A pull-based ship general assembly plan space-time resource collaborative optimization method and system

Abstract

The application relates to the technical field of ship intelligent manufacturing and advanced plan scheduling, and discloses a ship total assembly plan space-time resource collaborative optimization method based on a pull type, which comprises the following steps: S1: acquiring a mounting plan and scheduled hoisting dates D_i of each mounting unit, and simultaneously collecting physical boundary data of a total assembly site, gantry crane track coverage, necessary passage parameters and safety buffer zone standards; S2: establishing a one-to-one mapping relationship between the mounting unit and the total assembly unit, and deducing a latest completion date of the total assembly unit according to a formula, wherein is a fixed buffer time before total assembly completion and hoisting, and includes painting drying, inspection, transportation and emergency reserved time length. The application effectively solves the problems of extensive material quantity management and control, resource peak-valley resonance and space-time planning fragmentation in the prior art, and finally realizes the precision, feasibility, intelligence and agility of ship total assembly plan compilation, and guarantees that the total assembly plan and the mounting plan are accurately matched and resources are efficiently utilized.

Description

Technical Field

[0001] This invention relates to the field of intelligent ship manufacturing and advanced planning and scheduling technology, specifically to a pull-based method and system for the spatiotemporal resource collaborative optimization of ship group planning. Background Technology

[0002] The overall planning and assembly of shipbuilding is a core aspect affecting construction cycle and cost. Current industry-standard methods suffer from the following interconnected and deep-seated pain points: 1. Inefficient material control and disconnect between planning and production capacity: Existing plans only reflect processes and time nodes, failing to translate physical quantity data such as weld length and coating area into precise production capacity requirements for workshop manpower and dock cranes through standard working hours (ST). The feasibility of the plan heavily relies on the personal experience of the planning engineer, resulting in significant deviations from actual production capacity and poor plan executability.

[0003] 2. Isolated resource calculations lead to resonance peaks: The demand for resources (especially scarce cranes, core welders, and painters) at the main assembly site and the docking station is usually calculated and optimized independently, lacking a coordination mechanism. This easily leads to a "resonance peak" in the demand for the same resource in two phases at the same time, causing resource shortages and soaring costs; while at another time, demand falls into a trough, resulting in idle resources. This drastic resource fluctuation is a major cause of production inefficiency and cost control problems.

[0004] 3. Complex and Unquantified Spatial Constraints: The assembly site has a series of rigid spatial constraints, such as fixed boundaries, necessary passageways, and the coverage area of ​​the gantry crane rails. Furthermore, each segment / assembly section itself has three-dimensional dimensions (length, width, height), and the assembly process requires reserved safety buffer zones and material and tooling storage areas. These complex and multi-dimensional spatial constraints were not fully digitized and incorporated into automation considerations during the planning stage, leading to doubts about the spatial feasibility of the plan.

[0005] 4. Disconnect between planning and site layout, relying on inefficient manual arrangement: Due to the lack of quantified integration of the aforementioned spatial constraints, the overall project's time scheduling and site spatial layout are disconnected in practice. Layout schemes rely on engineers performing inefficient "manual tiling" on CAD drawings, making it difficult to assess the impact of multiple layout schemes on the overall cycle and resource chain in a short time. This method is inefficient and often yields only feasible solutions, not globally optimal ones, failing to maximize site utilization and logistical efficiency.

[0006] To address this issue, we propose a pull-based method and system for the spatiotemporal resource collaborative optimization of ship grouping planning. Summary of the Invention

[0007] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method and system for coordinated optimization of spatiotemporal resources in ship grouping planning based on a pull-based approach, thus solving the problems mentioned in the background technology.

