Shipyard cruise ship segmented storage yard scheduling optimization method based on improved genetic algorithm

Abstract

The invention relates to the technical field of shipyard storage yard dispatching, and particularly discloses a shipyard cruise ship segmented storage yard dispatching optimization method based on an improved genetic algorithm, which comprises the following steps: constructing a target mathematical model based on irregular combination segmented storage yard layout and sorting problems; based on the similarity of polygons, a strategy of irregular segmentation similarity combination is provided, and the strategy comprises similarity of angles and similarity of edges; determining initial heap positions of the segments, calculating the number of the minimum blocking segments, finding out paths generating the minimum blocking segments through depth-first search, and integrating the paths generating the minimum blocking segments to design a minimum blocking segment strategy; the method comprises the following steps: establishing a genetic algorithm of a fusion strategy, designing a coding rule of the genetic algorithm, preferentially considering departure segment scheduling, finding an optimal solution through iterative deepened depth-first search, and realizing optimization of segment scheduling of the cruise ship in the storage yard of the shipyard by applying an improved genetic algorithm and a polygon similarity decision theorem.

Description

Technical Field

[0001] The present invention relates to the technical field of shipyard yard scheduling, and more specifically, to a shipyard cruise ship segment yard scheduling optimization method based on an improved genetic algorithm. Background Art

[0002] With the continuous innovation and optimization of shipbuilding modes and technologies, shipbuilding efficiency has also been improved. Hull segments are called intermediate products in the shipbuilding process, and regional construction is the main production mode of the segmentation process, including hull design, material pretreatment, segment processing, hull assembly and ship launching. The segmented construction mode combines the advantages of total segment manufacturing and lean manufacturing, and is divided into several parts in time and space, and some parts involve the processing of multiple segments. During the construction process, the completion of a single segment includes four main stages, namely painting, pretreatment, equipment and final assembly. The construction of large ships involves a large number of segments, estimated to be tens of millions. For example, a large cruise ship currently under construction in China consists of 503 segmented structural blocks, requiring the assembly of approximately 25 million individual components. The general section of the ship is assembled in the dock. Each section has more detailed technology to ensure the quality of the segment. For example, the equipment is carried out with each sub-segment of the sub-section, and the equipment is completed through painting and pretreatment. Finally, after the assembly of the ship is completed, it is usually equipped and launched. In the entire complex cruise ship construction process, the segment is the most important and basic unit. Its irregular shape poses significant challenges in terms of storage and handling, requiring innovative strategies to ensure efficient progress.

[0003] As a meta-heuristic optimization search algorithm inspired by natural selection and genetics, the genetic algorithm constructs an initial population representing a potential solution set, and each individual (or chromosome) represents a candidate solution to the problem to be optimized in a specific encoding method. Subsequently, the algorithm simulates the three core evolutionary mechanisms in nature: selection, crossover, and mutation, and iteratively guides the population to evolve towards a better solution space, and finally converges to a group of individuals that are most adapted to the environment, thereby obtaining the optimal solution to the problem.

[0004] However, for this problem, simply using genetic algorithms may slow down the solution speed. It is necessary to set a solution strategy that meets this problem to improve the efficiency of the solution. The defects and shortcomings of the existing technology are as follows:

[0005] 1. From the in-depth analysis of the geometric and morphological characteristics of ship block construction, it can be seen that following the symmetry principle of ship design, the bow and stern blocks usually show obvious mirror symmetry. This means that the structural arrangement and configuration of these blocks on both sides of the ship are almost the same. Similarly, along the longitudinal axis of the ship, the blocks of the main hull usually have counterparts 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 block structure". This principle is an important consideration in the process of ship design and construction, and plays a key role in optimizing ship construction and improving construction efficiency. However, the similarity between ship blocks is not considered during their construction and storage. Therefore, we propose a combination strategy between blocks based on the polygon similarity determination theorem. The ultimate goal is to achieve a balanced and efficient use of available space and maximize the productivity of the yard;

[0006] 2. Although the traditional genetic algorithm can obtain the optimal solution when dealing with this problem, the number of solutions is greatly increased due to the different types of segmentation and scheduling time. 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 shipyard cruise ship block yard scheduling optimization method based on an improved genetic algorithm to solve the above problems. Summary of the invention

[0008] In order to overcome the problems in the prior art, the present invention proposes a shipyard cruise ship segment yard scheduling optimization method based on an improved genetic algorithm to maximize the yard space utilization, thereby improving the production efficiency of the shipyard.

