Fetal position scheduling optimization method and device, computer equipment and medium

Abstract

The invention relates to the technical field of resource scheduling, and discloses a fetal position scheduling optimization method and device, computer equipment and a medium. The method comprises the following steps: determining a segmented task set, a fetal position set and a pre-established workshop fetal position scheduling mathematical model; the segmented task set comprises at least one segmented task, and each segmented task comprises the length, the width, the in-tire period, the expected off-tire time and the on-tire time; determining an initial population according to the segmented task set, the fetal position set, an overdue risk-based priority initialization strategy and a segmented random initialization strategy; the initial population comprises a plurality of initial fetal position scheduling schemes; and optimizing the workshop tire position scheduling mathematical model based on the initial population to obtain a scheduling scheme of each tire position in the ship block construction process. Based on the technical scheme of the invention, the fetal position scheduling efficiency and the resource utilization rate in a multi-task and multi-constraint environment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling optimization in a ship segment construction process, and in particular to a method, device, computer equipment and medium for optimizing fetal position scheduling. Background Art

[0002] The modern shipbuilding industry adopts a modular block construction method, which greatly improves the construction efficiency by splitting the entire ship into multiple blocks for parallel manufacturing and assembly. Modular shipbuilding requires the rational use of limited space resources and the coordination of the processing sequence of each block to achieve efficient operation of block construction.

[0003] At present, the scheduling and spatial layout of ship construction mainly rely on manual scheduling. However, with the increasing complexity of ship projects, the limitations of manual scheduling in terms of efficiency and flexibility have become increasingly obvious, making it difficult to effectively respond to dynamic changes, resulting in waste of resources and delays in construction.

[0004] Therefore, traditional methods are difficult to meet the complex shipbuilding needs, and there is an urgent need for an efficient and automated scheduling method with dynamic response capabilities and global optimization to improve production efficiency and shorten the construction cycle. Summary of the invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the increasingly obvious limitations of manual scheduling in related technologies in terms of efficiency and flexibility, making it difficult to effectively cope with dynamic changes, resulting in waste of resources and delays in construction schedules.

[0006] In order to solve the above technical problems, the present invention provides a fetal position scheduling optimization method, the fetal position scheduling optimization method comprising:

[0007] Determine a segmented task set, a tire position set, and a pre-established workshop tire position scheduling mathematical model; the segmented task set includes at least one segmented task, and each segmented task includes: length, width, tire cycle, expected tire removal time, and tire placement time;

[0008] Determine an initial population according to a segmented task set, a fetal position set, a priority initialization strategy based on overdue risk, and a segmented random initialization strategy; the initial population includes multiple initial fetal position scheduling schemes;

[0009] The mathematical model of workshop position scheduling is optimized based on the initial population to obtain a scheduling plan for each position in the process of ship block construction.

[0010] In an optional implementation, before determining the segmented task set and the fetal position set, the method further includes:

[0011] With the goal of minimizing the total completion time, the first objective function is constructed;

[0012] The second objective function is constructed with the goal of minimizing the number of overdue tasks;

[0013] Determine multiple constraints; the multiple constraints include: space capacity constraints of fetal position, space placement constraints of segments, and time constraints of segment tasks;

[0014] The workshop fetal position scheduling mathematical model is constructed based on the first objective function, the second objective function and the multiple constraints.

[0015] In an optional implementation, the determining of the initial population according to the segmented task set, the fetal position set, the priority initialization strategy based on the overdue risk, and the segmented random initialization strategy includes:

[0016] Apply the priority initialization strategy based on overdue risk to the segmented task set to obtain the priority of each segmented task, and determine the expected start time of each segmented task according to the priority of each segmented task;

[0017] Generate several initial solutions according to the expected start time of each segment task and the fetal position set;

[0018] Encode several initial solutions to obtain the gene sequence corresponding to each initial solution;

[0019] The piecewise random initialization strategy is applied to the gene sequence corresponding to each initial solution to obtain an initial population.

[0020] In an optional implementation, the optimizing the workshop position scheduling mathematical model based on the initial population to obtain a scheduling scheme for each position in the ship block construction process includes:

[0021] S1: Determine the initial population as the parent population P with a population size of N t ;

[0022] S2: Initialization iteration number: i=1;

[0023] S3: construct a fitness function based on the mapping relationship and pre-set the fitness threshold;

[0024] S4: Calculate the fitness of individuals in the parent population and sort the individuals in the parent population according to their fitness; one individual corresponds to one gene sequence;

[0025] S5: Determine whether there is at least one individual in the parent population whose fitness is greater than a preset fitness threshold. If there is at least one individual in the parent population whose fitness is greater than the preset fitness threshold, output an optimal solution, which is a scheduling scheme for each position in the ship block construction process corresponding to the maximum fitness value;

[0026] If the fitness of the individuals in the parent population is not greater than the preset fitness threshold, the current number of iterations is increased by 1; it is determined whether the current number of iterations is greater than the preset number of iterations. If the current number of iterations is greater than the preset number of iterations, the individual with the largest fitness in the parent population is output as the optimal solution. If the current number of iterations is not greater than the preset number of iterations, S6 is executed;

[0027] S6: Perform crossover and mutation operations on the parent population to obtain a new offspring population of N in number, calculate the fitness of the individuals in the offspring population, re-determine the parent population based on the fitness of the individuals in the offspring population, and repeat S4-S6.

