Rectangular layout method based on minimum horizontal line adaptation degree

Abstract

The invention relates to the technical field of computer automatic auxiliary stock layout, in particular to a rectangular stock layout method based on the lowest horizontal line adaptation degree, and the method comprises the steps: firstly obtaining the related information of excess materials, raw materials and parts, sorting the parts, adopting an excess material priority stock layout strategy, and using all excess materials and then using the raw materials. And when each plate is subjected to stock layout, executing a lowest horizontal line adaptation degree arrangement strategy, generating an initial lowest horizontal line, calculating the adaptation degree of each part to be arranged on the current horizontal line, selecting the part with the highest adaptation degree to be arranged, and updating the lowest horizontal line. Meanwhile, a genetic algorithm is adopted for optimizing the part arrangement sequence, an initial population is generated through real number coding, the plate utilization rate serves as a fitness function, and the optimal arrangement sequence is obtained through self-adaptive crossover variation and population iterative optimization. According to the method, excess materials can be fully utilized, the part selection direction is defined, layout is prevented from falling into local optimum, the layout effect and efficiency are both considered, and the method is suitable for rectangular part layout in the field of machining of various raw materials.

Description

Technical Field

[0001] This invention relates to the field of computer-aided automatic nesting technology, and in particular to a rectangular nesting method based on the minimum horizontal line fit. Background Technology

[0002] In raw material processing industries such as furniture manufacturing and garment production, the layout of rectangular parts on rectangular raw materials is one of the core production steps. The layout effect directly affects the utilization rate of raw materials, production costs, and production efficiency. Currently, the main research direction of rectangular layout focuses on the layout of raw materials of single size. Most layout methods follow predetermined strategies, such as the bottom left strategy, the lowest horizontal line strategy, and the remaining rectangle strategy. In order to pursue higher raw material utilization, some methods combine the above strategies with heuristic algorithms, such as genetic algorithms and particle swarm optimization algorithms. However, these methods often sacrifice algorithmic efficiency.

[0003] However, existing technologies have obvious shortcomings: on the one hand, when considering the utilization rate of raw materials, existing technologies often ignore the issue of secondary utilization of leftover materials. In actual production, the full utilization of leftover materials can significantly reduce production costs and indirectly improve the overall production utilization rate of raw materials. On the other hand, existing layout strategies lack a clearer direction and mainly focus on the principle of prioritizing large blocks. Such fixed strategies are difficult to adapt to the situation where the minimum level line is constantly updated and changed during the layout process, which makes the layout results prone to getting trapped in local optima and unable to maximize the utilization rate of raw materials.

[0004] Therefore, there is an urgent need for a rectangular layout method that can make full use of surplus materials, clearly define the direction of part selection, and achieve better layout results. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of the prior art and provide a rectangular layout method based on the minimum horizontal line fit. This method solves the technical problems of insufficient utilization of surplus materials, lack of clear direction in part selection, easy trapping in local optima and insufficient efficiency in existing rectangular layout technology, resulting in low raw material utilization and high production costs.

[0006] The above objectives are achieved through the following technical solutions: A rectangular layout method based on minimum horizontal line fit includes the following steps: S1: Obtain the information on leftover materials, raw materials, and parts required for layout. The information on leftover materials includes the width, height, and quantity of leftover material rectangles. The information on raw materials includes the width, height, and quantity of raw material rectangles. The information on parts includes the width, height, and quantity of part rectangles. S2: Adopt the leftover material priority layout strategy. During the layout process, first traverse the leftover material container to layout the leftover material. Each time a leftover material is used, the quantity of that type of leftover material is reduced by one. After all types of leftover materials are used up, the raw materials are used for layout until the required number of parts are laid out. S3: When sorting leftover materials or raw materials, a minimum horizontal alignment strategy is adopted, specifically including: S31: Generate an initial minimum horizontal line, the information of which is ((x,y), width), where (x,y) is the lower left vertex of the rectangular plate to be arranged, and the width is the width of the rectangular plate to be arranged. S32: Traverse the parts container and calculate the fit of each part to be arranged on the current lowest horizontal line. The fit is the highest value when the parts are arranged horizontally or vertically. S33: Select the part with the highest fit and place it in the position corresponding to the current lowest horizontal line. Update the position and information of the lowest horizontal line. Return to step S32 and repeat the operation until all parts no longer fit the current lowest horizontal line and a new horizontal line cannot be updated. Then the layout of the board is completed. S4: A genetic algorithm is used to optimize the arrangement order of parts, specifically including: S41: Using the order of parts placement as the decision variable, the genetic chromosomes are encoded using real number encoding to generate the initial population; S42: Using the board utilization rate as the fitness function, calculate the fitness of each individual in the initial population. The formula for calculating the board utilization rate is as follows: ; S43: Select parent populations from the initial population, and use the parent populations to perform crossover mutations to generate offspring populations. The crossover mutations adopt adaptive crossover mutation probabilities, which are automatically adjusted according to the fitness changes of the offspring populations. S44: Retain chromosomes with superior genes from the parent population, replace inferior individuals in the offspring population to form a new generation population, return to step S43, iterate until the termination condition is met, and output the optimal part placement order.

