A shipyard cruise ship section yard scheduling optimization method based on improved genetic algorithm

Abstract

The present application relates to the shipyard yard scheduling technical field, specifically disclose a kind of shipyard cruise ship section yard scheduling optimization method based on improved genetic algorithm, including the following steps: based on irregular combination section yard layout and sequencing problem, construct target mathematical model;Based on the similarity of polygon, the strategy of irregular section similarity combination is proposed, including the similarity of angle and the similarity of side;Determine the initial stacking position of section, calculate the minimum number of blocking section, find the path of the least blocking section by depth-first search, integrate the path of the least blocking section to design the minimum blocking section strategy;Establish the genetic algorithm of fusion strategy, design the coding rule of genetic algorithm, give priority to the scheduling of outgoing section, find the optimal solution by iterative deepening depth-first search, by applying improved genetic algorithm and polygon similarity decision theorem, the optimization of shipyard yard internal cruise ship section scheduling is realized.

Description

Technical Field

[0001] This invention relates to the field of shipyard yard scheduling technology, and more specifically, to a method for optimizing the scheduling of cruise ship sections in shipyards based on an improved genetic algorithm. Background Technology

[0002] With continuous innovation and optimization of shipbuilding models and technologies, shipbuilding efficiency has also improved. Hull sections are considered intermediate products in the shipbuilding process, and section construction is the main production mode for the section process, encompassing hull design, material pretreatment, section processing, hull assembly, and ship launching. The section construction mode combines the advantages of modular manufacturing and lean manufacturing, dividing the process into several parts in time and space, some involving the processing of multiple sections. During construction, the completion of a single section includes four main stages: painting, pretreatment, equipment, and final assembly. The construction of large ships involves a vast number of sections, estimated to be in the tens of millions. For example, the large cruise ship currently under construction in China consists of 503 structural sections, requiring the assembly of approximately 25 million individual components. The general parts of the ship are assembled in the dry dock. Each section has more detailed technology to ensure the quality of the section. For example, equipment is carried out for each sub-section of the sub-section, and is completed through painting and pretreatment. Finally, after the ship is assembled, equipment and launching are usually carried out. Throughout the complex cruise ship construction process, the section is the most important and fundamental unit. Its irregular shape presents significant challenges in storage and processing, requiring innovative strategies to ensure efficient progress.

[0003] Genetic algorithms, as metaheuristic optimization search algorithms inspired by natural selection and genetic mechanisms, construct an initial population representing a potential solution set. Each individual (or chromosome) represents a candidate solution to the problem to be optimized using a specific encoding method. Subsequently, the algorithm simulates the three core evolutionary mechanisms in nature: selection, crossover, and mutation, iteratively guiding the population to evolve towards a better solution space, finally converging to a group of individuals best adapted to the environment, thus obtaining the optimal solution to the problem.

[0004] However, for this problem, simply using a genetic algorithm may slow down the solution process. A solution strategy tailored to this problem is needed to improve efficiency. The shortcomings and limitations of existing technologies are as follows:

[0005] 1. A thorough analysis of the geometric and morphological characteristics of ship section construction reveals that, following the principle of symmetry in ship design, the bow and stern sections typically exhibit a clear mirror symmetry. This means that the structural arrangement and configuration of these sections on both sides of the ship are almost identical. Similarly, along the longitudinal axis of the ship, the main hull sections usually have corresponding parts that are highly similar in form and structural elements to their symmetrical counterparts. We can summarize this phenomenon as the "mirror symmetry principle of ship section structures." This principle is an important consideration in ship design and construction, playing a crucial role in optimizing ship construction and improving construction efficiency. However, the similarity between ship blocks has not been considered during their construction and storage. Therefore, we propose a block combination strategy based on the polygon similarity criterion theorem. The ultimate goal is to achieve a balance and efficient utilization of available space and maximize the productivity of the storage yard.

[0006] 2. While traditional genetic algorithms can obtain the optimal solution to this problem, the number of solutions increases significantly due to the different types of segments and scheduling times. Therefore, it is necessary to design some optimization strategies to reduce the solution space and improve the efficiency of solving the problem.

