Method for optimizing the layout of logistics park facilities

Abstract

The application provides a logistics park facility layout optimization method, which comprises the following steps: 1, logistics park facility layout problem description, constructing a standard logistics park framework and establishing a general logistics park facility layout model; 2, model construction, constructing a plane rectangular coordinate system for the general logistics park optimization problem, setting the built traffic lines or transportation stations as fixed function areas in the comprehensive hub, and establishing a comprehensive relationship score maximization model; 3, model calculation, introducing a grid scaling method to simplify the problem, and adopting a genetic algorithm to calculate the logistics park layout optimization model; 4, simulation optimization, determining the road arrangement form and function area layout design of the logistics hub, simulating through a simulation model, and comparing with the original function area layout scheme. The application studies the combination optimization problem of the function areas of the logistics park, establishes the logistics park facility layout optimization method in combination with the actual situation, and provides certain theoretical support and guidance for the research and development of the logistics park and the multimodal transport.

Description

Technical Field

[0001] This invention belongs to the field of logistics park layout optimization, specifically involving a method for optimizing the layout of logistics park facilities. Background Technology

[0002] At present, the development goal of society has shifted from high-speed development to high-quality development, with improving quality and efficiency replacing speed and quantity as the primary task and key objective for development in all sectors of society. After years of construction, the existing domestic and international transportation system has been basically formed, and a multi-node, fully covered comprehensive transportation network has been initially established. However, newer construction goals and social needs have also placed higher demands on the transportation and logistics industry in the new era. Currently, my country's import and export transportation demand is growing rapidly, and the existing transportation structure and the services provided by the transportation system are difficult to match. The transportation efficiency of the transportation industry urgently needs to be improved. Under the requirements of improving comprehensive transportation efficiency, effectively reducing transportation costs, improving the transportation service system, and continuously optimizing the transportation structure, multimodal transport, as a major way to reduce costs and increase efficiency in modern logistics, has gradually received research and attention. Currently, when solving related problems globally, multimodal transport is mainly used internationally to improve transportation efficiency.

[0003] The "Work Plan for Promoting the Development of Multimodal Transport and Optimizing the Transportation Structure (2021-2025)" issued by the General Office of the State Council points out that vigorously developing multimodal transport is an important mode of transportation for optimizing and adjusting the transportation structure, improving comprehensive transportation efficiency, reducing social logistics costs, and promoting energy conservation, emission reduction, and carbon reduction. The main goals for the future development of logistics parks include three aspects: promoting the green and low-carbon transformation of transportation, improving regional comprehensive transportation efficiency, and cultivating a multimodal transport service system. Optimizing the layout of logistics parks and other logistics hubs as the main venues for logistics activities and ensuring efficient operation is the fundamental guarantee for the development of logistics parks. Therefore, optimizing the facility layout of logistics parks is a major method to comprehensively improve transportation efficiency. By adjusting the facility layout of essential functional areas, transshipment efficiency can be improved, operating costs reduced, and the organic integration of multimodal transport and the logistics industry can be ensured from an infrastructure perspective.

[0004] my country's multimodal transport industry started late, and there are relatively few high-efficiency, high-benefit, and systematic hub-type logistics parks that have been built domestically. There are even fewer cases with reference value and representativeness. The academic community's research and theoretical system on the construction of efficient hub-type logistics parks are still immature. As a result, existing logistics park hubs have problems in terms of facility site selection, functional area layout, and transshipment methods between different modes of transport. This leads to low overall operating efficiency of logistics parks, high input-output ratios, and failure to achieve the expected economic benefits. Summary of the Invention

[0005] The problem this invention aims to solve is to propose an optimization method for the layout of logistics park facilities, with the goal of establishing a complete, scientific, and reasonable optimization method for logistics park layout, providing a feasible approach to solving existing problems in the layout of real logistics parks, and playing a role in assisting decision-making.

[0006] The technical solution adopted in this invention is: an optimization method for the layout of logistics park facilities, comprising four parts: description of the logistics park facility layout problem, model construction, model calculation, and simulation optimization. The specific steps are as follows:

[0007] S1. Description of Logistics Park Facility Layout Problem: Construct a standard-shaped logistics park with several functional areas inside. The shape of the logistics park and all its functional areas are standard rectangles. Establish a model to minimize the total operating cost of a general logistics park.

[0008] S2. Model Construction: Define one vertex of the logistics park with the above standard shape as the origin of the coordinate system, construct a plane rectangular coordinate system, and set the existing transportation lines or transportation stations as fixed functional areas in the logistics park's comprehensive hub; establish the model objective function, namely the comprehensive relationship score maximization model;

[0009] S3. Model Calculation: The genetic algorithm is used to calculate the layout optimization model of the logistics park and obtain the genetic algorithm optimization scheme. In the model calculation stage, the grid scaling method is introduced to simplify the genetic algorithm.

