Hull sub-assembly workstation plan optimization method and apparatus, and device and storage medium

Abstract

A hull sub-assembly workstation plan optimization method, the method comprising the steps of: identifying influence factors of hull sub-assembly planning and production scheduling based on flow-direction-based batching, and acquiring certainty factors and uncertainty factors of sub-assembly planning and production scheduling; on the basis of the certainty factors of a sub-assembly plan, forming an initialized workstation plan work package set; on the basis of the uncertainty factors of the sub-assembly plan and the workstation plan work package set, constructing an uncertainty algorithm model; determining a solving strategy for the uncertainty algorithm model; and determining a sub-assembly operation plan on the basis of the solving strategy. Further disclosed are a hull sub-assembly workstation plan optimization apparatus, a computer device and a computer-readable storage medium.

Description

Ship body small assembly station planning optimization method, device, equipment and storage medium TECHNICAL FIELD

[0001] The present application relates to the ship assembly construction field, in particular to a ship body small assembly station planning optimization method, device, computer equipment and storage medium. BACKGROUND

[0002] Ship body section construction technology is a basic technology of modern shipbuilding mode, which classifies the intermediate products constituting the ship into groups for manufacturing according to the similarity of characteristics. The construction of the section is a combination of small assembly, medium assembly and large assembly. That is, small assembly forms small assembly parts, medium assembly forms multi-system modules, and large assembly forms sections, which are combined into a ship.

[0003] Ship body small assembly production is one of the key links of ship body section construction. The small assembly section production requires a large number of parts, and the demand time of small assembly parts is different, which will be used in downstream links of ship body construction (medium assembly, large assembly, total assembly, loading, etc.). If the small assembly section production plan is arranged improperly, each link of the upstream and downstream will be affected, which will easily cause the delay of ship body construction. Therefore, it is necessary to study the production plan of small assembly section. The traditional small assembly station plan is prepared according to manual experience, which has low preparation efficiency and large granularity of plan management. It does not consider preparing the plan in batches according to different flow directions for small assembly parts belonging to the same section, and has the problems of inventory accumulation and unbalanced production load. SUMMARY

[0004] Therefore, in order to effectively improve the efficiency and accuracy of the plan balance of ship section small assembly manufacturing, a ship body small assembly station planning optimization method, device, computer equipment and storage medium are provided, which solves the technical problems of traditional small assembly station plan prepared according to manual experience, low preparation efficiency, large granularity of plan management, not considering preparing the plan in batches according to different flow directions for small assembly parts belonging to the same section, and inventory accumulation and unbalanced production load.

[0005] The present application provides a ship body small assembly station planning optimization method, which comprises the following steps:

[0006] Identify the factors affecting the production scheduling of the ship body small assembly plan based on batch scheduling according to the flow direction, and obtain the certainty factors and uncertainty factors of the small assembly plan production scheduling;

[0007] Form an initialized station plan work package set according to the certainty factors of the small assembly plan;

[0008] According to the uncertainty factors of the team erection plan, an uncertainty algorithm model is constructed based on the set of the work package of the work station plan;

[0009] A solution strategy of the uncertainty algorithm model is determined;

[0010] According to the solution strategy, a team erection work plan is determined.

[0011] Further, the step of identifying the factors affecting the production scheduling of the ship hull team erection plan based on the batch division according to the flow direction and obtaining the certainty factors and uncertainty factors of the team erection plan production scheduling includes:

[0012] All factors affecting the production scheduling of the ship hull team erection plan based on the batch division according to the flow direction are identified, and the factors are divided into certainty factors and uncertainty factors according to whether they are controllable;

[0013] The certainty factors include the construction amount of each team erection plan package, the standard period of each team erection plan package, and the demand date of each team erection plan package, and the uncertainty factors include the delivery time when different parts of different work stations and team erection are set with different construction methods.

[0014] Further, the step of forming an initialized work station plan work package set according to the certainty factors of the team erection plan includes:

[0015] According to the construction method, the corresponding work station is set, and the material flow direction is the same as the work station setting;

[0016] According to the weld flow direction and welding method in the part weld detail table, a team erection plan package is prepared as a work package b, which represents a set of construction tasks of the same section, the same work station, and the same flow direction;

[0017] It is set that within a certain period, the team erection work station plan work package set B required for construction of the work station is B={1,…,|B|}, where b∈B;

[0018] According to the weld length information in the part weld detail table, the weld length of the work package b is counted;

[0019] According to the weld length and work time standard of the team erection plan package, the planned work time t of the work package b is calculated b ;

[0020] According to the weld length or planned work time of the team erection plan package, the standard period of the team erection plan package is calculated, that is, the number of consecutive days τ b ;

[0021] According to the planned work time t b of the work package b and the standard period, the average daily production work time of the team erection plan package is calculated

[0022] Based on the downstream demand dates and standard cycle of work package b, determine the initial delivery date and segmented initial start dates for work package b. The initial delivery date of work package b is the completion date of work package b in the initial plan. The initial start date for work package b is the start date of work package b in the initial plan.

[0023] Set the number of days α1 for advance production of work package b. Considering inventory, the cycle of α1 is set to 3, that is, the number of days for advance production is [0, 1, 2, 3].