[0007] In view of this, the present invention provides a method for optimizing the scheduling of shipyard cruise ship section yards based on an improved genetic algorithm to solve the above problems. Summary of the Invention

[0008] To overcome the problems in the existing technology, this invention proposes an optimized scheduling method for shipyard cruise ship section yards based on an improved genetic algorithm, which maximizes the utilization rate of yard space and thus improves the production efficiency of shipyards.

[0009] In a first aspect, the present invention provides a method for optimizing the scheduling of cruise ship section yards in shipyards based on an improved genetic algorithm, comprising the following steps:

[0010] S1: Construct a target mathematical model based on the irregular combination of segmented stockpile layout and sorting problem;

[0011] S2: Based on the similarity of polygons, a strategy of irregular segmented similarity combination is proposed. The similarity of polygons includes the similarity of angles and the similarity of sides.

[0012] S3: Determine the initial heap position of the segments, calculate the minimum number of blocking segments, find the path with the fewest blocking segments through depth-first search, and integrate the path with the fewest blocking segments to design the minimum blocking segment strategy.

[0013] S4: A genetic algorithm for the fusion strategy was established, and the encoding rules of the genetic algorithm were designed. Priority was given to the segmented scheduling of the products, and the optimal solution was found through iterative and deepening depth-first search.

[0014] As a preferred embodiment of the first aspect of the present invention, the construction logic of the target mathematical model is as follows:

[0015] The irregular combination of segmented yard layout is used as the segmented layout corresponding to the current yard, wherein each segment has a corresponding size and shape, and the process mode of scheduling the yard is determined based on the segmented layout.

[0016] When establishing the storage yard, the blocking segment equations are constructed by moving and scheduling segments within the storage yard. The blocking segment equations include entry blocking segment equations and exit blocking segment equations. Constraint equations are constructed by using obstacles. Based on the blocking segment equations and constraint equations, a target mathematical model is constructed.

[0017] As a preferred embodiment of the first aspect of the present invention, the target mathematical model includes:

[0018] The number of barrier segments entering the yard, the number of barrier segments leaving the yard, and the space utilization rate of the yard storage locations are calculated. Based on the number of barrier segments entering the yard, the number of barrier segments leaving the yard, and the space utilization rate of the yard storage locations, the overall objective of the problem is constructed by minimizing the number of barrier segments and maximizing the space utilization rate.

[0019] Based on task type restrictions, the task types include segmented inbound tasks and segmented outbound tasks. The area of ​​the segment and the area of ​​the stacking position are calculated based on the formula, and the following conditions are extracted: the area of ​​the segment is less than the area of ​​the stacking position, the segment is placed in the stacking position, and the segment is not stacked.

[0020] Segments can find suitable stacking locations in the yard directly or through combinations, ensuring the time required for segments to enter the yard, the time required for segments to exit the yard, and that task segments choose the route with the fewest obstacles to enter or exit the yard.

[0021] As a preferred embodiment of the first aspect of the present invention, the logic for obtaining the irregular segmentation similarity combination strategy is as follows:

[0022] S21: Determine the angles and sides of the segments based on the combination of irregular segments, and use the similarity standard of polygons to determine whether the two segments are similar; if they are similar, proceed to step S22; otherwise, proceed to step S23.

[0023] S22: If the current two segments are similar, calculate the similarity coefficient between the segments.

[0024] If the similarity coefficient is 1, it indicates that the two segments currently involved in the calculation are congruent and can be combined into a complete shape through rotation.

[0025] If the similarity coefficient is not 1, it indicates that there is room for improvement between the two segments currently involved in the calculation. Segments with similarity coefficients close to 1 are selected first for combination to synthesize a complete shape.

[0026] S23: When two segments are not similar, determine whether their angles are equal;

[0027] If the angles of two segments are equal, it indicates that there may be an imperfect combination between the two segments. In the case of an imperfect combination, the similarity ratio of the edges with similarity coefficients close to 1 is used to combine them. If the angles of the segments are not equal, there are no similar shapes.

