Tunnel hole slag preparation mechanism product production line plant layout optimization method and device, computer equipment, storage medium and computer program product

Abstract

The application relates to a tunnel hole slag preparation mechanism product production line factory layout optimization method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: grouping and partitioning different production line operation units according to functions; analyzing the material flow relationship and the non-material flow relationship between the production line operation units through a system layout planning method, determining a comprehensive relationship grade, obtaining an initial layout scheme, constructing a double-target function with the minimum total material transmission distance and the minimum factory area as the targets, setting a constraint condition that the production line operation units are not overlapped and are adapted to the factory site boundary, generating an initial population containing the initial layout scheme by using symbolic coding, iteratively optimizing the double-target function and the constraint condition based on a genetic algorithm until a preset termination condition is met, and outputting an optimal layout scheme that meets the constraint condition and is adapted to the terrain characteristics of the factory site. The method can improve the layout rationality and has good optimization effect.

Description

Technical Field

[0001] This application relates to the field of production line layout optimization technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for optimizing the layout of a tunnel muck preparation mechanism production line. Background Technology

[0002] With the rapid development of transportation infrastructure construction, the scale of tunnel engineering construction is constantly expanding, resulting in a significant increase in the amount of tunnel muck discharged. In response to the concept of green and low-carbon development, the resource utilization of tunnel muck has become an industry trend, and the technology of preparing manufactured products such as manufactured sand and manufactured aggregates from tunnel muck has been widely used.

[0003] The production line for tunnel muck preparation machinery includes multiple operational units such as crushing, screening, washing, and storage. The rationality of the plant layout directly affects production efficiency, material transportation costs, and plant space utilization. In traditional technologies, the layout of the production line often relies on the experience of engineering technicians. That is, based on the approximate area of ​​the site and their understanding of the functions of each operational unit, the layout area of ​​each unit is initially divided, and then the layout position is adjusted through simple material flow analysis to finally determine the layout scheme.

[0004] However, traditional experience-based layout methods have many problems: on the one hand, they do not systematically consider the logistics and non-logistics relationships between various work units, which can easily lead to overlapping material transmission paths and excessively long transmission distances, increasing transportation costs and reducing production efficiency; on the other hand, they lack scientific optimization objectives and constraints, which may result in overlapping work units, excessive plant area, or poor adaptability of the layout scheme to the site terrain, making it impossible to achieve synergistic optimization of production efficiency and space utilization, and making it difficult to meet the needs of large-scale, intensive tunnel muck resource production.

[0005] Therefore, there is an urgent need for a scientific and systematic method for optimizing the layout of tunnel muck preparation mechanism production lines to solve the problems of poor rationality and unsatisfactory optimization effect of traditional experience-based layouts. Summary of the Invention

[0006] Therefore, it is necessary to provide a method, apparatus, computer equipment, storage medium, and computer program product for optimizing the layout of a tunnel muck preparation mechanism production line, which can improve the rationality of the layout and has a good optimization effect, in order to address the above-mentioned technical problems.

[0007] Firstly, this application provides a method for optimizing the layout of a production line for tunnel muck preparation machinery. The method includes:

[0008] Different production line work units are grouped and partitioned according to their functions, and the area parameters and material transfer characteristic data of each production line work unit are determined.

[0009] The system layout planning method is used to first analyze the logistics and non-logistics relationships between the work units of each production line, determine the comprehensive relationship level, and then draw the relative position diagram and area relationship diagram in sequence to obtain the initial layout scheme.

[0010] Based on the initial layout scheme, a dual objective function is constructed with the goals of minimizing the total material transport distance and minimizing the plant area, and constraints are set to ensure that the production line operation units do not overlap and are adapted to the plant site boundary.

[0011] An initial population containing an initial layout scheme is generated using symbolic encoding. The population is then iteratively optimized using a genetic algorithm based on the bi-objective function and the constraints until a preset termination condition is met.

[0012] Output the optimal layout scheme that meets the constraints and is adapted to the terrain features of the construction site.

[0013] In some embodiments of the method, the step of grouping and partitioning different production line work units according to their functions, and determining the area parameters and material transfer characteristic data of each production line work unit, includes:

[0014] Based on the sequential process of preparing tunnel muck from crushing and screening to finished product storage, the functionally related production line work units are divided into the same functional group, and then partitioned according to the functional group.

[0015] The material transfer characteristic data includes at least one of the following: material transfer volume between each production line work unit, material transfer direction, and material transfer time threshold that meets the production cycle requirements.

[0016] In some embodiments of the method, the step of first analyzing the logistic and non-logistic relationships between each production line work unit using the system layout planning method to determine the comprehensive relationship level includes:

[0017] Obtain full data on material exchanges between each production line work unit within the complete production cycle. Construct a logistics relationship matrix based on the full data on material exchanges. The matrix elements represent the degree of logistics association between the corresponding production line work units. Then determine the strength of the logistics relationship based on the degree of logistics association. The full data on material exchanges includes at least one of the following: the number of material exchanges between each production line work unit and the weight of the material exchanged each time.

[0018] Collect multi-dimensional correlation information and determine the strength of non-logistics relationships based on the multi-dimensional correlation information;

[0019] The logistics relationship strength and the non-logistics relationship strength are weighted and summed using a preset weighted fusion algorithm, and the comprehensive relationship level is obtained by mapping the summation result.

[0020] In some embodiments of the method, the multi-dimensional association information includes at least two of the following: job association information, personnel collaboration requirements information, and safety distance requirements information.

[0021] In some embodiments of the method, the construction of a dual objective function based on the initial layout scheme, aiming to minimize the total material transport distance and the plant area, and setting constraints such as non-overlapping production line work units and adaptation to the plant site boundaries, includes:

[0022] With minimizing the total material transport distance and minimizing the plant area as the dual optimization objectives, corresponding weight coefficients are set for the two objectives, and a weighted summation form of the dual objective function is constructed.

[0023] The constraints include: the layout areas of each production line work unit have no spatial overlap, and the layout boundaries of all work units do not exceed the planned boundaries of the construction site.

[0024] In some embodiments of the method, the step of generating an initial population containing an initial layout scheme using symbolic encoding, and iteratively optimizing it using a genetic algorithm based on the biobjective function and the constraints until a preset termination condition is met, includes:

[0025] Based on the number of production line work units, a symbol encoding is designed to convert the layout information of each production line work unit into a unique encoding string. The encoding string corresponding to the initial layout scheme is used as the core individual, and multiple encoding strings corresponding to different layout positions are generated to form the initial population.

[0026] The calculation results of the bi-objective function are used as fitness evaluation index to score and rank the layout schemes corresponding to each encoding string in the initial population.

[0027] According to the preset selection rules, the matching encoding strings are sorted and filtered. The filtered encoding strings are cross-operated to generate new encoding strings. Then, the new encoding strings are mutated to introduce new arrangement position combinations.

