A simulation method and system for the layout of refrigerated and hazardous cargo yards at automated terminals considering mixed manual and unmanned operations

Abstract

A simulation method and system for the layout of refrigerated and hazardous cargo yards at automated terminals considering mixed manual and unmanned operations belongs to the field of layout of refrigerated and hazardous cargo yards at automated terminals. First, data is collected to generate an overall layout set. A ; Secondly, from A Any overall layout is selected to obtain the first-stage optimization model, and the genetic algorithm is used to solve it; then, the second-stage physical model is constructed, which is recorded as a simulation model set; finally, different simulation models are evaluated and compared to obtain the optimal layout and scale combination. The layout simulation system of the automated terminal cold and hazardous cargo yard includes a data acquisition module, a layout pattern generation module, a scale optimization module, a simulation model construction module, a comprehensive evaluation module and a judgment module. The present invention realizes the coordinated optimization of layout and scale through two-stage optimization and simulation, which can improve the port's operating efficiency, resource utilization and safety, while reducing energy consumption and carbon emissions, and provide scientific decision-making support for port design.

Description

Technical Field

[0001] The present invention belongs to the field of layout of refrigerated and hazardous goods yards at automated terminals, and relates to a layout simulation method and system for refrigerated and hazardous goods yards at automated terminals that considers mixed manual and unmanned operations. Background Art

[0002] Currently, the design of refrigerated container yard layouts at automated terminals often overlooks the safety of port personnel working in the port area. In fact, fully unmanned handling of refrigerated and hazardous cargo is currently difficult to achieve. Therefore, it is necessary to develop an efficient and safe yard layout simulation method for situations where unmanned machinery and manual labor operate within the same space. Furthermore, previous yard layout designs, due to incomplete evaluation systems and excessive subjective assumptions in the early stages of modeling, can lead to incomplete yard design logic, low operational efficiency, safety hazards, and wasted resources.

[0003] To address the above-mentioned issues, Chinese invention patent CN119417340A proposes a method for transporting and operating special containers at an automated terminal. This method sends the unloading operation plan through the front end of the TOS system, and the scheduling system obtains the special container operation plan and matches the IGV equipped with a loading rack to meet the needs of the special container type, thereby achieving efficient transportation and operation of special containers. However, this patent mainly focuses on the optimization of the transportation and operation process of special containers, and does not fully consider the safety and efficiency of the yard layout. Especially for cold and hazardous goods, the patent lacks safety protection measures for technical workers when working in collaboration with automated facilities and equipment, nor does it establish a complete evaluation system to optimize the yard layout to ensure operational efficiency and rational use of resources. In terms of yard operations, Chinese invention patent CN119579038A proposes an automated terminal yard operation method and storage medium, which improves terminal operation efficiency and rail crane equipment utilization by optimizing the sorting and scheduling of operation tasks. However, this patent mainly focuses on the scheduling optimization of yard operation tasks and does not involve the specific methods of special container transportation and operations. In particular, the safety and efficiency of the layout of the cold and hazardous cargo yard still need to be improved.

[0004] To sum up, how to provide a simulation system for the layout of refrigerated and hazardous cargo yards at automated terminals that takes into account mixed manual and unmanned operations, and to conduct a relatively comprehensive and accurate analysis of the layout of refrigerated and hazardous cargo yards at automated terminals that take into account mixed manual and unmanned operations, so as to achieve optimized layout, has become an urgent problem to be solved. Summary of the Invention

[0005] In order to solve the long-overlooked problem of cold and hazardous cargo storage in the discussion of yard layout in the existing technology, especially the safety, resource utilization efficiency and operational efficiency issues in the mixed manual and unmanned operation scenario, the present invention provides a cold and hazardous cargo yard layout simulation method and system for automated terminals considering mixed manual and unmanned operations. The method can comprehensively and accurately analyze the cold and hazardous cargo yard layout of automated terminals with mixed manual and unmanned operations, realize optimized layout, thereby improving port efficiency and resource utilization, while ensuring the safety of cold and hazardous cargo and meeting the green and energy-saving requirements of modern ports.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A simulation method for the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations includes the following steps:

[0008] The first step is to collect characteristic data of the automated terminal yard and parameters of the automated terminal facilities and equipment to be built, as follows:

[0009] The characteristic data of the automated terminal yard to be built mainly include: geographical location, available land depth, terrain conditions, natural conditions, main cargo, terminal operation days, expected annual throughput, predicted ship type, expected number of skilled workers and total port construction budget.

[0010] The parameters for the automated terminal facilities and equipment to be built include those for horizontal transport equipment, yard cranes, and quay cranes. Parameters for each facility and equipment include: unit price, operational efficiency (including loading and unloading efficiency and transport efficiency), hourly energy consumption, endurance, and maximum acceleration.

[0011] The second step is to generate an overall layout set. Based on the characteristic data of the automated terminal yard to be built and the parameters of the automated terminal facilities and equipment to be built obtained in the first step, n overall layouts are generated to form the overall layout set A. The overall layout generation is as follows:

[0012] Step 2.1: Based on the yard characteristic data of the automated terminal to be built and the yard layout determination rules obtained in the first step, determine whether the automated terminal to be built can be built in a vertical, horizontal, or U-shaped yard layout, and determine the available yard layout methods:

[0013] If the annual throughput is expected to be extremely small, the available land depth is small, the terrain conditions are restricted, and the total budget for port construction is low, then a vertical layout is not feasible;

[0014] If the annual throughput is expected to be extremely large, the terrain conditions are complex, or development is restricted, a horizontal layout is not feasible;

[0015] If the annual throughput is expected to be large but the terrain conditions are complex, the U-shaped layout is not suitable;

[0016] Identify one or more possible yard layouts.

[0017] Step 2.2: Based on the parameters of the automated terminal facilities and equipment to be built and the specifications for their selection and layout, determine the availability of various facilities and equipment. Specific specifications for the selection and layout of various facilities and equipment include industry standards JTS165-2013, General Design Specification for Seaports, and JTS144-1-2010, Specification for Loads in Port Engineering.

[0018] Step 2.3: Combine facilities and equipment with yard layout to form an overall layout set A = {S1, S2, S3...S i ...S n}, where i∈1......n.

[0019] The third step is to build and solve the optimization model of the first stage. Select any overall layout S from the overall layout set A generated in the second step. i , construct the first-stage mathematical model, obtain the optimization model of the scale of facilities and equipment for the refrigerated and hazardous cargo yard of the automated terminal, and use the genetic algorithm to solve the optimization model of the first stage; the details are as follows:

[0020] Step 3.1, determine the decision variables and key parameters of the optimization model.