[0028] S24: According to the Bottom-Left Fill (BLF) principle, all segments are systematically combined. This process involves repeating steps S21-S23 until all tasks are completed.

[0029] As a preferred technical solution of the first aspect of the present invention, the design logic of the minimum blocking segmentation strategy is as follows:

[0030] S31: Determine the initial stacking position of the segment. The minimum number of blocking segments tends to infinity. When the current number of blocking segments is zero, mark it as the starting point of the movement and move to the exit of the stacking yard without changing the direction. If the selected direction is blocked by a segment.

[0031] S32: If the number of obstructing segments encountered is returned to the initial stacking position and the next moving position is determined, a moving direction is selected, and the moving position is moved to the new moving position. Starting from the new moving position, the direction is kept unchanged and the movement continues until the exit of the stacking yard is reached. The number of obstructing segments is recorded.

[0032] S33: Repeat step S32 until the moving position is the outlet of the yard;

[0033] S34: Return to the initial heap position and execute step S32 until a path with zero blocking segments is found; otherwise, traverse all possible paths.

[0034] S35: Returns the heap position of the shortest path and the minimum number of blocking segments bm.

[0035] As a preferred embodiment of the first aspect of the present invention, the application logic of the genetic algorithm for the fusion strategy is as follows:

[0036] The initial population includes segmented scheduling plans at different time points, scheduling plans between segments already stored in the yard and segments about to arrive, and scheduling plans between segments at the same time point or of the same type.

[0037] Crossover operations follow the coding rules of the solution. Genes of the same type on the chromosome can be crossovered within the same scheduling period. The genes corresponding to the parents are divided into date-fixed genes and type-fixed genes.

[0038] The parameters of the genetic algorithm are designed, the fitness value of each segment plan is calculated, and the optimal solution is finally obtained through iteration.

[0039] As a preferred embodiment of the first aspect of the present invention, the coding rule for multi-cycle entry and exit segmented scheduling is as follows:

[0040] The segmentation order can be adjusted within the same period, while the segmentation order remains unchanged between different periods;

[0041] Given that entering a segment may increase the number of segments blocking the exit segment, the exit segment should be planned first.

[0042] The specific advantages of this invention are as follows:

[0043] This invention optimizes the scheduling of cruise ship sections within a shipyard by applying an improved genetic algorithm and polygon similarity decision theorem. This improves the utilization rate and production efficiency of the storage yard, reduces the number of obstructing sections, enhances the flexibility and adaptability of the scheduling plan, and maintains the orderliness and logical consistency of the operation process. It effectively avoids potential conflicts and chaos, and its significant effectiveness has been verified through experiments. Attached Figure Description

[0044] Figure 1 This application provides an illustrative diagram illustrating the segmented entry and exit of cruise ship sections in a shipyard storage yard, based on an improved genetic algorithm for optimizing the scheduling of cruise ship sections.

[0045] Figure 2 This application provides an irregular segmentation combination strategy diagram for an optimized method of cruise ship segment scheduling within a shipyard yard based on an improved genetic algorithm.

[0046] Figure 3 The minimum obstruction segmentation strategy diagram of a cruise ship segmentation scheduling optimization method based on an improved genetic algorithm provided in this application;

[0047] Figure 4 This application provides an encoded graph of the genetic algorithm solution for a cruise ship segment scheduling optimization method based on an improved genetic algorithm within a shipyard storage yard.

[0048] Figure 5 This application provides a cross-graph of the genetic algorithm solution for an optimized scheduling method of cruise ship sections within a shipyard based on an improved genetic algorithm.

[0049] Figure 6 This application provides a flowchart of a method for optimizing the scheduling of cruise ship sections within a shipyard yard based on an improved genetic algorithm. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0051] Example 1: Please refer to Figure 6 The present invention provides a technical solution: a method for optimizing the scheduling of cruise ship section yards in shipyards based on an improved genetic algorithm, comprising the following steps:

[0052] S1: Construct a target mathematical model based on the irregular combination of segmented stockpile layout and sorting problem;

[0053] For example, a rectangular storage yard can be likened to a chessboard with M rows and N columns, interspersed with certain passages. Given a task sequence for a given day within a scheduling period, each task consists of segment arrival times, departure times, and task quantities. A mathematical model is used to design the optimal layout strategy for these segments, optimizing their routes and determining the most reasonable segment scheduling order. The goal is to minimize the total number of obstacles generated within a given period and achieve the optimal combination of segments.