[0028] Repeat the fitness evaluation and genetic operations until the preset termination conditions are met.

[0029] According to a second aspect of the present disclosure, a plant layout optimization device for a tunnel muck preparation mechanism product production line is provided. The device includes:

[0030] The partitioning module is used to group and partition different production line work units according to their functions, and to determine the area parameters and material transfer characteristic data of each production line work unit.

[0031] The initial layout scheme generation module is used to first analyze the logistics and non-logistics relationships between the work units of each production line using the system layout planning method, determine the comprehensive relationship level, and then draw the relative position diagram and area relationship diagram in sequence to obtain the initial layout scheme.

[0032] The processing module is used to construct a dual objective function based on the initial layout scheme, with the objectives of minimizing the total material transport distance and minimizing the plant area, and to set constraints on the non-overlapping of production line operation units and the adaptation to the plant site boundary.

[0033] The iterative optimization module is used to generate an initial population containing an initial layout scheme using symbolic encoding, and to perform iterative optimization based on the bi-objective function and the constraints using a genetic algorithm until a preset termination condition is met.

[0034] The optimal solution output module is used to output the optimal layout scheme that meets the constraints and is adapted to the terrain characteristics of the construction site.

[0035] According to a third aspect of the present disclosure, a computer device is provided. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the above-described method for optimizing the layout of a tunnel muck preparation mechanism product production line.

[0036] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the above-described method for optimizing the plant layout of a tunnel muck preparation mechanism product production line.

[0037] According to a fifth aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program that, when executed by a processor, implements the above-described method for optimizing the layout of a tunnel muck preparation mechanism production line.

[0038] The tunnel muck preparation mechanism product production line layout optimization scheme provided in this application embodiment can divide the work units into functional groups and partitions, determine the initial layout scheme by combining the system layout planning method, and then use the genetic algorithm for iterative optimization. The system takes into account the logistics and non-logistics relationships between each work unit, effectively avoiding the problems of material transmission path intersection and excessively long transmission distance in the traditional experience-based layout. At the same time, by clarifying the constraints, it prevents the overlapping of work units, excessive plant area, or poor adaptability of layout to site terrain, etc., and achieves synergistic optimization of production efficiency and space utilization, reduces production and operation costs, and meets the needs of large-scale and intensive tunnel muck resource production.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0041] Figure 1 This is a schematic flowchart illustrating an in-plant layout optimization method for a tunnel muck preparation mechanism product production line according to an exemplary embodiment.

[0042] Figure 2 This is a schematic diagram illustrating the division, numbering, and material flow relationship of the main operating unit areas of a production line according to an exemplary embodiment;

[0043] Figure 3 This is a logistics-related diagram illustrated according to an exemplary embodiment;

[0044] Figure 4 This is a non-logistics relationship diagram illustrated according to an exemplary embodiment;

[0045] Figure 5 A comprehensive correlation diagram illustrated according to an exemplary embodiment;

[0046] Figure 6 This is a relative position diagram illustrated according to an exemplary embodiment;

[0047] Figure 7 This is an area relationship diagram illustrated according to an exemplary embodiment;

[0048] Figure 8 This is a diagram illustrating the cycle of biological evolution and natural selection according to an exemplary embodiment;

[0049] Figure 9 This is a schematic diagram illustrating the relationships between work units within a plant, according to an exemplary embodiment.

[0050] Figure 10 This is an example diagram illustrating the crossover operation of a genetic algorithm according to an exemplary embodiment;

[0051] Figure 11 This is an example diagram illustrating a mutation operation in a genetic algorithm according to an exemplary embodiment;

[0052] Figure 12 This is a flowchart illustrating a genetic algorithm according to an exemplary embodiment;

[0053] Figure 13 This is a schematic diagram of a 5×5 layout space according to an exemplary embodiment;

[0054] Figure 14 This is a graph illustrating the iterative curves of a genetic algorithm on a square field according to an exemplary embodiment.

[0055] Figure 15 This is a layout diagram of production line equipment on a square site according to an exemplary embodiment;

[0056] Figure 16 This is a graph illustrating the iterative curve of a genetic algorithm on a rectangular field according to an exemplary embodiment.

[0057] Figure 17 This is a layout diagram of production line equipment on a rectangular site according to an exemplary embodiment;

[0058] Figure 18 The genetic algorithm iteration curve on an "L"-shaped field is shown according to an exemplary embodiment.

[0059] Figure 19 This is a layout diagram of production line equipment on an "L"-shaped site according to an exemplary embodiment;

[0060] Figure 20 This is a structural block diagram of an in-plant layout optimization device for a tunnel muck preparation mechanism product production line, according to an exemplary embodiment.

[0061] Figure 21 This is a diagram illustrating the internal structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0062] 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.

[0063] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. For example, the use of terms such as "first," "second," etc., to denote names does not indicate any specific order.

[0064] In some embodiments provided in this disclosure, the execution of the layout optimization method for the tunnel muck preparation mechanism product production line can be controlled by a unified controller or by multiple controllers. These controllers may include controllers on local terminals or controllers on remote servers. In some embodiments, the controllers on local terminals and the controllers on servers may work together to complete the layout optimization control process for the tunnel muck preparation mechanism product production line. The local terminals mentioned in this disclosure may include, but are not limited to, various robotic devices, vehicle-mounted devices, personal computers, laptops, smartphones, tablets, wearable devices, medical devices, VR (Virtual Reality) devices, etc. The servers may also be servers, server clusters, distributed subsystems, cloud processing platforms, servers containing blockchain nodes, and combinations thereof. The controllers described in this disclosure may include various control units capable of implementing logic processing functions, including but not limited to CPU (Central Processing Unit), PLC (Programmable Logic Controller), ECU (Electronic Control Unit), MCU (Microcontroller Unit), FPGA (Field Programmable Gate Array), and CPLD (Complex Programmable Logic Device), as well as controllers composed of one or more logic function units, chips, etc.

[0065] In some embodiments of this disclosure, a method for optimizing the layout of a production line for tunnel muck preparation mechanisms is provided, such as... Figure 1 As shown, it includes the following steps:

[0066] S20. Divide and group different production line work units according to their functions, and determine the area parameters and material transfer characteristic data of each production line work unit.

[0067] A production line work unit typically refers to an independent area that performs a specific function in the process of producing tunnel muck. It is the basic functional unit that constitutes the production line, and the overall production process is achieved through material transfer or functional collaboration among these units. Material transfer characteristic data is a set of data describing the material transfer attributes between the various work units of the production line, providing a foundation for layout optimization analysis.

[0068] S22. First, analyze the logistics and non-logistics relationships between the work units of each production line using the system layout planning method, determine the comprehensive relationship level, and then draw the relative position diagram and area relationship diagram in sequence to obtain the initial layout scheme.