[0010] S4. Simulation Optimization: Simulation software is used to simulate vehicle operation, and the road layout of the logistics park's comprehensive hub is constructed. The functional area layout is designed using the genetic algorithm optimization scheme calculated by the model in step S3. The simulation is carried out through the construction of a simulation model and compared with the original functional area layout scheme.

[0011] Specifically, in step S1, construct a standard-shaped logistics park, defining its length and width dimensions as W and H, and setting the aspect ratio of the i-th functional area. The objective function for the model, i.e., minimizing total operating costs, is established as follows:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] aimin ≤a i ≤a imax (7)

[0019] Where N is the total number of functional areas, i and h are the sequence numbers of the functional areas, and d ij f is the center distance between functional areas i and j. ij c represents the volume of logistics transportation between the two functional areas. ij Operating costs per unit of transport distance;

[0020] w i h i Let w be the length and width of functional area i. j h j Let x be the length and width of functional area j; i y i (x) represents the center coordinates of functional area i, and (x) represents the center coordinates of functional area i. j y j Let be the center coordinates of functional area j;

[0021] Formula (1) above is the objective function, which represents the minimum value of the total transportation cost of the functional areas within the logistics park's integrated hub;

[0022] The constraint formulas (2), (3) and (4) indicate that there is no overlap between the two functional areas, that is, each functional area is independent and complete;

[0023] The constraint formulas (5) and (6) indicate that the functional area is entirely located within the logistics park;

[0024] The constraint formula (7) restricts the shape and aspect ratio of the functional area. Different values ​​are set according to the different characteristics of the logistics park. The logistics park includes: highway logistics park and port logistics park.

[0025] Specifically, step S2, model construction, includes the following sub-steps:

[0026] Step S2.1: Define one vertex of the rectangular logistics park as the origin of the coordinate system, and use the straight lines containing its adjacent length and width sides as the positive directions of the X and Y axes to construct a plane rectangular coordinate system;

[0027] Step S2.2: Set the existing transportation lines or stations as fixed functional areas in the logistics park's comprehensive hub, and calibrate and set the length, width, and coordinate parameters.

[0028] Step S2.3: Calculate the cargo volume and comprehensive relationship score of this transportation mode to obtain the comprehensive relationship score table, which serves as the score matrix;

[0029] Step S2.4: Establish the model objective function, namely the comprehensive relationship score maximization model. The model objective function is as follows:

[0030]

[0031] st count k ≥Area k , k = 1, 2, 3, ... (9)

[0032] Among them, score mn The total score value of the cell in the m-th row and n-th column is represented by count. k This indicates the number of cells occupied by the ribbon k. k This represents the area of ​​functional region k;

[0033] The cell is a designated number of cells into which the logistics park is divided according to a certain unit area. The size and area of ​​each cell are fixed, and the number of cells occupied by all functional areas is an integer.

[0034] Specifically, step S3 model calculation includes the following sub-steps:

[0035] Step S3.1, Raster Scaling: The problem is simplified by raster scaling, which is used for encoding and model calculation;

[0036] In the model calculation stage, in order to facilitate coding and model calculation, a grid scaling method is introduced to simplify the problem. The logistics park is divided into a specified number of cells according to a certain unit area, and the length and width of each functional area and the logistics park are enlarged to multiples of 100, so that the number of cells occupied by all functional areas are integers.

[0037] When calculating the overall relationship score, the sum of the scores of each cell and its surrounding adjacent cells is calculated as the score of that cell. When calculating the score, the score coefficient between two functional areas that are right-angled is taken as higher, and the score coefficient between two functional areas that are diagonally adjacent is taken as lower, so as to make the same functional areas as straight as possible rather than diagonally adjacent.

[0038] Step S3.2: Chromosome encoding: Defined using a digital matrix, each chromosome group has one gene, and the gene represents a set of layout optimization schemes;

[0039] Step S3.3: Initialize the population: Based on the premise that the functional areas of the facilities are as close or adjacent as possible, set several excellent initial solutions, and iterate on these solutions to obtain better solutions;

[0040] Step S3.4: Define a fitness function to determine the feasibility of the layout optimization scheme, as follows:

[0041]

[0042] The judgment function `judge` is set as a 0 / 1 variable to perform conditional judgment on each optimization scheme. When it meets the actual requirements, it is set to 1, which means it passes; otherwise, it is set to 0, which means it fails.

[0043] The final fitness function value is the product of the decision function and the objective function.

[0044] Step S3.5: Calculate the objective function: Use a genetic algorithm to use the comprehensive relationship score of each functional area as an indicator to measure the merits of the layout optimization scheme;

[0045] The objective function is calculated as follows: the coefficient of the score value adjacent to each other on a straight line is defined as 1, and the coefficient of the score value adjacent at an angle is a small value between 0 and 1. This value is dynamically set according to the actual calculation situation and the relationship between the functional regions:

[0046] score mn =score direct +β×score corner (11)

[0047] Among them, score direct The score is the sum of the scores of two cells that are directly adjacent to each other. corner β is the sum of the diagonally adjacent scores of a cell, and β is the coefficient of the diagonally adjacent scores of a cell.