[0054] Specifically, the construction logic of the target mathematical model is as follows:

[0055] The irregular combination of segmented yard layout is used as the segmented layout corresponding to the current yard, wherein each segment has a corresponding size and shape, and the process mode of scheduling the yard is determined based on the segmented layout.

[0056] It should be noted that, as the basic unit of a shipyard, the layout of the storage yard takes into account the actual conditions of the shipyard's storage area. To facilitate the processing of shipyard sections and ensure the smooth operation of the entire production process, the layout of the storage yard is closely related to scheduling efficiency. For example, a flat and wide rectangle is an ideal choice for the storage yard layout, as it reduces the distance sections need to travel within the yard and improves scheduling efficiency. However, the closer the storage yard shape is to a square, the more obstacles the sections may encounter when being removed from the storage yard under the same processing task. Therefore, when designing the storage yard, it is necessary to comprehensively consider both the layout shape and scheduling requirements to achieve the optimal scheduling effect.

[0057] Accessibility around the yard is also crucial for scheduling efficiency. A yard layout with accessibility on all four sides facilitates the movement and scheduling of sections within the yard. When constructing the yard, it is important to avoid setting up obstacles such as walls, reducing scheduling time and costs, and ensuring good accessibility around the yard.

[0058] The target mathematical model is:

[0059] Calculate the blocking segments for entry:

[0060]

[0061] In the formula: f1 represents the number of blocking segments entering the storage yard, t is the date number of each day within the scheduling period T, t∈T={0,1,2,3,...,t last}, t=0 represents the initial stacking position state of the storage yard before the scheduling task is issued, t=1 represents the stacking position state of the storage yard upon entry, t last Ta represents the last day; Ta represents the task number on date t, where Ta∈TA. t ={1,2,...,TA last}, TA last This represents the total number of segmented scheduling tasks on day t; i is the segment number, i total Represents the total number of segments; j is the number of the storage location within the storage yard. The storage yard numbering rule in the matrix is ​​to sort from left to right and from top to bottom, where j max This represents the bottom right corner of the storage yard;

[0062] Let x represent a segment with task Ta and segment number i within a given day t, and whether it can enter the storage yard; i,j This indicates whether segment i can be placed or combined in heap position j; This indicates whether segment i can be stacked at stack location j in the storage yard on a given day t; b i,j This represents the total number of obstructing segments on a given path.

[0063] Calculate the blocking segments at the exit:

[0064]

[0065] In the formula: f2 represents the number of obstruction segments for leaving the storage yard; This indicates a segment that satisfies the condition that the task is Ta and the segment number is i within a certain day t, and whether it can be retrieved from the storage yard; the meanings of the other symbols are the same as above.

[0066] Calculate the space utilization rate of the stack space:

[0067]

[0068] In the formula: f3 represents the space utilization rate of the storage yard; s i S represents the projected area of ​​segment i; j This represents the area of ​​heap position j;

[0069] The overall model is as follows:

[0070] f = min(f1 + f2 + 1 / f3);

[0071] In the formula: f represents the overall objective function of the problem, which means minimizing the number of blocking segments and maximizing space utilization;

[0072] The constraints are as follows:

[0073]

[0074] In the formula: Let T represent a segment that satisfies the condition that on a certain day t, the task is Ta and the segment number is i, and whether it can be moved out of the storage yard; At This represents the entire task cycle; the entire formula represents task type restrictions. Segmented scheduling has two task types: segmented inbound tasks and segmented outbound tasks. Tasks of the same type cannot be simultaneously moved in and out of the yard.

[0075]

[0076] In the formula: This indicates whether segment i can be stacked at stack location j in the storage yard within a certain day t; This represents the total area of ​​all segments stacked in stack position j; the formula ensures that the area of ​​each segment is less than the area of ​​the stack position.