[0024] Set the required inspection time α2 after work package b is completed;

[0025] Set the delivery date for work package b. b .

[0026] Furthermore, the step of constructing an uncertainty algorithm model based on the set of work packages for the workstation plan according to the uncertainty factors of the group's establishment plan includes:

[0027] Based on the aforementioned uncertainties, make scheme assumptions, convert the production time of cross-cycle work package b, and determine the variance of minimizing the daily workload.

[0028] Based on the work station's planned work package set, orders within the planning period are determined according to the delivery date δ. b Using α1 as a condition, and setting the start date of work package b through α2, the plan and average daily working hours are adjusted under the constraint of meeting the delivery date. The transformed parameters include:

[0029] The earliest time that work package b can start within the planning period [0, P].

[0030] The latest time for work package b to start within the planned period [0, P]

[0031] Number of days during which work package b is produced within the planning period [0, P]

[0032] Total working hours of work package b during the planning period [0, P]

[0033] The average daily working hours D during the planning period [0, P] a ;

[0034] The process for setting up the time conversion of cross-cycle work package b is as follows:

[0035] When construction begins before the planned period, set up

[0036] When construction begins before the planned period, set up

[0037] When setting the variance determination to minimize the daily workload, a first decision variable x is introduced. bd Second decision variable y bd ;

[0038] If x bd =1, the workload of work package b on day d∈{0,..,P} is If x bd =0, the workload of work package b on day d∈{0,..,P} is 0;

[0039] If y bd =1, work package b is in the... Work will begin on the day; if y bd =0, work package b is in the... Work has not yet started;

[0040] The objective function corresponding to the obtained uncertainty algorithm model is

[0041] The uncertainty algorithm model satisfies the constraints.

[0042] Wherein, objective function (1) represents minimizing the variance of daily workload; constraint (2) represents the total number of days for the production of work package b; constraint (3) represents the start date constraint of work package b; constraint (4) represents the continuous production constraint of work package b; constraint (5) is the first decision variable constraint; constraint (6) is the second decision variable constraint; the uncertainty algorithm model is a nonlinear programming mathematical model.

[0043] Furthermore, the step of determining the solution strategy for the uncertainty algorithm model includes:

[0044] A genetic algorithm is set up, wherein the operators of the genetic algorithm include encoding, fitness value, decoding and selection strategy, crossover strategy, mutation strategy and termination strategy;

[0045] The solution strategy for the uncertainty algorithm model is determined by a genetic algorithm.

[0046] Furthermore, the step of setting the genetic algorithm includes:

[0047] Based on the encoding rules, an initial solution for a population is randomly generated;

[0048] A superior population from the initial solution is selected using fitness, decoding, and selection strategies.

[0049] The optimal solution is updated by crossover or mutation of the superior population using a crossover strategy.

[0050] Termination strategies are used to control the termination of crossover or mutation processes.

[0051] Furthermore, in the step of setting the genetic algorithm,

[0052] The encoding adopts a sequential encoding method, where each chromosome represents a solution, each gene position represents a segmented small independent work package b, and the order of the segments indicates the order during production scheduling.

[0053] The fitness function selects the objective function.

[0054] The decoding process in the aforementioned decoding and selection strategy is a heuristic algorithm that schedules production according to the encoding order, sequentially placing the start dates of work package b in order. Chinese; calculation in For already ranked in In the segmented set of the sky, work package b was ultimately ranked [number]. The selection strategy described uses a roulette wheel betting method.

[0055] The crossover strategy employs a single-point crossover method to crossover at least one segment of gene loci on a chromosome to obtain crossover or mutated offspring chromosomes.

[0056] The termination strategy selects to terminate the crossover or mutation process after reaching a preset number of iterations.

[0057] Furthermore, the step of determining the group's work plan based on the solution strategy includes:

[0058] Obtain the planned balance scheme for the feasible hull group erection workstation calculated by the solution strategy;

[0059] When α1 is determined, the optimal plan balance scheme for the hull group erection workstation is determined based on minimizing the variance of the daily workload as the group erection work plan.

[0060] On the other hand, a device for optimizing the planning of ship hull group erection workstations is provided, the device comprising:

[0061] The influencing factor acquisition module is used to identify the influencing factors of the hull group assembly plan scheduling based on the flow direction and batch, and to acquire the deterministic and uncertain factors of the group assembly plan scheduling.

[0062] The initial plan acquisition module is used to generate an initial set of workstation plan work packages based on the deterministic factors of the group's initial plan.

[0063] An uncertainty algorithm model construction module is used to construct an uncertainty algorithm model based on the work station plan work package set and the uncertainty factors of the group establishment plan;

[0064] The solution strategy module is used to determine the solution strategy for the uncertain algorithm model.

[0065] The task planning module is used to determine the group's task plan based on the solution strategy.

[0066] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0067] Identify the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtain the deterministic and uncertain factors of the group assembly plan scheduling;

[0068] An initial set of workstation planning packages is formed based on the deterministic factors of the group's initial planning.

[0069] Based on the work station plan work package set, an uncertainty algorithm model is constructed according to the uncertainty factors of the group's independent plan;

[0070] Determine the solution strategy for the uncertainty algorithm model;

[0071] Based on the solution strategy, determine the group's work plan.

