Shipyard total group tray site layout optimization method based on simulated annealing in combination with local hill climbing algorithm

Abstract

The invention discloses a shipyard total group tray site layout optimization method based on simulated annealing in combination with a local hill climbing algorithm, which comprises the following steps: acquiring basic data of a shipyard total group tray site, constructing a multi-target optimization model, and optimizing the multi-target optimization model by taking the maximum space utilization rate, the minimum transportation cost and the minimum process conflict as optimization targets. And solving the multi-objective optimization model by adopting a simulated annealing-local hill climbing hybrid algorithm to obtain an optimal layout scheme, performing constraint verification and effect evaluation on the optimal layout scheme, and outputting a final layout scheme. Compared with a traditional empirical layout, the space utilization rate is improved by 15%-25%, the transportation cost is reduced by 20%-30%, the secondary transfer rate is reduced to 5% or below, and compared with a single simulated annealing algorithm, the convergence rate is improved by 40%-50%; compared with a single local hill climbing algorithm, the method has the advantages that the target function value is reduced by 10%-15%, parameters can be adjusted according to the actual condition of a shipyard, an output scheme can be directly connected with an MES system, and intelligent production decision making is supported.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of shipbuilding engineering, and particularly relates to a shipyard general assembly and positioning site layout optimization method based on simulated annealing combined with local hill climbing algorithm. BACKGROUND

[0002] The shipyard general assembly and positioning site is a key area of ship modular construction, and undertakes the temporary storage, assembly and pretreatment functions of large components such as ship sections and outfitting modules. Its layout rationality directly affects the production efficiency, transportation cost and safety production level.

[0003] At present, the layout of the shipyard general assembly and positioning site mainly relies on the experience of engineers, and the following problems exist: first, the space utilization rate is low, and the disorderly stacking of large modules leads to 20%-30% of the site being idle, while there is a contradiction between insufficient site and peak period; second, the transportation cost is high, and the secondary transportation rate of modules is more than 30%, and the invalid operation time of gantry cranes, flat cars and other equipment accounts for 15%; third, process conflicts occur frequently, and the layout of modules in pretreatment, general assembly and painting processes lacks continuity, and reverse handling is common.

[0004] In order to solve the above problems, an intelligent optimization algorithm is introduced for layout optimization. Among them, the simulated annealing algorithm has the ability to jump out of local optimum with probability, but the convergence speed is slow and the optimization precision is insufficient in the later period; the local hill climbing algorithm has fast convergence speed, but it is easy to fall into local optimal trap. Single algorithm is difficult to meet the dual needs of global search and local refinement for layout optimization, so it is urgent to build a hybrid optimization method with the advantages of both. SUMMARY

[0005] In view of the above problems, the purpose of the present application is to provide a shipyard general assembly and positioning site layout optimization method based on simulated annealing combined with local hill climbing algorithm, which can effectively improve the space utilization rate of the site, reduce the transportation cost, and reduce the process conflicts.

[0006] The specific technical scheme to achieve the purpose of the present application is:

[0007] A shipyard general assembly and positioning site layout optimization method based on simulated annealing combined with local hill climbing algorithm, comprising the following steps:

[0008] Step 1, collecting the basic data of the shipyard general assembly and positioning site;

[0009] Step 2, constructing a multi-objective optimization model, the model taking the maximum space utilization rate, the minimum transportation cost and the minimum process conflict as the optimization objectives;

[0010] Step 3, using simulated annealing-local hill climbing hybrid algorithm to solve the multi-objective optimization model to obtain the optimal layout scheme;

[0011] Step 4, constraint verification and effect evaluation are carried out on the optimal layout scheme, and the final layout scheme is output.

[0012] Compared with the prior art, the beneficial effects of the present application are that:

[0013] The optimization effect is remarkable: compared with the traditional experience layout, the space utilization rate is increased by 15%-25%, the transportation cost is reduced by 20%-30%, and the secondary transportation rate is reduced to less than 5%;

[0014] The algorithm performance is excellent: compared with a single simulated annealing algorithm, the convergence speed is increased by 40%-50%; compared with a single local hill climbing algorithm, the objective function value is reduced by 10%-15%;

[0015] The practicality is strong: the parameters can be adjusted according to the actual shipyard, the output scheme can be directly connected to the MES system, and the intelligent production decision is supported.

[0016] The present application will be further described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The present application is a shipyard general assembly layout optimization method based on simulated annealing combined with local hill climbing algorithm.

[0018] Figure 2 The present application is a flowchart of global search based on simulated annealing algorithm.