[0077] 0≤x i ≤l0-l i,m ;

[0078] In the formula: x i The x-coordinate of the upper right corner of the segment is represented by l0; l0 represents the length of the heap position; l i,m Indicates the length of the segment projection; ensures that the segment is placed within the stack location.

[0079] 0≤y i ≤l0-w i,m ;

[0080] In the formula: y i The vertical coordinate of the upper right corner of the segment is represented by l0; l0 represents the width of the heap position; w i,m Indicates the width of the segment projection; ensures the segment is placed within the stacking location.

[0081] x i +l i,m ≤x i ';

[0082] In the formula: x i ' represents the upper right x-coordinate of segment j combined with segment i; ensures that segments do not stack.

[0083] y i +w i,m ≤y′ i ;

[0084] In the formula: y′ i Represents the top-right ordinate of segment j, which is combined with segment i; ensures that segments do not stack.

[0085] This indicates that the segments can be placed in suitable locations in the yard, either directly or by combining them.

[0086]

[0087] In the formula: bt i is the time when segment i enters the storage yard; represents the time required for segment i to enter the storage yard.

[0088] This indicates the time required to complete the phased exit from the storage yard.

[0089]

[0090] In the formula: bt i The time for segment i to enter the storage yard; ensure that the task segment must choose the route with the fewest obstacles to enter or exit the storage yard.

[0091] In xi,j, a value of 1 indicates that segment i can be placed or combined in heap position j; otherwise, it is 0. In this context, a value of 1 indicates that segment i is stacked at position j in the storage yard within a certain time t on a given day; otherwise, it is 0. In the context of the equation, a value of 1 indicates that the task is Ta and the segment number is i within a certain day t, and it is a segment entering the storage yard; otherwise, it is 0. In this context, when its value is equal to 1, it means that the task is Ta and the segment number is i within a certain day t, and it is a segment that is being moved out of the storage yard; otherwise, it is 0.

[0092] More specifically, such as Figure 1 The process demonstrates a site with accessible roads that surround the site, allowing flatbed trucks to move sections of the site in and out for scheduling operations.

[0093] The yard status on day 7 shows that it contains many segments, such as 1210 and 228. The labels on these segments indicate that segment 12 was dispatched on day 10, and segment 22 on day 8. Segment 107 has three potential routes;

[0094] However, the downward route requires scheduling two blocking segments (2011 and 137) simultaneously, while the other two routes each require scheduling one segment (1112 and 189 respectively). Since the segments weigh hundreds of tons and are not easy to move, the path with fewer segments should be selected as much as possible. The left and right paths selected have fewer blocking segments, making them the optimal scheduling routes.

[0095] S2: Based on the similarity of polygons, an irregular segmented similarity combination strategy is proposed, including the similarity of angles and the similarity of sides;

[0096] Specifically, such as Figure 2 As shown, the logic for obtaining the irregular segmentation similarity combination strategy is as follows:

[0097] S21: Determine the angles and sides of the segments based on the combination of irregular segments, and use the similarity standard of polygons to determine whether the two segments are similar; if they are similar, proceed to step S22; otherwise, proceed to step S23.

[0098] S22: If the current two segments are similar, calculate the similarity coefficient between the segments.

[0099] If the similarity coefficient is 1, it indicates that the two segments currently involved in the calculation are congruent and can be combined into a complete shape through rotation.

[0100] If the similarity coefficient is not 1, it indicates that there is room for improvement between the two segments currently involved in the calculation. Segments with similarity coefficients close to 1 are selected first for combination to synthesize a complete shape.