[0072] In another aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0073] Identify the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtain the deterministic and uncertain factors of the group assembly plan scheduling;

[0074] An initial set of workstation planning packages is formed based on the deterministic factors of the group's initial planning.

[0075] Based on the work station plan work package set, an uncertainty algorithm model is constructed according to the uncertainty factors of the group's independent plan;

[0076] Determine the solution strategy for the uncertainty algorithm model;

[0077] Based on the solution strategy, determine the group's work plan.

[0078] The aforementioned method, device, computer equipment, and storage medium for optimizing the hull assembly workstation plan form an initial set of workstation plan packages based on deterministic factors of the assembly plan. Based on the workstation plan package set and combined with uncertain factors, an uncertainty algorithm model is constructed. By solving the uncertainty algorithm model, the assembly work plan is determined. This enables precise and balanced management of the assembly plan even when the ship section assembly manufacturing is characterized by a large number of parts, diverse types, wide demand span, and frequent demand changes. It improves the efficiency of the planning process, provides fine-grained planning management, avoids inventory backlog, and ensures a balanced production load. Attached Figure Description

[0079] Figure 1 is a flowchart illustrating a method for optimizing the hull group erection workstation plan according to Embodiment 1 of the present invention.

[0080] Figure 2 is a crossover diagram of the genetic algorithm provided in Embodiment 1 of the present invention.

[0081] Figure 3 is a schematic diagram of the mutation of the genetic algorithm provided in Embodiment 1 of the present invention.

[0082] Figure 4 shows the overall framework of the genetic algorithm provided in Embodiment 1 of the present invention.

[0083] Figure 5 is a flowchart illustrating a method for optimizing the hull group erection workstation plan according to Embodiment 2 of the present invention.

[0084] Figure 6 is a structural block diagram of the hull group erection station planning optimization device in one embodiment 3 of this application.

[0085] Figure 7 is an internal structural diagram of a computer device in one embodiment 4 of this application. Detailed Implementation

[0086] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0087] Example 1

[0088] Based on the background technology, Embodiment 1 of this application addresses the problems of large-scale ship section assembly manufacturing planning having large granularity, poor flexibility, inventory backlog or difficulty in meeting customer needs, uneven production load, and difficulty in responding quickly and reasonably to plan adjustments. It forms a method for optimizing the hull section assembly workstation plan, which can solve the technical problems of traditional section assembly workstation plans being compiled based on manual experience, resulting in low compilation efficiency, large granularity of plan management, failure to consider compiling plans for section assembly components belonging to the same section in batches according to different flow directions, and the existence of inventory backlog and uneven production load.

[0089] As shown in Figure 1, the method for optimizing the hull group erection workstation plan includes the following steps:

[0090] Step S1) Based on the characteristics of small-scale stand-up production, obtain the deterministic and uncertain factors of the small-scale stand-up production plan;

[0091] Step S2) Based on the deterministic factors of the group's initialization plan, form an initial set of workstation plan work packages;

[0092] Step S3) Construct an uncertainty algorithm model for the uncertainty factors of the group's establishment plan;

[0093] Step S4) Determine the solution strategy for the uncertainty algorithm model;

[0094] Step S5) Determine the group's work plan based on the solution strategy.

[0095] The production of sub-assemblies requires numerous components, and the time required for each sub-assembly component varies. These components will be used in multiple downstream stages of shipbuilding (mid-assembly, large-scale assembly, final assembly, and installation). Sub-assembly components have various structural types, therefore they must be grouped and manufactured according to the similarity of their structural characteristics. Taking a shipyard's sub-assembly workstation as an example, it is divided into sub-assembly panel assembly, sub-assembly C (a simpler component, mostly used in assembly line construction), sub-assembly S (a more difficult component to construct, mostly used for manual welding), and sub-assembly T-sections, etc., each manufactured at different workstations.

[0096] Since the time required for each batch of assembly parts varies from flow to flow, the production plan for each batch of assembly parts at this workstation should be arranged in batches according to the different time requirements of subsequent operations. This will reduce the waste caused by some assembly parts being provided too early, resulting in site occupation, ineffective handling, etc.

[0097] Furthermore, based on the characteristics of sub-assembly production, the deterministic and uncertain factors of the sub-assembly production schedule are identified and acquired. The deterministic factors include: setting up different workstations according to different construction methods; different components being constructed at different workstations due to different construction methods; the construction volume of each sub-assembly package being deterministic (workstation production efficiency is relatively constant, and planned working hours can be calculated using material quantity and time standards); the standard cycle of each sub-assembly package being deterministic (the standard cycle is determined based on the workload); and the required dates for each sub-assembly package (derived from the schedule in ship product production). The uncertain factor in the sub-assembly production schedule is the uncertainty of delivery time (before meeting subsequent requirements).

[0098] Furthermore, based on the aforementioned deterministic factors, small-scale independent planning work packages are created for each individual segment within the planning cycle, organized by flow direction, to initialize the basic information. This forms the foundation of the planning balancing model. The implementation method is as follows:

[0099] Different workstations are set up according to different construction methods, and the material flow direction in the production design is the same as the workstation setting.