[0007] Preferably, in step S1, after obtaining the part information, all parts are arranged in descending order of area.

[0008] Preferably, in step S31, the lower left vertex (x,y) of the initial lowest horizontal line is (0,0), the width of the initial lowest horizontal line is the width of the rectangular plates to be arranged, and the information of the initial lowest horizontal line is ((0,0),100).

[0009] Preferably, in step S32, when calculating the fit of the parts to be arranged, the fit value is assigned to 0, 1 or 2 by judging the degree of matching between the parts in the horizontal or vertical arrangement and the current lowest horizontal line, where the fit value 2 is the highest fit and the fit value 0 is the lowest fit.

[0010] Preferably, in step S32, the fitness threshold is varied from strict to lenient according to the iteration results of the genetic algorithm, so as to ensure that each part can be placed in the most suitable position.

[0011] Preferably, in step S41, the specific method of real number encoding is as follows: for N different parts, each part corresponds to a horizontal or vertical arrangement, where the horizontal arrangement is marked as 1 and the vertical arrangement is marked as 0, and the chromosome form is [N1, N2, ..., Nn], where N1, N2, ..., Nn represent the arrangement order of the corresponding parts. When there is a unique part with the highest fit, the part with the highest fit is selected and arranged; otherwise, the parts are arranged according to the arrangement order in the chromosome.

[0012] Preferably, in step S42, the area of ​​the arranged parts is the sum of the areas of all the parts arranged on the current plate, and the area of ​​the plate is the area of ​​the remaining or original material plate currently used for layout.

[0013] Preferably, in step S43, the adjustment rule for the adaptive crossover mutation probability is as follows: when the average fitness of the offspring population is higher than the average fitness of the parent population, the crossover mutation probability is reduced; when the average fitness of the offspring population is lower than the average fitness of the parent population, the crossover mutation probability is increased.

[0014] Preferably, in step S1, the surplus material information further includes the surplus material number and storage location, the raw material information further includes the raw material number and storage location, and the part information further includes the part number and production demand priority.

[0015] Preferably, in step S3, the specific judgment condition for completing the current board layout is: the fit between all parts to be arranged and the current lowest horizontal line is less than 0, and it is impossible to generate a new lowest horizontal line by merging the current lowest horizontal line.

[0016] The present invention provides a rectangular layout method based on the minimum horizontal line fit degree. It achieves full utilization of leftover materials through a leftover material priority layout strategy, achieves clear selection direction of parts through a minimum horizontal line fit degree layout strategy, and achieves layout avoidance of local optima and high convergence efficiency through genetic algorithm adaptive cross-mutation optimization. The three factors work together to greatly improve the utilization rate of raw materials and reduce production costs. It is applicable to rectangular parts layout scenarios in multiple industries. Attached Figure Description

[0017] Figure 1This is a schematic diagram of the structure of the initial minimum horizontal line in the rectangular layout method based on the minimum horizontal line adaptation described in this invention. Figure 2 This is a schematic diagram illustrating the fit of different parts relative to the lowest horizontal line in a rectangular layout method based on the fit of the lowest horizontal line as described in this invention. Figure 3 This is a flowchart of the lowest horizontal line algorithm in the rectangular layout method based on the lowest horizontal line adaptation described in this invention; Figure 4 This is a flowchart of the genetic algorithm in the rectangular layout method based on the minimum horizontal line fitness described in this invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The described embodiments are merely some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 3 As shown, this solution provides a rectangular layout method based on the minimum horizontal line fit, which includes the following steps: Step S1: Obtain layout-related information and sort the parts. Obtain the information on surplus materials, raw materials, and parts required for layout: The surplus material information includes the surplus material's number, storage location, rectangle width, rectangle height, and quantity; The raw material information includes the raw material's number, storage location, rectangle width, rectangle height, and quantity; The part information includes the part number, production demand priority, rectangle width, rectangle height, and quantity.