[0009] In a first aspect, the present invention provides a method for optimizing shipyard cruise ship staging yard scheduling based on an improved genetic algorithm, comprising the following steps:

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

[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 stacking position of the segments, calculate the minimum number of blocking segments, find the path with the least blocking segments through depth-first search, and integrate the paths with the least blocking segments to design the minimum blocking segment strategy;

[0013] S4: A genetic algorithm with fusion strategy was established, and the coding rules of the genetic algorithm were designed, giving priority to the segmented scheduling of the exits, and the optimal solution was found through iterative deepening depth-first search.

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

[0015] Based on the irregular combined segmented yard layout as the segmented layout corresponding to the current yard, wherein each segment has a corresponding size and shape, a flow pattern of the dispatching yard is determined based on the segmented layout;

[0016] When establishing a yard, a blocking segmental equation is constructed by moving and scheduling segments in the yard. The blocking segmental equation includes an entry blocking segmental equation and an exit blocking segmental equation. A constraint equation is constructed through obstacles, and a target mathematical model is constructed based on the blocking segmental equation and the constraint equation.

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

[0018] Calculate the number of blocking segments for yard entry, the number of blocking segments for yard exit and the space utilization rate of yard stacking positions, and construct the overall goal of the problem 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 stacking positions;

[0019] Based on the task type restriction, the task types include segment inbound tasks and segment outbound tasks. The area of ​​the segment and the area of ​​the stack are calculated based on the formula. The segment area is smaller than the area of ​​the stack, the segment is placed in the stack, and the segment is not stacked.

[0020] The segments can find a suitable stacking location in the yard directly or through combination, ensuring the time required for the segments to enter the yard, the time for the segments to exit the yard, and the task segments to choose the route with the least obstacles to enter or exit the yard.

[0021] As a preferred technical solution of the first aspect of the present invention, the acquisition logic of the irregular segment similarity combination strategy is:

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

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

[0024] If the similarity coefficient is 1, it means 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 in the two segments currently involved in the calculation, and the segments with a similarity coefficient close to 1 are preferentially selected 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 a non-perfect combination between the two segments. In the case of a non-perfect combination, they are combined using the similarity ratio of the sides with similarity coefficients close to 1; if the angles of the segments are not equal, there is no similar shape;

[0028] S24: All segments are systematically combined according to the Bottom-Left Fill (BLF) principle. This process includes repeatedly executing 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:

[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 movement, move to the exit of the yard without changing the direction, if the selected direction is blocked by the segment;

[0031] S32: If the number of blocked segments encountered returns to the initial stack position and determines the next moving position, a moving direction is selected, and the moving position is moved to a new moving position, and the new moving position is used as the starting point, the direction remains unchanged, and the movement continues until the exit of the yard is reached, and the number of blocked segments is recorded;

[0032] S33: repeat step S32 until the moving position is the exit of the yard;

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

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

[0035] As a preferred technical solution of the first aspect of the present invention, the application logic of the genetic algorithm of the fusion strategy is:

[0036] The initial population includes segment scheduling plans at different time points, scheduling plans between segments stored in the storage yard and segments that are about to arrive, and scheduling plans between segments at the same time point or of the same type;

[0037] The crossover operation follows the coding rules of the solution. The same type of genes on the chromosome can be crossovered in the same scheduling period. The genes corresponding to the parents are divided into date-fixed genes and type-fixed genes.

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

[0039] As a preferred technical solution of the first aspect of the present invention, the coding rule of multi-period entry and exit segment scheduling is:

[0040] The segment order within the same cycle can be adjusted, while the segment order between different cycles remains unchanged;

[0041] Considering that the incoming segments may increase the number of blocking segments for the outgoing segments, the outgoing segments should be planned first.