[0100] Based on the weld flow direction and welding method in the parts weld detail table, a group work plan package b (hereinafter referred to as work package b) is prepared; work package b represents a collection of construction tasks in the same section, the same work station, and the same flow direction;

[0101] Set the set of planned work packages for the substations to be constructed within a certain period (e.g., a two-month planning period) as B = {1, ..., |B|};

[0102] Based on the weld length information in the parts weld detail sheet, calculate the weld length t for work package b. b (When using weld length for planned quantitative balancing);

[0103] Based on the weld length and working hour standards of the group's plan, calculate the planned working hours (t) for work package b. b (When using planned hours for quantitative balancing);

[0104] The standard cycle of the sub-assembly plan package is calculated based on the weld length or planned working hours, i.e., the number of consecutive days τ required for work package b. b ;

[0105] According to the group's established plan, the planned working hours are t. b Standard cycle calculation of the average daily production time of work package b (or the average daily weld length produced);

[0106] The initial delivery date for work package b (the completion date of work package b in the initial plan) is determined based on the downstream demand date and standard cycle of work package b. ), Initial start date of work package b (the start date of work package b in the initial plan) );

[0107] Set the number of days α1 that work package b can be prepared in advance. Considering inventory, α1 is set to 3, meaning the number of days to prepare in advance can be [0, 1, 2, 3].

[0108] Set the required inspection time α2 after work package b is completed;

[0109] Set the delivery date δ for work package b. b .

[0110] With a planning period of two months, the initial basic information includes the following:

[0111] Table 1 Initialization Information Table

[0112] Furthermore, based on the aforementioned uncertainties regarding delivery time, a batch-by-batch hull assembly station planning optimization model is established. The goal is to schedule the start date of work package b to minimize the daily manufacturing volume deviation within a planning period (e.g., two months) while meeting the delivery deadline. Specifically, this includes: making assumptions based on the aforementioned uncertainties, converting the production time of work package b across cycles, and determining the variance of minimizing the daily workload.

[0113] Without loss of generality, we make the following assumptions: daily planning is coarse-grained, and daily workload does not exceed capacity constraints. Work package b in the initial plan exhibits cross-cycle production. The time allotted for cross-cycle work package b within the planning period is proportionally allocated to the planning cycle, and it is produced first within the planning period. This assumption is similar to that in the shop floor scheduling problem; work items already started cannot be interrupted.

[0114] Furthermore, since some segments from the previous cycle that were not completed may be produced across cycles when the new cycle plan is formulated, it is necessary to convert the production information of these segments. Orders within the planning period can be processed based on the delivery date δ. b Using α1 as a condition, the start date of work package b is set via α2. This allows for adjustments to the schedule and average daily working hours while meeting delivery deadlines. The converted parameters include:

[0115] The earliest time that work package b can start within the planning period [0, P].

[0116] The latest time for work package b to start within the planned period [0, P].

[0117] Number of days during which work package b is produced within the planning period [0, P]

[0118] Total working hours of work package b within the planning period [0, P]

[0119] The average daily working hours D within the period [0, P] a .

[0120] Table 2 Optimization Parameters for Work Package B

[0121] The process of time conversion for cross-cycle work package b is as follows.

[0122] when That is, to start construction before the planned period.

[0123] when Construction will commence during the planned period. This ensures that all work packages (b) produced under the initial plan can be completed within the adjusted planning period.

[0124] Minimize the variance of daily workload. Introduce the following two decision variables, x bd and y bd .

[0125] If x bd =1, the workload of work package b on day d∈{0,..,P} is If x bd =0, the workload of work package b on day d∈{0,..,P} is 0;

[0126] If y bd =1, work package b is in the... Work will begin on the day; if y bd =0, work package b is in the... Construction has not yet begun. Therefore, we can obtain the following model:

[0127] Satisfying constraints

[0128] The objective function (1) represents minimizing the variance of the daily workload. This function aims to make the daily workload as even as possible, thus achieving a more balanced workload. Constraint (2) represents the total number of days required to produce work package b. Constraint (3) represents the start date constraint for work package b. Constraint (4) represents the continuous production constraint for work package b. Constraints (5-6) are the decision variable constraints. This model is a nonlinear programming mathematical model.

[0129] For ease of description, an illustrative example is introduced, as shown in Table 3. This example contains three segments, 0, 1, and 2, of work package b at the same workstation. These three segments represent three scenarios: a. the segment spans the previous cycle; b. the segment is within the current cycle; c. the segment spans the next cycle. The cycle is set to [0,3], i.e., P = 3. Through transformation, we can obtain... The transformation results and optimal solution are shown in Table 1. The data in the table is explained using segment 0 as an example. The total workload t for segment 0 is... b The value is 178. Since it spans the previous cycle, it is proportionally allocated within the planning cycle and rounded to obtain the workload for the planning period. The value is 132. Therefore, although the plan can be adjusted by α1 = 7 days, it spans multiple cycles, hence the earliest start date is... and latest start date All are 0. The earliest and latest start dates for other segments can be obtained using the same method. In this example, there are only two options for the plan: (1) y 00 =1, y10 = 1 (indicating that the "1" segment starts from day 0), y 22 = 1, the target value f1 = 1578.188; (2) y 00 = 1, y 11 = 1 (indicating that the "1" segment starts from day 1), y 22 = 1, f2 = 1446.688. The target value f1 < f2. The optimal case is (2).