[0020] After obtaining the above information, arrange all the parts to be arranged in descending order of area so that the larger parts are given priority in the subsequent layout process, thereby initially improving the utilization rate of raw materials.

[0021] Step S2: Implementation of the surplus material priority sorting strategy Leftover materials are managed separately from raw materials, stored in separate containers for leftover materials and raw materials. During the sorting process, the leftover material containers are traversed first to sort the leftover materials. Each time, select one type of unused material from the waste material container as the current layout material; Perform a nesting operation on the current surplus sheet material. For each part nested, the remaining quantity of the corresponding part decreases by one. When the current leftover material can no longer be placed into any parts, determine whether the leftover material has been used up (i.e., the remaining quantity of leftover material is 0). If it has not been used up, continue to try to place the leftover material as the current layout material; if it has been used up, select the next type of leftover material that has not been used up from the leftover material container as the current layout material. Repeat the above operation until all types of leftover materials in the leftover material container are used up (the remaining quantity of all leftover materials is 0). At this point, start traversing the raw material container and use the raw materials to arrange the layout until the remaining quantity of the required parts is 0 (all parts are arranged).

[0022] Step S3: Implementation of the minimum horizontal line adaptation emission strategy When laying out each piece of leftover or raw material sheet, a minimum horizontal alignment layout strategy is adopted, which includes the following sub-steps: S31: Generate the initial lowest horizontal line The information of the initial minimum horizontal line is represented in the form of ((x,y), width), where (x,y) is the lower left vertex of the rectangular material to be arranged, and the width is the width of the rectangular material to be arranged. In this embodiment, the lower left vertex (x,y) of the rectangular material to be arranged is set to (0,0), therefore the information of the first initial minimum horizontal line is ((0,0),100), and its structure is as follows. Figure 1 As shown, Figure 1 The image shows the position and width parameters of the initial lowest horizontal line, where the horizontal length is 100 and the vertical starting point is (0,0).

[0023] S32: Calculate the fit of the parts to be arranged Iterate through all parts in the parts container with a remaining quantity greater than 0. For each part, calculate its fit in both horizontal and vertical arrangement at the current lowest horizontal level. Take the higher value between the two states as the final fit of the part.

[0024] Fit is used to characterize the degree of matching between a part and the current lowest level. In this embodiment, fit is divided into three levels: fit 0, fit 1, and fit 2, where fit 2 is the highest fit and fit 0 is the lowest fit. The specific fit levels are as follows: Figure 2 As shown: Fit 0: The parts cannot be matched with the current lowest horizontal line in either horizontal or vertical arrangement, and cannot be placed into the area corresponding to the current lowest horizontal line; Compatibility 1: When parts are arranged horizontally or vertically, they can be matched with the current lowest horizontal line and placed into the area corresponding to the current lowest horizontal line, but the matching effect is generally poor. Fit 2: When parts are arranged horizontally or vertically, they can perfectly match the current lowest horizontal line. After being placed, the space of the board can be fully utilized, and the matching effect is optimal.

[0025] Meanwhile, the fitness threshold will change from strict to lenient according to the iteration results of the subsequent genetic algorithm. In the early stage of iteration, the fitness threshold is stricter, allowing only parts with high fitness to be placed; in the later stage of iteration, the fitness threshold is relaxed to ensure that as many parts as possible can be placed into the board, ensuring that each part can be placed in the most suitable position.

[0026] S33: Select parts, insert them, and update the lowest horizontal line. Select the part with the highest fit from all the parts to be arranged, and place it in the area corresponding to the current lowest horizontal line in either a horizontal or vertical arrangement (using the optimal state for fit calculation). After placement, update the remaining quantity of the part (decrease by one), and update the position and width information of the lowest horizontal line according to the position and size of the placed part.

[0027] After updating the lowest horizontal line, determine whether the current lowest horizontal line can be merged: if there are multiple adjacent lowest horizontal lines with the same height, merge them into a new lowest horizontal line. The width of the merged lowest horizontal line is the sum of the widths of the horizontal lines before the merge, and the position is the original position of the horizontal line.

[0028] After the merger is complete, return to step S32 and continue to calculate the fit of all remaining parts to be arranged at the lowest level after the update (or merger). Repeat the above operation until one of the following conditions is met: All parts to be arranged have a fit of less than 0 with the current lowest level (no parts can be arranged). It is impossible to generate a new lowest level by merging the current lowest level, and there are no parts that can be placed into the current lowest level.

