Method for automatic layout and optimization of construction machinery assembly workshop based on logistics simulation and genetic algorithm

Abstract

The present application relates to a kind of engineering machinery assembly workshop automation layout and optimization method based on logistics simulation and genetic algorithm, belong to logistics simulation and genetic algorithm field.The present application is by integrating genetic algorithm module, data processing and storage module, simulation module, evaluation analysis module and visual module on logistics simulation platform, realizes the discrete type assembly workshop automation layout and optimization.Specifically including: using genetic algorithm module to encode and generate sample, sample coding information is converted into the coordinate required by simulation using data processing and storage module, index data is output by simulation module simulation operation, evaluation analysis module calculates sample fitness value, genetic algorithm module is selected according to fitness value, cross, mutation operation, and iteration optimization is carried out, and optimal layout scheme is obtained.Visual module is used to show simulation process and result.The present application improves the efficiency of complex production system optimization, and overcomes the limitation of prior art.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of logistics simulation and genetic algorithm, and relates to an engineering machinery assembly workshop automation layout and optimization method based on logistics simulation and genetic algorithm. BACKGROUND

[0002] At present, the layout optimization of engineering machinery assembly workshops mainly relies on experience and manual design, which has the following problems:

[0003] The existing logistics simulation technology mainly focuses on the simulation verification of existing schemes, and cannot automatically generate optimization schemes, relies on the experience and subjective judgment of designers, and is low in efficiency and easy to make mistakes.

[0004] The optimization module of the logistics simulation software is usually equipped with an enumeration method, which is limited to non-continuous optimization, and is difficult to deal with complex and variable actual problems, and the optimization efficiency is low.

[0005] Although the genetic algorithm is a powerful optimization algorithm, it has the advantages of strong search ability, parallelism and good robustness, but when dealing with complex problems in a multi-dimensional and multi-factor coupled space, it has problems such as complex solution, high time cost and difficulty in convergence.

[0006] Therefore, in view of the deficiencies of the prior art, the present application provides an engineering machinery assembly workshop automation layout and optimization method based on logistics simulation and genetic algorithm, which aims to realize automatic layout, improve optimization efficiency, and overcome the limitations of genetic algorithm in complex problems. SUMMARY

[0007] Therefore, the present application provides an engineering machinery assembly workshop automation layout and optimization method based on logistics simulation and genetic algorithm, which is used to solve the problems of complex production system layout optimization, weak optimization of logistics simulation software, and inability of genetic algorithm to solve problems in a multi-dimensional and multi-factor coupled space.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0009] The engineering machinery assembly workshop automation layout and optimization method based on logistics simulation and genetic algorithm comprises the following steps:

[0010] Step 1: using a genetic algorithm module to genetically code the functional areas divided in the workshop, and randomly generating samples;

[0011] Step 2: using a data processing and storage module to grid the layout space, and converting the position information of each partition in the sample, i.e. genetic code, into position coordinate information required for simulation simulation and storing;

[0012] Step 3: read the position information of each functional area using the simulation module, establish the logical relationship and operation flow among the modules, run the model and output the transportation distance, logistics efficiency and logistics cost index data;

[0013] Step 4: use the evaluation analysis module to calculate and output the fitness value of the current sample scheme layout according to the output index data;

[0014] Step 5: use the genetic algorithm module to select, cross and mutate according to the fitness value of each sample to obtain a new generation of sample population;

[0015] Step 6: repeat steps 2-5 until the fitness value converges and no longer changes, and obtain the optimal layout scheme under the current input;

[0016] Step 7: use the visualization module to display the current iteration number, simulation layout, simulation running characteristic index and fitness value of the current scheme.

[0017] Further, the functional areas include unloading areas, storage areas, material picking areas, production areas and material distribution points.

[0018] Further, the simulation module is one of Plant Simulation, DELMIA, FlexSim, VisualComponents, Simio, Arena, AutoMod, SIMUL8, AnyLogic, AutoMod, ExtendSim or Demo3d.

[0019] An engineering machinery assembly plant automatic layout and optimization system based on logistics simulation and genetic algorithm, the system comprises:

[0020] A genetic algorithm module for genetic coding of the functional areas divided in the plant and randomly generating samples;

[0021] A data processing and storage module for grid processing of the layout space, converting the position information of each partition in the sample, i.e. genetic code, into position coordinate information required for simulation and storing;

[0022] A simulation module for reading the position information of each functional area, establishing the logical relationship and operation flow among the modules, running the model and outputting the index data of transportation distance, logistics efficiency and logistics cost;

[0023] An evaluation analysis module for calculating and outputting the fitness value of the current sample scheme layout according to the output index data;

[0024] A visualization module for displaying the current iteration number, simulation layout, simulation running characteristic index and fitness value of the current scheme.