[0048] Step S3.6: Perform crossover operation: Use the multi-point crossover method, that is, randomly select a column or a row in the parent chromosome and exchange it with the gene in the same column or row in the mother chromosome. The resulting gene is then used as the chromosome gene of the offspring. Set the crossover probability according to the actual needs and scale of this optimization problem.

[0049] Step S3.7, Mutation Calculation: Set up a random exchange of genes in two columns or two rows in an individual, so that the mutation result satisfies the constraint formula (9), that is, the area of ​​each functional region in each chromosome gene can meet the minimum area requirement of that region.

[0050] Furthermore, in step S4, the layout of the logistics park's integrated hub road system is established. Specifically, this involves analyzing the differences in the layout of vehicle operation roads within the logistics park, selecting a road transport mode that aligns with the logistics park's positioning, and adopting a multi-layout approach to construct the logistics park's road layout.

[0051] Compared with existing methods for optimizing the layout of logistics park facilities, the technical solution of this invention has the following beneficial technical effects:

[0052] (1) When setting up functional areas, this invention can include areas such as traffic routes and natural terrain that do not affect the layout of facilities in the logistics park as part of the functional area area and fix them inside the integrated hub. This avoids the impact of these environmental factors on the facility layout, effectively simplifies the complexity of the problem, and does not affect the final optimization result.

[0053] (2) In this invention, the grid scaling method and the genetic algorithm are effectively combined, and the system layout design method is combined to solve the problems of high complexity and slow solution process of traditional SLP method. It can be applied to the same type of problem. Attached Figure Description

[0054] Figure 1 A generalized simulated logistics park parameter diagram provided by the present invention;

[0055] Figure 2 This is an example diagram of the functional area layout of a general logistics park provided by the present invention;

[0056] Figure 3 This is a schematic diagram of the cross-process provided by the present invention;

[0057] Figure 4 This is a schematic diagram of the grid scaling method layout provided by the present invention;

[0058] Figure 5 The fitness function convergence curve provided by this invention;

[0059] Figure 6 This is a diagram of the final functional area layout scheme provided by the present invention;

[0060] Figure 7 A simulation model was built for the optimized functional area layout scheme of this invention;

[0061] Figure 8 This is a flowchart of the method for optimizing the layout of logistics park facilities according to the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below in conjunction with embodiments. The described embodiments are only a part of the embodiments involved in this invention. All non-innovative embodiments based on these embodiments by other researchers in the art are within the protection scope of this invention.

[0063] In one embodiment of the present invention, such as Figure 8 As shown, the optimization method for the layout of logistics park facilities includes the following sub-steps:

[0064] S1: Description of Logistics Park Facility Layout Issues

[0065] For ease of understanding, we will describe it by defining a general logistics park and its functional areas. The functional areas within a logistics park mainly include the following categories:

[0066] (1) Comprehensive office area. The office area and branch offices are set up to mainly undertake the functions of enterprise services, comprehensive management and government services of the logistics park. In conjunction with national agencies such as public security, industry and commerce, insurance and banking, the government and business are integrated.

[0067] (2) Intermodal transport area. It mainly undertakes the transshipment of goods when multiple modes of transport are connected, and transfers goods in the form of containers to another transport route to improve the efficiency of goods transshipment and facilitate the next transport task of goods.

[0068] (3) Warehousing and Processing Area. Logistics parks typically undertake warehousing and processing services. Some transported goods are processed within the logistics park, awaiting further transportation activities. These goods are mostly handled by other modes of transportation and also complete various types of services such as processing, distribution, and sales, in accordance with the park's positioning. The warehousing and processing area is often the core area of ​​a multimodal transport hub, undertaking tasks such as loading, unloading, collection, and warehousing of goods, as well as distribution, transshipment, and sales activities. It is the main working area for the distribution of goods within the hub.

[0069] (4) Vehicle Service Area. This mainly includes parking, repair, and maintenance of vehicles inside and outside the park. It should not only ensure the normal operation of vehicles inside the park, but also provide corresponding support services for external vehicles that come to the hub for office work, communication, and transactions, thereby improving the service quality of the hub.

[0070] (5) Living and service area. Large hubs not only include cargo handling, but modern hubs also need to have cooperative sectors such as sales, trade, and exhibition. These areas are the main functional areas for improving the economic benefits of the hub.

[0071] The parameters used in this embodiment are shown in Table 1 below:

[0072] Table 1 Model Definition Parameter Table

[0073]

[0074] In this embodiment, the problem of optimizing the layout of logistics park facilities is described as follows:

[0075] Step 1: Construct a standard-shaped logistics park, meaning the overall shape of the logistics park and all its internal functional areas are standard rectangles, such as... Figure 1 As shown.

[0076] Its length and width are defined as W and H, and the length and width of each internal functional area are defined as w. i and hi Meanwhile, to ensure that the shape of the logistics functional area closely matches actual application and avoids overly long and narrow functional areas, the aspect ratio of the functional area is set. In addition, it is also necessary to define the coordinates of the functional area location. Here, it is simply defined as the intersection of the diagonals of the rectangular functional area, that is, the center coordinates (x, y). i y i Based on this, the distance d between the two functional areas is defined. ij The actual distance is taken as the straight-line distance between the two center coordinates.