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

[0043] The present invention optimizes the scheduling of cruise ships in shipyard sections by applying an improved genetic algorithm and polygon similarity decision theorem, improves the space utilization and production efficiency of the shipyard, reduces the number of blocked sections, enhances the flexibility and adaptability of the scheduling plan, and maintains the orderliness and logical consistency of the operation process, effectively avoiding potential conflicts and confusions, and its remarkable effectiveness is verified by experiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 An illustration of the segmented entry and exit of a shipyard yard of a cruise ship segmented scheduling optimization method based on an improved genetic algorithm is provided for this application;

[0045] Figure 2 An irregular segmentation combination strategy diagram of an optimization method for segmentation scheduling of cruise ships within a shipyard yard based on an improved genetic algorithm is provided for this application;

[0046] Figure 3 A minimum blocking segmentation strategy diagram for a method for optimizing segmentation scheduling of cruise ships within a shipyard yard based on an improved genetic algorithm provided in this application;

[0047] Figure 4 Provided for this application is a coding diagram of a genetic algorithm solution for a method for optimizing the block scheduling of cruise ships within a shipyard yard based on an improved genetic algorithm;

[0048] Figure 5 A cross-plot of genetic algorithm solutions for an optimization method for cruise ship block scheduling within a shipyard yard based on an improved genetic algorithm is provided for this application.

[0049] Figure 6 A flow chart of a method for optimizing cruise ship block scheduling within a shipyard yard based on an improved genetic algorithm is provided for this application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 shipyard cruise ship segmentation yards based on an improved genetic algorithm, comprising the following steps:

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

[0053] Exemplarily, a rectangular yard is similar to a chessboard with M rows and N columns, interspersed with certain passages. Given a sequence of tasks for a day in a scheduling cycle, each task consists of a segment arrival time, a departure time, and a task amount. A mathematical model is used to design the best layout strategy for these segments, optimize their routes, and determine the most reasonable segment scheduling sequence. The goal is to minimize the total number of obstacles generated within a given cycle and achieve the best segment combination.

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

[0055] Based on the irregular combined segmented yard layout as the segmented layout corresponding to the current yard, wherein each segment has a corresponding size and shape, a flow pattern of the dispatching yard is determined based on the segmented layout;

[0056] It should be noted that: as the basic unit of a shipyard, the actual situation of the shipyard yard is taken into consideration. In order to realize the segment processing of the shipyard and the smoothness of the entire production process, it is proposed that the yard layout is closely related to the scheduling efficiency. For example, a flat and wide rectangle is an ideal choice for the yard layout, which can reduce the moving distance of the segment in the yard and improve the scheduling efficiency. At the same time, the closer the yard shape is to a square, the more obstructions the unloading segment may encounter under the same segmentation task. Therefore, when designing the yard, it is necessary to comprehensively consider the layout shape and scheduling requirements to achieve the best scheduling effect.

[0057] Accessibility around the yard is also crucial to dispatch efficiency. A yard layout with accessibility on all sides can facilitate the movement and dispatch of sections within the yard. When building the yard, avoid setting up obstacles such as walls, reduce dispatch time and costs, and ensure good accessibility around the yard.

[0058] The target mathematical model is:

[0059] Calculate the barrier segment for market entry:

[0060]

[0061] Where: f1 represents the number of blocking segments for the yard to enter, t is the date number of each day in the scheduling period T, t∈T={0,1,2,3,...,t last}, t = 0 represents the initial stacking state of the yard before the dispatch task comes out, t = 1 represents the stacking state of the yard when entering the yard, t last Indicates the last day; Ta indicates the number of a task on date t, Ta∈TA t ={1,2,...,TA last},TA last represents the total number of segment scheduling tasks in day t; i is the segment number, i total Represents the total number of segments; j is the number of the stack in the yard. The matrix yard numbering rule is to sort from left to right and from top to bottom. max Represents the bottom right corner of the stack;

[0062] Indicates whether the segment with task Ta and segment number i can enter the yard within a certain day t; x i,j Indicates whether segment i can be placed or combined in stack position j; Indicates whether segment i can be stacked at location j in the yard within a certain day t; b i,j Indicates the total number of blocked segments on a path.