[0069] System layout planning is a quantitative and qualitative equipment layout method that comprehensively considers factors such as logistics processes, staff needs, and equipment configuration. It relies on accurate data collection and analysis to provide a basis for decision-making, while emphasizing visualization to make layout design more intuitive. Logistics relationships refer to the connections formed between various work units on the production line during the transfer of goods, materials, and products. Non-logistics relationships refer to the connections formed between various work units on the production line based on work collaboration needs, excluding material transfer factors. The comprehensive relationship level is a weighted fusion of logistics and non-logistics relationships between work units, reflecting the overall tightness of the connections between units.

[0070] S24. Based on the initial layout scheme, construct a dual objective function with the goals of minimizing the total material transport distance and minimizing the plant area, and set constraints such as non-overlapping production line operation units and adapting to the plant site boundary.

[0071] The initial layout scheme is a theoretical and idealized scheme obtained through systematic layout planning. The dual objective function is a mathematical model constructed to achieve the production line layout optimization objective. The constraints are the rules that the genetic algorithm must follow when performing layout optimization, used to ensure the feasibility and compliance of the layout scheme.

[0072] S26. An initial population containing the initial layout scheme is generated using symbolic encoding, and iterative optimization is performed using a genetic algorithm based on the bi-objective function and the constraints until the preset termination condition is met.

[0073] Symbolic encoding is a method used in genetic algorithms to convert the location information of production line work cells into an algorithm-recognizable form. The initial population is the starting group for iterative optimization in the genetic algorithm, consisting of multiple different encoded strings. A genetic algorithm is a globally randomized search algorithm that simulates the evolution and natural selection processes of species in nature. A preset termination condition is the criterion for stopping iterative optimization in the genetic algorithm, used to ensure that the optimization process is efficient and achieves the desired results.

[0074] S28. Output the optimal layout scheme that meets the constraints and is adapted to the terrain features of the construction site.

[0075] After iterative optimization, the optimal layout scheme that best suits the terrain features of the construction site is selected from the layout schemes that meet the constraints and output. In some implementations, the optimal layout scheme can be output for construction sites with different terrain features such as square, rectangular, and L-shaped. The scheme clearly defines the specific location, size boundaries, and material transport paths of each production line operation unit, ensuring that the layout scheme is highly adapted to the site terrain and simultaneously meets the dual objectives of minimizing the total material transport distance and minimizing the plant area.

[0076] In some embodiments of this disclosure, work units can be divided by functional grouping and partitioning, an initial layout scheme can be determined by combining system layout planning method, and then iterative optimization can be performed by using genetic algorithm. The system takes into account the logistics and non-logistics relationships between each work unit, effectively avoiding the problems of material transmission path intersection and excessive transmission distance in traditional experience-based layout. At the same time, by clarifying the constraints, it prevents the overlapping of work units, excessive plant area, or poor adaptability of layout to site terrain, etc., and achieves synergistic optimization of production efficiency and space utilization, reduces production and operation costs, and meets the needs of large-scale and intensive tunnel muck resource production.

[0077] In some embodiments of this disclosure, S20 includes:

[0078] Based on the sequential process of preparing tunnel muck from crushing and screening to finished product storage, the functionally related production line work units are divided into the same functional group, and then partitioned according to the functional group.

[0079] The material transfer characteristic data includes at least one of the following: material transfer volume between each production line work unit, material transfer direction, and material transfer time threshold that meets the production cycle requirements.

[0080] In some implementations, by integrating the sequential logic of the complete preparation process of tunnel muck from crushing and screening to finished product storage, closely related production line work units are divided into the same functional group, and then arranged in zones according to functional groups to ensure smooth functional integration. When determining the area parameters of each production line work unit, the actual occupied area is calculated based on equipment specifications, operating space requirements, and production scale, and the dimensional boundaries of each unit are clearly defined. The collection of material transfer characteristic data needs to cover the entire production cycle, including the material transfer volume and direction between each production line work unit. Simultaneously, in conjunction with production cycle requirements, the material transfer time threshold is determined to provide data support for subsequent layout optimization.

[0081] In some examples, the production line equipment can be divided into 11 main operating unit areas: 1. Feeding area; 2. Core crushing area; 3. Waste storage area; 4. Dust removal system area; 5. Grinding area; 6. High-quality sand (0-5) storage area; 7. High-quality crushed stone (5-10) storage area; 8. High-quality crushed stone (10-20) storage area; 9. High-quality crushed stone (20-31.5) storage area; 10. Power distribution room; 11. Spare parts room. The material flow between each operating unit is as follows: Figure 2 As shown.

[0082] In some embodiments of this disclosure, work units are grouped and partitioned according to the sequence of the process flow and functional relevance, making the work units within each functional group more closely connected, reducing ineffective logistics activities across functional groups, and improving the smoothness of the production process. At the same time, comprehensive collection of area parameters and various material transport characteristic data provides comprehensive and accurate data support for subsequent layout analysis and optimization, ensuring that the layout design meets actual production needs and further improving the scientificity and rationality of layout optimization.

[0083] In some embodiments of this disclosure, S22 includes:

[0084] Obtain full data on material exchanges between each production line work unit within the complete production cycle. Construct a logistics relationship matrix based on the full data on material exchanges. The matrix elements represent the degree of logistics association between the corresponding production line work units. Then determine the strength of the logistics relationship based on the degree of logistics association. The full data on material exchanges includes at least one of the following: the number of material exchanges between each production line work unit and the weight of the material exchanged each time.

[0085] Collect multi-dimensional correlation information and determine the strength of non-logistics relationships based on the multi-dimensional correlation information;

[0086] The logistics relationship strength and the non-logistics relationship strength are weighted and summed using a preset weighted fusion algorithm, and the comprehensive relationship level is obtained by mapping the summation result.

[0087] In some implementations, when conducting analysis using the system layout planning method, the first step is to collect full data on material flows between each production line's operational units throughout the entire production cycle. This data includes the number of material flows between units and the weight of materials in each flow. Based on this data, a logistics relationship matrix is ​​constructed. The elements in the matrix directly reflect the degree of logistics association between the corresponding production line's operational units. Then, the strength of the logistics relationship is classified according to the strength of the logistics association. Subsequently, multi-dimensional association information is collected, including at least two types of information such as operational association information, personnel collaboration needs information, and safety distance requirements information. Based on this information, the strength of non-logistic relationships between units is comprehensively evaluated.

[0088] A pre-defined weighted fusion algorithm is used to sum the strength of logistics relationships and the strength of non-logistics relationships. The summation result is then mapped to obtain a comprehensive relationship level, with higher levels indicating closer comprehensive connections between corresponding units. Based on the comprehensive relationship level, a relative position diagram that reflects the relative positional relationships of each unit is first drawn. Then, combined with the area parameters of each unit, an area relationship diagram is adjusted and optimized to obtain the final initial layout scheme.

[0089] In some embodiments of this disclosure, the multi-dimensional association information includes at least two of the following: job association information, personnel collaboration requirements information, and safety distance requirements information.