[0077] Step 2: Establish the objective function of the model, namely, the model that minimizes the total operating cost.

[0078] First, calculate the minimum total transportation cost for the functional areas within the logistics hub, f. ij c represents the volume of logistics transportation between the two functional areas. ij Operating costs per unit of transport distance:

[0079]

[0080] The following formula ensures that there is no overlap between two functional areas, that is, that each functional area is independent and complete:

[0081] like

[0082] like

[0083] like

[0084] The following formula represents a functional area that is entirely located within the logistics park:

[0085]

[0086]

[0087] The shape and aspect ratio of the functional areas are restricted, and different values ​​are set according to the different characteristics of logistics parks such as highway logistics parks and port logistics parks.

[0088] a imin ≤a i ≤a imax

[0089] S2: Model Building

[0090] Step 1: Define one vertex of the rectangular logistics park as the origin of the coordinate system, and use the lines containing its adjacent length and width sides as the positive directions of the X and Y axes to construct a Cartesian coordinate system, as follows: Figure 2 As shown.

[0091] Secondly, the existing transportation lines or stations are designated as fixed functional areas within the integrated hub, and their length, width, and coordinate parameters are calibrated and set. Then, the cargo volume and comprehensive relationship score of the transportation mode are calculated to obtain a comprehensive relationship score table, which serves as the final score matrix.

[0092] Step 2: Establish the objective function of the model, namely the comprehensive relationship score maximization model.

[0093]

[0094] st count i ≥Area i i = 1, 2, 3, ...

[0095] Among them, score mn This represents the total score value of the cell in the m-th row and n-th column. i This indicates the number of cells occupied by ribbon i. i This represents the area of ​​functional region i.

[0096] S3: Model Calculation

[0097] A genetic algorithm is used to calculate the layout optimization model of the logistics park. In the model calculation stage, a grid scaling method is introduced to simplify the problem in order to facilitate coding and model calculation.

[0098] (1) Grid scaling

[0099] The logistics hub is divided into a specified number of cells based on a certain unit area, with each cell having a fixed size and area. The selected cell area is set as the common divisor of the areas of all functional areas, using the area of ​​the smallest functional area as a reference, thus ensuring that the number of cells occupied by each functional area is an integer.

[0100] In this embodiment, a 100×100 square block is used as the unit area grid, and the length and width of each functional area and logistics park are enlarged to multiples of 100 to ensure that the number of cells occupied by all functional areas is an integer.

[0101] When calculating the overall relationship score, the sum of the scores of each cell and its surrounding adjacent cells is used as the score for that cell. In calculating the score, a higher score coefficient is applied to two functional areas that are right-angled, and a lower score coefficient is applied to two functional areas that are diagonally adjacent. This ensures that identical functional areas are as close as possible to each other in a straight line rather than at an angle.

[0102] (2) Chromosome coding

[0103] The system is defined using a numerical matrix, where each chromosome group has a gene that represents a set of layout optimization schemes.

[0104] In this embodiment, it is assumed that a logistics hub contains 7 functional areas and one railway transport line. The railway transport line and its transport station are defined as functional area 0. Therefore, the logistics hub has a total of 8 functional areas, numbered 0, 1, 2, 3, 4, 5, 6, and 7. Generally, since the total area of ​​the functional areas is often less than the total area of ​​the transport hub, it is necessary to allocate the actual area occupied by each functional area according to its proportion in the total area of ​​the logistics hub, thus filling the area occupied by the transport hub. This ensures that the area of ​​the functional areas in subsequent calculations matches practical applications and also leaves room for improvements and road paving, facilitating adjustments and optimizations.

[0105] Since the logistics hub is a rectangular area, the logistics hub is mapped to a matrix, with its length and width values ​​serving as the row and column parameters of the matrix. This ensures that each matrix value corresponds one-to-one with the functional area label.

[0106]

[0107] (3) Initialize the population

[0108] In optimizing the layout of logistics parks, functional areas of facilities should be as close or adjacent as possible. This method sets some initial excellent solutions and iterates based on these to obtain better solutions to a greater extent.

[0109] (4) Define the fitness function

[0110] A fitness function is defined to determine the feasibility of a layout optimization scheme. In this model, the fitness function determines whether the number of each functional region in the gene matrix is ​​greater than the area of ​​each functional region. If this condition is met, the scheme meets the practical requirements and satisfies the area requirements of the functional regions. If the number of any functional region does not meet this expression, the optimization scheme does not meet the practical requirements and should be eliminated.

[0111]

[0112] In this approach, a judgment function, `judge`, is set as a 0 / 1 variable. It performs a conditional judgment on each optimization solution, setting it to 1 if it meets the actual requirements (passing) and 0 if it does not (failing). The final fitness function value should be the product of the judgment function and the objective function. This avoids situations where a final solution has an excessively high objective function score but does not meet the actual functional area requirements, ensuring that the final optimized solution is feasible.