[0029] When any of the above conditions are met, it indicates that the current board has been laid out and the process will begin with laying out the next board.

[0030] Step S4: Genetic algorithm for optimization, such as... Figure 4 As shown Since the arrangement order of parts of different sizes has a significant impact on the layout result (raw material utilization), this invention uses a genetic algorithm to iteratively optimize the arrangement order of parts in order to find a better arrangement result. Specifically, it includes the following sub-steps: S41: Chromosome Coding and Initial Population Generation The order in which parts are arranged is used as the decision variable, and the genetic chromosome is encoded using real number encoding. For example, when there are 3 different parts (part 1, part 2, and part 3), each part corresponds to a horizontal or vertical arrangement, where the horizontal arrangement is marked as 1 and the vertical arrangement is marked as 0. The chromosome form is [1,2,3], where 1, 2, and 3 represent the order in which parts 1, 2, and 3 are arranged, respectively.

[0031] It should be noted that the arrangement order is affected by the fitness calculated in step S3: when there is only one part with the highest fitness, the part with the highest fitness is selected first and arranged, regardless of the initial arrangement order in the chromosome; when there are multiple parts with the same highest fitness, they are arranged according to the arrangement order in the chromosome. According to the above encoding method, several chromosomes are generated to form an initial population. The size of the initial population can be set according to the actual sampling requirements (in this embodiment, the initial population size is set to 50).

[0032] S42: Fitness Calculation Using the board utilization rate as the fitness function, the fitness of each individual (each chromosome) in the initial population is calculated. The formula for calculating fitness is: ,in This represents the sum of the areas of all parts already placed on the current sheet metal. This represents the area of ​​the remaining or raw material sheet currently used for layout. A higher fitness value indicates a better arrangement order for the corresponding parts and a higher utilization rate of the sheet material.

[0033] S43: Parent Selection and Offspring Generation Individuals with high fitness are selected from the initial population to form the parent population. Selection methods can include roulette wheel selection or tournament selection (this example uses tournament selection). The parent population is then subjected to crossover and mutation to generate the offspring population. Crossover operation: Using a single-point crossover method, the crossover point of two parent chromosomes is randomly selected, and the gene segments after the crossover point are exchanged to generate two new chromosomes; Mutation operation: Randomly select one or more gene loci in the offspring chromosome and change the corresponding part arrangement order or state (horizontal / vertical).

[0034] In this invention, the crossover mutation adopts an adaptive crossover mutation probability, which is automatically adjusted according to the fitness changes of the offspring population: when the average fitness of the offspring population is higher than the average fitness of the parent population, the crossover mutation probability is reduced to preserve superior genes; when the average fitness of the offspring population is lower than the average fitness of the parent population, the crossover mutation probability is increased to enhance the search capability of the algorithm and avoid getting trapped in local optima.

[0035] S44: Population Iterative Optimization The parent and offspring populations are merged, and the fitness of all individuals after the merge is calculated. Individuals with higher fitness in the merged population (i.e., chromosomes with superior genes in the parent population) are retained and replaced with individuals with lower fitness in the offspring population to form a new generation population.

[0036] The new generation of population is used as the new initial population. Return to step S43 and repeat the crossover mutation and population update operations until the iteration termination condition is met. The iteration termination condition is: the number of iterations reaches the preset maximum number of iterations (in this embodiment, the preset maximum number of iterations is 100), or the average fitness of the population no longer significantly improves after multiple consecutive iterations (in this embodiment, when the average fitness improvement is less than 0.001 after 10 consecutive iterations, it is considered no longer significantly improved).

[0037] When the iteration termination condition is met, the part placement order corresponding to the individual with the highest fitness in the output population is output, and this placement order is the optimal placement order.

[0038] Step S5: Stopping the sorting process When the remaining quantity of all parts is 0 (all parts are laid out), the entire layout process terminates and the layout results are output, including the types and quantities of leftover sheet materials used, the types and quantities of raw material sheet materials, the layout and utilization rate of each sheet material, and other information.

[0039] As a specific embodiment of this solution, the layout objects in this embodiment are 10 different sizes of parts, totaling 50. The specific information of the parts is shown in Table 1. The layout plates are divided into surplus material plates and raw material plates. There are 3 types of surplus material plates, with 1 of each. The specific information is shown in Table 2. There is 1 type of raw material plate, with a size of 1220mm×2440mm, and the quantity is unlimited.