[0090] In some implementations, a "from" to "to" table can be used to represent the material handling volume and direction. Here, "from" represents the starting unit of the logistics operation, indicated by the row number in Table 1 or Table 2; "to" represents the ending unit of the logistics operation, indicated by the column number in Table 1 or Table 2. The data in the intersection of rows and columns represents the material handling volume or distance between two operations. The following rules can be established: data appearing above the diagonal indicates forward logistics, and vice versa; each work area unit on the production line is numbered as follows: 1-Feeding area, 2-Crushing area, 3-Waste area, 4-Dust removal area, 5-Powder selection area, 6-Finished product warehouse 1, 7-Finished product warehouse 2, 8-Finished product warehouse 3, 9-Finished product warehouse 4, 10-Power distribution room, 11-Spare parts room. Based on the processing technology of machine-made products, the logistics distance "from" and the transport volume "from" tables can be drawn as follows, as shown in Table 1 and Table 2 respectively.

[0091] Table 1 Distance from to table

[0092]

[0093] Table 2. Transport Volume from Destination to Destination

[0094]

[0095] Based on the process flow and the approximate size of the production equipment, the layout should avoid any overlap in planar position between equipment, and the initial distance between each area should be set. The transport capacity considers the consumables for connecting equipment used during production line construction, the amount of material processed, and the replacement of parts during production. The transport capacity for conveyor belt connections between areas can be set to 100; the transport capacity for pipeline connections between the dust removal area and the crushing and powder selection areas can be set to 30; and the power supply connection between the power distribution room and each piece of equipment can be set to 1.

[0096] The current logistics intensity is calculated based on the distance and volume between each production area, as shown in Table 3.

[0097] Table 3 Logistics Intensity from Table

[0098]

[0099] In some examples, five symbols, A, E, I, O, and U, can be used to represent the logistics intensity level between workshop equipment. The meanings and symbols of different levels are shown in Table 4.

[0100] Table 4. Classification of Logistics Intensity Levels

[0101]

[0102] In some implementations, a logistics correlation diagram can be drawn based on logistics intensity and its symbols, such as... Figure 3 As shown, the powder selection area, finished product bin 1, crushing area, and finished product bin 4 have extremely high material flow intensity; the waste area and finished product bin 3 have relatively high material flow intensity; the dust removal area and finished product bin 2 have relatively high material flow intensity; and the feeding area and power distribution room have moderate material flow intensity. Although the spare parts room has no material flow relationship with other production areas, it is an area that must be considered in the actual production line construction, therefore, non-material flow analysis between areas is required.

[0103] In other implementations, the non-logistics relationship analysis of equipment operations in the production line area is mainly arranged based on the closeness of the relationships between various work units. Currently, the closeness of the relationships between production area units is divided into 6 levels, as shown in Table 5. Arabic numerals can be used to represent the reasons for the close relationships between different areas: 1- continuity between processes; 2- related connections; 3- similar work nature; 4- convenience for on-site maintenance and management.

[0104] Table 5 Classification of Relationship Closeness

[0105]

[0106] Based on the above evaluation criteria and the relationships between different areas of the production line, a non-logistics relationship level table is created, as shown in Table 6.

[0107] Table 6 Non-Logistics Relationship Level Table

[0108]

[0109] Based on the non-logistics relationship hierarchy table, draw a non-logistics relationship diagram, such as... Figure 4 As shown.

[0110] In some examples, logistics relationships and non-logistics relationships can be combined to conduct a comprehensive correlation analysis between regional units. When conducting this comprehensive correlation analysis, the weight of logistics relationships versus non-logistics relationships can be measured. When quantifying the two relationships, a 1:1 ratio is adopted. The weighted calculation formula for the comprehensive correlation between different regions of the production line can be found in the following formula (1):

[0111] (1)

[0112] In formula (1): The comprehensive interrelationship score between region i and region j; The score represents the logistics relationship between region i and region j. The score represents the non-logistical relationship between region i and region j.

[0113] The level of a comprehensive relationship can be quantified. For the classification of levels, refer to the relationship type value table, see Table 7.

[0114] Table 7. Values ​​for Relationship Types

[0115]

[0116] Quantitative calculations can be performed. The logistics and non-logistics relationships of each regional unit are scored and summarized, and a comprehensive relationship quantification table is obtained after the summary, as shown in Table 8.

[0117] Table 8. Quantitative Table of Comprehensive Relationships

[0118]

[0119] A comprehensive correlation level can be assigned. In the quantitative table, 7 points is defined as A, 6 points as E, 5-3 points as I, 2-1 points as O, and 0 points as U. After considering non-logistical relationships, the levels in some local areas change. A comprehensive correlation diagram can be drawn, such as... Figure 5 As shown.

[0120] In some implementations, after determining the relationships between different areas through logistics and non-logistics relationship analysis, line graphs are used to determine their relative positions. In some examples, level A relationships between equipment can be represented by four lines, level E by three lines, level I by two lines, level O by one line, level U by no line, and level X by a broken line. The length relationships are: level A is the shortest, level E is twice the length of level A's line segment, level I is three times the length of level A's line segment, and so on. The drawing order can be A, E, I, O; if level X exists, it should be placed as far away as possible. (Refer to...) Figure 6 A map showing the relative positions of each production area unit in the high-quality utilization production line of tunnel slag from the Sichuan-Tibet Railway was drawn.

[0121] In some implementations, after the relative positions of each piece of equipment are determined, the actual locations need to be arranged based on the area relationships of each production piece of equipment. Since the area of ​​the plant site and the area of ​​the crushing zone are not fixed, the areas of other areas are relatively fixed. The area of ​​the crushing zone can be tentatively determined based on equipment parameters and production line requirements. According to the relative position diagram of the regional units and the area requirements per unit area, the conveyor belt connects from the screening machine end to each finished product bin, waste area, and sorting area, and from the crusher end to the feeding area. (Refer to...) Figure 7 It can draw a diagram showing the area relationship between different regions of the production line. It can determine the initial layout scheme. However, the initial layout scheme does not take into account sufficient constraints, and the positions of each work unit are not precisely calculated. It can only represent a theoretical and ideal layout scheme. It must be adjusted and revised to become the final feasible layout scheme.

[0122] In some embodiments of this disclosure, a logistics relationship matrix can be constructed using full data on material flows, making the determination of logistics relationship strength more objective and accurate, and avoiding biases caused by subjective judgment. Combining multi-dimensional correlation information to determine the strength of non-logistics relationships comprehensively considers various collaborative needs and constraints between work units, making the analysis of non-logistics relationships more comprehensive. The weighted fusion algorithm yields a comprehensive relationship level, which objectively reflects the comprehensive correlation degree between each work unit, providing a scientific basis for the formulation of the initial layout plan, ensuring that the relative positions of each unit in the initial layout plan are reasonable, and laying a good foundation for subsequent optimization.