[0063] Calculate the blocking segment of the field:

[0064]

[0065] Where: f2 represents the number of blocking sections at the yard exit; It indicates whether the segment with task Ta and segment number i can be transferred out of the yard within a certain day t; the meanings of other symbols are the same as above.

[0066] Calculate the space utilization of the stack:

[0067]

[0068] Where: f3 represents the space utilization rate of the storage yard; s i represents the projected area of ​​segment i; S j represents the area of ​​location j;

[0069] The overall model is:

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

[0071] Where: 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] Where: It indicates whether the segment with task Ta and segment number i can be transferred out of the yard within a certain day t; T At Indicates the entire task cycle; the entire formula indicates the task type restriction. There are two types of tasks in segmented scheduling, one is segmented inbound task and the other is segmented outbound task. Tasks of the same type cannot be transferred in and out of the yard at the same time.

[0075]

[0076] Where: Indicates whether segment i can be stacked at location j in the yard within a certain day t; Represents the area of ​​all segments stacked in location j; the formula ensures that the area of ​​the segment is smaller than the area of ​​the location.

[0077] 0≤x i ≤l0-l i,m ;

[0078] Where: x i Indicates the horizontal coordinate of the upper right corner of the segment; l0 indicates the length of the stack; l i,m Indicates the length of the segment projection; ensures that the segment fits within the stack.

[0079] 0≤y i ≤l0-w i,m ;

[0080] Where: y i Indicates the ordinate of the upper right corner of the segment; l0 indicates the width of the stack; w i,m Indicates the width of the segment projection; ensures that the segment fits within the stack.

[0081] x i +l i,m ≤x i ';

[0082] Where: x i ' represents the upper right corner abscissa of segment j combined with segment i; ensures that segments are not stacked.

[0083] y i +w i,m ≤y′ i ;

[0084] Where: y′ i Indicates the upper right ordinate of segment j combined with segment i; ensures that segments are not stacked.

[0085] It shows that the segments can find suitable stacking locations in the yard directly or through combination.

[0086]

[0087] Where: bt i It is the time for segment i to enter the yard; it indicates the time required for the segment to enter the yard.

[0088] Indicates the time required for segmented exit from the yard.

[0089]

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

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

[0092] More specifically, if Figure 1 The process of a site with accessible roads running around the perimeter of the site for flatbed trucks to carry sections in and out of the site for dispatch operations is shown;

[0093] The yard status on Day 7 shows that there are many segments included, such as 1210 and 228. The labels on these segments indicate that segment 12 is out on Day 10 and segment 22 is out on Day 8. There are three potential routes for segment 107;

[0094] However, the downward route requires dispatching two blocking segments (2011 and 137) at the same time, while the other two routes each require dispatching one segment (1112 and 189 respectively). Since the segments weigh hundreds of tons and are not easy to move, the path with fewer segments is chosen as much as possible. The left and right paths encounter fewer blocking segments, making them the optimal dispatch 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 edges;

[0096] Specifically, if Figure 2 As shown, the acquisition logic of the irregular segment similarity combination strategy is:

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

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

[0099] If the similarity coefficient is 1, it means 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 in the two segments currently involved in the calculation, and the segments with a similarity coefficient close to 1 are preferentially selected for combination to synthesize a complete shape;

[0101] It should be noted that although two segments can be combined together, they are not completely consistent. Combining dissimilar segments will cause greater space waste. Therefore, segments with a similarity coefficient close to 1 are preferred for combination; the similarity coefficient is calculated based on the area ratio of the segments, as follows:

[0102]

[0103] Among them, S i is the segment planned to be stored in the yard, S now,j is the segment currently stored in the yard; if δ-1=0, then S i YesS now,j The optimal combination segmentation of

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

[0105] If the angles of two segments are equal, this indicates that there may be a non-perfect combination between the two segments;

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

[0107]

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

[0109] S24: All segments are systematically combined according to the Bottom-Left Fill (BLF) principle. This process includes repeatedly executing steps S21-S23 until all tasks are completed.

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

[0111] Check whether A and B are similar. Assuming that the angles and sides of A and B are similar, calculate their similarity coefficient and find that the coefficient is 0.9, indicating that they can be combined but not completely matched.

[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, and combine them using the similarity ratio.