[0101] It should be noted that although two segments can be combined, they are not perfectly matched. Combining dissimilar segments will result in greater wasted space. Therefore, segments with a similarity coefficient close to 1 should be selected for combination first. The similarity coefficient is calculated based on the area ratio of the segments, as follows:

[0102]

[0103] Among them, S i These are the sections planned to be stored in the yard, S now,j These are the segments currently stored in the storage yard; if δ-1=0, then S i It is S now,j The optimal combination of segments;

[0104] S23: When two segments are not similar, determine whether their angles are equal;

[0105] If the angles of two segments are equal, it indicates that there may be an imperfect combination between the two segments;

[0106] It should be noted that in the case of imperfect combinations, edges with similarity coefficients close to 1 are grouped together by their similarity ratio. The similarity coefficient is calculated as follows:

[0107]

[0108] Among them, l i,jIt is the side length of the segment planned to be stored in the yard, l now,j This refers to the side length of the segments currently stored in the yard. If the angles of the segments are not equal, then there are no similar shapes. Set δ = 0.5, and then proceed to step S24;

[0109] S24: According to the Bottom-Left-Fill (BLF) principle, all segments are systematically combined. This process involves repeating steps S21-S23 until all tasks are completed.

[0110] More specifically, suppose we have a shipyard yard that needs to manage the following four sections: A, B, C, and D. These sections have different shapes and sizes and need to be placed in the yard while minimizing obstruction and maximizing space utilization.

[0111] Check if A and B are similar. Assuming that the angles and sides of A and B are similar, calculate their similarity coefficient. The coefficient is found to be 0.9, indicating that they can be combined but are not a perfect match.

[0112] Check if C and D are similar. Assuming C and D are not similar, check their angles and find that one of them is equal. Use the similarity ratio to combine them.

[0113] S3: Determine the initial heap position of the segments, calculate the minimum number of blocking segments, find the path with the fewest blocking segments through depth-first search, and integrate the path with the fewest blocking segments to design the minimum blocking segment strategy.

[0114] Specifically, the process is as follows: Figure 3 As shown, the design logic of the minimum blocking segmentation strategy is as follows:

[0115] S31: Determine the initial stacking position p0 of the segment, the minimum number of obstructing segments bm = +∞, the current number of obstructing segments b = 0, use this position as the starting point for movement, and move to the exit of the stacking yard without changing direction. If the selected direction is blocked by segments, then b = b + n, where n is the number of obstructing segments encountered. If b is 0, proceed to step S34; otherwise, bm = b, proceed to step S32.

[0116] S32: Return to p0 and determine the next position pnext. Select a suitable moving direction di (left or right) and move pnext to the new position pnew. Starting from the new position pnew, keep the direction unchanged and continue moving until the exit of the storage yard is reached. Record the number of blocking segments b. If b is less than bm, update the value of bm to b. Otherwise, the value of bm remains unchanged.

[0117] S33: Repeat step S32 until the position of pnext is the exit of the yard.

[0118] S34: Return to position p0 and execute step S32 until a path with b = 0 is found; otherwise, traverse all possible paths.

[0119] S35: Returns the heap position of the shortest path and the minimum number of blocking segments bm;

[0120] It should be noted that a minimum obstruction segmentation strategy was designed. Since each segment at each stack location has feasible movement directions, a depth-first search could search every path from that location to the destination, thus finding the path with the fewest obstructing segments. However, this increases the difficulty and time required for the solution. To reduce the number of paths in the results, an iteratively deepening depth-first search is used to find the path with the fewest obstructing segments for an incoming segment entering a stack location or for an outgoing segment transported from a stack location to the edge of the stack yard.

[0121] S4: A genetic algorithm for the fusion strategy was established, and the encoding rules of the genetic algorithm were designed. Priority was given to the segmented scheduling of the products, and the optimal solution was found through iterative and deepening depth-first search.

[0122] Specifically, such as Figure 4 As shown, the application logic of the genetic algorithm for the fusion strategy is as follows:

[0123] The initial population includes segmented scheduling plans at different time points, scheduling plans between segments already stored in the yard and segments about to arrive, and scheduling plans between segments at the same time point or of the same type.

[0124] More specifically, the segmented scheduling plan for different time points is as follows:

[0125] In scheduling planning, the segmented scheduling plans for time points T1 and T2 must remain independent to ensure the orderliness and logical consistency of the operation process. For example, the scheduling plans between segments belonging to different time points (such as block 5 and block 6) cannot be interchanged to maintain the accuracy of the time series and the continuity of the process.