[0130] Table 3 Illustrative examples

[0131] Furthermore, determine the solution strategy for the uncertainty algorithm model. In the genetic algorithm solution steps, the operation operators include encoding, fitness value, decoding and selection strategy, crossover strategy, mutation strategy, termination strategy, etc. for design and operation.

[0132] Encoding, in this paper, the classical sequential encoding method is adopted. Each chromosome represents a solution. Each gene position represents a work package b. The order of the work package b represents the order during production scheduling. For example, the encoding [2, 1, 3, 5, 4] means that when scheduling the segmented work package b, it is scheduled in the order of 2, 1, 3, 5, 4. According to the encoding rule, randomly generate an initial solution for a population.

[0133] Fitness value, the fitness value function selects the objective function

[0134] Decoding and selection strategy, the decoding process is a heuristic algorithm. According to the encoding order for production scheduling, sequentially arrange the start date of the segmented b ∈ B in Calculate Here is the work package b that has been arranged on the day. The work package b is finally arranged on the day. If there are multiple such positions, place it in the first position. The selection strategy adopts the classical roulette wheel method.

[0135] Crossover strategy, single-point crossover is adopted. Taking a chromosome with 5 gene positions as an example, randomly select two chromosomes P1, P2; randomly select 1 crossover position 2. The genes on the left side of the crossover position remain unchanged, and the gene order on the right side of the crossover is the front-back order of the corresponding gene positions in the other chromosome. Through crossover, two offspring C1 and C2 are obtained. The crossover structure is shown in Figure 2.

[0136] Mutation strategy, the crossover strategy adopts the strategy of swapping two gene positions. For a chromosome P1 [2, 1, 3, 5, 4] with 5 gene positions, swap genes 1 and 5, and the chromosome obtained after mutation is C1 [2, 5, 3, 1, 4].

[0137] The termination strategy is selected after a certain number of iterations. The mutation structure is shown in Figure 3.

[0138] The overall framework of the algorithm is shown in Figure 4.

[0139] Furthermore, based on the aforementioned solution strategy, a group-based work plan is determined. The optimal plan balancing scheme is determined by minimizing the variance of the daily workload under the given condition of α1.

[0140] The above-mentioned method for optimizing the hull assembly workstation plan establishes a mathematical model for plan balance and designs an implementation of a genetic algorithm. This solves the problem of difficulty in accurately balancing the hull assembly workstation plan management due to the large number of hull sections, diverse types, wide range of requirements, and frequent changes in requirements.

[0141] Example 2

[0142] Example 2 includes all the technical features of Example 1, and the specific implementation methods are the same.

[0143] As shown in Figure 5, Embodiment 2 of this application provides a method for optimizing the hull group erection workstation plan, including the following steps:

[0144] Step S11) Identify the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtain the deterministic and uncertain factors of the group assembly plan scheduling.

[0145] Step S12) Based on the deterministic factors of the group's planning, form an initial set of workstation planning packages;

[0146] Step S13) Construct an uncertainty algorithm model based on the set of work packages in the workstation plan according to the uncertainty factors of the group's establishment plan;

[0147] Step S14) Determine the solution strategy for the uncertainty algorithm model;

[0148] Step S15) Determine the group's work plan based on the solution strategy.

[0149] Furthermore, the step of identifying the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtaining the deterministic and uncertain factors of the group assembly plan scheduling, includes:

[0150] Identify all influencing factors for the production planning of hull groups based on batch production according to flow direction, and categorize the influencing factors into deterministic factors and uncertain factors according to whether they are controllable;

[0151] When setting up different work stations and different work packages b in different groups, and using different construction methods, the deterministic factors include the construction volume of each group's work package, the standard cycle of each group's work package, and the required date of each group's work package, while the uncertain factors include delivery time.

[0152] Furthermore, the step of forming an initial set of workstation plan work packages based on the deterministic factors of the group's establishment plan includes:

[0153] The corresponding workstations are set up according to the construction method, and the material flow direction is the same as the workstation settings.

[0154] Based on the weld flow direction and welding method in the parts weld detail table, a group stand-up planning package is prepared as work package b. Work package b represents a collection of construction tasks in the same segment, the same work station, and the same flow direction.

[0155] Set a set of planned work packages for the work station within a certain period, B = {1,…,|B|}, where b∈B;

[0156] The length of the weld in work package b is calculated based on the weld length information in the parts weld detail table.

[0157] Calculate the planned working hours (t) for work package b based on the weld length and working hour standards. b ;

[0158] Calculate the standard cycle of work package b based on the weld length or planned working hours, i.e., the number of consecutive days τ required to produce work package b. b ;

[0159] According to the work package b, the planned work hours t b Standard cycle calculation group establishes the average daily production time of the planning package. Or the average daily weld length;

[0160] Based on the downstream demand date and standard cycle of work package b, determine the initial delivery date and initial start date for each segment. The initial delivery date for each segment is the completion date of work package b in the initial plan. The initial start date for the segment is the start date for work package b in the initial plan.

[0161] Set the number of days α1 required to prepare work package b in advance;

[0162] Set the required inspection time α2 after work package b is completed;

[0163] Set the delivery date for work package b. b .