[0021] Decision variables include: number of quay cranes ;Number of field bridges ; Number of horizontal transport equipment ;

[0022] Key parameters include: single quay crane loading and unloading efficiency ; Loading and unloading efficiency of a single yard crane ;Transportation efficiency of a single horizontal transport equipment ; The equipment unit price is divided into the field bridge unit price , quay crane unit price , unit price of water transport equipment The total construction budget of the automated terminal to be built is G, Means a positive integer, the number of days of terminal operation , estimated annual throughput Q.

[0023] Step 3.2: Construct the objective function of the optimization model for the scale of the refrigerated and hazardous cargo yard facilities at the automated terminal. Ensure the highest operational efficiency U, maximize the throughput of the system bottleneck, and ensure a balanced collaborative operation capability among quay cranes, yard cranes, and horizontal transport vehicles:

[0024] (1)

[0025] Step 3.2, construct the constraint conditions of the optimization model for the scale of facilities and equipment of the automated terminal refrigerated and hazardous cargo yard, as follows;

[0026] (2)

[0027] Formula (2) shows that the total purchase cost of quay cranes, yard cranes, and horizontal transportation equipment cannot exceed the total construction budget G of the terminal. This ensures that equipment configuration is carried out within an economically feasible range.

[0028] (3)

[0029] (4)

[0030] (5)

[0031] Formulas (3) to (5) ensure that the combined handling capacity of the terminal's quay cranes, yard cranes, and horizontal transport equipment meets or exceeds the annual throughput Q requirement. That is, the handling capacity of each piece of equipment (number multiplied by the efficiency of each unit) must be able to handle at least the average daily cargo volume. , thereby ensuring the terminal's operating efficiency and service level.

[0032]

[0033] Formula (6) is a non-negative integer constraint.

[0034] Step 3.3: Use the genetic algorithm to solve the optimization model of the scale of the facilities and equipment in the automated terminal's cold and hazardous cargo yard, and obtain the scale optimization results of each facility and equipment.

[0035] The fourth step is to construct the second-stage physical model, that is, the simulation model of the automated terminal cold and hazardous cargo yard, which is recorded as the simulation model set B={M1,M2,M3…M i …M n}, where i∈1......n, specifically as follows:

[0036] Step 4.1: In the simulation software AnyLogic, model all the overall layouts in A separately. Overall layout S i Modeled as the corresponding simulation model M in the simulation software i . S i The facilities and equipment in the i In particular, in order to reflect the influence of human-computer interaction in the model, the present invention also adds a technical worker agent to the model. Traverse the overall layout set A={S1,S2,S3...S i ...S n}, the corresponding simulation model set B={M1,M2,M3…M i …M n}.

[0037] Step 4.2: Determine the agent scale. Each simulation model uses the same facility and equipment scale, which is the optimized scale of each facility and equipment obtained in Step 3. Specifically, the scale of the skilled worker agent is determined based on the estimated number of skilled workers in the yard characteristic data of the automated terminal to be built in Step 1.

[0038] Step 4.3: Construct the agent's behavior. In the simulation model, the facility and equipment agent uses the built-in random function of the simulation software to generate the agent's random behavior. The technical worker agent uses Lévy Flight to describe the random movement behavior of humans. The description formula is as follows:

[0039] The position of the skilled worker is described by two-dimensional coordinates (xp, yp), and its position change is expressed as:

[0040] (7)

[0041] (8)

[0042] Among them, μxp and μyp are the drift coefficients in the xp and yp directions; σxp and σyp are the diffusion coefficients in the xp and yp directions; 、 are independent Wiener process increments.

[0043] In order to facilitate calculation, the step length of Lévy Flight is generated by the method based on the Mantegna algorithm. , the probability density function of its step size s for:

[0044] (9)

[0045] Where β is the distribution exponent, which is between (0, 2] and controls the heavy-tail characteristic of the step size, and D is the normalization constant.

[0046] In step 4.4, the model is set up to simulate the loading and unloading operations of the refrigerated and hazardous cargo yard of the automated terminal for one year. The output is the average operating speed of the horizontal transportation equipment, the average operating speed of the yard crane, the average speed of the quay crane, labor cost, construction cost, utilization rate of the horizontal transportation equipment, utilization rate of the storage space, yard crane utilization rate, quay crane utilization rate, total loading and unloading time of the horizontal transportation equipment, total loading and unloading time of the yard crane, total loading and unloading time of the quay crane, average charging time of the horizontal transportation equipment, cargo throughput, proportion of automated facilities, total energy consumption of refrigerated containers, and the position coordinates and driving speed of the horizontal transportation equipment or the yard crane when they recognize the presence of skilled workers on the path ahead.

[0047] The fifth step is to evaluate each simulation model. First, we construct an evaluation index system for the layout of the automated port area's refrigerated and hazardous cargo yard. Based on this evaluation index system, we select the required port operation data and process it using the entropy weight method and TOPSIS method to obtain a comprehensive score for each simulation model. Note that the i-th simulation model is the i-th evaluation object. The details are as follows:

[0048] Step 5.1: Construct an evaluation index system for the layout of automated port area refrigerated and hazardous cargo yards. This evaluation system is comprised of six indicators: automation level, cost input, resource utilization, operational efficiency, human-machine interaction safety, and energy conservation. The evaluation indicators are categorized as either benefit-based or cost-based. Indicators dealing with cost input are considered cost-based, while the remaining indicators are considered benefit-based.

[0049] Step 5.2: Calculate the comprehensive collision time T. The comprehensive collision time T in the evaluation index system is not data that can be directly output by the simulation model. It is an innovative human-computer interaction safety evaluation index designed by the present invention and needs to be calculated separately. The specific calculation is given later.

[0050] In step 5.3, the required port operation data is selected based on the evaluation index system. Then, the required operation data and the comprehensive collision time T obtained in step 5.2 are dimensionlessly processed. The specific process is as follows:

[0051] (10)

[0052] Among them, X max is the maximum value of the jth indicator of the evaluation system; X min is the minimum value of the jth indicator of the evaluation system; x ij is the preprocessed data; X ij It is the original value in the evaluation system.

[0053] Step 5.4: Calculate the information entropy and weight of the required operational data and the integrated collision time T obtained in step 5.2:

[0054]

[0055] in, is the information entropy of the j-th indicator, is the weight of the j-th indicator; is the i-th simulation model M i , that is, the probability of occurrence of the jth indicator of the i-th evaluation object; n represents n evaluation objects, that is, all simulation models {M1, M2, M3…M i …M n}; m means there are m evaluation indicators.