[0123] In some embodiments of this disclosure, S24 includes:

[0124] With minimizing the total material transport distance and minimizing the plant area as the dual optimization objectives, corresponding weight coefficients are set for the two objectives, and a weighted summation form of the dual objective function is constructed.

[0125] The constraints include: the layout areas of each production line work unit have no spatial overlap, and the layout boundaries of all work units do not exceed the planned boundaries of the construction site.

[0126] In some implementations, based on the initial layout scheme obtained above, the core optimization objectives are minimizing the total material transport distance and minimizing the plant area. According to the actual production needs, corresponding weight coefficients are set for the two objectives respectively, and a weighted summation form of dual objective function is constructed to achieve a synergistic balance between the two optimization objectives.

[0127] At the same time, clear constraints are set to ensure that the layout areas of each production line operation unit do not overlap, and that sufficient operation and passage space is reserved between units to meet the actual needs of personnel operation and material replenishment; on the other hand, it is ensured that the layout boundaries of all operation units do not exceed the planned boundaries of the construction site, so as to ensure that the layout plan is consistent with the planning requirements of the construction site.

[0128] Genetic algorithms are global random search algorithms that simulate the evolutionary and natural selection processes of species in nature, such as... Figure 8As shown. This method is based on genetic theory and the process of natural selection, using the law of survival of the fittest and the process of chromosome and gene crossover and mutation within a biological population as simulation objects, and continuously searching for optimization through computer simulation. Genetic algorithms are characterized by strong robustness and high efficiency. By repeatedly selecting, replicating, crossing over, and mutating the research object, they obtain continuously optimized research results, and finally obtain a near-optimal solution or a true optimal solution.

[0129] In some implementations, an objective function can be established to minimize the total material handling distance, thereby optimizing the layout. Non-logistics factors can be considered to provide a more comprehensive view of the company's actual situation. Based on different terrain conditions and actual production situations, the production line layout problem is simplified into a multi-row equipment layout problem, and a corresponding mathematical model is established. The following assumptions can be set: the entire production line area is considered as a two-dimensional plane, and all work units are on the same two-dimensional plane; each work unit within the plant area is simplified into a rectangle, and its length, width, and area are further determined; the lower left corner of the entire production line area is marked as the origin of a rectangular coordinate system, the positive X-axis is the long side of the entire production line area, and the positive Y-axis is the short side of the entire production line area, with the length and width of each rectangle parallel to the X-axis and Y-axis, respectively; when calculating the transmission distance between work units, the center point of each rectangle is used as the coordinate, and the Euclidean distance formula is used to calculate the distance between work unit areas. Based on the above assumptions, a schematic diagram of the relationship between work units in the production line area is obtained as follows: Figure 9 As shown in Table 9, the specific meanings of each symbol in the model assumptions can be found therein.

[0130] Table 9. Symbols and their meanings in the model assumptions

[0131]

[0132] Constructing the objective function is a crucial step in solving equipment layout optimization problems, as its selection directly impacts the overall effectiveness of the optimized layout. The goal of in-plant equipment layout optimization is to rationally optimize the layout of each work unit on the production line. Optimized layout can reduce material transport distances, thereby lowering construction and operating costs, enhancing coordination between different work units, and effectively improving production efficiency. Specifically, minimizing the total material transport distance is crucial, as the total material transport distance between equipment within the plant is influenced by transport frequency and distance. Let... and To represent any two work units that need to be laid out within the factory, use Indicates work unit and The material transfer volume between them is shown in equation (2); using Indicates work unit and The material transfer distance between them is calculated using the Euclidean distance method, as shown in equation (3); Indicates work unit and The cost per unit distance for material transport between them is shown in Equation (4).

[0133] (2)

[0134] (3)

[0135] (4)

[0136] In equations (2) to (4), the subscripts Indicates the first Several layout schemes are proposed. Based on the principle that material transport cost equals the product of unit price, transport quantity, and transport distance, a cost function is established, deriving the expression for material transport cost. Refer to the following formula (5):

[0137] (5)

[0138] To minimize the plant area, the plant area is defined as the area of ​​the smallest bounding rectangle of all working units within the plant. For the... For each of the following layout schemes, the coordinates of the lower left corner and upper right corner of the minimum bounding rectangle are calculated using equations (6) to (9).

[0139] (6)

[0140] (7)

[0141] (8)

[0142] (9)

[0143] In equations (6) to (9), Indicates the first The length of the rectangular area for each work unit. Indicates the first The width of the rectangular area for each work unit. The factory area for this layout scheme can be referenced by the following formula (10):

[0144] (10)

[0145] To facilitate the calculation and solution of the model, the multi-objective function needs to be transformed into a single-objective function. Since the total material transport distance and the plant area have different dimensions, they need to be standardized first, and then the minimum value of their sum is calculated and combined to obtain the final single-objective function expression, as shown in the following formula (11):

[0146] (11)

[0147] In formula (11) and They represent and Standardized coefficients, This indicates the total number of layout options.

[0148] Constraints are the restrictions imposed on the initial population generated by the genetic algorithm. In addition to establishing the objective function expression, the model also needs to satisfy the following constraints: no overlap between work units and boundary constraints.

[0149] Among them, for non-overlapping work units, in order to ensure personnel operation, material replenishment and other operations, work units should also meet a certain minimum interval distance. The mathematical expression of this constraint is as follows (12):

[0150] (12)

[0151] In equation (12), and Representing work units Length and width, and Representing work units Length and width, and Representing work units and The horizontal and vertical spacing between them.

[0152] Boundary constraints ensure that the equipment must be located inside the production plant area, that is, the horizontal placement of the equipment cannot exceed the total length of the plant area, and the vertical placement of the equipment cannot exceed the total width of the plant area. The mathematical expression of this constraint is shown in the following equation (13):

[0153] (13)

[0154] In equation (13), and These represent the total length and total width of the factory area, respectively. and These represent the minimum horizontal and vertical spacing between work units, respectively. and These represent the number of columns and rows of the work unit in the current layout scheme, respectively.

[0155] In some embodiments of this disclosure, the dual optimization objectives of minimizing the total material transport distance and minimizing the plant area are achieved, and a synergistic balance between the two is realized through weighting coefficients. This effectively reduces material transportation costs, improves production efficiency, increases plant space utilization, and reduces land resource occupation. Clear constraints limit the layout scheme from both spatial overlap and site boundary perspectives, ensuring the feasibility and compliance of the layout scheme, avoiding increased later modification costs due to unreasonable layout, and guaranteeing the stable operation of the production line.

[0156] In some embodiments of this disclosure, S26 includes:

[0157] Based on the number of production line work units, a symbol encoding is designed to convert the layout information of each production line work unit into a unique encoding string. The encoding string corresponding to the initial layout scheme is used as the core individual, and multiple encoding strings corresponding to different layout positions are generated to form the initial population.

[0158] The calculation results of the bi-objective function are used as fitness evaluation index to score and rank the layout schemes corresponding to each encoding string in the initial population.