[0025] The genetic algorithm module is connected to the data processing and storage module, and the generated samples are transmitted to the data processing and storage module for subsequent gridding processing and coordinate information conversion;

[0026] The data processing and storage module is connected to the simulation module, and the functional area coordinate information is transmitted to the simulation module for establishing a simulation model and performing simulation running;

[0027] The simulation module is connected to the evaluation and analysis module, and the index data are transmitted to the evaluation and analysis module for calculating the fitness value of the sample; the simulation module is connected to the visualization module, and the simulation running result is transmitted to the visualization module for displaying the simulation layout and running characteristic index;

[0028] The evaluation and analysis module is connected to the genetic algorithm module, and the fitness value is transmitted to the genetic algorithm module for selection, crossover and mutation operations; the evaluation and analysis module is connected to the visualization module for displaying the fitness value of the current layout scheme;

[0029] The visualization module is connected to the genetic algorithm module to obtain the current iteration number and sample information from the genetic algorithm module; the visualization module is connected to the simulation module to obtain the simulation running characteristic index and layout scheme from the simulation module; the visualization module is connected to the evaluation and analysis module to obtain the fitness value of the current scheme from the evaluation and analysis module.

[0030] The beneficial effects of the present application are as follows:

[0031] (1) The genetic algorithm is combined with the logistics simulation technology to realize automatic generation and optimization of the layout scheme, avoid manual design and experience dependence, and improve efficiency and accuracy.

[0032] (2) The optimal layout scheme is quickly found through the parallel search capability and iterative optimization mechanism of the genetic algorithm, effectively shortening the optimization time and improving the optimization efficiency.

[0033] (3) The logistics simulation technology provides a multi-dimensional, multi-factor coupling space modeling tool for the genetic algorithm, which can effectively handle complex problems and overcome the limitations of the genetic algorithm in complex problems.

[0034] (4) The visualization module can clearly display the simulation process and result, facilitate user understanding and evaluation of the layout scheme, and improve decision-making efficiency.

[0035] (5) The present application can be applied to layout optimization of various engineering mechanical assembly workshops, and has strong universality.

[0036] Additional advantages, objects, and features of the application will be apparent to those skilled in the art upon examination of the following specification. It is intended that the application not be limited by the disclosed BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:

[0038] Figure 1 Flow chart of the present application. DETAILED DESCRIPTION

[0039] The other advantages and effects of the present application can be easily understood by those skilled in the art from the content disclosed in the specification. The present application can also be implemented or applied by different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0040] The drawings are only used for exemplary illustration, and the representation is only a schematic diagram, not a physical diagram, and should not be understood as a limitation of the present application; in order to better illustrate the embodiments of the present application, some components in the drawings can be omitted, enlarged or reduced, and do not represent the size of the actual product; it can be understood by those skilled in the art that some known structures and their descriptions in the drawings can be omitted.

[0041] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and should not be understood as a limitation of the present application, for those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0042] As Figure 1As shown, the application proposes an engineering machinery assembly workshop automation layout and optimization method based on logistics simulation and genetic algorithm, the logistics simulation model and genetic algorithm model involved in the method are established on a logistics simulation platform, the logistics simulation platform comprises a genetic algorithm module, a data processing and storage module, a simulation module, an evaluation analysis module, a visualization module and the like.

[0043] Optionally, the genetic algorithm module is responsible for genetic coding of the functional areas divided in the workshop, such as unloading area, storage area, material picking area, production area, material distribution point and the like, and randomly generating 100 samples.

[0044] Optionally, the divided functional areas can be increased, reduced or adjusted according to actual needs, and are not limited to the above needs; the solution space of 100 samples is an empirical value, which can be adjusted according to the actual situation of the project.

[0045] Optionally, the data processing and storage module is responsible for grid processing of the layout space, dividing the space into square grids with a certain side length according to the size of the space, each grid having its own number and position, and then the data processing and storage module converts the position information of each partition in each sample into position coordinate information required for simulation and stores it.

[0046] Optionally, the simulation module reads the position information of each functional area, establishes the logical relationship and operation process among the modules, runs the model and outputs index data such as transportation distance, logistics efficiency and logistics cost.

[0047] Optionally, the evaluation analysis module calculates and outputs the fitness value of the current sample scheme layout according to the output index data.