[0056] Step 5.5 Calculate the positive ideal solution and negative ideal solutions .in Represents the optimal value combination of each indicator. Represents the worst value combination of each indicator:

[0057] (14)

[0058] (15)

[0059] } (16)

[0060] (17)

[0061] in, represents the positive ideal solution value of the first index; represents the positive ideal solution value of the nth index; Indicates the negative ideal solution value of the first index; represents the negative ideal solution value of the nth index; represents j indicators; J1 represents the set of benefit indicators; Indicates that for the benefit indicator, the maximum value of the indicator among all the options is taken; Indicates that for the benefit indicator, the minimum value of the indicator among all options is taken; Represents a set of cost-type indicators; Indicates that for cost indicators, the minimum value of the indicator among all solutions is taken; Indicates that for cost-type indicators, the maximum value of the indicator among all solutions is taken.

[0062] Step 5.6, use Represents the i-th simulation model M i right The distance to the positive ideal solution; , represents the i-th simulation model M i arrive The distance to the negative ideal solution, calculates the relative closeness of the i-th evaluation object to the positive and negative ideal solutions , the formula is as follows:

[0063] (18)

[0064] The sixth step is to determine the optimal layout. Evaluate and compare different simulation models, where 0≤δ i ≤1, according to The values ​​are sorted by size, and the larger the value, the closer it is to the optimal level. The details are as follows:

[0065] If the overall layout S i Corresponding M i Is closeness The largest automated terminal cold and hazardous cargo yard simulation model, it is believed that M i The scale and overall layout of facilities and equipment used in the model are optimal;

[0066] If the overall layout S i The corresponding M i Not closeness The simulation model of the largest automated terminal cold and hazardous cargo yard, the model with the highest degree of closeness is M k , then M k The corresponding S k As the overall layout selected in the third step, then complete steps 3 to 6 in sequence until the scale of facilities and equipment and the overall layout are optimal.

[0067] Furthermore, the evaluation system in step 5.2 is composed of evaluation indicators in six dimensions, including automation level, cost input, resource utilization, operational efficiency, human-computer interaction safety and green energy saving. Specifically:

[0068] The automation level is measured by using the quantitative indicator of the proportion of automated facilities.

[0069] The cost input includes labor cost and construction cost.

[0070] In terms of resource utilization, the evaluation system uses four evaluation indicators that are more frequently used, namely, horizontal transportation equipment utilization, storage space utilization, yard crane utilization and quay crane utilization.

[0071] In terms of operational efficiency, the total loading and unloading time of transport equipment, the average charging time of horizontal transport equipment, and cargo throughput are used to describe the total loading and unloading time of transport equipment, among which the total loading and unloading time of transport equipment includes: the total loading and unloading time of horizontal transport equipment, the total loading and unloading time of yard cranes, and the total loading and unloading time of quay cranes.

[0072] In terms of green energy conservation, based on the characteristics of refrigerated container yards, the total energy consumption of refrigerated containers is introduced to describe the energy consumption problem caused by continuous power supply of refrigerated containers;

[0073] In terms of human-machine interaction safety, the average operating speed of the equipment can reflect the safety of human-machine interaction to a certain extent. For human-machine interaction situations formed in a small range, the present invention introduces a comprehensive collision time T. The details are as follows:

[0074] The parameter settings are as follows: When the horizontal transport equipment or the field bridge recognizes that there are skilled workers on the path ahead, the projection coordinates of the horizontal transport equipment or the field bridge on the equipment's motion trajectory are: , the projection coordinates of the two-dimensional coordinates (xp, yp) of the technical worker's position on the equipment motion trajectory are: The speed of the horizontal transport equipment or the field crane is v (vector), and the direction of this speed is the positive direction. The speed of the technical worker is v r , the maximum acceleration of the facility equipment is a and the lane width is L. When the equipment recognizes a person, the relative distance between the person and the equipment is expressed as The comprehensive collision time T is calculated as follows:

[0075] (19)

[0076] (20)

[0077] (twenty one)

[0078] (twenty two)

[0079] (twenty three)

[0080] (twenty four)

[0081] (25)

[0082] (26)

[0083] (27)

[0084] Among them, the coefficient μ is a conversion coefficient, the purpose of which is to unify the two states of human-computer interaction. Describes the relative positions of people and equipment. The distance between the vehicle and the person in two critical states is described: one is when the distance between the vehicle and the person is equal to the braking distance, and the other is when the distance between the vehicle and the person is equal to the distance the person would have traveled without braking and at their original speed after the person had completed the crossing. y describes the distance the vehicle decelerated during the time it took the person to cross the road.

[0085] The meaning of the comprehensive collision time T is: when the same straight line =1, in fact, the TTC transformed formula is used to describe the relative safety of human-computer interaction; when That is, people and equipment are in the same direction. That is, if a person is in front of the vehicle and the distance between the person and the vehicle is less than the braking distance of the vehicle, it is considered that there is a potential danger; when When people and equipment are moving towards or in opposite directions, if they are moving in opposite directions, , there is danger; if you go in the opposite direction , stay safe;

[0086] when When the running directions are perpendicular to each other =0, in fact, the PET deformed formula is used to describe the relative safety of human-computer interaction: when When it is considered dangerous; when or When it is considered very safe;

[0087] In summary, the value of the comprehensive collision time T is: When T is set, the human-computer interaction is considered to be in a safe state, and the larger the value of T, the safer it is; , it is believed that human-computer interaction is potentially dangerous.

[0088] A simulation system for the layout of a cold and hazardous cargo yard at an automated terminal, which considers mixed manual and unmanned operations, divides each step of the simulation method for the layout of a cold and hazardous cargo yard at an automated terminal into modules. The simulation system includes a data acquisition module, a layout pattern generation module, a scale optimization module, a simulation model construction module, a comprehensive evaluation module, and a judgment module. Specifically:

[0089] The data acquisition module is mainly used to obtain characteristic data of the automated terminal yard to be built and parameters of the automated terminal facilities and equipment to be built from the port environment. It covers three functional units: data cleaning, formatting and classified storage, pre-processes the data to ensure that the data is complete and accurate, and provides high-quality input data for subsequent modules.

[0090] The layout pattern generation module: This layout pattern generation module is connected to the data acquisition module and inputs the port yard characteristic data and port facility equipment performance data. The layout pattern generation module is based on the yard layout judgment rules and equipment layout specifications, covering three functional units: layout mode judgment, multi-scheme generation and index establishment, and supports the automatic generation and management of multiple feasible yard overall layouts. The final output contains the overall layout set A={S1,S2,S3,...,S i ,...,S n}structured data and store it for future reference.