[0164] Furthermore, the step of constructing an uncertainty algorithm model based on the set of work packages for the workstation plan according to the uncertainty factors of the group's establishment plan includes:

[0165] Based on the aforementioned uncertainties, make scheme assumptions, convert the production time of cross-cycle work package b, and determine the variance of minimizing the daily workload.

[0166] Based on the work station's planned work package set, orders within the planning period are determined according to the delivery date δ. b Using α1 as a condition, and setting the start date of work package b through α2, the schedule and average daily working hours of work package b are adjusted under the constraint of meeting the delivery date. The transformed parameters include:

[0167] The earliest time that work package b can start within the planning period [0, P].

[0168] The latest time for work package b to start within the planned period [0, P]

[0169] Number of days during which work package b is produced within the planning period [0, P]

[0170] Total working hours of work package b during the planning period [0, P]

[0171] The average daily working hours D during the planning period [0, P] a ;

[0172] The process for setting up the time conversion of cross-cycle work package b is as follows:

[0173] When construction begins before the planned period, set up

[0174] When construction begins before the planned period, set up

[0175] When setting the variance determination to minimize the daily workload, a first decision variable x is introduced. bd Second decision variable y bd ;

[0176] If x bd =1, the workload of work package b on day d∈{0,..,P} is If x bd =0, the workload of work package b on day d∈{0,..,P} is 0;

[0177] If y bd =1, work package b is in the... Work will begin on the day; if ybd =0, work package b is in the... Work has not yet started;

[0178] The objective function corresponding to the obtained uncertainty algorithm model is

[0179] The uncertainty algorithm model satisfies the constraints.

[0180] Wherein, objective function (1) represents minimizing the variance of daily workload; constraint (2) represents the number of days of work package b; constraint (3) represents the start date of work package b; constraint (4) represents the continuous production constraint of work package b; constraint (5) is the first decision variable constraint; constraint (6) is the second decision variable constraint; the uncertainty algorithm model is a nonlinear programming mathematical model.

[0181] Furthermore, the step of determining the solution strategy for the uncertainty algorithm model includes:

[0182] A genetic algorithm is set up, wherein the operators of the genetic algorithm include encoding, fitness value, decoding and selection strategy, crossover strategy, mutation strategy and termination strategy;

[0183] The solution strategy for the uncertainty algorithm model is determined by a genetic algorithm.

[0184] Furthermore, the step of setting the genetic algorithm includes:

[0185] Based on the encoding rules, an initial solution for a population is randomly generated;

[0186] A superior population from the initial solution is selected using fitness, decoding, and selection strategies.

[0187] The optimal solution is updated by crossover or mutation of the superior population using a crossover strategy.

[0188] Termination strategies are used to control the termination of crossover or mutation processes.

[0189] Furthermore, in the step of setting the genetic algorithm,

[0190] The encoding adopts a sequential encoding method, where each chromosome represents a solution, each gene position represents a segment of working package b, and the order of working package b indicates the order during production scheduling.

[0191] The fitness function selects the objective function.

[0192] The decoding process in the aforementioned decoding and selection strategy is a heuristic algorithm that schedules production according to the encoding order, sequentially placing the start dates of work package b in order. Chinese; calculation in For already ranked in In the segmented set of the sky, work package b was ultimately ranked [number]. The selection strategy described uses a roulette wheel betting method.

[0193] The crossover strategy employs a single-point crossover method to crossover at least one segment of gene loci on a chromosome to obtain crossover or mutated offspring chromosomes.

[0194] The termination strategy selects to terminate the crossover or mutation process after reaching a preset number of iterations.

[0195] Furthermore, the step of determining the group's work plan based on the solution strategy includes:

[0196] Obtain the planned balance scheme for the feasible hull group erection workstation calculated by the solution strategy;

[0197] Given a fixed α1, the optimal plan balance scheme for the hull group erection workstation is determined based on minimizing the variance of the daily workload, serving as the group erection work plan.

[0198] Among them, the optimal hull group erection work station planning balance scheme is determined by minimizing the variance of the daily workload. The group erection work planning method aims to minimize the variance between the daily weld length or planned working hours and the daily weld length or planned working hours.

[0199] This application addresses the difficulties in scheduling and production of sub-assembly units in ship assembly due to their large quantity, variety, wide range of demands, and frequent changes in requirements. While ensuring the availability of sub-assembly components for subsequent processes and maintaining the habit of continuous production management for sub-assembly components at the same workstation and within the same section, it proposes an optimization method for hull sub-assembly workstation planning based on flow direction and batch processing. With the objective of maintaining a relatively constant production capacity (personnel and daily work capacity) at the same workstation, it provides a planning optimization method that minimizes the variance between the daily weld length (or planned working hours) and the daily weld length (or planned working hours), and solves the problem using a genetic algorithm. This provides a technical solution to the problem of accurately balancing the planning and management of sub-assembly units in ship final assembly construction.

[0200] The aforementioned method for optimizing the hull assembly workstation plan involves forming an initial set of workstation plan packages based on deterministic factors in the assembly plan, constructing an uncertainty algorithm model based on the workstation plan package set and combining it with uncertain factors, and determining the assembly workstation plan by solving the uncertainty algorithm model. This method enables precise and balanced planning management for ship section assembly even when there are many types of sections, large demand spans, and frequent demand changes. It improves planning efficiency, provides fine-grained planning management, avoids inventory backlog, and ensures balanced production load.