[0113] The comprehensive score for each functional area will be determined by setting the logistics and non-logistics relationships between them. The comprehensive relationship score will be calculated using a specific formula and focus to obtain the comprehensive relationship score between each functional area.

[0114] (5) Calculate the objective function

[0115] A genetic algorithm is used to evaluate the effectiveness of the layout optimization scheme by using the comprehensive relationship score of each functional area. In this method, the score coefficient of linearly adjacent areas is defined as 1, while the score coefficient of diagonally adjacent areas is a small value between 0 and 1. This value is dynamically set according to the actual calculation and the relationship between the functional areas.

[0116] score mn =score direct +β×score corner

[0117] (6) Cross operation

[0118] The multi-point crossover method is used, which involves randomly selecting a column or row from the parent chromosome and exchanging it with the gene in the same column or row from the mother chromosome. The resulting gene is then used as the chromosome gene of the offspring.

[0119] Based on the actual needs and scale of the optimization problem according to the present invention, the crossover probability is set to 0.4 to 0.8 in this embodiment. The crossover operation process is as follows: Figure 3 As shown.

[0120] (7) Mutation operation

[0121] To ensure that the mutation results meet the constraints in step 2, namely that the area of ​​each functional region in each chromosome gene meets the minimum area requirement of that region, the mutation operation will be set to the exchange of genes in two random columns or two random rows in an individual.

[0122] In this embodiment, the mutation probability is set to 0 to 0.1. In actual calculations, it can be adjusted according to the situation to avoid excessive impact of an excessively high mutation rate on the calculation.

[0123] (8) Selecting an optimal population

[0124] The product of the objective function and the fitness function is used as the fitness function value for selection. The selected chromosomes are then incorporated into a new population for further iterations and optimization calculations. After iteration, the chromosome with the highest fitness function value or objective function value in the population is selected, and its genes are used as the final initial optimization scheme.

[0125] Through the above process, a feasible solution that meets the actual requirements and practical applications can be calculated in a short period of time as an initial optimization scheme.

[0126] S4: Simulation Optimization

[0127] Simulation software is used to simulate vehicle operation. By setting the input and output of lanes and traffic flow, the transportation activities of the logistics hub and logistics park in actual application are simulated. The differences in the road layout of vehicle operation within the logistics park are analyzed, and a road transportation mode that conforms to the positioning of the logistics park is selected. A multi-layout approach is adopted to build the road layout of the logistics hub. The functional area layout is designed using the genetic algorithm optimization scheme calculated by the model in step S3. The simulation model is built and compared with the original functional area layout scheme to prove that the final optimization scheme of the present invention is effective and feasible.

[0128] Specific implementation examples:

[0129] Based on the above methods, and considering the limitations of data acquisition and the periodicity of investigation and research, this implementation case only selects the calculation model S2 and the calculation steps S3 in the stage of selecting cases to verify the algorithm feasibility. The data source is the railway-road intermodal logistics park hub in Feng Hao's 2019 article "Research on the Layout Optimization of Railway Logistics Park Based on Operation Flow Organization" as the reference data for case verification.

[0130] The logistics park is designed as a standard rectangle, 1200 meters long and 1000 meters wide, with a standard planned area of ​​1,200,000 square meters. The logistics park does not include natural dividing lines within its boundaries, and the planned functional areas comprise nine sections, with specific parameters shown in Table 2.

[0131] Table 2 Functional Area Names and Their Area Parameters

[0132]

[0133]

[0134] The S1 railway siding and railway station are 1200 meters long and 30 meters wide; the Q1 railway operation area is 720 meters long and 216 meters wide.

[0135] For ease of calculation, and because the dedicated railway line, railway station, and railway operation area need to be closely connected, the S1 dedicated railway line and railway station, along with the Q1 railway operation area, are fixed within the logistics park. The functional area areas have been adjusted, and the results are shown in Table 3.

[0136] Table 3 Functional Area Names and Area Parameter Corrections

[0137] Functional area number Function Area Name <![CDATA[Functional area area / m 2 > 0 <![CDATA[S1 Special Railway Line and Railway Yard Station]]> 36000 1 <![CDATA[Q1 Railway Operation Area]]> 155520 2 <![CDATA[Q2 Highway Work Zone]]> 51062.57 3 <![CDATA[Q3 Container Intermodal Transport Zone]]> 182068.66 4 <![CDATA[Q4 storage operation area]]> 173923.6 5 <![CDATA[Q5 distribution processing area]]> 56081.6 6 <![CDATA[Q6 Urban Distribution Zone]]> 17164.3 7 <![CDATA[Q7 transaction display area]]> 24000 8 <![CDATA[Q8 Commercial and Trade Service Area]]> 18000 9 <![CDATA[Q9 Living Service Area]]> 14000