[0008] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for coordinated optimization of spatiotemporal resources in a pull-based ship grouping plan, comprising: S1: Obtain the installation plan and the scheduled hoisting date D_i for each installation unit, and simultaneously collect the physical boundary data of the overall site, the coverage area of ​​the gantry crane track, the necessary passage parameters, and the safety buffer zone standards; S2: Establish a one-to-one mapping relationship between the mounting unit and the overall unit, according to the formula. Determine the latest completion date of the main unit. ,in This is a fixed buffer period from the completion of the assembly to the hoisting, including time for paint drying, inspection, transportation, and emergency contingency. S3: Convert the physical quantities of each process in the overall unit into human resource requirements for each job type using standard working hours. The physical quantities include weld length. Coating area The formula for calculating human resource demand is: ,in For job type Standard man-hours per unit weld, For job type Standard man-hours for unit painting This is calculated by determining the effective working hours per day, which in turn leads to the total daily manpower requirement for each job type. Simultaneously, the lifting requirements of each main unit are statistically analyzed. ; S4: Based on the latest completion date of the main unit With rigid time constraints, the goal is to minimize resource fluctuations. With space efficiency goals The weighted sum is the overall optimization objective, while satisfying human resource constraints. Crane daily capacity constraints Under the constraints of spatial non-overlapping and process logic, the start time of the total group of cells is optimized using a genetic algorithm with constraint processing mechanism. Site layout coordinates and orientation Among them, resource fluctuation targets , Total daily manpower requirements standard deviation This represents the peak demand for single-day lifting operations. For weighting coefficients; spatial efficiency target Utilization represents the average utilization rate of the site area within the total group period. These are weighting coefficients; S5: Output the overall group plan, dynamic site layout sequence diagram and resource demand-supply comparison diagram that meet all constraints, and verify the feasibility of the scheme through visual simulation.

[0009] Preferably, step S3 further includes error calibration of the quantity data of each process in the overall unit, using the following calibration formula: ,in The original material quantity data is δ, which is the error correction coefficient based on historical construction data, with a value range of -0.05 to 0.08, to ensure the accuracy of manpower demand calculation.

[0010] Preferably, the spatial non-overlapping constraint specifically means that for any two units T_m and T_n located in the same time period t within the overall site, their envelope rectangles must satisfy the Euclidean distance ≥ (r_m + r_n + s), where r_m and r_n are the circumcircles of the envelope rectangles of the two units, respectively, and s is a preset safety distance, ranging from 1.5 to 3.0 meters. Furthermore, the envelope rectangles must be completely within the physical boundaries of the site and must not cross necessary passages.

[0011] Preferably, the process logic constraints include preceding process constraints and dependency constraints: the preceding process constraints require that the start time S_b of unit T_b ≥ S_a + Dur_a, where T_a is the preceding process unit of T_b, and Dur_a is the total group duration of T_a; the dependency constraints require that units sharing the same core equipment have no overlapping process time windows, and the core equipment includes dedicated welding fixtures and non-destructive testing equipment.

[0012] Preferably, in step S4, the fitness function of the genetic algorithm is set to... , where Viol is the quantification value of the degree of violation of each constraint, and λ is the penalty coefficient. When the constraints are fully satisfied, Viol=0, and the fitness function degenerates into the overall optimization objective F, ensuring that the algorithm prioritizes searching the feasible solution space.

[0013] Preferably, a dynamic adjustment mechanism is also included: when resource changes (R_k change ≥ 10%) or hoisting date adjustments (D_i change ≥ 3 days) occur in actual production, the recalculation of steps S3 to S5 is automatically triggered to generate an optimized solution that adapts to the adjustment. During the adjustment process, the reasonable process logic of the original solution is retained, and the rescheduling time is shortened.

[0014] Preferably, in step S4, the average site area utilization rate (Utilization) is calculated as follows: Utilization = (∫(t0~t1)S_used(t)dt) / (S_total×(t1-t0)), where S_used(t) is the site area occupied at time t, S_total is the total site area of ​​the group, t0 is the planned start time, and t1 is the planned end time. The average occupancy level within the period is calculated by integration.

[0015] Preferably, in step S3, the calculation of the lifting demand C_i^{lift} adopts a hierarchical statistical method: it is divided into unit entry lifting times, inter-process transfer lifting times, and exit lifting times. Among them, the inter-process transfer lifting times are adjusted by a coefficient k_lift based on the structural complexity of the unit, with a value range of 1.2 to 2.5. The higher the structural complexity, the larger k_lift is, ensuring that the crane demand is fully quantified.

[0016] Preferably, step S4 further includes a resource priority allocation strategy: when resource requirements of multiple trades conflict, resources are allocated according to the importance of the trades. The importance ranking is determined based on the criticality coefficient of the process. The criticality coefficient = the weight of the impact of the delay of the process on the total construction period × the difficulty coefficient of the process. The weight ranges from 0.6 to 1.0, and the difficulty coefficient is derived from the historical construction qualification rate.