[0091] The scale optimization module: This scale optimization module is connected to the layout pattern generation module and the data acquisition module, covering two functional units: parameter update and model solution. Randomly select an overall layout pattern S from the layout pattern generation module. i (If it is not the first iteration, the overall layout mode S returned from the judgment module is obtained k ), inputting port yard characteristic data and port facility and equipment performance data into the optimization model for the scale of facilities and equipment in the automated terminal's refrigerated and hazardous cargo yard, updating the model parameters, and then solving the optimization model. Finally, the scale optimization results for each facility and equipment are output and fed back to the simulation model construction module.

[0092] The simulation model construction module: This simulation model construction module is connected to the data acquisition module, layout pattern generation module and scale optimization module, covering three functional units: parameter update, model construction and dynamic simulation. Input the yard characteristic data, facility equipment parameters, overall layout set and scale optimization results of each facility equipment, update the simulation model related parameters, and construct different automated terminal cold and hazardous cargo yard simulation models M1, M2, M3...M i …M n , (where i∈1...n). It supports the simulation of dynamic processes of mixed operations of manual and unmanned equipment, reflects the random drift and diffusion characteristics of skilled workers, and ultimately outputs various port operation data.

[0093] The comprehensive evaluation module: This module is connected to the simulation model construction module to obtain various port operation data. The module is based on the evaluation index system and covers three functional units: data preprocessing, weight distribution (entropy weight method) and multi-index comprehensive ranking (using TOPSIS method). It supports the parallel evaluation of multiple simulation models and finally outputs the closeness index of each simulation model (0≤δ i ≤1).

[0094] The judgment module: This module is connected to the comprehensive evaluation module and contains two functional units: comparative decision-making and feedback control. Input the closeness index of each simulation model and sort and optimize different overall layout and facility equipment scale plans. This module automatically identifies the optimal model with the highest closeness. If the overall layout corresponding to the optimal model is the overall layout S used by the scale optimization module, i (If it is not the first iteration, then S k ), the system concludes that the facility scale and overall layout are optimal, terminates the iteration, and outputs the final solution. Otherwise, the overall layout corresponding to the optimal model is fed back to the scale optimization module, initiating a new round of iteration. This continues until the facility scale and overall layout are optimal, ultimately achieving dynamic coordinated optimization of layout and facility scale.

[0095] The beneficial effects of the present invention are:

[0096] This design utilizes a two-stage approach to address overall layout and facility sizing. It integrates a data-driven optimization model for automated terminal refrigerated and hazardous cargo yard facility sizing, a simulation model for automated terminal refrigerated and hazardous cargo yards, and an evaluation system, enhancing the scientific nature of the entire port design process. It also innovatively introduces a comprehensive collision time (T) metric to enhance human-machine interaction safety, and incorporates green energy-saving indicators to reduce energy consumption and carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is an implementation step of the port refrigerated and hazardous cargo yard layout method considering the interaction between manual and unmanned operations.

[0098] Figure 2 It is an evaluation index system for the layout of refrigerated and hazardous cargo yards in automated port areas.

[0099] Figure 3 It is the intelligent agent composition of the simulation model building block and the relationship between the intelligent agents.

[0100] Figure 4 It is a structural diagram of the simulation system for the layout of the refrigerated and hazardous cargo yard at an automated terminal taking into account mixed manual and unmanned operations. DETAILED DESCRIPTION

[0101] The present invention is further described below with reference to specific implementation cases.

[0102] A simulation method for the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations includes the following steps:

[0103] The first step is data collection. Collect the yard characteristic data of the automated terminal to be built and the parameters of the automated terminal facilities and equipment to be built, as follows:

[0104] The data collected for the proposed automated terminal mainly includes: taking a specific port as an example, its location is in the South China Sea, the available land depth is approximately 1,300 meters, the terrain is flat and open, there are no special restrictions, the natural conditions are relatively stable, the main cargo is refrigerated containers, the terminal operating days are expected to be 300 days / year, the annual throughput is expected to be 1 million TEUs, the ship type is expected to be mainly container ships, the number of technical workers is expected to be 100, and the total budget for port construction is 1 billion yuan;

[0105] Regarding the parameters of the automated terminal facilities and equipment to be built, horizontal transport equipment options include AGVs and L-AGVs. The AGV has a purchase cost of 1 million RMB per unit, an operating efficiency of 40 TEUs / h, and an operating speed of 6 m / s. The L-AGV has a similar purchase cost and operates at the same efficiency and speed as the AGV, but consumes slightly more energy. Both transport vehicles utilize a battery replacement system to maintain power, with a battery life of 6 hours and a replacement time of 6 minutes. There is no power loss when stationary. The yard cranes include both cantilevered and non-cantilevered ARMGs, with an operating efficiency of 25 TEUs / h and a purchase cost of 3 million RMB per unit. The quay crane has an operating efficiency of 30 TEUs / h and a purchase cost of 5 million RMB per unit. Parameters such as maximum acceleration also require detailed collection.

[0106] The second step is to generate an overall layout set. Based on the characteristic data of the automated terminal yard to be built and the parameters of the automated terminal facilities and equipment to be built obtained in the first step, n overall layouts are generated to form the overall layout set A, as follows:

[0107] Step 2.1: Based on the yard characteristic data of the automated terminal to be built obtained in the first step, determine whether the automated terminal to be built can be built in a vertical, horizontal, or U-shaped yard layout, and determine the available yard layout methods:

[0108] The port's assessment revealed a high projected annual throughput, favorable land depth and terrain conditions, and a relatively sufficient total port construction budget. Therefore, both vertical and horizontal layouts were feasible, and a U-shaped layout was also considered due to the relatively simple terrain. However, after considering various factors, the two options were ultimately considered.

[0109] In step 2.2, based on the parameters of the automated terminal facilities and equipment to be built in the first step, the availability of various facilities and equipment is determined. The specific layout is determined by reference to industry standards JTS165-2013, Specification for General Design of Seaports, and JTS144-1-2010, Specification for Loads in Port Engineering. This specific embodiment also considers that in a horizontal layout, AGVs and cantilevered ARMGs work well together, efficiently completing cargo transportation and loading and unloading tasks; in a vertical layout, the combination of L-AGVs and non-cantilevered ARMGs is more advantageous.

[0110] Step 2.3. Combine the facilities and equipment with the yard layout. Combine the feasible yard layouts and the transport routes of the horizontal transport equipment to generate two overall layout patterns: S1 (horizontal layout with AGVs and cantilevered ARMGs) and S2 (vertical layout with L-AGVs and non-cantilevered ARMGs). This is denoted as the overall layout pattern set A = {S1, S2}.