[0138] Furthermore, by analyzing the logistics and non-logistics relationships of each functional area, a comprehensive relationship scoring table was obtained. In order to arrange functional areas with the same serial number in a straight line as much as possible, the score value between cells in the same functional area needs to be greater than the score value between cells in other functional areas, as shown in Table 4:

[0139] Table 4. Overall Relationship Scoring Table Among Functional Zones

[0140]

[0141]

[0142] This implementation case uses a grid scaling method. In the model, a square with an area of ​​100 meters × 100 meters is used as the unit area. The logistics park is divided into 120 cells. Simultaneously, to ensure that the number of cells occupied by each functional area is an integer (i.e., each cell is assigned to only one functional area), the length and width of each functional area should be an integer multiple of the cell's side length (i.e., an integer multiple of 100 meters). The area of ​​each functional area should also be scaled up to an integer multiple of the cell's area (i.e., an integer multiple of 10,000 square meters). Based on the above assumptions, the functional area parameters are further modified as shown in Table 5.

[0143] Table 5 Final Correction Results of Functional Area Names and Area Parameters

[0144] Functional area number Function Area Name <![CDATA[Functional area area / ten thousand m 2 > Functional area length and width dimensions (100 meters) 0 <![CDATA[S1 Special railway line and railway station yard]]> 12 12×1 1 <![CDATA[Q1 Railway Operation Area]]> 24 12×2 2 <![CDATA[Q2 Highway Work Zone]]> 6 3×2 3 <![CDATA[Q3 Container Intermodal Transport Zone]]> 20 5×4 4 <![CDATA[Q4 warehousing operation area]]> 18 6×3 5 <![CDATA[Q5 distribution processing zone]]> 6 3×2 6 <![CDATA[Q6 urban distribution area]]> 2 2×1 7 <![CDATA[Q7 Transaction Display Area]]> 3 3×1 8 <![CDATA[Q8 Commercial Service Area]]> 2 2×1 9 <![CDATA[Q9 Living Service Area]]> 2 2×1

[0145] Functional areas S1 and Q1, being existing structures, will serve as non-natural dividing lines that do not affect the layout of facilities within the functional areas, and will be fixed in designated locations within the logistics park. Other functional areas will be randomly arranged according to their size and the number of cells they occupy. Figure 4 As shown.

[0146] Figure 4The layout diagram shown is a random layout that meets the minimum construction area requirements for each functional area. The blank areas represent areas without functional areas, and no functional areas will be constructed in this scheme. However, in actual calculations and applications, blank areas are often connected to functional areas, making it impossible to calculate the area's score, which can lead to errors in the optimization calculation process. Therefore, in the actual model calculation, the functional area sequence numbers within each cell are randomly generated according to the proportion of their respective functional area areas. To fill all cells, the frequency of each functional area sequence number is generally greater than the scaled area of ​​its functional area. This ensures the optimization scheme is as feasible as possible without affecting the layout.

[0147] The principle of population iterative selection in this model is to iterate through a relatively optimal initial solution, retaining the 100 best fitness function solutions in each iteration. Through continuous adjustments, the final population size is set at 100, and the number of iterations is set at 500.

[0148] To compare with the optimal solution, this embodiment sets up multiple control experiments to find the most suitable crossover probability parameter. After comparison, the crossover probability is set to 0.8, and the model's computation time is approximately 60 seconds. The obtained optimal gene solution and objective function value are as follows: Figure 5 As shown. Analysis revealed that the optimal solution converged around the 330th generation, and this optimal solution remained unchanged in subsequent iterations. Theoretically, it can be considered the optimal solution for this problem and can serve as a valid reference scheme. By manually adjusting this scheme while ensuring that the final fitness function value does not decrease, the functional areas are made rectangular, as shown... Figure 6 As shown.

[0149] Combining the final solution, it can be seen that in the optimal genetic solution, i.e., the final optimized solution, the codes of the same numbers are grouped together, forming a standard rectangular layout. This indicates that the functional area layout in the final optimized solution is constructed in a rectangular shape, which conforms to the internal functional area layout of logistics parks. Compared with irregularly shaped functional area layouts, this significantly improves the operational efficiency of the hub, and the fitness function and objective function values ​​are higher than those of irregularly shaped functional area layouts. Figure 5 As shown.

[0150] Meanwhile, the functional areas are closely connected, and the functional areas with larger scale and heavier transportation tasks (i.e., 3 and 4) can fully meet the actual needs. However, because the functional area numbers are used to fill all the cells generated by the grid scaling method, the construction area of ​​some functional areas will exceed the actual required area. Therefore, the area of ​​the functional areas with the basic shape will remain unchanged. Without changing the final objective function score and fitness function value, the shape of all functional areas will be manually adjusted to a standard rectangle to facilitate the construction of the simulation model.