[0028] In an optional implementation, the calculating the fitness of individuals in the parent population includes:

[0029] Decoding the individuals in the parent population according to a preset fetal position selection mode to obtain a fetal position scheduling scheme corresponding to each individual;

[0030] The fitness of the fetal position scheduling scheme corresponding to each individual is calculated according to the fitness function.

[0031] In an optional implementation, the preset fetal position selection mode includes:

[0032] Mode 1: The current segment task shares the fetal position with the segment with higher priority;

[0033] Mode 2: independently assign fetal position to the current segment task;

[0034] Mode 3: When there is insufficient space for a single fetal position, two adjacent fetal positions are shared with the segmentation tasks with higher priority;

[0035] Mode 4: The current segment task exclusively occupies two adjacent fetal positions;

[0036] Among them, the selection order of the preset fetal position selection modes is mode 1, mode 2, mode 3, and mode 4.

[0037] In an optional implementation, the method further includes: determining a mutation probability in the mutation operation according to a current number of iterations.

[0038] In a second aspect, the present invention provides a fetal position scheduling optimization device, the fetal position scheduling optimization device comprising:

[0039] The first processing module is used to determine a segmented task set, a tire position set and a pre-established workshop tire position scheduling mathematical model; the segmented task set includes at least one segmented task, and each segmented task includes: length, width, tire cycle, expected tire removal time and tire placement time;

[0040] A second processing module is used to determine an initial population according to a segmented task set, a fetal position set, a priority initialization strategy based on overdue risk, and a segmented random initialization strategy; the initial population includes multiple initial fetal position scheduling schemes;

[0041] The third processing module is used to optimize the workshop position scheduling mathematical model based on the initial population to obtain a scheduling plan for each position in the process of ship block construction.

[0042] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the fetal position scheduling optimization method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a single computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the fetal position scheduling optimization method of the first aspect or any corresponding embodiment thereof.

[0044] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the fetal position scheduling optimization method of the first aspect or any corresponding embodiment thereof.

[0045] The technical solution provided by the present invention has the following technical effects:

[0046] When determining the set of segment tasks, multiple key attributes of each segment task are taken into consideration, such as length, width, tire cycle, expected tire unloading time and tire loading time, etc. These attributes can comprehensively and meticulously describe the characteristics of each segment task, so that the subsequent generated scheduling plan can be fully arranged according to the actual situation of the task, avoiding unreasonable scheduling due to missing information or incomplete consideration, so as to better meet the complex reality of the shipyard's ship segment construction process.

[0047] By considering the risk of overdue to determine the priority of tasks, those segmented tasks that are more likely to be overdue can get more attention in the initial stage and be given priority in arranging appropriate fetal positions. This helps to deal with possible delays in advance, lays the foundation for the overall scheduling plan to complete tasks on time from the beginning, and improves the rationality and effectiveness of the scheduling plan in terms of time control.

[0048] The segmented random initialization strategy can further enrich the diversity of fetal position scheduling solutions in the initial population. It avoids generating only a single type or partially similar solutions during initialization, and increases the richness of the population. It is like looking for the optimal solution from multiple different "starting points" in a large search space at the beginning, which provides a wider exploration range for the subsequent optimization process, reduces the risk of falling into the local optimum, and increases the possibility of finally finding the global optimal scheduling solution.

[0049] Based on the initial population containing a variety of different initial fetal position scheduling schemes, the mathematical model of workshop fetal position scheduling is optimized. Since the initial population already has the characteristics of rationality and diversity, the algorithm can make full use of these advantages in the optimization process, and gradually select better schemes through continuous iteration, comparison and optimization. This enables the final fetal position scheduling schemes to achieve more efficient resource utilization, shorter total completion time, and fewer overdue tasks on the basis of meeting various constraints (such as fetal position resource restrictions, task time requirements, etc.), thereby improving the production efficiency and resource allocation efficiency of the entire ship section construction process.