[0111] The third step is to build and solve the optimization model of the first stage. Randomly select an overall layout S from the overall layout set A generated in the second step. i , construct the first-stage mathematical model, obtain the optimization model of the scale of facilities and equipment for the refrigerated and hazardous cargo yard of the automated terminal, and use the genetic algorithm to solve the first-stage optimization model; the details are as follows:

[0112] In step 3.1, based on this specific implementation case, the overall layout S1 is randomly selected and the decision variables and key parameters of the optimization model are determined.

[0113] Step 3.2 constructs the objective function of the optimization model for the scale of the refrigerated and hazardous cargo yard facilities at the automated terminal. This ensures the highest operational efficiency U, maximizes the throughput of the system bottleneck, and ensures a balanced collaborative operation capability among the quay crane, cantilever ARMG, and AGV, as shown in formula (1).

[0114] Step 3.2: The constraints for constructing the optimization model for the scale of facilities and equipment for the refrigerated and hazardous cargo yard at the automated terminal are shown in formula (2). Specifically:

[0115] Formula (2) shows that the total purchase cost of quay cranes, yard cranes, and horizontal transportation equipment cannot exceed the total construction budget G of the terminal. This ensures that equipment configuration is carried out within an economically feasible range.

[0116] Formulas (3) to (5) ensure that the combined handling capacity of the terminal's quay cranes, yard cranes, and horizontal transport equipment meets or exceeds the annual throughput Q requirement. That is, the handling capacity of each type of equipment (number multiplied by the efficiency of each unit) must be able to handle at least the average daily cargo volume. , thereby ensuring the terminal's operating efficiency and service level.

[0117] Formula (6) is a non-negative integer constraint.

[0118] In step 3.3, the genetic algorithm is used to solve the optimization model of the scale of facilities and equipment in the refrigerated and hazardous cargo yard of the automated terminal, and the optimal scale of facilities and equipment corresponding to the overall layout S1 is obtained: 12 quay cranes, 20 cantilever ARMGs, and 40 AGVs.

[0119] The fourth step is to construct the second-stage physical model, namely the simulation model of the automated terminal cold and hazardous cargo yard, which is denoted as the simulation model set B = {M1, M2,} as follows:

[0120] Step 4.1: In the simulation software AnyLogic, model all the overall layouts in A separately. In the simulation software AnyLogic, model S1 and S2 in the overall layout set A separately to form the corresponding simulation models M1 and M2. i The facilities and equipment in the i The facility equipment agent in . Figure 3 As shown, the agent composition of the simulation model building module and the relationship between the agents are described.

[0121] Step 4.2: Determine the agent scale. Each simulation model uses the same facility and equipment scale, which is the optimal facility and equipment scale obtained in Step 3. Specifically, the scale of the skilled worker agent is determined based on the estimated number of skilled workers (100) as determined by the yard characteristic data of the automated terminal to be built in Step 1.

[0122] Step 4.3: Construct the agent's behavior. In the simulation model, the facility and equipment agent uses the built-in random function of the simulation software to generate the agent's random behavior. The technical worker agent uses Lévy Flight to describe the random movement behavior of humans. The description formula is as follows:

[0123] The position of the skilled worker is described by two-dimensional coordinates (xp, yp), and its position change is shown in formula (7) and formula (8).

[0124] In order to facilitate calculation, a method based on the Mantegna algorithm is used to generate the step length of the Lévy Flight. The density function of the step length is shown in formula (9).

[0125] In step 4.4, the model is set up to simulate the loading and unloading operations of the refrigerated and hazardous cargo yard of the automated terminal for one year. The output is the average operating speed of the horizontal transportation equipment, the average operating speed of the yard crane, the average speed of the quay crane, labor cost, construction cost, utilization rate of the horizontal transportation equipment, utilization rate of the storage space, yard crane utilization rate, quay crane utilization rate, total loading and unloading time of the horizontal transportation equipment, total loading and unloading time of the yard crane, total loading and unloading time of the quay crane, average charging time of the horizontal transportation equipment, cargo throughput, proportion of automated facilities, total energy consumption of refrigerated containers, and the position coordinates and driving speed of the horizontal transportation equipment or the yard crane when they recognize the presence of skilled workers on the path ahead.

[0126] The fifth step is to evaluate each simulation model. First, we build an evaluation index system for the layout of the automated port area cold and hazardous cargo yard. Based on the evaluation index system, we select the required port operation data and process it using the entropy weight method and TOPSIS method to obtain the comprehensive score of each simulation model. The details are as follows:

[0127] Step 5.1: Construct an evaluation index system for the layout of the automated port area cold and hazardous cargo yard, such as Figure 2 The evaluation system is comprised of six evaluation indicators: automation level, cost input, resource utilization, operational efficiency, human-machine interaction safety, and green energy conservation. Evaluation indicators are categorized as either benefit-based or cost-based. Indicators dealing with cost input are considered cost-based, while the remaining indicators are considered benefit-based.

[0128] Step 5.2: Calculate the comprehensive collision time T. Combining formulas (19) to (27), the comprehensive collision time T in the evaluation index system is obtained.

[0129] In step 5.3, the required port operation data are selected according to the evaluation index system, and then the required operation data and the comprehensive collision time T obtained in step 5.2 are dimensionlessly processed as shown in formula (10).

[0130] Step 5.4, calculate the information entropy and weight of the required operational data and the comprehensive collision time T obtained in step 5.2, as shown in formulas (11) to (13);

[0131] Step 5.5 Calculate the positive ideal solution and negative ideal solutions , as shown in formula (14) to formula (17). Represents the optimal value combination of each indicator. Represents the worst value combination of each indicator.

[0132] Step 5.6, use Represents the i-th simulation model M i right The distance to the positive ideal solution; , represents the i-th simulation model M i arrive The distance to the negative ideal solution is calculated as The value is 0.755, The value is 0.245.

[0133] The sixth step is to determine the optimal layout based on the results obtained in the fifth step. Evaluate and compare different simulation models. In this implementation case, as shown in Table 1, the layout mode S1 corresponds to M1. The value is 0.755, and the layout mode S2 corresponds to M2 The value is 0.245. M1, corresponding to layout mode S1, is the highest-scoring automated terminal refrigerated and hazardous cargo yard simulation model. It is believed that the optimal facility equipment scale and overall layout mode is S1, which is a horizontal layout with AGVs and cantilevered ARMGs.