[0151] To determine the merits of the optimized functional area layout scheme and compare it with the original layout scheme of the logistics park, this method uses AnyLogic software for simulation to dynamically observe the vehicle operation status and transportation process in real time. The simulation model primarily adopts a ring-shaped layout scheme, with a hybrid road layout method using dead-end layouts in some areas. While ensuring the efficient and safe operation of basic transportation activities, certain free transportation routes are set up to provide backup road transportation schemes to handle emergency issues. If, under the optimized functional area layout scheme, there are no excessively long waiting times or congestion during vehicle transportation, it proves that the optimized layout scheme meets the transportation task requirements of the logistics hub, i.e., it satisfies the research objective of maximizing the internal and external operational efficiency of the system.

[0152] The volume of materials flowing between each functional area is shown in Table 6.

[0153] Table 6 Material Flow Between Functional Zones

[0154] S1 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 S1 304 496 Q1 188.8 115.2 Q2 364.16 188.8 Q3 152 168 Q4 472 33.6 37.44 Q5 Q6 Q7 Q8 Q9

[0155] Since different functional areas use different types of vehicles and have different sizes when carrying out transportation activities and tasks, the number of vehicles required to complete one transportation task between different functional areas is calculated based on the actual transportation direction and the selected transportation vehicles, combined with the material flow between functional areas in Table 6. The final statistical results are shown in Table 7.

[0156] Table 7 Loading capacity of vehicles transporting goods between functional zones

[0157]

[0158]

[0159] The number of vehicles required to meet the corresponding cargo flow between each functional area was calculated, as shown in Table 8.

[0160] Table 8 Number of vehicles between functional zones

[0161] S1 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 S1 6 20 Q1 4 3 Q2 10 5 Q3 7 7 Q4 12 19 21 Q5 Q6 Q7 Q8 Q9

[0162] Based on the above assumptions and the already generated optimized layout, the basic road network and functional area locations are constructed as follows: Figure 7 As shown.

[0163] The simulated loading and unloading process will utilize parking lines. After vehicles are generated in the parking lot, they will perform loading, transport, and unloading operations in three steps. When a vehicle reaches the target functional area, it will enter the internal roads of that area and stop at the designated parking lines. The stopping time will be equal to the total time spent on loading and unloading in that area, simulating the loading and unloading process. Subsequent vehicles will queue up to wait for loading and unloading. The parking lines are located in the middle of the functional area roads, which will help avoid traffic congestion to some extent.

[0164] The logistics park selected in this simulation has a total of 11 transportation tasks, involving 8 functional areas.

[0165] By comparing the results of the two functional area layout schemes, it can be found that the functional area layout scheme optimized by the genetic algorithm takes 17,430 seconds (approximately 4 hours and 50 minutes) to simulate a complete transportation task. However, when using the original functional area layout scheme of the logistics park to perform a complete transportation task, the simulation takes 19,942 seconds (approximately 5 hours and 32 minutes), achieving an optimization of 42 minutes. This represents a 12.6% improvement in transportation efficiency compared to the original transportation time, thus achieving the goals of improving transportation efficiency, reducing transportation time and costs. This proves that the optimized scheme calculated by the model is feasible and effective.

[0166] The embodiments described above are merely illustrative of some implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for optimizing the layout of logistics park facilities, characterized in that: The system comprises four parts: description of the logistics park facility layout problem, model building, model calculation, and simulation optimization. The specific steps are as follows: S1. Description of Logistics Park Facility Layout Problem: Construct a standard-shaped logistics park with several functional areas inside. The shape of the logistics park and all its functional areas are standard rectangles. Establish a model to minimize the total operating cost of a general logistics park. S2. Model Construction: Define one vertex of the logistics park with the above standard shape as the origin of the coordinate system, construct a plane rectangular coordinate system, and set the existing transportation lines or transportation stations as fixed functional areas in the logistics park's comprehensive hub; establish the model objective function, namely the comprehensive relationship score maximization model; S3. Model Calculation: A genetic algorithm is used to calculate the layout optimization model of the logistics park, resulting in an optimized genetic algorithm scheme. During the model calculation stage, a grid scaling method is introduced to simplify the genetic algorithm. This includes the following sub-steps: Step S3.1, Raster Scaling: The problem is simplified by raster scaling, which is used for encoding and model calculation; Step S3.2: Chromosome encoding: Defined using a digital matrix, each chromosome group has one gene, and the gene represents a set of layout optimization schemes; Step S3.3: Initialize the population: Based on the premise that the functional areas of the facilities are as close or adjacent as possible, set several excellent initial solutions, and iterate on these solutions to obtain better solutions; Step S3.4: Define a fitness function to determine the feasibility of the layout optimization scheme; Step S3.5: Calculate the objective function: Use a genetic algorithm to use the comprehensive relationship score of each functional area as an indicator to measure the merits of the layout optimization scheme; Step S3.6: Perform cross-operation; Step S3.7, Mutation Calculation: Set up a random exchange of genes in two columns or two rows in an individual, so that the mutation result satisfies the constraint formula, that is, the area of ​​each functional region in each chromosome gene can meet the minimum area requirement of that region. In step S3.1, during the model calculation stage, in order to facilitate coding and model calculation, a grid scaling method is introduced to simplify the problem. The logistics park is divided into a specified number of cells according to a certain unit area, and the length and width of each functional area and the logistics park are enlarged to multiples of 100, so that the number of cells occupied by all functional areas are integers. When calculating the overall relationship score, the sum of the scores of each cell and its surrounding adjacent cells is calculated as the score of that cell. When calculating the score, the score coefficient between two functional areas that are right-angled is taken as higher, and the score coefficient between two functional areas that are diagonally adjacent is taken as lower, in order to make the same functional areas as straight as possible rather than diagonally adjacent. In step S3.5, the objective function is calculated as follows: the coefficient of the score value adjacent to a straight line is defined as 1, and the coefficient of the score value adjacent at an angle is a small value between 0 and 1. The coefficient of the score value adjacent at an angle is dynamically set according to the actual calculation situation and the relationship between the functional intervals. （11） in, Indicates the first Line number The total score value of the cells in the column, This represents the sum of the scores for cells that are directly adjacent to each other. This represents the sum of the diagonally adjacent score values ​​in the cell. The coefficient for the diagonally adjacent score values ​​in the cells; S4. Simulation Optimization: Simulation software is used to simulate vehicle operation, and the road layout of the logistics park's comprehensive hub is constructed. The functional area layout is designed using the genetic algorithm optimization scheme calculated by the model in step S3. The simulation is carried out through the construction of a simulation model and compared with the original functional area layout scheme.