[0050] The technical solution of the present invention, through the synergistic effect of the advantages of the above-mentioned aspects, can not only make the position scheduling in the process of ship segment construction more reasonable and orderly, reduce the overdue of tasks, but also make full use of limited position resources and improve space utilization, etc. For example, it can reasonably arrange segment tasks of different sizes (reflected by length and width) and different processing cycles (reflected by the cycle of the tire, the expected time of the tire, etc.) to the appropriate position, avoid idle waste of the position or delay of the task due to inappropriate position, so as to adapt to the complex production environment of limited shipyard resources and multiple tasks in parallel, and bring about the improvement of overall production efficiency.

[0051] The technical solution of the present invention can be flexibly applied and adjusted for ship segment construction tasks of different scales (such as different numbers of segment tasks, different numbers of fetal positions, etc.) because it is based on a clear set of task attributes, a reasonable initialization strategy, and an effective optimization mechanism. It is not designed for specific, fixed scenarios, but has a certain degree of versatility. It can be adapted accordingly with various changes in the actual production of the shipyard (such as new task types, changes in fetal position resources, etc.), and continue to play a role in optimizing fetal position scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 is a flow chart of a fetal position scheduling optimization method according to an embodiment of the present invention;

[0054] Figure 2 is a flow chart of a differential evolution algorithm according to an embodiment of the present invention;

[0055] Figure 3 is a diagram of a differential evolution coding method according to an embodiment of the present invention;

[0056] Figure 4 is a crossover and mutation operation diagram in the differential evolution algorithm of an embodiment of the present invention;

[0057] Figure 5 It is a fitness function convergence curve diagram of the traditional differential evolution and adaptive Levy differential evolution algorithms of the embodiment of the present invention;

[0058] Figure 6 is a structural schematic diagram of a fetal position scheduling optimization device according to an embodiment of the present invention;

[0059] Figure 7 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0061] The modern shipbuilding industry adopts a modular segmented construction method, which greatly improves construction efficiency by splitting the entire ship into multiple blocks for parallel manufacturing and assembly. Modular shipbuilding requires the rational use of limited space resources and the coordination of the processing sequence of each segment to achieve efficient operation of segmented construction. At present, the scheduling and spatial layout of ship construction mainly rely on manual scheduling, but with the increasing complexity of ship projects, the limitations of manual scheduling in terms of efficiency and flexibility have become more and more obvious, making it difficult to effectively respond to dynamic changes, resulting in waste of resources and delays in construction. In order to improve efficiency, some shipyards have tried to adopt automated scheduling methods, but most methods are fixed to a single model and lack the flexibility to respond to dynamic changes. They can usually only optimize a single goal and are difficult to meet actual needs.

[0062] The scheduling problem of ship block construction involves the allocation of multiple positions and the optimization of the processing sequence of the blocks, which is highly complex and has multiple constraints. Traditional scheduling algorithms are prone to fall into local optimality, fail to fully utilize resources, and lack the flexibility to deal with dynamic adjustments such as changes in block priorities and reallocation, resulting in slow response, waste of resources and delays in construction. Therefore, related methods are difficult to meet the complex needs of shipbuilding, and an efficient and automated scheduling method with dynamic response capabilities and global optimization is urgently needed to improve production efficiency and shorten the construction cycle.

[0063] The embodiments of the present invention provide a fetal position scheduling optimization method, device, computer equipment and medium to solve the above problems.

[0064] The technical solution of the present invention provides a method for optimizing the fetal position scheduling, which is specifically a multi-task multi-constraint scheduling method based on an improved differential evolution algorithm, aiming to solve the fetal position scheduling problem in a shipyard. The method introduces innovative initialization strategies, encoding and decoding mechanisms, adaptive differential evolution operations, and multi-mode allocation strategies based on the differential evolution algorithm, effectively improving the completion efficiency and space utilization of scheduling, and is suitable for production environments with limited resources and multi-task parallelism.

[0065] In the first stage, by constructing a mathematical model for workshop fetal position scheduling, the spatial requirements, processing time, expected completion time and resource constraints of the fetal position of the segmented tasks were clarified, with the optimization goal of minimizing the total completion time and minimizing the number of overdue tasks. The model sets multiple constraints, including the spatial capacity of the fetal position, the spatial placement requirements of the segments, the time requirements of the segmented tasks, etc. In addition, different segments may need to occupy the fetal position exclusively or share multiple fetal positions to meet their spatial requirements, and four fetal position allocation modes are proposed. In order to improve the quality of the initial solution and accelerate the convergence speed, the present invention adopts a priority initialization method based on overdue risk, and enhances the diversity of solutions through the segmented random initialization strategy, laying a solid foundation for the optimization of the differential evolution algorithm.

