solve the integer linear programming model. The genetic algorithm parameters are set as follows:

[0089] Table 1

[0090] Parameter name Configuration Values n_dim 6 size_pop 200 max_iter 1200 prob_mut 0.02 lb 0 precision (1,1,1,1,1,1)

[0091] The optimal solution is x = [0 0 4 0 1 2], and the mold optimization combination scheme is as follows:

[0092] Table 2

[0093]

[0094] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

[0095] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for optimizing prefabricated molds for assembled residential wall panels, characterized in that: The following steps are involved: S1. Scene Description Determine information on prefabricated wall panel production modules for prefabricated housing; The prefabricated wall panel production module includes a mold platform and a mold. A plurality of the molds are spaced apart and arranged in a straight line on the mold platform. The mold platform has a length a and a width b. The left side of all the mold platforms as a whole is at a distance of not less than d1 from the edge of the mold. The right side of the mold platform as a whole is at a distance of not less than d3 from the edge of the mold. The distance between the upper and lower sides of the mold platform as a whole and the mold is not less than d4. The distance between two adjacent molds is not less than d2. The wall panels are all rectangular. The length, height, and quantity of a batch of wall panels required for production are represented by arrays e, f, and g, respectively. e = [e1 e2 …e i …], f=[f1 f2 … f i …], g=[g1 g2 … g i …], where e i and f i represents the length and height of the i-th wall panel, and f i All satisfy f i ≤b-2d4,g i represents the number of type i wall panels; S2. Calculation of mold combination First, for all wall panels that satisfy f i <e i <b - 2d4, swap e i and f i in their dimensions. Then, merge the wall panels with the same length and height, add up the quantities of the merged wall panels, and together form the final e, f, g; Secondly, all possible combinations of molds are calculated through recursive functions, that is, all possible combinations of different molds placed on the mold platform that meet the requirements of the distance between the mold platform and the mold, and are represented by matrix Z. z mn Indicates the number of m-th wall panel molds in the n-th combination; S3. Establish an integer linear programming model Each mold combination corresponds to a prefabrication batch, and the decision variable x is the number of times each combination in Z is used, x = [x1x2…x i …], where x i is the number of times the truncation combination of column i in Z is used; the constraint condition is that the number of wall panels corresponding to x is equal to the production demand of the corresponding wall panels, and an integer linear programming model is established; S4. Solve integer linear programming models By solving the integer linear programming model based on genetic algorithm, the optimal solution x of the integer linear programming can be obtained, which is the optimization result of the wall panel prefabrication mold combination.

2. The method for optimizing the prefabricated mold for prefabricated residential wall panels according to claim 1, characterized in that: In S2, all combinations of molds are calculated through a recursive function, which specifically includes the following steps: S1. Calculate the initial combination matrix Z0 = [0 0…0], where the matrix length is len(e), where len(e) is the number of elements in e. S2. Create a recursive function Z with the following input parameters: a, d1, d2, d3, and e. The function calculation steps are as follows: S21, calculate (a-d1-d3+d2) / (e i +d2), and round down to get h=[h1 h2…h i …], where h i =[(a-d1-d3+d2) / (e i +d2)], "[]" means round down; S22. Determine whether len(e) is 1. If it is 1, then Z = [0 1…h1] and output Z. If not, proceed to the next step. S23, remove the first element of array e and recursively call function Z, Z t =Z(a d1 d2 d3 e); S24. Set a variable k. The initial value of k is 0. Each time the loop is repeated, k increases by 1. When k is greater than h1, the loop is exited. In each loop, the matrix Z is established. k , matrix Z k Z t Add a row above, all elements in this row are k; S25. Combine matrices Z0, Z1, ... into Z; S26. Calculate the length L and the number of molds c of each column combination in Z, delete the columns where L + d2 (c-1) + d1 + d3 > a, and output Z. S3. Finally, the combined matrix Z is obtained.

3. The method for optimizing the prefabricated mold for prefabricated residential wall panels according to claim 1, characterized in that: In S3, the integer linear programming model is as follows:

4. The method for optimizing the prefabricated mold for prefabricated residential wall panels according to claim 1, characterized in that: In S3 and S4, the parameter configuration of the genetic algorithm is as follows: constraint_eq represents the objective function, n_dim represents the dimension of the objective function, that is, the dimension of the decision variable x, precision represents the accuracy of the variable value, and since all elements in x are non-negative integers, precision is 1; size_pop is the population size, which is 200, max_iter is the maximum number of iterations, which is 1200, prob_mut is the mutation probability, which is 0.02, and lb is the minimum value of each independent variable, which is 0; the constraint conditions are expressed as

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