[0017] Another technical problem to be solved by this invention is to provide a spatiotemporal resource collaborative optimization system for ship grouping planning, comprising: The loading plan interface module is used to receive loading plan data, site parameter data, and resource configuration data, and supports data interaction with the shipbuilding management system (MPS). The ST capacity conversion module is used to convert physical quantity data into manpower requirements for different job types through standard working hours, while also completing quantity error calibration and hierarchical statistics of lifting demand. The spatiotemporal resource coupling optimization engine has a built-in genetic algorithm with a constraint processing mechanism to achieve coordinated optimization of the overall group unit time scheduling and spatial arrangement, including dynamic adjustment trigger unit and resource priority configuration unit; The dynamic visualization simulation module is used to generate dynamic site layout time sequence diagrams and resource demand-supply comparison diagrams, and supports 3D visualization browsing and constraint violation warnings; The output module is used to output the optimized master plan, simulation report and data interface file. It supports export in Excel and PDF formats and can be integrated with the workshop production execution system (MES).

[0018] (III) Beneficial Effects Compared with the prior art, the present invention provides a method and system for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach, which has the following beneficial effects: This invention utilizes a "cargo-driven overall assembly" logic, taking the cargo plan as a rigid input and accurately converting material quantities into the production capacity requirements of different work types through standard working hours. It constructs an integrated reverse optimization model of "time-space-crane-manpower," and under multiple constraints such as resources, cranes, and space, it uses intelligent algorithms to collaboratively optimize the start time and site layout of overall assembly units. This effectively solves problems such as extensive material quantity control, resource peak-valley resonance, and fragmented time-space planning in existing technologies. Ultimately, it achieves precision, feasibility, intelligence, and agility in ship overall assembly planning, ensuring accurate matching between the overall assembly plan and the cargo plan and efficient resource utilization. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] A pull-based method for spatiotemporal resource collaborative optimization of ship grouping planning includes: S1: Obtain the installation plan and the scheduled hoisting date D_i for each installation unit, and simultaneously collect the physical boundary data of the overall site, the coverage area of ​​the gantry crane track, the necessary passage parameters, and the safety buffer zone standards; S2: Establish a one-to-one mapping relationship between the mounting unit and the overall unit, according to the formula. Determine the latest completion date of the main unit. ,in This is a fixed buffer period from the completion of the assembly to the hoisting, including time for paint drying, inspection, transportation, and emergency contingency. S3: Convert the physical quantities of each process in the overall unit into human resource requirements for each job type using standard working hours. The physical quantities include weld length. Coating area The formula for calculating human resource demand is: ,in For job type Standard man-hours per unit weld, For job type Standard man-hours for unit painting This is calculated by determining the effective working hours per day, which in turn leads to the total daily manpower requirement for each job type. Simultaneously, the lifting requirements of each main unit are statistically analyzed. In addition, it also includes error calibration of the quantity data of each process in the overall unit, and the calibration formula is as follows: ,in The original material quantity data is δ, which is the error correction coefficient based on historical construction data, with a value range of -0.05 to 0.08, to ensure the accuracy of manpower demand calculation. The calculation of lifting demand C_i^{lift} adopts a hierarchical statistical method: it is divided into unit entry lifting times, inter-process transfer lifting times, and exit lifting times. Among them, the inter-process transfer lifting times are adjusted by a coefficient k_lift based on the structural complexity of the unit, with a value range of 1.2~2.5. The higher the structural complexity, the larger k_lift is, to ensure that the crane demand is fully quantified. S4: Based on the latest completion date of the main unit With rigid time constraints, the goal is to minimize resource fluctuations. With space efficiency goals The weighted sum is the overall optimization objective, while satisfying human resource constraints. Crane daily capacity constraints Under the constraints of spatial non-overlapping and process logic, the start time of the total group of cells is optimized using a genetic algorithm with constraint processing mechanism. Site layout coordinates and orientation Among them, resource fluctuation targets , Total daily manpower requirements standard deviation This represents the peak demand for single-day lifting operations. For weighting coefficients; spatial efficiency target Utilization represents the average utilization rate of the site area within the total group period. These are weighting coefficients; Specifically, the spatial non-overlap constraint is as follows: for any two units T_m and T_n located in the overall site during the same time period t, their envelope rectangles must satisfy the Euclidean distance ≥ (r_m + r_n + s), where r_m and r_n are the circumcircles of the envelope rectangles of the two units, and s is a preset safety distance, ranging from 1.5 to 3.0 meters. Furthermore, the envelope rectangles must be completely within the physical boundaries of the site and must not cross the necessary passages. Process logic constraints include prerequisite process constraints and dependency constraints: Prerequisite process constraints require that the start time of unit T_b is S_b≥S_a+Dur_a, where T_a is the prerequisite process unit of T_b and Dur_a is the total group duration of T_a; Dependency constraints require that units sharing the same core equipment have no overlapping process time windows, and the core equipment includes dedicated welding fixtures and non-destructive testing equipment. The fitness function of the genetic algorithm is set as follows: , where Viol is the quantification value of the degree of violation of each constraint, λ is the penalty coefficient, when the constraints are fully satisfied Viol=0, the fitness function degenerates into the overall optimization objective F, ensuring that the algorithm prioritizes searching the feasible solution space; The average utilization rate of the site area is calculated as follows: Utilization = (∫(t0~t1)S_used(t)dt) / (S_total×(t1-t0)), where S_used(t) is the site area occupied at time t, S_total is the total site area of ​​the group, t0 is the planned start time, and t1 is the planned end time. The average occupancy level within the period is calculated by integration. In addition, step S4 also includes a resource priority allocation strategy: when resource requirements of multiple trades conflict, resources are allocated according to the importance of the trades. The importance ranking is determined based on the criticality coefficient of the process. The criticality coefficient = the weight of the impact of the delay of the process on the total project duration × the difficulty coefficient of the process. The weight ranges from 0.6 to 1.0, and the difficulty coefficient is derived from the historical construction qualification rate. S5: Output the overall group plan, dynamic site layout timeline diagram and resource demand-supply comparison diagram that meet all constraints, and verify the feasibility of the scheme through visual simulation. S6: Includes a dynamic adjustment mechanism: When resource changes (R_k change ≥ 10%) or hoisting date adjustments (D_i change ≥ 3 days) occur in actual production, the recalculation of steps S3~S5 is automatically triggered to generate an optimized solution that adapts to the adjustment. During the adjustment process, the reasonable process logic of the original solution is retained, and the rescheduling time is shortened.