[0066] In the second stage, the scheduling scheme of the workshop fetal position is generated by the improved differential evolution algorithm. First, the selection operation based on the fitness function is used. The fitness function comprehensively considers the total completion time, the number of overdue tasks, the overdue time and the fetal space utilization rate to ensure that high-quality individuals enter the next generation. Then, through the adaptive crossover operation, the parent genes are combined with random positions and lengths to generate new individuals, retaining high-quality features and enhancing gene diversity. The mutation operation sets a higher mutation probability for overdue tasks to increase their priority and reduce the number of overdue tasks, while other tasks maintain a lower mutation probability to enhance the stability of the scheme. At the same time, the adaptive Levy mechanism is introduced. In the early stage of iteration, a higher probability is used for global exploration. In the later stage of iteration, the probability of global exploration is reduced and the probability of local exploration is increased. After each round of differential evolution operation, the scheme is evaluated based on the fitness function, and after multiple rounds of iterative optimization, it gradually converges to a high-quality solution, and finally forms an efficient fetal position scheduling scheme that meets the complex multi-constraint environment.

[0067] According to an embodiment of the present invention, an embodiment of a fetal position scheduling optimization method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer device such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0068] Figure 1 4 is a flow chart of a fetal position scheduling optimization method according to an embodiment of the present invention.

[0069] like Figure 1 As shown, an embodiment of the present invention provides a fetal position scheduling optimization method, the fetal position scheduling optimization method comprising:

[0070] S101: Determine a segmented task set, a fetal position set, and a pre-established workshop fetal position scheduling mathematical model.

[0071] In this embodiment, the segmented task set includes at least one segmented task, and each segmented task includes: length, width, tire cycle, expected tire removal time and tire placement time.

[0072] S102: Determine an initial population according to a segmented task set, a fetal position set, a priority initialization strategy based on overdue risk, and a segmented random initialization strategy.

[0073] In this embodiment, the initial population includes a plurality of initial fetal position scheduling schemes.

[0074] S103: Optimizing the mathematical model of workshop position scheduling based on the initial population to obtain a scheduling plan for each position during the ship block construction process.

[0075] In an optional implementation, before determining the segmented task set and the fetal position set, the fetal position scheduling optimization method further includes:

[0076] With the goal of minimizing the total completion time, the first objective function is constructed.

[0077] The second objective function is constructed with the goal of minimizing the number of overdue tasks.

[0078] Determine multiple constraints. Multiple constraints include: spatial capacity constraints for fetal position, spatial placement constraints for segments, and time constraints for segment tasks.

[0079] A mathematical model for workshop fetal position scheduling is constructed based on the first objective function, the second objective function and multiple constraints.

[0080] In an optional implementation, determining the initial population according to the segmented task set, the fetal position set, the priority initialization strategy based on the overdue risk, and the segmented random initialization strategy includes:

[0081] The priority initialization strategy based on overdue risk is applied to the segmented task set to obtain the priority of each segmented task, and the expected start time of each segmented task is determined according to the priority of each segmented task.

[0082] Several initial solutions are generated according to the expected start time of each segmented task and the fetal position set.

[0083] Several initial solutions are encoded to obtain the gene sequence corresponding to each initial solution.

[0084] The piecewise random initialization strategy is applied to the gene sequence corresponding to each initial solution to obtain the initial population.

[0085] In an optional implementation, the workshop position scheduling mathematical model is optimized based on the initial population to obtain a scheduling scheme for each position in the ship block construction process, including:

[0086] S1: Determine the initial population as the parent population P with a population size of N t .

[0087] S2: Initialize the number of iterations: i=1.

[0088] S3: Construct a fitness function according to the mapping relationship and pre-set the fitness threshold.

[0089] S4: Calculate the fitness of individuals in the parent population and sort the individuals in the parent population according to their fitness. One individual corresponds to one gene sequence.

[0090] S5: Determine whether there is at least one individual in the parent population whose fitness is greater than a preset fitness threshold. If there is at least one individual in the parent population whose fitness is greater than the preset fitness threshold, output the optimal solution, which is the scheduling plan for each position in the ship segment construction process corresponding to the maximum fitness value.

[0091] If the fitness of the individuals in the parent population is not greater than the preset fitness threshold, the current iteration number is increased by 1. It is determined whether the current iteration number is greater than the preset iteration number. If the current iteration number is greater than the preset iteration number, the individual with the largest fitness in the parent population is output as the optimal solution. If the current iteration number is not greater than the preset iteration number, S6 is executed.

[0092] S6: Perform crossover and mutation operations on the parent population to obtain a new offspring population of N in number, calculate the fitness of the individuals in the offspring population, re-determine the parent population based on the fitness of the individuals in the offspring population, and repeat S4-S6.