[0019] Figure 3 The present application is a flowchart of obtaining a globally optimal solution by using a local hill climbing algorithm for local fine-tuning. DETAILED DESCRIPTION

[0020] EMBODIMENT

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. The described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application.

[0022] As shown in the present application and claims, unless the context clearly indicates otherwise, the words "one", "a", "an", and / or "the" do not refer to the singular, but can also include the plural. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0023] The relative arrangement of parts and steps, numerical expressions, and numerical values set forth in the Examples are not intended to limit the scope of the application unless specifically so stated. Also, it is to be understood that the dimensions of the various parts shown in the drawings are not necessarily to scale. Techniques, methods, and equipment known to those of ordinary skill are not discussed in detail because such techniques, methods, and equipment are considered standard to the relevant art. In all examples shown and discussed herein, any specific value is to be interpreted as illustrative only and not as a limitation. Thus, other examples of exemplary embodiments can have different values. It is to be noted that like numbers and letters refer to like elements throughout the several views of the drawings, and that the use of or insertion of a reference character is not required in subsequent drawings where the element has already been discussed.

[0024] In this embodiment, a certain shipyard general assembly layout site is taken as an example, which has a length of 150 m, a width of 80 m, a usable area of 10000 m2, and a gantry crane working radius of 50 m; 20 general assembly modules (size 5-20 m, weight 20-150 t) need to be arranged, including 3 dangerous modules.

[0025] In combination with Figure 1 A shipyard general assembly layout site optimization method based on simulated annealing combined with local hill climbing algorithm, comprising the following steps:

[0026] Step 1, collect the basic data of the shipyard general assembly layout site, including site parameters, general assembly module parameters and constraint parameters;

[0027] The site parameters include site size, fixed facility location, ground bearing limit and taboo area coordinates;

[0028] The general assembly module parameters include module quantity, size, weight, process attribute and process priority;

[0029] The constraint parameters include minimum safety distance between modules, transportation path constraint and equipment coverage constraint.

[0030] In this embodiment, the site parameters are: length 150 m, width 80 m, gantry crane track located on both sides of the site, ground bearing limit 200 t, and taboo area is the underground pipeline area with a diameter of 10 m in the center of the site;

[0031] The module parameters: the size, weight, and process attribute of the 20 modules are shown in Table 1 (specific data is omitted here), of which 3 dangerous modules need to be isolated separately;

[0032] The constraint parameters: the safety distance of ordinary modules is ≥3 m, the safety distance of dangerous modules is ≥8 m, and the turning radius of a flat car is ≥8 m.

[0033] Step 2: Construct a multi-objective optimization model, with the optimization objectives being to maximize space utilization, minimize transportation costs, and minimize process conflicts.

[0034] The objective function of the multi-objective optimization model is:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] in, As a reverse indicator of space utilization, , The first The length and width of each module, This refers to the usable area of ​​the site. As a transportation cost indicator, For the first The weight of each module, For the first The total transport distance of each module For process conflict indicators, For module and The process correlation coefficient, For module and The actual distance For module and Ideal process distance; , , These are the weighting coefficients, and In this embodiment, the corresponding weighting coefficients are determined using an expert scoring method. ;

[0040] Its constraints include physical constraints, safety constraints, and process constraints;

[0041] The physical constraints include that the module size does not exceed the size of the placement area and the module weight does not exceed the ground bearing capacity limit.

[0042] The safety constraints include that the center distance between any two modules is not less than the safety threshold, and the width of the fire lane and the main transportation road is not less than the required safety width, for example, 4m;

[0043] The process constraints include the requirement that the lifting module be located within the working radius of the gantry crane, and that the distances between the pretreatment, final assembly, and painting modules increase sequentially.

[0044] Step 3, a simulated annealing-local hill climbing hybrid algorithm is used to solve the multi-objective optimization model to obtain an optimal layout scheme:

[0045] Step 3-1, global search is performed based on the simulated annealing algorithm to generate a globally optimal solution, as shown in FIG. 3-1: Figure 2

[0046] Step 3-1-1, initial solution generation: according to the process priority sorting module, sequentially place the modules in the nearest feasible region to the target station, and randomly adjust a certain proportion (e.g., 5%-10%) of the module positions;

[0047] Step 3-1-2, state generation: use translation operators, rotation operators, or exchange operators to perturb the current solution to generate a new solution;

[0048] wherein the translation operator is to randomly select a module to translate ±(0.5-2)m along the X-axis or Y-axis, and the step size linearly decreases as the temperature decreases;

[0049] the rotation operator is to rotate the rectangular module by 90°;