[0126] The scheduling plan between segments already stored in the yard and segments about to arrive is as follows:

[0127] The scheduling plans between segments already stored in the yard and segments about to arrive must not be exchanged (e.g., block 2 and block 4), in order to ensure logistics efficiency and avoid potential conflicts or chaos;

[0128] Scheduling plans between segments of the same time point or of the same type:

[0129] At the same time point or between segments of the same type (such as block 2 and block 3), their scheduling plans can be flexibly adjusted according to actual needs to optimize resource allocation and improve overall operation efficiency.

[0130] More specifically, in actual operation, according to the encoding rules of the genetic algorithm, the number of segments entering the field often increases the number of obstacles to the exiting field, thus affecting the overall scheduling efficiency. Therefore, a strategy that prioritizes exiting field scheduling is adopted to accelerate the solution speed by reducing the size of the solution. The encoding rules for multi-cycle entry and exit segment scheduling are as follows:

[0131] ① The segmentation order within the same period is allowed to be adjusted, but the segmentation order between different periods remains unchanged to ensure the logical consistency of the time series;

[0132] ② Given that entering a segment may increase the number of segments blocking the exiting segment, prioritize the planning of exiting segments to optimize resource allocation and improve overall operational efficiency.

[0133] It should be noted that this division into three parts is to ensure that the genetic algorithm considers the logical consistency of the time series, logistical efficiency, and resource allocation optimization during the optimization process. In this way, the algorithm can more effectively handle complex segmented scheduling problems and find optimal or near-optimal solutions.

[0134] Crossover operations follow the coding rules of the solution. Genes of the same type on the same chromosome can undergo crossover operations within the same scheduling period. For the parent Pa, all its genes are randomly divided into two groups: date-fixed genes and type-fixed genes.

[0135] Specifically, the genes of parent Pb correspond to the date-fixed genes of parent Pa, forming the sequence-fixed genes of Pb; while the genes of Pb correspond to the type-fixed genes of Pa, forming the position-fixed genes of Pb. For example... Figure 5 As shown, offspring C inherited the date-fixed gene and type-fixed gene from P1 and P2.

[0136] The genetic algorithm parameters are designed as follows: maximum number of iterations 200, population size 30, elite selection ratio 0.15, roulette wheel selection ratio 0.1, crossover probability 0.7, and mutation probability 0.05. The optimal solution is obtained through iteration.

[0137] More specifically, the fitness value of each segmented plan is calculated using a formula, and the optimal solution is selected. The formula used for the fitness value of the initial population is:

[0138] f * = 1 / f, where f is the overall objective function mentioned above.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0140] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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 shipyard cruise ship section yard scheduling optimization method based on an improved genetic algorithm, characterized in that, The method comprises the following steps: S1: based on the irregular combination segmentation yard layout and sequencing problem, a target mathematical model is constructed; S2: based on the similarity of polygons, an irregular segmentation similarity combination strategy is proposed, the similarity of polygons includes the similarity of angles and the similarity of edges; the acquisition logic of the irregular segmentation similarity combination strategy is: S21: according to the combination of irregular segments, the angles and edges of the segments are determined, and the similarity criterion of polygons is used to determine whether two segments are similar; if similar, step S22 is entered; Otherwise, step S23 is entered; S22: if the current two segments are similar, the similarity coefficient between the segments is calculated, If the similarity coefficient is 1, it means that the two segments participating in the current calculation are congruent, and they are combined by rotation to form a complete shape; If the similarity coefficient is not 1, it means that there is room for improvement in the current two segments participating in the calculation, and the segment with a similarity coefficient close to 1 is preferentially selected for combination, so as to synthesize a complete shape; S23: when the two segments are not similar, it is judged whether their angles are equal; If the angles of the two segments are equal, it means that there may be a non-perfect combination between the two segments, and in the case of non-perfect combination, the similar edges with a similarity coefficient close to 1 are used to combine them; if the angles of the segments are not equal, there is no similar shape; S24: according to the Bottom-Left Fill principle, all segments will be systematically combined, and this process includes repeated execution of steps S21-S23 until all tasks are completed; S3: determine the initial position of the segment, calculate the minimum number of blocking segments, find the path with the minimum number of blocking segments by depth-first search, and integrate the design of the minimum blocking segment strategy; S4: a genetic algorithm integrating the strategy is established, the coding rules of the genetic algorithm are designed, the scheduling of the outgoing segment is preferentially considered, and the optimal solution is found by iterative deepening depth-first search.