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

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

[0115] S31: Determine the initial stacking position p0 of the segment, the minimum number of blocking segments bm=+∞, the current number of blocking segments b=0, take this position as the starting point of movement, and move to the exit of the yard without changing the direction. If the selected direction is blocked by the segment, then b=b+n, n is the number of blocking segments encountered. If b is 0, go to step S34; otherwise bm=b, go 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 a new position pnew, starting from the new position pnew, keep the direction unchanged, and continue moving until reaching the exit of the yard, and 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 being 0 is found; otherwise, all possible paths are traversed;

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

[0120] It should be noted that a minimum blocking segmentation strategy is designed. Since there is a feasible moving direction for the segments on each stack, each path from this position to the end position can be searched based on the depth-first search, and then the path with the least blocking segments can be obtained. At the same time, this increases the difficulty and time of the solution. In order to reduce the number of paths in the result, an iteratively deepened depth-first search is used to find the path with the least blocking segments for the entry segment to enter a certain stack or the exit segment to be transported from a certain stack to the edge of the yard.

[0121] S4: A genetic algorithm with fusion strategy was established, and the coding rules of the genetic algorithm were designed, giving priority to the segmented scheduling of the exits, and the optimal solution was found through iterative deepening depth-first search.

[0122] Specifically, if Figure 4 As shown, the application logic of the genetic algorithm of the fusion strategy is:

[0123] The initial population includes segment scheduling plans at different time points, scheduling plans between segments stored in the yard and segments that are 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 at different time points is:

[0125] In scheduling planning, the block scheduling plans at 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 blocks 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 the segments stored in the yard and the segments that are about to arrive is:

[0127] The scheduling plans between the blocks stored in the yard and the blocks that are about to arrive (such as block 2 and block 4) must not be exchanged. This is to ensure logistics efficiency and avoid potential conflicts or confusion;

[0128] Scheduling between segments at 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), the scheduling plan 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 coding rules of the genetic algorithm, the entry segmentation often increases the number of blocks in the exit segmentation, thus affecting the overall scheduling efficiency. The strategy of exit segmentation scheduling is given priority to speed up the solution by reducing the scale of the solution. The coding rules for multi-period entry and exit segmentation scheduling are:

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

[0132] ② Considering that the incoming segments may increase the number of blocking segments for outgoing segments, priority should be given to planning the outgoing segments to optimize resource allocation and improve overall operational efficiency.

[0133] It should be noted that the division of these three parts is to ensure that the genetic algorithm can take into account the logical consistency of the time series, logistics efficiency, and optimization of resource allocation 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] The crossover operation follows the encoding rules of the solution. The same type of genes on the chromosome can be crossovered 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 order-fixed genes of Pb; and the genes of Pb correspond to the type-fixed genes of Pa, forming the position-fixed genes of Pb. Figure 5 As shown, offspring C inherited the date-fixing genes and type-fixing genes from P1 and P2.

[0136] The genetic algorithm parameters are designed as follows: maximum iterations of the genetic algorithm are 200, population size is 30, the proportion of elite selection is 0.15, the proportion of individuals selected by roulette is 0.1, the probability of individuals obtained by crossover operation is 0.7, and the probability of individual mutation is 0.05. Through iteration, the optimal solution is finally obtained.

[0137] More specifically, the fitness value of each segment plan is calculated using a formula to select the optimal solution. The fitness value of the initial population is calculated using the formula:

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

[0139] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0140] Finally: 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 in the protection scope of the present invention.

Claims

1. A method for optimizing shipyard cruise ship block yard scheduling based on improved genetic algorithm, characterized in that: The following steps are involved: S1: Construct the target mathematical model based on the layout and sorting problem of irregular combined segmented yard; 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. S3: Determine the initial stacking position of the segments, calculate the minimum number of blocking segments, find the path with the least blocking segments through depth-first search, and integrate the paths with the least blocking segments to design the minimum blocking segment strategy; S4: A genetic algorithm with fusion strategy was established, and the coding rules of the genetic algorithm were designed, giving priority to the segmented scheduling of the exits, and the optimal solution was found through iterative deepening depth-first search.