[0159] According to the preset selection rules, the matching encoding strings are sorted and filtered. The filtered encoding strings are cross-operated to generate new encoding strings. Then, the new encoding strings are mutated to introduce new arrangement position combinations.

[0160] Repeat the fitness evaluation and genetic operations until the preset termination conditions are met.

[0161] In some implementations, symbol coding rules are designed based on the number of production line work units. The specific layout information of each production line work unit is converted into a unique code string. The code string corresponding to the initial layout scheme obtained by the system layout planning method is used as the core individual. Then, multiple code strings corresponding to different layout positions are supplemented by random generation to form the initial population. This ensures both the diversity of the population and the presence of high-quality initial individuals.

[0162] The results of the biobjective function calculation are used as fitness evaluation indicators to score the fitness of layout schemes corresponding to each coding string in the initial population, and the schemes are sorted according to the scores. Based on the preset selection rules and the sorting results, coding strings that meet the fitness criteria are selected. A two-point crossover method is used to perform a crossover operation on the selected coding strings. Two crossover points are randomly selected, the genes in the middle of the crossover points are swapped, and duplicate genes are removed to generate new coding strings.

[0163] Then, random mutation is used to mutate the new coding string, randomly selecting a portion of genes on a chromosome to replace them, introducing new placement combinations to maintain population diversity. Fitness evaluation, selection, crossover, and mutation are repeated until a preset termination condition is reached. The preset termination condition is a fixed number of generations to ensure the optimization effect meets the target.

[0164] In some implementations, selection operators are used to choose individuals with high fitness from the current population to serve as parents for the next generation. Selection operators have various implementations, such as roulette wheel selection, tournament selection, and random selection. Roulette wheel selection is the most commonly used; its basic idea is to assign a probability value based on the individual's fitness and then select parent individuals through random probability. The selection operator is one of the most important genetic operators in genetic algorithms. Its main function is to select a subset of individuals as parents for the next generation based on their fitness values, thereby gradually improving the population's fitness and achieving optimized search. The purpose of the selection operator is to continuously improve the overall fitness of the population by retaining excellent individuals and eliminating inferior ones. Therefore, the individual's fitness value is a crucial selection criterion; generally, individuals with higher fitness have a greater probability of being selected. The implementation method and specific parameter settings of the selection operator affect the performance and search effect of the genetic algorithm and need to be adjusted and optimized according to the specific application scenario. Roulette wheel selection can be used, where the fitness value of each individual in the population is considered as a proportion, and a corresponding region is divided on the wheel according to this proportion. Then, the roulette wheel is randomly rotated to select individuals from a certain region as parents. The higher the fitness value of an individual, the greater its probability of being selected. In the roulette wheel selection operator, let the total number of individuals in a population be... Then the individual The probability of being selected to be passed on to the next generation. You can refer to the following formula (14):

[0165] (14)

[0166] In equation (14), , representing an individual This represents the individual's fitness value.

[0167] The crossover operator improves the fitness of a population by generating new individuals through gene recombination. It is typically performed after the selection operator and is a crucial component of genetic algorithms. The basic idea of ​​the crossover operator is to exchange certain genes between two individuals to produce a new one. The implementation and parameter settings of the crossover operator significantly impact the overall algorithm performance. Generally, the crossover probability should not be too high or too low; too high a probability can lead to excessively rapid population convergence, while too low a probability reduces population diversity. The specific implementation of the crossover operator should also be selected and adjusted based on the characteristics of the problem. This paper uses a paired crossover method, where the system randomly selects two pairs of chromosomes to form a pairing segment. Genes within the paired segment are replaced, while genes outside the segment are replaced according to a mapping relationship, thus forming a new individual. Figure 10 An example of a cross operation is shown.

[0168] Mutation operators are used to introduce random mutations at certain gene loci in offspring individuals to increase population diversity. The main purposes of using mutation operators in genetic algorithms are: ① Improving the local searchability of the genetic algorithm. Genetic algorithms using crossover operators find some good individual coding structures from a global perspective. These structures are close to or help converge to the optimal solution of the problem. However, crossover operators alone cannot achieve local search of the details of the search space. If mutation operators are used to adjust the values ​​of some genes in the individual coding, the individuals can be made closer to the optimal solution locally, thereby improving the local search capability of the genetic algorithm. ② Maintaining population diversity and preventing premature convergence. Mutation operators replace the original gene values ​​with new gene values, thereby changing the structure of the individual coding string and maintaining population diversity, which helps prevent premature convergence.

[0169] The design of mutation operators can include two aspects: determining the location of mutation points and replacing gene values. Random mutation can be used, where a chromosome is randomly selected for mutation, and the mutated chromosome replaces the original individual, generating a new individual, such as... Figure 11 As shown, genetic manipulation is then performed to determine whether the individual has a tendency to mutate.

[0170] The termination method for a genetic algorithm is to limit the population size. Using genetic operators, a new population can be generated iteratively until an individual with high fitness is found and the required number of iterations is met. Generally, the population size can be set between 100 and 500. The solution process of the genetic algorithm is as follows: Figure 12 As shown.

[0171] Encoding is a crucial component of genetic algorithms. It transforms the problem-solving process into a form that the genetic algorithm can understand, allowing the computer to perform computations and solutions. The encoding process typically involves careful analysis of the problem to find the most suitable encoding scheme for the genetic algorithm. Encoding methods can include binary encoding, symbolic encoding, and floating-point encoding. Binary encoding converts the problem into a string of 0s and 1s, similar to the representation of genes in the human genetic system. 0s and 1s represent human chromosome genes, and the length of the encoding represents the accuracy of the result. This is currently the most commonly used encoding method. Symbolic encoding typically uses a set of symbols to represent chromosomes. This encoding method effectively simplifies the representation of chromosomes, making their description more concise and precise. Through symbolic encoding, we can better understand and analyze chromosomes, providing important tools and methods for genomics and genetics research. Examples include {a, b, c, d, f} or {0, 1, 5, 3, 4}, where each symbol represents a gene on the chromosome, rather than a specific numerical value. In genetic algorithms, floating-point encoding primarily maps real-valued solutions to a finite binary string for genetic operations, offering advantages such as strong global search capabilities, low programming complexity, and simple structure. However, using a finite binary number to represent a real number introduces precision errors. This can prevent achieving high accuracy when optimizing the objective function. Encoding is a prerequisite for genetic algorithms to solve practical problems. In this study, layout optimization aims to minimize the total material transport distance and plant area by optimizing the location of equipment within the plant. Encoding equipment location coordinates only requires considering the relative positions of equipment symbols, making symbol encoding suitable. Arabic numerals are used to number the possible locations of each equipment unit. The numbering order starts from the lower left corner of the plant area, proceeding from left to right and bottom to top. For example, a 5×5 layout space is used (e.g....). Figure 13 As shown), the layout positions are numbered from 1 to 25. Based on the division of the main working areas within the factory, a single chromosome has 11 genes, meaning the chromosome has 11 dimensions, such as {11, 12, 13, 9, 6, 16, 17, 18, 5, 7, 20} and {11, 12, 15, 16, 5, 18, 13, 14, 4, 8, 21}, etc.