2. The shipyard cruise ship block yard scheduling optimization method based on the improved genetic algorithm according to claim 1, characterized in that, The construction logic of the target mathematical model is: The irregular combination segmentation yard layout is the segmentation layout corresponding to the current yard, wherein each segmentation has a corresponding size and shape, and the flow mode of scheduling the yard is determined based on the segmentation layout; When establishing the yard, the blocking segment equation is constructed by moving and scheduling the segments in the yard, the blocking segment equation includes the incoming blocking segment equation and the outgoing blocking segment equation; the constraint equation is constructed by the obstacle, and the target mathematical model is constructed based on the blocking segment equation and the constraint equation.

3. The shipyard cruise ship block yard scheduling optimization method based on the improved genetic algorithm according to claim 2, characterized in that, The target mathematical model comprises: The number of blocking segments for yard entry, the number of blocking segments for yard exit, and the space utilization rate of yard position are calculated, and the total target of the problem is constructed by minimizing the number of blocking segments and maximizing the space utilization rate based on the number of blocking segments for yard entry, the number of blocking segments for yard exit, and the space utilization rate of yard position; Based on the task type limitation, the task type includes segment inbound task and segment outbound task, the area of the segment and the area of the position are calculated based on the formula, and the area of the segment is smaller than the area of the position, the segment is placed in the position, and the segment is not stacked; The segments can be directly or through combination to find a suitable stacking position in the yard, to ensure the time required for the segments to enter the yard, the time for the segments to exit the yard, and the route for the task segments to select the least number of obstacles to enter or turn out of the yard.

4. The shipyard cruise ship block yard scheduling optimization method based on the improved genetic algorithm according to claim 3, characterized in that, The design logic of the minimum blocking segment strategy is as follows: S31: Determine the initial stacking position of the segment, and when the number of minimum blocking segments tends to infinity and the number of current blocking segments is zero, mark the starting point of movement, move to the exit of the yard without changing direction, and if the selected direction is blocked by the segment; S32: If the number of blocking segments encountered is returned to the initial stacking position and the next movement position is determined, a movement direction is selected, and the movement position is moved to the new movement position. With the new movement position as the starting point, continue to move without changing direction until reaching the exit of the yard, and record the number of blocking segments; S33: Repeat step S32 until the movement position is the exit of the yard; S34: Return to the initial stacking position and perform step S32 until the path with zero blocking segments is found; otherwise, all possible paths are traversed; S35: Return the stacking position of the shortest path and the minimum number of blocking segments bm.

5. The shipyard cruise ship block yard scheduling optimization method based on the improved genetic algorithm according to claim 4, characterized in that, The application logic of the genetic algorithm of the fusion strategy is as follows: The initial population includes segment scheduling plans at different time points, scheduling plans between segments stored in the yard and segments to be arrived, and scheduling plans between segments at the same time point or of the same type; The crossover operation follows the coding rules of the solution. The same type of genes on the same scheduling period and chromosome can be crossed. The genes corresponding to the parents are divided into date-fixed genes and type-fixed genes; The genetic algorithm parameters are designed, the fitness value of each segment plan is calculated, and the optimal solution is finally obtained through iteration.

6. The shipyard cruise ship block yard scheduling optimization method based on the improved genetic algorithm according to claim 5, characterized in that, The coding rules of the multi-cycle in-and-out yard segment scheduling are as follows: The order of segments in the same cycle is allowed to be adjusted, and the order of segments between different cycles remains unchanged; In view of the fact that the incoming segments may increase the number of blocking segments of the outgoing segments, the outgoing segments are prioritized for planning.

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