[0093] In an optional implementation, calculating the fitness of individuals in the parent population includes:

[0094] The individuals in the parent population are decoded according to the preset fetal position selection mode to obtain the fetal position scheduling plan corresponding to each individual.

[0095] The fitness of the fetal position scheduling scheme corresponding to each individual is calculated according to the fitness function.

[0096] In an optional implementation, the preset fetal position selection mode includes:

[0097] Mode 1: The current segment task shares the fetal position with the segment with higher priority.

[0098] Mode 2: Independently assign fetal position to the current segment task.

[0099] Mode 3: When there is insufficient space for a single fetal position, two adjacent fetal positions are shared with segmented tasks with higher priority.

[0100] Mode 4: The current segmented task exclusively occupies two adjacent fetal positions.

[0101] Among them, the selection order of the preset fetal position selection modes is mode 1, mode 2, mode 3, and mode 4.

[0102] In an optional implementation, the fetal position scheduling optimization method further includes: determining a mutation probability in a mutation operation according to a current number of iterations.

[0103] The fetal position scheduling optimization method of the present invention specifically comprises the following steps:

[0104] 1. Problem modeling and optimization objective function design

[0105] First, a mathematical model is constructed for the fetal position scheduling problem, assuming that the segmented task set is N and the fetal position set is M. Each segmented task n has the following properties: length l n , width w n , during fetal cycleΔ n , expected time of pregnancy T n and tire insertion time t n To describe the correspondence between segmentation and fetal position within any time τ, a time-varying Boolean variable a is defined as m,n (τ), as shown in formula (1). In the following expression, t will be omitted for simplicity:

[0106]

[0107] The corresponding Boolean matrix A between the segment and the fetal position is further represented as shown in formula (2). The segmented fetal placement time series t is used to represent the segmented fetal position layout scheme, as shown in formula (3):

[0108]

[0109] t=[t1,t2,…,t N ](3)

[0110] Based on the above problem description and assumptions, a spatiotemporal layout optimization model with the goal of minimizing the total segment task completion time can be established to obtain a reasonable segment time and space layout plan. Formulas (4)-(8) are used to describe the fitness function and corresponding constraint relationships in the model.

[0111] (1) Define the fitness function:

[0112]

[0113] (2) Define three types of spatial constraints: assign one fetal position to one segment, assign multiple spatially adjacent fetal positions to a larger segment, and assign the same fetal position to multiple smaller segments at the same time:

[0114]

[0115] When there are multiple segments, the segment tire removal time should be before the expected tire removal time, with time constraints:

[0116] t n +Δ n ≤T n (8)

[0117] 3. Fetal position spatiotemporal layout optimization algorithm based on differential evolution algorithm

[0118] In order to accelerate the convergence of the differential evolution algorithm and ensure the feasibility of the solution, the present invention first determines the gene encoding and decoding method to effectively represent the segmented tasks and their attributes. Then, a number of initial solutions are generated according to the expected start time of each segment to form an initial population. Then, the differential evolution algorithm is iteratively optimized. Finally, the optimal solution is selected and output to obtain the spatiotemporal layout plan of the fetal position. The specific main process of the algorithm is as follows Figure 2 In order to more clearly describe the specific implementation process of the differential evolution algorithm in the spatiotemporal layout of fetal position, the key steps of the algorithm can be summarized into the following three aspects:

[0119] (1) Gene encoding and decoding

[0120] The differential evolution operator in the differential evolution algorithm acts directly on the digital string, using the M-ary integer encoding method, such as Figure 3 As shown in the figure, each gene sequence corresponds to a unique fetal position distribution scheme, where the sequence length is equal to the number of tasks, the position index represents the task number, and the gene value represents the fetal position allocation order of the task. The gene sequence reflects the priority of the segmented task. The smaller the gene value, the higher the priority of the segmented task, and the priority is given to the fetal position allocation. The low-priority segment selects the first free fetal position for processing based on the layout results of the high-priority segment.

[0121] The decoding process includes four fetal position selection modes: OneSharedMode, OneAloneMode, TwoNeighSharedMode, and TwoNeighAloneMode. Mode 1 attempts to share the fetal position between the current segment and a higher priority segment. Mode 2 allocates the fetal position independently for the current segment. When the space for a single fetal position is insufficient, mode 3 attempts to share two adjacent fetal positions with a higher priority segment. Mode 4 is that the current segment exclusively occupies two adjacent fetal positions. The order of mode selection is mode 1 to mode 4, giving priority to sharing fetal position resources to improve space utilization. When sharing a fetal position with a high-priority segment, time and space constraints must be met, that is, the placement of the current segment does not affect the layout of the existing segments and is no later than the departure time of the shared segment. Among the fetal position combinations that meet the conditions, the earliest available fetal position is selected as the allocation result.