[0172] Genetic algorithms are not highly dependent on external information during the search process, primarily relying on the fitness function as the basis for genetic operations. The fitness function, also called the evaluation function, is used to judge the quality of individuals or solutions. The better the individual, the higher its fitness value, and the greater the likelihood that its traits will be passed on to the next generation, thus ensuring that the characteristics of superior individuals are continuously inherited, ultimately leaving the best individuals. In genetic algorithms, the convergence stability time and whether the final solution meets the requirements are both affected by the fitness function. Therefore, the fitness function needs to be selected based on the research objectives. Furthermore, during the design process, the fitness function should adhere to the following conditions: ① consistency and rationality; ② continuity, maximization, single value, and non-negativity; ③ strong generality; ④ low computational cost.

[0173] Based on fitness function analysis and research objectives, a fitness function constructed by taking its reciprocal can be used. Generally, the objective function is... In other words, the fitness function is used to represent the fitness function. The reciprocal method first extracts a value from the objective function and then further processes it into a fitness function. The expression for this method is:

[0174] When the objective function is a minimum value problem, the transformation formula can be referred to as equation (15):

[0175] (15)

[0176] When the objective function is a problem of finding the maximum value, the transformation formula can be referred to as the following formula (16):

[0177] (16)

[0178] For the current research objective, the fitness function can be referred to as Equation (17):

[0179] (17)

[0180] in, In genetic algorithms, the evolutionary process of the population is based on the fitness of each individual in the population. Through repeated iterations, individuals with higher fitness are continuously searched, and finally, the optimal solution or a near-optimal solution to the problem is obtained.

[0181] In some examples, MATLAB can be used to program genetic algorithms. Since different terrain features may exist at the tunnel entrances in mountainous areas, the corresponding factory construction sites may be constrained by the terrain. Therefore, simulation experiments were conducted to optimize the layout of production line equipment on square, rectangular, and "L"-shaped construction sites. During the optimization process, the parameters of the genetic algorithm were set as follows: population size 200; maximum generations 100; crossover rate 0.5; mutation rate 0.2. The size of the site in the simulation experiment was expressed as a relative value, i.e., the coordinate values ​​in the simulation results graph are in unit size.

[0182] The genetic algorithm described above is used to optimize the layout of production line equipment in a square area. The optimization process of the genetic algorithm is as follows: Figure 14 As shown, the optimal solution obtained is as follows: Figure 15 As shown in the diagram, the arrows indicate the direction of material flow. The optimized layout design of production line equipment in a square site is primarily suitable for factory sites with large, open, and flat areas.

[0183] The genetic algorithm described above is used to optimize the layout of production line equipment in a rectangular area. The optimization process of the genetic algorithm is as follows: Figure 16 As shown, the optimal solution obtained is as follows: Figure 17 As shown in the diagram, the arrows indicate the direction of material flow. The optimized layout design of production line equipment in a rectangular area is primarily suitable for site selection in areas with long and narrow terrain.

[0184] The genetic algorithm described above is used to optimize the layout of production line equipment in an "L"-shaped site area. The optimization process of the genetic algorithm is as follows: Figure 18 As shown, the optimal solution obtained is as follows: Figure 19 As shown in the diagram, the arrows indicate the direction of material flow. The optimized layout design of production line equipment in an "L"-shaped site is mainly suitable for factory site selection in areas with "L"-shaped terrain.

[0185] The material transport distance and transport volume ratio of the work unit before and after optimization can be analyzed and compared quantitatively to verify the layout effect of the genetic algorithm on the equipment layout optimization design of the tunnel muck high-quality utilization production line. See Table 10 for details, where the unit of transport distance is set as... The total amount of material transferred is in units of .

[0186] Table 10 Comparison of material transport distance before and after optimization

[0187]

[0188] According to Table 10, the total material transport distance before optimization was 154.78 km. The total material transfer volume is 24.25. The optimized total material transport distance is 104.78. The total material transfer volume is 16.10. Therefore, the optimized scheme using the genetic algorithm reduces the total material transport distance by 32.30% and the total material transport volume by 33.60% compared to the original scheme, significantly reducing the initial construction costs and subsequent operation and maintenance costs of the production line. Thus, the genetic algorithm demonstrates a good improvement effect in optimizing the equipment layout design of the high-quality utilization production line for tunnel muck.

[0189] The present disclosure provides a method for optimizing the layout of a production line for tunnel muck preparation products. This method divides the work units into functional groups and zones, determines the initial layout scheme by combining a system layout planning method, and then uses a genetic algorithm for iterative optimization. The system considers the logistics and non-logistics relationships between each work unit, effectively avoiding the problems of material transmission path intersections and excessively long transmission distances in traditional experience-based layouts. At the same time, by clarifying constraints, it prevents overlapping of work units, excessive plant area, or poor adaptability of the layout to the site terrain. This achieves synergistic optimization of production efficiency and space utilization, reduces production and operating costs, and meets the needs of large-scale and intensive tunnel muck resource production.

[0190] It is understood that the various embodiments of the methods described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. Related details can be found in the descriptions of other method embodiments.

[0191] It should be understood that although the steps in the flowcharts shown in the accompanying drawings are displayed sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated otherwise, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the steps or stages of other steps.

[0192] Based on the description of the above-described embodiments of the method for optimizing the layout of a tunnel muck preparation mechanism product production line, this disclosure also provides a device for optimizing the layout of a tunnel muck preparation mechanism product production line to implement the above-described method. The device may include a system (including a distributed system), software (application), module, component, controller, server, terminal, etc., using the method described in the embodiments of this specification, combined with necessary hardware implementation. Based on the same innovative concept, the devices in one or more embodiments provided in this disclosure are as described in the following embodiments. Since the implementation schemes and methods for solving the problem are similar, the implementation of specific devices in the embodiments of this specification can refer to the implementation of the aforementioned method, and repeated details will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0193] Figure 20 This is a schematic block diagram illustrating an in-plant layout optimization device for a tunnel muck preparation mechanism product production line, according to an exemplary embodiment. The device can be the aforementioned terminal, a server, or a module, component, device, control unit, etc., integrated into the terminal. For details, please refer to... Figure 20 The device 100 may include: a partitioning module 120, an initial layout scheme generation module 140, a processing module 160, an iterative optimization module 180, and an optimal scheme output module 190. The partitioning module 120 is used to group and partition different production line operation units according to their functions, and determine the area parameters and material transfer characteristic data of each production line operation unit; the initial layout scheme generation module 140 is used to first analyze the logistics and non-logistics relationships between each production line operation unit through system layout planning, determine the comprehensive relationship level, and then draw relative position diagrams and area relationship diagrams in sequence to obtain the initial layout scheme; the processing module 160 is used to construct a dual objective function based on the initial layout scheme, with the objectives of minimizing the total material transfer distance and minimizing the plant area, and set constraints such as non-overlapping production line operation units and adaptation to the plant site boundary; the iterative optimization module 180 is used to generate an initial population containing the initial layout scheme using symbolic encoding, and perform iterative optimization based on the dual objective function and the constraints using a genetic algorithm until a preset termination condition is met; the optimal scheme output module 190 is used to output the optimal layout scheme that meets the constraints and is adapted to the terrain characteristics of the plant site.