[0050] the exchange operator is to randomly select two modules to exchange positions, and the exchanged positions satisfy all constraint conditions;

[0051] Step 3-1-3, acceptance criterion: if the new solution has a smaller objective function value than the current solution, it is directly accepted; otherwise, it is accepted according to the Metropolis criterion P , wherein is the current temperature;

[0052] wherein and represent the new objective function value and the current objective function value, respectively;

[0053] Step 3-1-4, temperature iteration: according to the set initial temperature =1000 and the temperature reduction coefficient =0.95, the temperature is reduced once every 100 iterations until the temperature is reduced to the termination temperature =1e-5—1e-4, and the globally optimal solution is output;

[0054] In actual operation, the initial temperature can be set between 800-1200 for better effect, and the temperature reduction coefficient is set between 0.90-0.98, in this embodiment, the globally optimal solution is obtained when the temperature T=1e−5, the space utilization rate is 76.8%, and the transportation cost is 68200 tons・m;

[0055] ​Step 3-2, taking the global optimal solution as the initial solution, a local hill climbing algorithm is used for local refinement to obtain a global optimal solution, as shown in Figure 3

[0056] Step 3-2-1, define neighborhood: take each module in the global optimal solution output by the simulated annealing as the center to construct a rectangular neighborhood, and set the search step size;

[0057] Step 3-2-2, local search: arrange the modules in descending order of transportation cost weight, and traverse all feasible positions in the neighborhood in turn to calculate the objective function value;

[0058] Step 3-2-3, iterative update: if there is a better solution, update the module position, repeat the search until there is no improvement for n consecutive iterations, and output the global optimal solution.

[0059] In this embodiment, the neighborhood radius is set to 3m, the step size is 0.2m, the modules are arranged in order of transportation cost weight, the positions in the neighborhood are traversed, and after 50 iterations, the global optimal solution is obtained, the space utilization rate is 80.8%, and the transportation cost is 62100 tons・m.

[0060] Step 4, constraint checking and effect evaluation of the optimal layout scheme, output the final layout scheme;

[0061] When performing constraint checking and effect evaluation, if the global optimal solution does not satisfy the physical, safety and process constraints, return to step 3 to adjust the algorithm parameters and determine the global optimal solution again;

[0062] At this time, the penalty function method is used to handle the constraint violation problem, and the objective function is modified:

[0063] ;

[0064] wherein, is the violation degree of the cth constraint, is the penalty coefficient, wherein corresponding to the safety constraint is 80-120, corresponding to the physical constraint is 40-60.

[0065] The effect evaluation includes calculating the space utilization rate, transportation cost and total assembly cycle by a simulation tool, and comparing with the traditional layout scheme. In this example, the optimal solution obtained compared with the traditional layout has a space utilization rate increased by 18.3% and a transportation cost reduced by 27.6%;

[0066] The application also discloses a shipyard total assembly layout optimization system based on simulated annealing combined with a local hill climbing algorithm, comprising the following steps:

[0067] A data acquisition module is used to collect the basic data of the shipyard total assembly layout.​

[0068] An optimization module is configured to construct a multi-objective optimization model, the model taking maximum space utilization, minimum transportation cost and minimum process conflict as optimization objectives; a simulated annealing-local hill climbing hybrid algorithm is used to solve the multi-objective optimization model to obtain an optimal layout scheme; the optimal layout scheme is subjected to constraint checking and effect evaluation, and a final layout scheme is output.

[0069] The above-described embodiments only express one implementation of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for optimizing the layout of a shipyard assembly station based on simulated annealing combined with a local hill-climbing algorithm, characterized in that, Includes the following steps: Step 1: Collect basic data of the shipyard's final assembly site; Step 2: Construct a multi-objective optimization model, with the optimization objectives being to maximize space utilization, minimize transportation costs, and minimize process conflicts. Step 3: Use the simulated annealing-local hill climbing hybrid algorithm to solve the multi-objective optimization model and obtain the optimal layout scheme; Step 4: Perform constraint verification and effect evaluation on the optimal layout scheme, and output the final layout scheme.

2. The method for optimizing the layout of shipyard assembly station based on simulated annealing combined with local hill-climbing algorithm according to claim 1, characterized in that, The basic data includes site parameters, overall module parameters, and constraint parameters; The site parameters include site dimensions, location of fixed facilities, ground bearing capacity limit, and coordinates of prohibited areas; The overall module parameters include the number of modules, size, weight, process attributes, and process priority; The constraint parameters include minimum safe distance between modules, transportation path constraints, and equipment coverage constraints.