[0122] (2) Initialization

[0123] Priority initialization based only on the expected start time may make the gene sequence outperform other randomized initial sequences at the beginning of the iteration, thereby reducing the diversity of the population and causing the algorithm to fall into a local optimum. To this end, the segmented random initialization strategy can further improve the diversity of the population, that is, the gene sequence is divided into several subsequences containing N1 genes, and each subsequence is randomly arranged, as shown in formula (9):

[0124]

[0125] (3) Differential Evolution Algorithm Iteration

[0126] The core iterative process of the differential evolution algorithm includes selection, crossover and mutation operations. In the present invention, the selection operation determines the selection probability of an individual based on the normalized fitness value, where the fitness value represents the evaluation function value of the solution after the gene sequence is decoded. The probability of individual i being selected is I is the number of individuals in the current iteration, fitness i is the fitness of the individual. Figure 4 As shown, the crossover operation recombines the parent gene fragments to generate offspring individuals (the offspring individuals are obtained by the crossover operation) by randomly selecting the exchange site and the exchange fragment length. In the mutation operation (the offspring individuals obtained by the crossover operation are mutated to obtain new offspring individuals), a hierarchical strategy is adopted for the mutation probability of the gene loci, and the Levy mechanism is introduced in the mutation, that is, two mutation modes are defined. One is the global exploration mode, and the gene mutation amount Δx obeys the π1(Δx) distribution, as shown in formula (10). The other is the local exploration mode, which obeys the π2(Δx) distribution, as shown in formula (11). The selection probabilities of the two exploration modes are p1 and p2, respectively. In the early stage of the iteration, in order to ensure the global exploration ability of the algorithm, p1 is relatively large. In the later stage of the iteration, p2 increases as p1 decays, and local exploration is mainly used. p1 is p1=p0 ki Attenuation, p2 = (1-p1), where p0 is the initial attenuation rate, k is the attenuation factor, and i is the number of iterations.

[0127]

[0128]

[0129] Through the above gene encoding and decoding, initialization and differential evolution algorithm iteration process, the whole process from gene optimization, initial population generation to the final optimal solution of fetal position spatiotemporal layout is completed. The following data is to verify the effectiveness of the designed fetal position spatiotemporal layout optimization method. The number of segmented tasks is 157, the number of fetal positions available for scheduling is 26, and there are two types of space capacity. Figure 5 As shown in the figure, the average optimization result of the evolutionary algorithm with the Levy mechanism added is 66 days, which is lower than the 68 days of the optimization result of the evolutionary algorithm without the Levy mechanism added, and the convergence speed is significantly improved compared with the traditional differential evolution algorithm.

[0130] Beneficial effects of the present invention:

[0131] (1) Accelerated convergence performance

[0132] The present invention significantly improves the fetal position scheduling efficiency and resource utilization in a multi-task and multi-constraint environment by improving the differential evolution algorithm. First, through the priority initialization based on the overdue time and the segmented random initialization strategy, the initial population is made more reasonable and diverse, avoiding the ineffective exploration of traditional random initialization in high-dimensional space, thereby accelerating the convergence speed of the algorithm.

[0133] (2) Adapting to the multi-dimensional coupling constraints of time and space

[0134] The present invention proposes an innovative gene encoding and decoding scheme, in which the priorities of segmented tasks constitute a gene sequence, and a unique task scheduling scheme is generated based on the priority sequence, which can adapt to the multi-dimensional coupling constraints of time and space and solve the problem of difficulty in local adjustment of the scheduling scheme.

[0135] (3) Improve the utilization of space resources

[0136] The present invention adopts four modes of fetal position allocation strategy, which adapts to the size difference of the segments and ensures full utilization of resources on the basis of unchanged fetal position layout and unchanged fetal position division granularity. It flexibly responds to the segment task size and layout requirements, and improves the adaptability and space utilization of the overall scheduling.

[0137] (4) Enhanced global search performance

[0138] The adaptive Levy mechanism is introduced to ensure global exploration capability and avoid falling into local optimal solutions. Experimental results show that the method of the present invention is superior to traditional evolutionary algorithms and manual scheduling in terms of total completion time, number of overdue tasks and resource utilization, and has wide application value and significant practical effects.

[0139] It should be noted that the contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

[0140] In this embodiment, a fetal position scheduling optimization device is also provided. A single device is used to implement the above-mentioned embodiment and optional implementation methods. The descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0141] Figure 6 Schematic diagram of the structure of the fetal position scheduling optimization device according to an embodiment of the present invention.

[0142] The present invention provides a fetal position scheduling optimization device, such as Figure 6 As shown, the fetal position scheduling optimization device includes:

[0143] The first processing module 11 is used to determine a segmented task set, a tire position set and a pre-established workshop tire position scheduling mathematical model. The segmented task set includes at least one segmented task, and each segmented task includes: length, width, tire cycle, expected tire removal time and tire placement time.