[0134] Table 1. Evaluation results of specific implementation cases

[0135]

[0136] This embodiment also provides a layout simulation system for a refrigerated and hazardous cargo yard at an automated terminal that considers mixed manual and unmanned operations, including a data acquisition module, a layout pattern generation module, a scale optimization module, a simulation model construction module, a comprehensive evaluation module, and a judgment module, specifically as follows:

[0137] Data acquisition module: Figure 4 The system first obtains the characteristic data of the automated terminal yard to be built and the parameters of the automated terminal facilities and equipment to be built from the port environment through the data acquisition module, which covers three functional units: data cleaning, formatting, and classified storage. Based on this specific implementation case, the data cleaning unit is responsible for eliminating missing, duplicate, and abnormal data in the characteristic data of the automated terminal yard to be built and the parameters of the automated terminal facilities and equipment to be built; the formatting unit converts the collected multi-dimensional heterogeneous data, including numerical data, categorical data, and time series data, into a standard format; the classified storage unit archives the yard characteristics and equipment parameters in an orderly manner according to the data type and purpose, ensuring the integrity and accuracy of the data and supporting rapid retrieval, providing high-quality input for the layout generation and scale optimization modules.

[0138] Layout pattern generation module: such as Figure 4 The layout pattern generation module is connected to the data acquisition module, and inputs the port's yard characteristic data and the performance data of port facilities and equipment. The layout pattern generation module is based on the yard layout judgment rules and equipment layout specifications, covering three functional units: layout mode judgment, multi-scheme generation and index establishment. Based on specific implementation cases, the layout mode judgment unit screens out feasible overall yard layout types as horizontal layout and vertical layout by analyzing the port's geographical environment, cargo attributes and equipment characteristics. The multi-scheme generation unit arranges and combines different layout forms and equipment to form multiple overall layout schemes, forming a horizontal layout with AGV and cantilever ARMG (S1 scheme) and a vertical layout with L-AGV and non-cantilever ARMG (S2 scheme). Index establishment unit: The generated multiple overall layout schemes are numbered, classified and structured to form an overall layout set A={S1,S2} containing all layout schemes, and stored for future reference.

[0139] Scale optimization module: such as Figure 4The layout pattern generation module and the data acquisition module are connected to the scale optimization module together, covering two functional units: parameter updating and model solving. Based on a specific implementation case, the parameter updating unit randomly selects the overall layout S1 from the overall layout set, and at the same time imports the corresponding port yard characteristic data and facility equipment performance parameters, and dynamically updates the relevant parameters in formulas (1) to (6). Based on the updated parameters, the model solving unit constructs a scale optimization model for the facilities and equipment of the automated terminal cold and hazardous cargo yard, and uses genetic algorithms and other solving methods to calculate the optimal equipment scale configuration plan. The solution result is the construction of 12 quay cranes, 20 cantilever ARMGs and 40 AGVs. The output results are fed back to the simulation model construction module

[0140] Simulation model building blocks: e.g. Figure 4 The simulation model construction module is connected to the data acquisition module, layout pattern generation module and scale optimization module, covering three functional units: parameter update, model construction and dynamic simulation. Based on the specific implementation case, the parameter update unit updates the relevant parameters of the simulation models M1 and M2 according to the yard characteristic data, the parameters of the facilities and equipment, the overall layout set and the scale optimization results of each facility and equipment. The model construction unit establishes corresponding simulation models M1 and M2 for the schemes S1 and S2 of the overall layout set A in the layout pattern generation module. Construct a network of relationships between intelligent agents (such as Figure 3 As shown in Figure 2, M1 determines 12 quay cranes, 20 cantilevered ARMGs, and 40 AGVs; M2 determines 12 quay cranes, 20 non-cantilevered ARMGs, and 40 L-AGVs. The number of skilled worker agents in each scenario is set to 100. The dynamic simulation unit simulates one year of loading and unloading operations at the automated terminal's cold and hazardous cargo yard. The skilled worker agents are modeled using the Lévy flight random mobility model based on the Mantegna algorithm to simulate the random behavior of skilled workers in two-dimensional space. This model conforms to the mathematical descriptions of equations (7) to (9), dynamically reflecting the operational flow of mixed manual and unmanned equipment operations. Finally, various port operation data are output.

[0141] Comprehensive evaluation module: Figure 4 As shown, the comprehensive evaluation module is connected to the simulation model construction module to obtain various port operation data. The comprehensive evaluation module is based on the evaluation index system and covers three functional units: data preprocessing, weight allocation (entropy weight method) and multi-index comprehensive ranking (using TOPSIS method). Based on the specific implementation case, the data preprocessing unit is embedded with formula (10) and formula (19) to (27) to complete the preprocessing of the data. The weight allocation unit is embedded with formula (11) to formula (13) to complete the entropy weight method to weight individual indicators. The multi-index comprehensive ranking unit is embedded with formula (14) to formula (17) to support the parallel evaluation of multiple simulation models. Finally, the output of the closeness index of each simulation model is calculated to obtain the M1. The value is 0.755, M2 The value is 0.245.

[0142] Judgment module: Figure 4 As shown, the judgment module is connected to the comprehensive evaluation module, which includes two functional units: comparison decision and feedback control. Based on the specific implementation case, the comparison decision unit inputs the closeness index of each simulation model: M1 The value is 0.755, M2 The value is 0.245, automatically identifying the optimal model M1 with the highest degree of closeness. The overall layout corresponding to M1 is the overall layout S1 adopted by the scale optimization module. The system concludes that the facility equipment scale and overall layout are optimal, and the system terminates the iteration, achieving dynamic coordinated optimization of the layout and facility scale.