[0021] The ship group planning spatiotemporal resource collaborative optimization system includes: The loading plan interface module is used to receive loading plan data, site parameter data, and resource configuration data, and supports data interaction with the shipbuilding management system (MPS). The ST capacity conversion module is used to convert physical quantity data into manpower requirements for different job types through standard working hours, while also completing quantity error calibration and hierarchical statistics of lifting demand. The spatiotemporal resource coupling optimization engine has a built-in genetic algorithm with a constraint processing mechanism to achieve coordinated optimization of the overall group unit time scheduling and spatial arrangement, including dynamic adjustment trigger unit and resource priority configuration unit; The dynamic visualization simulation module is used to generate dynamic site layout time sequence diagrams and resource demand-supply comparison diagrams, and supports 3D visualization browsing and constraint violation warnings; The output module is used to output the optimized master plan, simulation report and data interface file. It supports export in Excel and PDF formats and can be integrated with the workshop production execution system (MES).

[0022] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-based approach, characterized in that, include: S1: Obtain the installation plan and the scheduled hoisting date D_i for each installation unit, and simultaneously collect the physical boundary data of the overall site, the coverage area of ​​the gantry crane track, the necessary passage parameters, and the safety buffer zone standards; S2: Establish a one-to-one mapping relationship between the mounting unit and the overall unit, according to the formula. Determine the latest completion date of the main unit. ,in This is a fixed buffer period from the completion of the assembly to the hoisting, including time for paint drying, inspection, transportation, and emergency contingency. S3: Convert the physical quantities of each process in the overall unit into human resource requirements for each job type using standard working hours. The physical quantities include weld length. Coating area The formula for calculating human resource demand is: ,in For job type Standard man-hours per unit weld, For job type Standard man-hours for unit painting This is calculated by determining the effective working hours per day, which in turn leads to the total daily manpower requirement for each job type. Simultaneously, the lifting requirements of each main unit are statistically analyzed. ; S4: Based on the latest completion date of the main unit With rigid time constraints, the goal is to minimize resource fluctuations. With space efficiency goals The weighted sum is the overall optimization objective, while satisfying human resource constraints. Crane daily capacity constraints Under the constraints of spatial non-overlapping and process logic, the start time of the total group of cells is optimized using a genetic algorithm with constraint processing mechanism. Site layout coordinates and orientation Among them, resource fluctuation targets , Total daily manpower requirements standard deviation This represents the peak demand for single-day lifting operations. For weighting coefficients; spatial efficiency target Utilization represents the average utilization rate of the site area within the total group period. These are weighting coefficients; S5: Output the overall group plan, dynamic site layout sequence diagram and resource demand-supply comparison diagram that meet all constraints, and verify the feasibility of the scheme through visual simulation.

2. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach, as described in claim 1, is characterized in that... Step S3 further includes error calibration of the material quantity data of each process in the overall unit, using the following calibration formula: ,in The original material quantity data is δ, which is the error correction coefficient based on historical construction data, with a value range of -0.05 to 0.08, to ensure the accuracy of manpower demand calculation.

3. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach, as described in claim 1, is characterized in that... The spatial non-overlapping constraint is specifically defined as follows: for any two units T_m and T_n located in the same time period t within the overall site, their envelope rectangles must satisfy the Euclidean distance ≥ (r_m + r_n + s), where r_m and r_n are the circumcircles of the envelope rectangles of the two units, respectively, and s is a preset safety distance, ranging from 1.5 to 3.0 meters. Furthermore, the envelope rectangles must be completely within the physical boundaries of the site and must not cross necessary passages.

4. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach as described in claim 1, characterized in that, The process logic constraints include preceding process constraints and dependency constraints: the preceding process constraints require that the start time of unit T_b is S_b≥S_a+Dur_a, where T_a is the preceding process unit of T_b and Dur_a is the total grouping period of T_a. Dependency constraints require that units sharing the same core equipment have no overlapping process time windows. The core equipment includes dedicated welding fixtures and non-destructive testing equipment.

5. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach as described in claim 1, characterized in that, In step S4, the fitness function of the genetic algorithm is set to... , where Viol is the quantification value of the degree of violation of each constraint, and λ is the penalty coefficient. When the constraints are fully satisfied, Viol=0, and the fitness function degenerates into the overall optimization objective F, ensuring that the algorithm prioritizes searching the feasible solution space.

6. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach, as described in claim 1, is characterized in that... It also includes a dynamic adjustment mechanism: when resource changes occur in actual production (R_k change ≥ 10%) or hoisting date adjustments occur (D_i change ≥ 3 days), the recalculation of steps S3 to S5 is automatically triggered to generate an optimized solution that adapts to the adjustment. During the adjustment process, the reasonable process logic of the original solution is retained, and the rescheduling time is shortened.

7. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach, as described in claim 1, is characterized in that... In step S4, the average site area utilization rate (Utilization) is calculated as follows: Utilization = (∫(t0~t1)S_used(t)dt) / (S_total×(t1-t0)), where S_used(t) is the site area occupied at time t, S_total is the total site area of ​​the group, t0 is the planned start time, and t1 is the planned end time. The average occupancy level within the period is calculated by integration.

8. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach, as described in claim 1, is characterized in that... In step S3, the calculation of the lifting demand C_i^{lift} adopts a hierarchical statistical method: it is divided into unit entry lifting times, inter-process transfer lifting times, and exit lifting times. Among them, the inter-process transfer lifting times are adjusted by a coefficient k_lift based on the structural complexity of the unit, with a value range of 1.2 to 2.

5. The higher the structural complexity, the larger k_lift is, to ensure that the crane demand is fully quantified.

9. The method for spatiotemporal resource collaborative optimization of ship grouping planning based on a pull-type approach as described in claim 1, characterized in that, Step S4 also includes a resource priority allocation strategy: when resource requirements of multiple trades conflict, resources are allocated according to the importance of the trades. The importance ranking is determined based on the criticality coefficient of the process. The criticality coefficient = the weight of the impact of the delay of the process on the total project duration × the difficulty coefficient of the process. The weight ranges from 0.6 to 1.0, and the difficulty coefficient is derived from the historical construction qualification rate.

10. A spatiotemporal resource collaborative optimization system for ship grouping planning that implements the method of any one of claims 1-9, characterized in that, include: The loading plan interface module is used to receive loading plan data, site parameter data, and resource configuration data, and supports data interaction with the shipbuilding management system (MPS). The ST capacity conversion module is used to convert physical quantity data into manpower requirements for different job types through standard working hours, while also completing quantity error calibration and hierarchical statistics of lifting demand. The spatiotemporal resource coupling optimization engine has a built-in genetic algorithm with a constraint processing mechanism to achieve coordinated optimization of the overall group unit time scheduling and spatial arrangement, including dynamic adjustment trigger unit and resource priority configuration unit; The dynamic visualization simulation module is used to generate dynamic site layout time sequence diagrams and resource demand-supply comparison diagrams, and supports 3D visualization browsing and constraint violation warnings; The output module is used to output the optimized master plan, simulation report and data interface file. It supports export in Excel and PDF formats and can be integrated with the workshop production execution system (MES).

Images