[0144] The second processing module 12 is used to determine an initial population according to the segmented task set, the fetal position set, the priority initialization strategy based on the overdue risk, and the segmented random initialization strategy. The initial population includes multiple initial fetal position scheduling schemes.

[0145] The third processing module 13 is used to optimize the workshop position scheduling mathematical model based on the initial population to obtain a scheduling plan for each position in the process of ship block construction.

[0146] In an optional embodiment, the fetal position scheduling optimization device further includes: a function construction module, which is used to construct a first objective function with the goal of minimizing the total completion time before determining the segmented task set and the fetal position set. Construct a second objective function with the goal of minimizing the number of overdue tasks. Determine multiple constraints. The multiple constraints include: spatial capacity constraints of fetal positions, spatial placement constraints of segments, and time constraints of segmented tasks. Construct a workshop fetal position scheduling mathematical model based on the first objective function, the second objective function and the multiple constraints.

[0147] In an optional implementation, the second processing module 12 is specifically configured to apply a priority initialization strategy based on overdue risk to the segmented task set, obtain the priority of each segmented task, and determine the expected start time of each segmented task according to the priority of each segmented task.

[0148] Several initial solutions are generated according to the expected start time of each segmented task and the fetal position set.

[0149] Several initial solutions are encoded to obtain the gene sequence corresponding to each initial solution.

[0150] The piecewise random initialization strategy is applied to the gene sequence corresponding to each initial solution to obtain the initial population.

[0151] In an optional implementation, the third processing module 13 is specifically configured to perform the following operations:

[0152] S1: Determine the initial population as the parent population P with a population size of N t .

[0153] S2: Initialize the number of iterations: i=1.

[0154] S3: Construct a fitness function according to the mapping relationship and pre-set the fitness threshold.

[0155] S4: Calculate the fitness of individuals in the parent population and sort the individuals in the parent population according to their fitness. One individual corresponds to one gene sequence.

[0156] S5: Determine whether there is at least one individual in the parent population whose fitness is greater than a preset fitness threshold. If there is at least one individual in the parent population whose fitness is greater than the preset fitness threshold, output the optimal solution, which is the scheduling plan for each position in the ship segment construction process corresponding to the maximum fitness value.

[0157] If the fitness of the individuals in the parent population is not greater than the preset fitness threshold, the current iteration number is increased by 1. It is determined whether the current iteration number is greater than the preset iteration number. If the current iteration number is greater than the preset iteration number, the individual with the largest fitness in the parent population is output as the optimal solution. If the current iteration number is not greater than the preset iteration number, S6 is executed.

[0158] S6: Perform crossover and mutation operations on the parent population to obtain a new offspring population of N in number, calculate the fitness of the individuals in the offspring population, re-determine the parent population based on the fitness of the individuals in the offspring population, and repeat S4-S6.

[0159] In an optional embodiment, the third processing module 13 includes a fitness calculation unit, which is used to decode the individuals in the parent population according to the preset fetal position selection mode to obtain the fetal position scheduling scheme corresponding to each individual, and calculate the fitness of the fetal position scheduling scheme corresponding to each individual according to the fitness function.

[0160] In an optional implementation, the preset fetal position selection mode includes:

[0161] Mode 1: The current segment task shares the fetal position with the segment with higher priority.

[0162] Mode 2: Independently assign fetal position to the current segment task.

[0163] Mode 3: When there is insufficient space for a single fetal position, two adjacent fetal positions are shared with segmented tasks with higher priority.

[0164] Mode 4: The current segmented task exclusively occupies two adjacent fetal positions.

[0165] Among them, the selection order of the preset fetal position selection modes is mode 1, mode 2, mode 3, and mode 4.

[0166] In an optional implementation, the third processing module 13 further includes a mutation probability determination unit, which is used to determine the mutation probability in the mutation operation according to the current number of iterations.

[0167] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0168] The fetal position scheduling optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0169] The embodiment of the present invention also provides a computer device having the above Figure 6 The fetal position scheduling optimization device shown.

[0170] See also Figure 7 , Figure 7 Schematic diagram of the hardware structure of the computer device according to the embodiment of the present invention. Figure 7 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In an optional embodiment, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor device). Figure 7 A processor 10 is taken as an example.

[0171] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0172] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0173] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating device, an application required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In an optional embodiment, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0174] The memory 20 may include a volatile memory, such as a random access memory. The memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive. The memory 20 may also include a combination of the above types of memory.