2. According to claim 1, a method for optimizing shipyard cruise ship staging yard scheduling based on improved genetic algorithm is characterized in that: The construction logic of the target mathematical model is: Based on the irregular combined segmented yard layout as the segmented layout corresponding to the current yard, wherein each segment has a corresponding size and shape, a flow pattern of the dispatching yard is determined based on the segmented layout; When establishing a yard, a blocking segmental equation is constructed by moving and scheduling segments in the yard. The blocking segmental equation includes an entry blocking segmental equation and an exit blocking segmental equation. A constraint equation is constructed through obstacles, and a target mathematical model is constructed based on the blocking segmental equation and the constraint equation.

3. The method for optimizing shipyard cruise ship staging yard scheduling based on improved genetic algorithm according to claim 2 is characterized in that: The target mathematical model includes: Calculate the number of blocking segments for yard entry, the number of blocking segments for yard exit and the space utilization rate of yard stacking positions, and construct the overall goal of the problem 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 stacking positions; Based on the task type restriction, the task types include segment inbound tasks and segment outbound tasks. The area of ​​the segment and the area of ​​the stack are calculated based on the formula. The segment area is smaller than the area of ​​the stack, the segment is placed in the stack, and the segment is not stacked. The segments can find a suitable stacking location in the yard directly or through combination, ensuring the time required for the segments to enter the yard, the time for the segments to exit the yard, and the task segments to choose the route with the least obstacles to enter or exit the yard.

4. The method for optimizing shipyard cruise ship staging yard scheduling based on improved genetic algorithm according to claim 3 is characterized in that: The acquisition logic of the irregular segment similarity combination strategy is: S21: Determine the angles and sides of the segments according to the combination of irregular segments, and use the polygon similarity standard to determine whether two segments are similar; if they are similar, proceed to step S22; Otherwise, proceed to step S23; S22: If the two current segments are similar, calculate the similarity coefficient between the segments. If the similarity coefficient is 1, it means that the two segments currently involved in the calculation are congruent and can be combined into a complete shape through rotation; If the similarity coefficient is not 1, it indicates that there is room for improvement in the two segments currently involved in the calculation, and the segments with a similarity coefficient close to 1 are preferentially selected for combination to synthesize a complete shape; S23: When two segments are not similar, determine whether their angles are equal; If the angles of two segments are equal, it indicates that there may be a non-perfect combination between the two segments. In the case of a non-perfect combination, they are combined using the similarity ratio of the sides with similarity coefficients close to 1; if the angles of the segments are not equal, there is no similar shape; S24: All segments are systematically combined according to the Bottom-Left Fill (BLF) principle. This process includes repeatedly executing steps S21-S23 until all tasks are completed.

5. The method for optimizing shipyard cruise ship staging yard scheduling based on improved genetic algorithm according to claim 4 is characterized in that: The design logic of the minimum blocking segmentation strategy is: 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 movement, move to the exit of the yard without changing the direction, if the selected direction is blocked by the segment; S32: If the number of blocked segments encountered returns to the initial stack position and determines the next moving position, a moving direction is selected, and the moving position is moved to a new moving position, and the new moving position is used as the starting point, the direction remains unchanged, and the movement continues until the exit of the yard is reached, and the number of blocked segments is recorded; S33: repeat step S32 until the moving position is the exit of the yard; S34: Return to the initial stack and execute step S32 until a path with zero blocking segments is found; otherwise, all possible paths are traversed; S35: Return the stack position of the shortest path and the minimum number of blocking segments bm.

6. The method for optimizing shipyard cruise ship staging yard scheduling based on improved genetic algorithm according to claim 5 is characterized in that: The application logic of the genetic algorithm of the fusion strategy is: The initial population includes segment scheduling plans at different time points, scheduling plans between segments stored in the storage yard and segments that are about to arrive, 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 chromosome can be crossovered in the same scheduling period. 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.

7. The method for optimizing shipyard cruise ship staging yard scheduling based on improved genetic algorithm according to claim 6 is characterized in that: The coding rules for multi-cycle entry and exit segment scheduling are: The segment order within the same cycle can be adjusted, while the segment order between different cycles remains unchanged; Considering that the incoming segments may increase the number of blocking segments for the outgoing segments, the outgoing segments should be planned first.

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