[0201] Example 3

[0202] In Embodiment 3, as shown in Figure 6, a hull group erection workstation planning optimization device 10 is provided, including: an influencing factor acquisition module 1, an initial plan acquisition module 2, an uncertainty algorithm model construction module 3, a solution strategy setting module 4, and a work plan solution module 5.

[0203] The influencing factor acquisition module 1 is used to identify the influencing factors of the hull group assembly plan based on the flow direction and batch production, and to acquire the deterministic and uncertain factors of the group assembly plan production.

[0204] The initial plan acquisition module 2 is used to form an initial workstation plan work package set based on the deterministic factors of the group's initial plan.

[0205] The uncertainty algorithm model construction module 3 is used to construct an uncertainty algorithm model based on the work station plan work package set and the uncertainty factors of the group establishment plan.

[0206] The solution strategy setting module 4 is used to determine the solution strategy for the uncertainty algorithm model.

[0207] The solution task planning module 5 is used to determine the group assembly task plan based on the solution strategy.

[0208] In the aforementioned hull assembly workstation planning optimization device, an initial set of workstation plans is formed based on the deterministic factors of the assembly plan. An uncertainty algorithm model is then constructed based on the set of workstation plans and combined with the uncertainty factors. The assembly workstation plan is determined by solving the uncertainty algorithm model. This enables precise and balanced planning management of ship section assembly even when there are many types of sections, large demand spans, and frequent demand changes. It improves the efficiency of planning, provides fine-grained planning management, avoids inventory backlog, and ensures a balanced production load.

[0209] Specific limitations regarding the hull assembly station planning optimization device can be found in the limitations of the hull assembly station planning optimization method described above, and will not be repeated here. Each module in the aforementioned hull assembly station planning optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0210] Example 4

[0211] In embodiment 4, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 7. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores optimization data for hull group erection workstation plans. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a hull group erection workstation plan optimization method.

[0212] Those skilled in the art will understand that the structure shown in Figure 7 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0213] In embodiment 4, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0214] Identify the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtain the deterministic and uncertain factors of the group assembly plan scheduling;

[0215] An initial set of workstation planning packages is formed based on the deterministic factors of the group's initial planning.

[0216] Based on the work station plan work package set, an uncertainty algorithm model is constructed according to the uncertainty factors of the group's independent plan;

[0217] Determine the solution strategy for the uncertainty algorithm model;

[0218] Based on the solution strategy, determine the group's work plan.

[0219] For specific limitations on the steps implemented by the processor when executing a computer program, please refer to the limitations on the method for optimizing the hull group's workstation plan mentioned above, which will not be repeated here.

[0220] Example 5

[0221] In embodiment 5, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps:

[0222] Identify the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtain the deterministic and uncertain factors of the group assembly plan scheduling;

[0223] An initial set of workstation planning packages is formed based on the deterministic factors of the group's initial planning.

[0224] Based on the work station plan work package set, an uncertainty algorithm model is constructed according to the uncertainty factors of the group's independent plan;

[0225] Determine the solution strategy for the uncertainty algorithm model;

[0226] Based on the solution strategy, determine the group's work plan.

[0227] For specific limitations on the steps implemented when a computer program is executed by a processor, please refer to the limitations on the method for optimizing the hull group's workstation plan mentioned above, which will not be repeated here.

[0228] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0229] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0230] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the inventive concept, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the planning of hull erection workstations, characterized in that, Including the following steps: Identify the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtain the deterministic and uncertain factors of the group assembly plan scheduling; An initial set of workstation planning packages is formed based on the deterministic factors of the group's initial planning. Based on the work station plan work package set, an uncertainty algorithm model is constructed according to the uncertainty factors of the group's independent plan; Determine the solution strategy for the uncertainty algorithm model; Based on the solution strategy, determine the group's work plan.

2. The method for optimizing the hull group erection workstation plan according to claim 1, characterized in that, The steps for identifying the influencing factors of the hull group assembly plan scheduling based on batches according to flow direction, and obtaining the deterministic and uncertain factors of the group assembly plan scheduling, include: Identify all influencing factors for the production planning of hull groups based on batch production according to flow direction, and categorize the influencing factors into deterministic factors and uncertain factors according to whether they are controllable; When different workstations and different components are assembled in groups, and different construction methods are used, the deterministic factors include the construction quantity of each group assembly plan package, the standard cycle of each group assembly plan package, and the required date of each group assembly plan package, while the uncertain factors include delivery time.