[0194] Each module in the above-mentioned tunnel muck preparation mechanism product production line layout 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 in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0195] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 21 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for optimizing the layout of a tunnel muck preparation mechanism product production line.

[0196] Those skilled in the art will understand that Figure 21 The structure shown 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 combine certain components, or have different component arrangements.

[0197] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the plant layout optimization method for the tunnel muck preparation mechanism product production line described in any embodiment of this specification.

[0198] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by the processor of a computer device, enables the computer device to implement the in-plant layout optimization method for tunnel muck preparation mechanism product production line as described in any embodiment of this disclosure.

[0199] Based on the foregoing description of the relevant methods and apparatus embodiments, this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the plant layout optimization method for the tunnel muck preparation mechanism product production line described in any embodiment of this specification.

[0200] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0201] Those skilled in the art will understand that all or part of the processes in 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 described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0202] It should be noted that the apparatus, computer equipment, storage medium, and computer program products described above may also include other implementation methods according to the description of the method embodiments. Specific implementation methods can be found in the description of the relevant method embodiments. Furthermore, new embodiments formed by combinations of features from various methods, apparatuses, devices, and server embodiments still fall within the scope of this disclosure and will not be elaborated upon here.

[0203] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, when implementing one or more of these specifications, the functions of each module can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling and communication connections between the devices or units shown or described can be implemented through direct and / or indirect coupling / connection, through standard or custom interfaces or protocols, and can be implemented electrically, mechanically, or in other forms.

[0204] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0205] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for optimizing the layout of a production line for preparing tunnel muck products, characterized in that, The method includes: Different production line work units are grouped and partitioned according to their functions, and the area parameters and material transfer characteristic data of each production line work unit are determined. The system layout planning method is used to first analyze the logistics and non-logistics relationships between the work units of each production line, determine the comprehensive relationship level, and then draw the relative position diagram and area relationship diagram in sequence to obtain the initial layout scheme. Based on the initial layout scheme, a dual objective function is constructed with the goals of minimizing the total material transport distance and minimizing the plant area, and constraints are set to ensure that the production line operation units do not overlap and are adapted to the plant site boundary. An initial population containing an initial layout scheme is generated using symbolic encoding. The population is then iteratively optimized using a genetic algorithm based on the bi-objective function and the constraints until a preset termination condition is met. Output the optimal layout scheme that meets the constraints and is adapted to the terrain features of the construction site.

2. The method according to claim 1, characterized in that, The process of grouping and partitioning different production line work units according to their functions, and determining the area parameters and material transfer characteristic data of each production line work unit, includes: Based on the sequential process of preparing tunnel muck from crushing and screening to finished product storage, the functionally related production line work units are divided into the same functional group, and then partitioned according to the functional group. The material transfer characteristic data includes at least one of the following: material transfer volume between each production line work unit, material transfer direction, and material transfer time threshold that meets the production cycle requirements.

3. The method according to claim 1, characterized in that, The process involves first analyzing the logistic and non-logistic relationships between various production line work units using a system layout planning method to determine the overall relationship level, including: Obtain full data on material exchanges between each production line work unit within the complete production cycle. Construct a logistics relationship matrix based on the full data on material exchanges. The matrix elements represent the degree of logistics association between the corresponding production line work units. Then determine the strength of the logistics relationship based on the degree of logistics association. The full data on material exchanges includes at least one of the following: the number of material exchanges between each production line work unit and the weight of the material exchanged each time. Collect multi-dimensional correlation information and determine the strength of non-logistics relationships based on the multi-dimensional correlation information; The logistics relationship strength and the non-logistics relationship strength are weighted and summed using a preset weighted fusion algorithm, and the comprehensive relationship level is obtained by mapping the summation result.

4. The method according to claim 3, characterized in that, The multi-dimensional related information includes at least two of the following: job-related information, personnel collaboration needs information, and safety distance requirements information.

5. The method according to claim 1, characterized in that, Based on the initial layout scheme, a dual objective function is constructed with the goals of minimizing the total material transport distance and minimizing the plant area. Constraints are set to ensure that production line work units do not overlap and that the function adapts to the plant site boundaries, including: With minimizing the total material transport distance and minimizing the plant area as the dual optimization objectives, corresponding weight coefficients are set for the two objectives, and a weighted summation form of the dual objective function is constructed. The constraints include: the layout areas of each production line work unit have no spatial overlap, and the layout boundaries of all work units do not exceed the planned boundaries of the construction site.

6. The method according to claim 1, characterized in that, The process involves generating an initial population containing an initial layout scheme using symbolic encoding, and then iteratively optimizing it using a genetic algorithm based on the dual objective function and the constraints until a preset termination condition is met. This includes: Based on the number of production line work units, a symbol encoding is designed to convert the layout information of each production line work unit into a unique encoding string. The encoding string corresponding to the initial layout scheme is used as the core individual, and multiple encoding strings corresponding to different layout positions are generated to form the initial population. The calculation results of the bi-objective function are used as fitness evaluation index to score and rank the layout schemes corresponding to each encoding string in the initial population. According to the preset selection rules, the matching encoding strings are sorted and filtered. The filtered encoding strings are cross-operated to generate new encoding strings. Then, the new encoding strings are mutated to introduce new arrangement position combinations. Repeat the fitness evaluation and genetic operations until the preset termination conditions are met.

7. A device for optimizing the layout of a production line for preparing tunnel muck, characterized in that, The device includes: The partitioning module is used to group and partition different production line work units according to their functions, and to determine the area parameters and material transfer characteristic data of each production line work unit. The initial layout scheme generation module is used to first analyze the logistics and non-logistics relationships between the work units of each production line using the system layout planning method, determine the comprehensive relationship level, and then draw the relative position diagram and area relationship diagram in sequence to obtain the initial layout scheme. The processing module is used to construct a dual objective function based on the initial layout scheme, with the objectives of minimizing the total material transport distance and minimizing the plant area, and to set constraints on the non-overlapping of production line operation units and the adaptation to the plant site boundary. The iterative optimization module is used to generate an initial population containing an initial layout scheme using symbolic encoding, and to perform iterative optimization based on the bi-objective function and the constraints using a genetic algorithm until a preset termination condition is met. The optimal solution output module is used to output the optimal layout scheme that meets the constraints and is adapted to the terrain characteristics of the construction site.

8. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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