[0040] Table 1 Parts Information Table width high quantity Part 1 1143 1385 7 Part 2 1215 1385 2 Part 3 1210 1385 1 Part 4 1215 1200 6 Part 5 1210 1200 5 Part 6 1215 765 5 Part 7 1210 765 4 Part 8 400 975 8 Part 9 1835 975 2 Part 10 1030 270 10 Table 2 Surplus Material Information Table width high quantity 1 piece of leftover sheet material 1210 1500 1 2 scrap boards 800 1220 1 3 scrap boards 1220 300 1 The rectangular layout method based on the lowest horizontal line adaptation of the present invention is used for layout, and the specific steps are as follows: Execute step S1: Obtain the part information shown in Table 1, the surplus material information shown in Table 2, and the raw material information (size 1220mm×2440mm, unlimited quantity). Sort the 10 parts by area from largest to smallest. The sorting result is: part 9 > part 1 > part 2 > part 3 > part 4 > part 5 > part 6 > part 7 > part 8 > part 10.

[0041] Execution step S2: Prioritize the use of leftover material for layout, and select leftover material 1, leftover material 2, and leftover material 3 in sequence for layout.

[0042] For scrap sheet 1 (1210mm × 1500mm), implement the minimum horizontal alignment placement strategy of step S3: S31: Generate the initial minimum horizontal line ((0,0),1210) (because the width of the leftover material plate 1 is 1210mm, the width of the initial horizontal line is adjusted to 1210). S32: Calculate the fit of all remaining parts. When part 1 (width 1143mm, height 1385mm) is arranged horizontally, its fit with the initial lowest horizontal line is 2, which is the highest fit. S33: Place part 1 into the remaining material plate 1, update the remaining quantity of part 1 to 6, and update the lowest level information; Repeat steps S32-S33 until no more parts can be inserted into the remaining material plate 1. At this point, the utilization rate of the remaining material plate 1 is 0.919.

[0043] Following the steps above, the layout of leftover material sheet 2 and leftover material sheet 3 is completed in sequence, with utilization rates of 0.948 and 0.965, respectively.

[0044] After the surplus sheet material is used up, the original sheet material (1220mm×2440mm) is used for layout. When laying out each original sheet material, the minimum horizontal line fit placement strategy in step S3 is executed, and the placement order of parts is optimized by the genetic algorithm in step S4. S41: Using the order of 10 parts as decision variables, generate an initial population (size 50) using real number encoding. S42: Calculate the fitness (board utilization rate) of each individual. S43: Select the parent population and generate the offspring population using adaptive crossover and mutation probability; S44: After 100 iterations of optimization, output the optimal emission order; Arrange each raw material board according to the optimal arrangement order.

[0045] Arrangement termination: When all 50 parts were arranged, a total of 19 plates were used, including 3 leftover plates and 16 original plates. The utilization rate of each plate is shown in Table 3, and the overall average utilization rate is 0.923.

[0046] Table 3. Sheet Material Utilization Rate Board Name Utilization Board Name Utilization 1 piece of leftover sheet material 0.919 Raw material board 11 0.937 2 scrap boards 0.948 Raw material board 12 0.937 3 scrap boards 0.965 Raw material board 13 0.937 Raw material board 4 0.929 Raw material board 14 0.979 Raw material board 5 0.975 Raw material board 15 0.979 Raw material board 6 0.975 Raw material board 16 0.979 Raw material board 7 0.924 Raw material board 17 0.968 Raw material board 8 0.924 Raw material board 18 0.787 Raw material board 9 0.943 Raw material board 19 0.601 10 raw material boards 0.937 After arranging 50 parts, 3 leftover sheet materials and 16 raw sheet materials were used, for a total of 19 sheet materials, with an average utilization rate of 0.923.