[0175] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0176] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0177] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0178] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A fetal position scheduling optimization method, characterized in that: include: Determine the segmented task set, the fetal position set and the pre-established mathematical model for workshop fetal position scheduling; The segmented task set includes at least one segmented task, and each segmented task includes: length, width, tire cycle, expected tire removal time and tire placement time; Determine an initial population according to a segmented task set, a fetal position set, a priority initialization strategy based on overdue risk, and a segmented random initialization strategy; the initial population includes multiple initial fetal position scheduling schemes; The mathematical model of workshop position scheduling is optimized based on the initial population to obtain a scheduling plan for each position in the process of ship block construction.

2. The method according to claim 1, characterized in that Before determining the segmented task set and fetal position set, it also includes: With the goal of minimizing the total completion time, the first objective function is constructed; The second objective function is constructed with the goal of minimizing the number of overdue tasks; Determine multiple constraints; the multiple constraints include: space capacity constraints of fetal position, space placement constraints of segments, and time constraints of segment tasks; The workshop fetal position scheduling mathematical model is constructed based on the first objective function, the second objective function and the multiple constraints.

3. The method according to claim 1, characterized in that The step of determining the initial population according to the segmented task set, the fetal position set, the priority initialization strategy based on the overdue risk, and the segmented random initialization strategy includes: Apply the priority initialization strategy based on overdue risk to the segmented task set to obtain the priority of each segmented task, and determine the expected start time of each segmented task according to the priority of each segmented task; Generate several initial solutions according to the expected start time of each segment task and the fetal position set; Encode several initial solutions to obtain the gene sequence corresponding to each initial solution; The piecewise random initialization strategy is applied to the gene sequence corresponding to each initial solution to obtain an initial population.

4. The method according to claim 1, characterized in that The method of optimizing the workshop position scheduling mathematical model based on the initial population to obtain a scheduling scheme for each position in the ship block construction process includes: S1: Determine the initial population as the parent population P with a population size of N t ; S2: Initialization iteration number: i=1; S3: construct a fitness function based on the mapping relationship and pre-set the fitness threshold; S4: Calculate the fitness of individuals in the parent population and sort the individuals in the parent population according to their fitness; one individual corresponds to one gene sequence; S5: Determine whether there is at least one individual in the parent population whose fitness is greater than a preset fitness threshold. If there is at least one individual in the parent population whose fitness is greater than the preset fitness threshold, output an optimal solution, which is a scheduling scheme for each position in the ship block construction process corresponding to the maximum fitness value; If the fitness of the individuals in the parent population is not greater than the preset fitness threshold, the current number of iterations is increased by 1; it is determined whether the current number of iterations is greater than the preset number of iterations. If the current number of iterations is greater than the preset number of iterations, the individual with the largest fitness in the parent population is output as the optimal solution. If the current number of iterations is not greater than the preset number of iterations, S6 is executed; S6: Perform crossover and mutation operations on the parent population to obtain a new offspring population of N in number, calculate the fitness of the individuals in the offspring population, re-determine the parent population based on the fitness of the individuals in the offspring population, and repeat S4-S6.

5. The method according to claim 4, characterized in that The calculation of the fitness of individuals in the parent population includes: Decoding the individuals in the parent population according to a preset fetal position selection mode to obtain a fetal position scheduling scheme corresponding to each individual; The fitness of the fetal position scheduling scheme corresponding to each individual is calculated according to the fitness function.

6. The method according to claim 5, characterized in that The preset fetal position selection mode includes: Mode 1: The current segment task shares the fetal position with the segment with higher priority; Mode 2: independently assign fetal position to the current segment task; Mode 3: When there is insufficient space for a single fetal position, two adjacent fetal positions are shared with the segmentation tasks with higher priority; Mode 4: The current segment task exclusively occupies two adjacent fetal positions; Among them, the selection order of the preset fetal position selection modes is mode 1, mode 2, mode 3, and mode 4.

7. The method according to claim 4, characterized in that The method further includes: determining a mutation probability in the mutation operation according to a current number of iterations.

8. A fetal position scheduling optimization device, characterized in that: include: The first processing module is used to determine the segmented task set, the fetal position set and the pre-established workshop fetal position scheduling mathematical model; The segmented task set includes at least one segmented task, and each segmented task includes: length, width, tire cycle, expected tire removal time and tire placement time; A second processing module is used to determine an initial population according to a segmented task set, a fetal position set, a priority initialization strategy based on overdue risk, and a segmented random initialization strategy; the initial population includes multiple initial fetal position scheduling schemes; The third processing module is used to optimize the workshop position scheduling mathematical model based on the initial population to obtain a scheduling plan for each position in the process of ship block construction.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the fetal position scheduling optimization method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the fetal position scheduling optimization method according to any one of claims 1 to 7.

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