[0143] The above-described embodiments merely express the implementation methods of the present invention, but should not be understood as limiting the scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A simulation method for the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations, characterized in that: The automated terminal refrigerated and hazardous cargo yard layout simulation method comprises the following steps: The first step is to collect the characteristic data of the automated terminal yard to be built and the parameters of the automated terminal facilities and equipment to be built. The second step is to generate an overall layout set. Based on the characteristic data of the automated terminal yard to be built and the parameters of the automated terminal facilities and equipment to be built obtained in the first step, n overall layouts are generated to form an overall layout set A. The third step is to construct and solve the optimization model of the first stage; select any overall layout S from the overall layout set A generated in the second step. i , construct the first-stage mathematical model, obtain the optimization model of the scale of facilities and equipment for the refrigerated and hazardous cargo yard at the automated terminal, and use the genetic algorithm to solve the optimization model of the first stage; The fourth step is to construct the second-stage physical model, that is, the simulation model of the automated terminal cold and hazardous cargo yard, which is recorded as the simulation model set B={M1,M2,M3…M i …M n }, where i∈1......n; The fifth step is to evaluate each simulation model. First, an evaluation index system for the layout of the refrigerated and hazardous cargo yard in the automated port area is constructed. The required port operation data is selected based on the evaluation index system, and the entropy weight method and TOPSIS method are used to process the data to obtain the comprehensive score of each simulation model. Note that the i-th simulation model is the i-th evaluation object. Step 5.1: Construct an evaluation index system for the layout of automated port area refrigerated and hazardous cargo yards. The evaluation system is composed of evaluation indicators in six dimensions, including automation level, cost input, resource utilization, operational efficiency, human-machine interaction safety, and green energy conservation. The evaluation indicators are divided into benefit-based indicators and cost-based indicators. Indicators dealing with cost input are considered cost-based indicators, while the remaining indicators are considered benefit-based indicators. Step 5.2: Calculate the comprehensive collision time T. The comprehensive collision time T in the evaluation index system is the human-machine interaction safety evaluation index, which is calculated separately. Specifically: The parameter settings are as follows: When the horizontal transport equipment or the field bridge recognizes that there are skilled workers on the path ahead, the projection coordinates of the horizontal transport equipment or the field bridge on the equipment's motion trajectory are: , the projection coordinates of the two-dimensional coordinates (xp, yp) of the technical worker's position on the equipment motion trajectory are: The speed of the horizontal transport equipment or the yard bridge is v, which is a vector, and the direction of this speed is the positive direction; the speed of the technical worker is v r , the maximum acceleration of the facility equipment is a and the lane width is L; when the equipment recognizes a person, the relative distance between the person and the equipment is expressed as ; Comprehensive collision time T, calculated as follows: （19）， （20）， （21）， （22）， （23）， （24）， （25）， （26）， （27）， Among them, the coefficient μ is a conversion coefficient, the purpose of which is to unify the two states of human-computer interaction; Describe the relative positions of people and equipment; describes the distance between the device and the person in the two critical states; y describes the distance the vehicle decelerates when the person crosses the road; The meaning of the comprehensive collision time T is: when the same straight line =1, in fact, the TTC transformed formula is used to describe the relative safety of human-computer interaction; when That is, people and equipment are in the same direction, if 0> That is, if a person is in front of the vehicle and the distance between the person and the vehicle is less than the braking distance of the vehicle, it is considered that there is a potential danger; when When people and equipment are moving towards or in opposite directions, if they are moving towards , there is danger; if you go in the opposite direction , stay safe; when When the running directions are perpendicular to each other =0, in fact, the PET deformed formula is used to describe the relative safety of human-computer interaction: when When it is considered dangerous; when or When it is considered very safe; The value of the comprehensive collision time T is: When T is set, the human-computer interaction is considered to be in a safe state, and the larger the value of T, the safer it is; , then it is believed that human-computer interaction is potentially dangerous; In step 5.3, the required port operation data is selected based on the evaluation index system. Then, the required operation data and the comprehensive collision time T obtained in step 5.2 are dimensionlessly processed. The specific process is as follows: （10）， Among them, X max is the maximum value of the jth indicator of the evaluation system; X min is the minimum value of the jth indicator of the evaluation system; x ij is the preprocessed data; X ij It is the original value in the evaluation system; Step 5.4: Calculate the information entropy and weight of the required operational data and the integrated collision time T obtained in step 5.2: ， in, is the information entropy of the j-th indicator, is the weight of the j-th indicator; is the i-th simulation model M i , that is, the probability of occurrence of the jth indicator of the i-th evaluation object; n represents n evaluation objects, that is, all simulation models {M1, M2, M3…M i …M n }; m means there are m evaluation indicators; Step 5.5 Calculate the positive ideal solution and negative ideal solutions ;in Represents the optimal value combination of each indicator; Represents the worst value combination of each indicator: （14）， （15）， }（16）， （17）， in, represents the positive ideal solution value of the first index; represents the positive ideal solution value of the nth index; Indicates the negative ideal solution value of the first index; represents the negative ideal solution value of the nth index; represents j indicators; J1 represents the set of benefit indicators; Indicates that for the benefit indicator, the maximum value of the indicator among all the options is taken; Indicates that for the benefit indicator, the minimum value of the indicator among all options is taken; Represents a set of cost-type indicators; Indicates that for cost indicators, the minimum value of the indicator among all solutions is taken; Indicates that for cost-type indicators, the maximum value of the indicator among all solutions is taken; Step 5.6, use Represents the i-th simulation model M i right The distance to the positive ideal solution; , represents the i-th simulation model M i arrive The distance to the negative ideal solution, calculates the relative closeness of the i-th evaluation object to the positive and negative ideal solutions , the formula is as follows: (18); Step 6, determine the optimal layout; according to the fifth step Evaluate and compare different simulation models, where 0≤δ i ≤1, according to The values ​​are sorted by size, and the larger the value, the closer it is to the optimal level.

2. The method for simulating the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations according to claim 1 is characterized in that: In the first step: The characteristic data of the automated terminal yard to be built mainly include: geographical location, available land depth, terrain conditions, natural conditions, main cargoes, terminal operation days, estimated annual throughput, predicted ship types, estimated number of skilled workers, and total port construction budget; The parameters of the automated terminal facilities and equipment to be built consider the parameters of horizontal transportation equipment, yard cranes and quay cranes respectively; the parameters considered for each facility and equipment specifically include: unit price of the facility and equipment, operating efficiency, energy consumption per unit hour, endurance, and maximum acceleration.

3. The method for simulating the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations according to claim 1 is characterized in that: The second step is specifically as follows: Step 2.1: Based on the yard characteristic data of the automated terminal to be built obtained in the first step, determine whether the automated terminal to be built can be built in a vertical, horizontal, or U-shaped yard layout, and determine the available yard layout methods: Step 2.2: Based on the parameters of the automated terminal facilities and equipment to be built in the first step, determine the availability of various facilities and equipment; Step 2.3: Combine facilities and equipment with yard layout to form an overall layout set A = {S1, S2, S3...S i ...S n }, where i∈1......n.

4. The method for simulating the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations according to claim 1 is characterized in that: The third step is specifically as follows: Step 3.1, determine the decision variables and key parameters of the optimization model; Decision variables include: number of quay cranes ;Number of field bridges ;Number of horizontal transport equipment ; Key parameters include: single quay crane loading and unloading efficiency ; Loading and unloading efficiency of a single yard crane ;Transportation efficiency of a single horizontal transport equipment ; Step 3.2: Construct the objective function of the optimization model for the scale of the refrigerated and hazardous cargo yard facilities at the automated terminal; ensure the highest operational efficiency U, maximize the throughput of the system bottleneck, and ensure a balanced collaborative operation capability among quay cranes, yard cranes, and horizontal transport vehicles: （1）， Step 3.2: Construct the constraint conditions of the optimization model for the scale of facilities and equipment for the refrigerated and hazardous cargo yard at the automated terminal; Step 3.3, use genetic algorithm to solve the optimization model of the scale of facilities and equipment in the automated terminal cold and hazardous cargo yard, and obtain the overall layout S of each facility and equipment. i The corresponding optimal scale of facilities and equipment.