[0047] The above description is merely illustrative of the embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rectangular layout method based on the minimum horizontal line fit, characterized in that, Includes the following steps: S1: Obtain the information on leftover materials, raw materials, and parts required for layout. The information on leftover materials includes the width, height, and quantity of leftover material rectangles. The information on raw materials includes the width, height, and quantity of raw material rectangles. The information on parts includes the width, height, and quantity of part rectangles. S2: Adopt the leftover material priority layout strategy. During the layout process, first traverse the leftover material container to layout the leftover material. Each time a leftover material is used, the quantity of that type of leftover material is reduced by one. After all types of leftover materials are used up, the raw materials are used for layout until the required number of parts are laid out. S3: When sorting leftover materials or raw materials, a minimum horizontal alignment strategy is adopted, specifically including: S31: Generate an initial minimum horizontal line, the information of which is ((x,y), width), where (x,y) is the lower left vertex of the rectangular plate to be arranged, and the width is the width of the rectangular plate to be arranged. S32: Traverse the parts container and calculate the fit of each part to be arranged on the current lowest horizontal line. The fit is the highest value when the parts are arranged horizontally or vertically. S33: Select the part with the highest fit and place it in the position corresponding to the current lowest horizontal line. Update the position and information of the lowest horizontal line. Return to step S32 and repeat the operation until all parts no longer fit the current lowest horizontal line and a new horizontal line cannot be updated. Then the layout of the board is completed. S4: A genetic algorithm is used to optimize the arrangement order of parts, specifically including: S41: Using the order of parts placement as the decision variable, the genetic chromosomes are encoded using real number encoding to generate the initial population; S42: Using the board utilization rate as the fitness function, calculate the fitness of each individual in the initial population. The formula for calculating the board utilization rate is as follows: ; S43: Select parent populations from the initial population, and use the parent populations to perform crossover mutations to generate offspring populations. The crossover mutations adopt adaptive crossover mutation probabilities, which are automatically adjusted according to the fitness changes of the offspring populations. S44: Retain chromosomes with superior genes from the parent population, replace inferior individuals in the offspring population to form a new generation population, return to step S43, iterate until the termination condition is met, and output the optimal part placement order.

2. The rectangular layout method based on the lowest horizontal line fit as described in claim 1, characterized in that, In step S1, after obtaining the part information, all parts are arranged in descending order of area.

3. The rectangular layout method based on the lowest horizontal line fit as described in claim 1, characterized in that, In step S31, the lower left vertex (x,y) of the initial lowest horizontal line is (0,0), the width of the initial lowest horizontal line is the width of the rectangular plate to be arranged, and the information of the initial lowest horizontal line is ((0,0),100).

4. The rectangular layout method based on the lowest horizontal line fit as described in claim 1, characterized in that, In step S32, when calculating the fit of the parts to be arranged, the fit value is assigned to 0, 1 or 2 by judging the degree of matching between the parts in the horizontal or vertical arrangement and the current lowest horizontal line. The fit value is 2, which is the highest fit, and the fit value is 0, which is the lowest fit.

5. A rectangular layout method based on minimum horizontal line fit as described in claim 1, characterized in that, In step S32, the fitness threshold changes from strict to lenient according to the iteration results of the genetic algorithm to ensure that each part can be placed in the most suitable position.

6. A rectangular layout method based on minimum horizontal line fit as described in claim 1, characterized in that, In step S41, the specific method of real number encoding is as follows: For N different types of parts, each part corresponds to a horizontal or vertical arrangement, where the horizontal arrangement is marked as 1 and the vertical arrangement is marked as 0. The chromosome form is [N1, N2, ..., Nn], where N1, N2, ..., Nn represent the arrangement order of the corresponding parts. When there is a unique part with the highest fit, the part with the highest fit is selected and arranged; otherwise, the parts are arranged according to the arrangement order in the chromosome.

7. A rectangular layout method based on minimum horizontal line fit as described in claim 1, characterized in that, In step S42, the area of ​​the arranged parts is the sum of the areas of all the parts arranged on the current plate, and the area of ​​the plate is the area of ​​the remaining or raw material plate currently used for layout.

8. A rectangular layout method based on minimum horizontal line fit according to claim 1, characterized in that, In step S43, the adjustment rule for the adaptive crossover mutation probability is as follows: when the average fitness of the offspring population is higher than the average fitness of the parent population, the crossover mutation probability is reduced; when the average fitness of the offspring population is lower than the average fitness of the parent population, the crossover mutation probability is increased.

9. A rectangular layout method based on minimum horizontal line fit according to claim 1, characterized in that, In step S1, the surplus material information also includes the surplus material number and storage location, the raw material information also includes the raw material number and storage location, and the part information also includes the part number and production demand priority.

10. A rectangular layout method based on minimum horizontal line fit according to claim 1, characterized in that, In step S3, the specific judgment condition for completing the current board layout is: the fit between all parts to be arranged and the current lowest horizontal line is less than 0, and it is impossible to generate a new lowest horizontal line by merging the current lowest horizontal line.

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