2. The method for optimizing the layout of logistics park facilities according to claim 1, characterized in that: Step S1: Construct a standard-shaped logistics park, defining its length and width dimensions as follows. and Set the aspect ratio of the i-th functional area. The objective function of the model, namely the model that minimizes the total operating cost, is established as follows: （1） （2） （3） （5） （6） （7） in, Let i be the total number of functional areas, and j be the sequence number of the functional areas. Functional area and The center distance between them This represents the volume of logistics transportation between the two functional areas. Operating costs per unit of transport distance; , Let i be the length and width of the functional area. , Let j be the length and width of the functional area; Let i be the center coordinates of functional area i. Let J be the center coordinates of functional area j; Formula (1) above is the objective function, which represents the minimum value of the total transportation cost of the functional areas within the logistics park's integrated hub; The constraint formulas (2) and (3) indicate that there is no overlap between the two functional areas, that is, each functional area is independent and complete; The constraint formulas (5) and (6) indicate that the functional area is entirely located within the logistics park; The constraint formula (7) restricts the shape and aspect ratio of the functional area. Different values ​​are set according to the different characteristics of the logistics park. The logistics park includes: highway logistics park and port logistics park.

3. The method for optimizing the layout of logistics park facilities according to claim 2, characterized in that: Step S2, model construction, includes the following sub-steps: Step S2.1: Define one vertex of the rectangular logistics park as the origin of the coordinate system, and use the straight lines containing its adjacent length and width sides as the positive directions of the X and Y axes to construct a plane rectangular coordinate system; Step S2.2: Set the existing transportation lines or stations as fixed functional areas in the logistics park's comprehensive hub, and calibrate and set the length, width, and coordinate parameters. Step S2.3: Calculate the cargo volume and comprehensive relationship score of this transportation mode to obtain the comprehensive relationship score table, which serves as the score matrix; Step S2.4: Establish the objective function of the model, namely the comprehensive relationship score maximization model.

4. The method for optimizing the layout of logistics park facilities according to claim 3, characterized in that: In step S2.4, the objective function of the model is as follows: （8） （9） in, Indicates the first Line number The total score value of the cells in the column, , These represent the total number of rows and columns in the cell, respectively. Indicates functional area The number of cells occupied Indicates functional area The number of cells corresponding to the area; The cell is a designated number of cells into which the logistics park is divided according to a certain unit area. The size and area of ​​each cell are fixed, and the number of cells occupied by all functional areas is an integer.

5. The method for optimizing the layout of logistics park facilities according to claim 4, characterized in that: In step S3.4, a fitness function is defined to determine the feasibility of the layout optimization scheme, as follows: （10） Among them, the judgment function is set. As a 0 / 1 variable, each optimization scheme is conditionally judged. When it meets the actual requirements, it is set to 1, which means it passes; otherwise, it is set to 0, which means it fails. The final fitness function value is the product of the decision function and the objective function.

6. The method for optimizing the layout of logistics park facilities according to claim 1, characterized in that: Step S3.6: In the crossover operation stage, a multi-point crossover method is adopted, that is, a column or a row is randomly selected from the parent chromosome and exchanged with the gene in the same column or row from the mother chromosome individual. The resulting gene is then used as the chromosome gene of the offspring. The crossover probability is set according to the actual needs and scale of the logistics park facility layout optimization problem.

7. The method for optimizing the layout of logistics park facilities according to any one of claims 1 to 6, characterized in that: In step S4, the construction of the road layout of the logistics park's comprehensive hub specifically involves: analyzing the differences in the road layout of vehicles operating within the logistics park, selecting a road transport mode that conforms to the positioning of the logistics park, and constructing the road layout of the logistics park using a multi-layout approach.

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