5. The method for simulating the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations according to claim 4 is characterized in that: The constraints of step 3.2 are as follows: （2）， （3）， （4）， （5）， ， Formula (2) states that the total purchase cost of quay cranes, yard cranes, and horizontal transportation equipment cannot exceed the total construction budget G of the terminal; Formulas (3) to (5) ensure that the combined handling capacity of the terminal's quay cranes, yard cranes, and horizontal transport equipment meets or exceeds the annual throughput Q requirement; that is, the handling capacity of each equipment must be able to handle at least the average daily cargo volume. , to ensure the terminal's operating efficiency and service level; the equipment unit price is divided into the yard crane unit price , quay crane unit price , unit price of horizontal transportation equipment The total construction budget of the automated terminal to be built is G, Means a positive integer, the number of days of terminal operation , estimated annual throughput Q; Formula (6) is a non-negative integer constraint.

6. The method for simulating the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations according to claim 4 is characterized in that: The fourth step is specifically as follows: Step 4.1: In the simulation software, model all the overall layouts in A separately; the overall layout S i Modeled as the corresponding simulation model M in the simulation software i ;S i The facilities and equipment in the i The facility equipment agent in the simulation model; add the technical worker agent to the simulation model; traverse the overall layout set A={S1,S2,S3...S i ...S n }, the corresponding simulation model set B={M1,M2,M3…M i …M n }; Step 4.2: Determine the agent scale. Each simulation model uses the same facility and equipment scale, which is the optimal facility and equipment scale obtained in Step 3. The scale of the skilled worker agent is determined based on the estimated number of skilled workers in the yard characteristic data of the automated terminal to be built in Step 1. Step 4.3: Construct the agent behavior. In the simulation model, the facility and equipment agent uses the built-in random function of the simulation software to generate the random behavior of the agent. For the technical worker agent, the random movement behavior is described using the Levy flight method. The description formula is as follows: The position of the skilled worker is described by two-dimensional coordinates (xp, yp), and its position change is expressed as: （7）， （8）， Among them, μxp and μyp are the drift coefficients in the xp and yp directions; σxp and σyp are the diffusion coefficients in the xp and yp directions; 、 are independent Wiener process increments; Generate the step length of Lévy flight using the method based on Mantegna algorithm , the probability density function of its step size s for: （9）， Where β is the distribution index, which is between (0,2] and controls the heavy-tail characteristic of the step size, and D is the normalization constant; In step 4.4, the model is set up to simulate the loading and unloading operations of the refrigerated and hazardous cargo yard of the automated terminal for one year. The output is the average operating speed of the horizontal transportation equipment, the average operating speed of the yard crane, the average speed of the quay crane, labor cost, construction cost, utilization rate of the horizontal transportation equipment, utilization rate of the storage space, yard crane utilization rate, quay crane utilization rate, total loading and unloading time of the horizontal transportation equipment, total loading and unloading time of the yard crane, total loading and unloading time of the quay crane, average charging time of the horizontal transportation equipment, cargo throughput, proportion of automated facilities, total energy consumption of refrigerated containers, and the position coordinates and driving speed of the horizontal transportation equipment or the yard crane when they recognize the presence of skilled workers on the path ahead.

7. The method for simulating the layout of a cold and hazardous cargo yard at an automated terminal considering mixed manual and unmanned operations according to claim 1, characterized in that: The sixth step is specifically as follows: If the overall layout S i Corresponding M i Is closeness The largest automated terminal cold and hazardous cargo yard simulation model, it is believed that M i The scale and overall layout of facilities and equipment used in the model are optimal; If the overall layout S i Corresponding M i Not closeness Simulation model of the largest automated terminal refrigerated and hazardous cargo yard, close to The largest model is M k , then M k The corresponding S k As the overall layout selected in the third step, then complete steps 3 to 6 in sequence until the scale of facilities and equipment and the overall layout are optimal.

8. An automated terminal cold and hazardous cargo yard layout simulation system considering mixed manual and unmanned operations, characterized by: The automated terminal refrigerated and hazardous goods yard layout simulation method described in any one of claims 1 to 7 is implemented through the automated terminal refrigerated and hazardous goods yard layout simulation system, which includes a data acquisition module, a layout pattern generation module, a scale optimization module, a simulation model construction module, a comprehensive evaluation module and a judgment module.

9. The automated terminal cold and hazardous cargo yard layout simulation system considering mixed manual and unmanned operations according to claim 8 is characterized in that: The automated terminal refrigerated and hazardous cargo yard layout simulation system is specifically: The data acquisition module is used to obtain characteristic data of the automated terminal yard to be built and parameters of the automated terminal facilities and equipment to be built from the port environment and pre-process the data; The layout pattern generation module is connected to the data acquisition module, and inputs the port yard characteristic data and the performance data of the port facilities and equipment. The layout pattern generation module outputs the overall layout set A={S1,S2,S3,...,S i ,...,S n }structured data; The scale optimization module is connected to the layout pattern generation module and the data acquisition module, and randomly selects an overall layout pattern S from the layout pattern generation module. i , the port yard characteristic data and the performance data of port facilities and equipment are input into the optimization model of the scale of the automated terminal cold and hazardous cargo yard facilities and equipment, the model parameters are updated, and the optimization model is solved; finally, the scale optimization results of each facility and equipment are output and fed back to the simulation model construction module; The simulation model construction module is connected to the data acquisition module, the layout pattern generation module and the scale optimization module, inputs the yard characteristic data, the parameters of the facilities and equipment, the overall layout set and the scale optimization results of each facility and equipment, updates the relevant parameters of the simulation model, and constructs different automated terminal cold and hazardous cargo yard simulation models M1, M2, M3...M i …M n ; Output various port operation data; The comprehensive evaluation module: accesses the simulation model construction module to obtain various port operation data; based on the evaluation index system, supports parallel evaluation of multiple simulation models, and finally outputs the closeness index of each simulation model; The judgment module is connected to the comprehensive evaluation module, which includes two functional units: comparative decision-making and feedback control; inputs the closeness index of each simulation model, sorts and optimizes different overall layout and facility equipment scale plans; automatically identifies the optimal model with the highest closeness; and realizes dynamic coordinated optimization of layout and facility scale.

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