[0048] Optionally, the genetic algorithm module is also responsible for selection, crossover, mutation and the like according to the fitness value of each sample, to obtain a new generation of sample population. After several iterations and evolution, until the fitness value converges and no longer changes, the optimal layout scheme under the current input can be obtained.

[0049] Optionally, the visualization module is responsible for obtaining data from the genetic algorithm module, the simulation module and the evaluation analysis module, clearly and clearly displaying the current iteration number, simulation layout, simulation running characteristic index and fitness value of the current scheme.

[0050] Optionally, the logistics simulation platform is one of Plant Simulation, DELMIA, FlexSim, VisualComponents, Simio, Arena, AutoMod, SIMUL8, AnyLogic, AutoMod, ExtendSim, Demo3d.

[0051] Embodiment

[0052] Problem Description: A construction machinery assembly workshop uses traditional layout, which has problems such as long material transportation distance, low logistics efficiency, high cost, etc. It needs to be optimized for automated layout.

[0053] Step 1: Function Area Division

[0054] The workshop is divided into the following functional areas:

[0055] Unloading area (1)

[0056] Warehouse area (2)

[0057] Material picking area (3)

[0058] Production area (4)

[0059] Material distribution point (5)

[0060] Step 2: Genetic Algorithm Encoding

[0061] Use the genetic algorithm module to encode the functional areas, with each functional area represented by a set of genes for its position coordinates (x, y). For example, the unloading area coordinates are (1010, 1010), warehouse area 1 coordinates are (10100, 1010), warehouse area 2 coordinates are (11110, 1010), material picking area 1 coordinates are (101000, 1010), material picking area 2 coordinates are (110010, 1010), material picking area 3 coordinates are (111100, 1010), production area 1 coordinates are (1000110, 1010), production area 2 coordinates are (1010000, 1010), production area 3 coordinates are (1011010, 1010), production area 4 coordinates are (1100100, 1010), material distribution point 1 coordinates are (1101110, 1010), material distribution point 2 coordinates are (1111000, 1010), material distribution point 3 coordinates are (10000010, 1010), material distribution point 4 coordinates are (10001100, 1010), and material distribution point 5 coordinates are (10010110, 1010).

[0062] Step 3: Generate Samples

[0063] 100 samples are randomly generated, each representing a layout scheme. For example, sample 1 has the following layout scheme: unloading area (1010, 1010), warehouse area 1 (10100, 1010), warehouse area 2 (11110, 1010), material picking area 1 (101000, 1010), material picking area 2 (110010, 1010), material picking area 3 (111100, 1010), production area 1 (1000110, 1010), production area 2 (1010000, 1010), production area 3 (1011010, 1010), production area 4 (1100100, 1010), material distribution point 1 (1101110, 1010), material distribution point 2 (1111000, 1010), material distribution point 3 (10000010, 1010), material distribution point 4 (10001100, 1010), material distribution point 5 (10010110, 1010). The layout scheme of sample 2 is: unloading area (1111, 10100), warehouse area 1 (11110, 1010), warehouse area 2 (110010, 101000), material picking area 1 (10100, 100011), material picking area 2 (10100, 101000), material picking area 3 (10100, 110010), production area 1 (1000110, 110010), production area 2 (1011010, 110010), production area 3 (1011010, 110111), production area 4 (1100100, 111100), material distribution point 1 (1100100, 1000001), material distribution point 2 (1101110, 111100), material distribution point 3 (1111000, 111100), material distribution point 4 (10000111, 111100), material distribution point 5 (10010001, 111100).

[0064] Step 4: Grid processing

[0065] The workshop layout space is divided into square grids with a side length of 1 meter, each grid has number and position information. Then the genetic code representing the position information of each functional area is converted into coordinate information that can be recognized by the simulation module, such as the unloading area genetic code (1010, 1010), the position coordinate after data processing and storage module conversion is (10, 10).

[0066] Step 5: Simulation

[0067] The function area coordinate information of the sample is read using the simulation module, the logical relationship and operation process among the modules are established, the model is run and index data such as transportation distance, logistics efficiency and logistics cost are output. For example, the transportation distance of sample 1 is 500 meters, the logistics efficiency is 80%, and the logistics cost is 1000 yuan.

[0068] Step 6: Fitness value calculation

[0069] The evaluation analysis module is used to calculate the fitness value of each sample according to the output index data. The higher the fitness value, the better the layout scheme. For example, the fitness value of sample 1 is 0.8, and the fitness value of sample 2 is 0.9.