3. The method for optimizing the layout of shipyard assembly and positioning sites based on simulated annealing combined with local hill-climbing algorithm according to claim 1, characterized in that, The objective function of the multi-objective optimization model in step 2 is: ； ； ； ； in, As a reverse indicator of space utilization, , The first The length and width of each module, This refers to the usable area of ​​the site. As a transportation cost indicator, For the first The weight of each module, For the first The total transport distance of each module For process conflict indicators, For module and The process correlation coefficient, For module and The actual distance For module and Ideal process distance; , , These are the weighting coefficients, and .

4. The method for optimizing the layout of shipyard assembly and positioning sites based on simulated annealing combined with local hill-climbing algorithm according to claim 3, characterized in that, The constraints of the multi-objective optimization model include physical constraints, safety constraints, and process constraints; The physical constraints include that the module size does not exceed the size of the placement area and the module weight does not exceed the ground bearing capacity limit. The safety constraints include that the center distance between any two modules is not less than the safety threshold, and the width of the fire lane and the main transportation road is not less than the required safety width. The process constraints include the requirement that the lifting module be located within the working radius of the gantry crane, and that the distances between the pretreatment, final assembly, and painting modules increase sequentially.

5. The method for optimizing the layout of shipyard assembly and positioning sites based on simulated annealing combined with local hill-climbing algorithm according to claim 1, characterized in that, The solution to the multi-objective optimization model in step 3 is specifically as follows: Step 3-1: Perform a global search based on the simulated annealing algorithm to generate a globally optimal solution; Step 3-2: Using the aforementioned globally optimal solution as the initial solution, a local hill-climbing algorithm is used for local refinement to obtain the globally optimal solution.

6. The method for optimizing the layout of shipyard assembly station based on simulated annealing combined with local hill-climbing algorithm according to claim 5, characterized in that, The global search in step 3-1, which generates the optimal solution, specifically involves: Step 3-1-1: Generate initial solution: Sort modules according to process priority, place them in the feasible area closest to the target station in sequence, and randomly adjust the positions of a certain proportion of modules; Step 3-1-2, State Generation: Perturb the current solution using translation, rotation, or exchange operators to generate a new solution; Step 3-1-3, Acceptance Criterion: If the objective function value of the new solution is less than that of the current solution, accept it directly; otherwise, follow the Metropolis criterion P. Accept, among which The current temperature; Step 3-1-4, Cooling Iteration: Based on the set initial temperature = and cooling coefficient The temperature is reduced once every certain number of iterations until it reaches the termination temperature. Output the globally optimal solution.

7. The method for optimizing the layout of shipyard assembly and positioning sites based on simulated annealing combined with local hill-climbing algorithm according to claim 5, characterized in that, In step 3-2, a local hill-climbing algorithm is used for local refinement to obtain the globally optimal solution. Specifically: Step 3-2-1: Define the neighborhood: Construct a rectangular neighborhood centered on each module in the globally optimal solution output by simulated annealing, and set the search step size; Step 3-2-2, Local Search: Sort the modules in descending order of transportation cost weight, traverse all feasible positions in the neighborhood in turn, and calculate the objective function value; Step 3-2-3, Iterative Update: If a better solution exists, update the module position and repeat the search until there is no improvement after n consecutive iterations, then output the global optimal solution.

8. The method for optimizing the layout of shipyard assembly and positioning sites based on simulated annealing combined with local hill-climbing algorithm according to claim 1, characterized in that, If the global optimal solution does not meet the physical, safety, and process constraints during constraint verification and effect evaluation in step 4, the algorithm parameters are adjusted in step 3, and the global optimal solution is re-determined.

9. The method for optimizing the layout of shipyard assembly station based on simulated annealing combined with local hill-climbing algorithm according to claim 8, characterized in that, When the global optimal solution does not meet physical, safety, and technological constraints, and the algorithm returns to step 3 to adjust the parameters and redetermine the global optimal solution, the penalty function method is used to handle constraint violations and correct the objective function. ； in, To determine the degree of violation of constraint c, This is the penalty coefficient.

10. A shipyard assembly site layout optimization system based on simulated annealing combined with a local hill-climbing algorithm, characterized in that, Includes the following steps: Data acquisition module: used to collect basic data of the shipyard's final assembly and positioning site; The optimization module is used to construct a multi-objective optimization model, which aims to maximize space utilization, minimize transportation costs, and minimize process conflicts. The model is solved using a simulated annealing-local hill-climbing hybrid algorithm to obtain the optimal layout scheme. The optimal layout scheme is then constrained and evaluated to output the final layout scheme.

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