3. The method for optimizing the hull group erection workstation plan according to claim 2, characterized in that, The step of forming an initial set of workstation plans based on the deterministic factors of the group's initialization plan includes: The corresponding workstations are set up according to the construction methods and categories, and the material flow is the same as the workstation settings. Based on the weld flow direction and welding method in the parts weld detail table, a group stand-up planning package is prepared as work package b. Work package b represents a collection of construction tasks in the same segment, the same work station, and the same flow direction. Set a set of work packages B = {1,…,|B|} for the work station within a certain period, where b∈B; The length of segment b weld is calculated based on the weld length information in the part weld detail table. Based on the group's plan, weld length, and time standards, calculate the segmented planned time (t) for section b. b ; The standard cycle of the sub-assembly plan package is calculated based on the weld length or planned working hours, i.e., the number of consecutive days τ required for work package b. b ; According to the group's established plan, the planned working hours are t. b Standard cycle calculation group establishes the average daily production time of the planning package. Based on the downstream demand date and standard cycle of work package b, determine the initial delivery date and initial start date for each segment. The initial delivery date for each segment is the completion date of work package b in the initial plan. The initial start date for the segment is the start date for work package b in the initial plan. Set the number of days α1 required to prepare work package b in advance; Set the required inspection time α2 after work package b is completed; Set the delivery date for work package b. b .

4. The method for optimizing the hull group erection workstation plan according to claim 3, characterized in that, The steps of constructing an uncertainty algorithm model based on the set of work packages for the workstation plan according to the uncertainty factors of the group's establishment plan include: Based on the aforementioned uncertainties, make scheme assumptions, convert the production time of cross-cycle work package b, and determine the variance of minimizing the daily workload. Based on the work station's planned work package set, orders within the planning period are determined according to the delivery date δ. b Using α1 as a condition, and setting the start date of work package b through α2, the plan and average daily working hours are adjusted under the constraint of meeting the delivery date. The transformed parameters include: The earliest time that work package b can start within the planning period [0, P]. The latest time for work package b to start within the planned period [0, P] Number of days during which work package b is produced within the planning period [0, P] Total working hours of work package b during the planning period [0, P] The average daily working hours D during the planning period [0, P] a ; The process for setting up the time conversion of cross-cycle work package b is as follows: When construction begins before the planned period, set up When construction begins during the planning period, set up When setting the variance determination to minimize the daily workload, a first decision variable x is introduced. bd Second decision variable y bd ; If x bd =1, the workload of work package b on day d∈{0,..,P} is If x bd =0, the workload of work package b on day d∈{0,..,P} is 0; If y bd =1, work package b is in the... Work will begin on the day; if y bd =0, work package b is in the... Work has not yet started; The objective function corresponding to the obtained uncertainty algorithm model is The uncertainty algorithm model satisfies the constraints. Wherein, objective function (1) represents minimizing the variance of daily workload; constraint (2) represents the total number of days for the production of work package b; constraint (3) represents the start date constraint of work package b; constraint (4) represents the continuous production constraint of work package b; constraint (5) is the first decision variable constraint; constraint (6) is the second decision variable constraint; the uncertainty algorithm model is a nonlinear programming mathematical model.

5. The method for optimizing the hull group erection workstation plan according to claim 4, characterized in that, The steps for determining the solution strategy of the uncertainty algorithm model include: A genetic algorithm is set up, wherein the operators of the genetic algorithm include encoding, fitness value, decoding and selection strategy, crossover strategy, mutation strategy and termination strategy; The solution strategy for the uncertainty algorithm model is determined by a genetic algorithm.

6. The method for optimizing the hull group erection workstation plan according to claim 5, characterized in that, The steps for setting up the genetic algorithm include: Based on the encoding rules, an initial solution for a population is randomly generated; A superior population from the initial solution is selected using fitness, decoding, and selection strategies. The optimal solution is updated by crossover or mutation of the superior population using a crossover strategy. Termination strategies are used to control the termination of crossover or mutation processes.

7. The method for optimizing the hull group erection workstation plan according to claim 6, characterized in that, In the step of setting up the genetic algorithm, The encoding adopts a sequential encoding method, where each chromosome represents a solution, each gene position represents a working package b, and the order of working packages b indicates the order during production scheduling. The fitness function selects the objective function. The decoding process in the aforementioned decoding and selection strategy is a heuristic algorithm that schedules production according to the encoding order, sequentially placing the start dates of work package b in order. Chinese; calculation in For already ranked in Task package B in the sky, task package B was ultimately ranked [number]. The selection strategy described uses a roulette wheel betting method. The crossover strategy employs a single-point crossover method to crossover at least one segment of gene loci on a chromosome to obtain crossover or mutated offspring chromosomes. The termination strategy selects to terminate the crossover or mutation process after reaching a preset number of iterations.

8. The method for optimizing the hull group erection workstation plan according to claim 1, characterized in that, The steps for determining the group assembly work plan based on the solution strategy include: Obtain the planned balance scheme for the feasible hull group erection workstation calculated by the solution strategy; The optimal plan balance scheme for the hull group erection workstation is determined by minimizing the variance of the daily workload, which serves as the group erection work plan.

9. A device for optimizing the planning of ship hull group erection workstations, characterized in that, The device includes: The influencing factor acquisition module is used to identify the influencing factors of the hull group assembly plan scheduling based on the flow direction and batch, and to acquire the deterministic and uncertain factors of the group assembly plan scheduling. The initial plan acquisition module is used to generate an initial set of workstation plan work packages based on the deterministic factors of the group's initial plan. An uncertainty algorithm model construction module is used to construct an uncertainty algorithm model based on the work station plan work package set and the uncertainty factors of the group establishment plan; The solution strategy module is used to determine the solution strategy for the uncertain algorithm model. The task planning module is used to determine the group's task plan based on the solution strategy.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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