[0070] Step 7: Iterative optimization of genetic algorithm

[0071] The genetic algorithm module is used to select, cross, mutate and other operations according to the fitness value to generate a new generation of sample population. For example, samples with higher fitness values are selected for crossing to generate new samples, and mutation is performed on them to increase the diversity of samples.

[0072] Step 8: Visual display

[0073] The current iteration number, simulation layout, simulation running characteristic index and fitness value of the current scheme are displayed using the visualization module.

[0074] Step 9: Repeat iteration

[0075] Steps 5 to 8 are repeated until the fitness value converges and no longer changes, and the optimal layout scheme under the current input is obtained.

[0076] After multiple iterations and optimization, the optimal layout scheme is finally obtained, which places the material distribution points close to the production area, shortens the material transportation distance, improves the logistics efficiency, and reduces the logistics cost. For example, the transportation distance of the optimal scheme is 300 meters, the logistics efficiency is 90%, and the logistics cost is 800 yuan.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should be covered by the claims of the present application.

Claims

1. An automated layout and optimization method for engineering machinery assembly workshops based on logistics simulation and genetic algorithms, characterized by: The method includes the following steps: Step 1: Use the genetic algorithm module to genetically encode the functional areas of the workshop and randomly generate samples; Step 2: Use the data processing and storage module to perform gridding on the layout space, convert the position information of each partition in the sample, i.e. the genetic code, into the position coordinate information required for simulation and store it. Step 3: Use the simulation module to read the location information of each functional area, establish the logical relationship and operation process between each module, run the model and output the transportation distance, logistics efficiency and logistics cost index data; Step 4: Use the evaluation and analysis module to calculate and output the fitness value of the current sample layout based on the output index data; Step 5: Using the genetic algorithm module, perform selection, crossover, and mutation operations based on the fitness values ​​of each sample to obtain a new generation of sample population; Step 6: Repeat steps 2 to 5 until the fitness value converges and no longer changes, thus obtaining the optimal layout scheme under the current input; Step 7: Use the visualization module to display the current iteration algebra, simulation layout, simulation running characteristic indicators, and the fitness value of the current scheme; The functional areas include unloading area, storage area, material picking area, production area and material distribution points; The simulation module is one of Plant Simulation, DELMIA, FlexSim, Visual Components, Simio, Arena, AutoMod, SIMUL8, AnyLogic, AutoMod, ExtendSim, or Demo3d.

2. An automated layout and optimization system for engineering machinery assembly workshops based on logistics simulation and genetic algorithms, characterized in that: The system includes: The genetic algorithm module is used to genetically encode the unloading area, storage area, material picking area, production area and material distribution point in the engineering machinery assembly workshop, randomly generate an initial sample population, and perform selection, crossover and mutation operations based on the fitness value of each sample to obtain a new generation of sample population. The data processing and storage module is used to perform gridding processing on the layout space, converting the position information of each partition in the sample, i.e. the genetic code, into the position coordinate information required for simulation and storing it. The simulation module is used to read the location information of each functional area, establish the logical relationship and operation process between each module, run the model and output indicator data such as transportation distance, logistics efficiency and logistics cost. The evaluation and analysis module is used to calculate and output the fitness value of the current sample solution layout based on the output data of transportation distance, logistics efficiency and logistics cost indicators. The visualization module is used to display the current iteration algebra, simulation layout, simulation running characteristic indicators, and the fitness value of the current scheme; The genetic algorithm module is connected to the data processing and storage module, and the generated samples are passed to the data processing and storage module for subsequent gridding and coordinate information transformation. The data processing and storage module is connected to the simulation module, which transmits the functional area coordinate information to the simulation module for building a simulation model and performing simulation operations. The simulation module is connected to the evaluation and analysis module, which transmits the indicator data to the evaluation and analysis module for calculating the fitness value of the sample; the simulation module is also connected to the visualization module, which transmits the simulation results to the visualization module for displaying the simulation layout and running characteristic indicators. The evaluation and analysis module is connected to the genetic algorithm module, which passes the fitness value to the genetic algorithm module for selection, crossover, and mutation operations; the evaluation and analysis module is connected to the visualization module to display the fitness value of the current layout scheme. The visualization module is connected to the genetic algorithm module to obtain the current iteration generation and sample information; the visualization module is connected to the simulation module to obtain simulation running characteristic indicators and layout schemes; the visualization module is connected to the evaluation and analysis module to obtain the fitness value of the current scheme.

Images