Multi-target general cargo wharf stockpiling method based on cargo types and berth distribution

Abstract

The invention relates to a multi-target general cargo wharf stockpiling method based on cargo types and berth allocation, and belongs to the technical field of stockpiling, and the method comprises the following steps: S1, defining and initializing parameters; s2, calculating the average turnover of each kind of goods according to the annual throughput and the stockpiling period of the goods; s3, taking the overall shortest distance of each cargo from the berth to the storage yard as an optimization target, adding constraint conditions, and defining a target function; and S4, solving the target function by utilizing multi-target linear programming, and outputting the unloading berth of each cargo, the number and the number of the storage yards, the total storage amount and the difference between the total storage amount and the average turnover amount.

Description

Technical Field

[0001] The invention belongs to the technical field of stockpiling, and relates to a multi-objective general cargo terminal stockpiling method based on cargo type and berth allocation. Background Art

[0002] Port logistics management is a core component of the modern logistics system. With the continuous expansion of global trade, ports have increasingly stringent requirements for efficiency and resource utilization. In this context, general cargo terminals, as a traditional but important port facility, not only have broad growth potential in market demand, but also show significant cost-effectiveness advantages, becoming an indispensable link in the port logistics system. With the year-on-year increase in port cargo throughput, the storage demand at general cargo terminals has increased sharply, resulting in increasingly tight yard space. The existing yard storage strategy can no longer meet the efficient needs of modern port operations.

[0003] In recent years, the research on storage strategy has mainly focused on the container field. The research on storage strategy, berth allocation and transportation route optimization of breakbulk terminals is gradually increasing. The research field focuses on reducing the yard cargo transfer time, improving the yard space utilization and overall operation efficiency. However, the current technology still has limitations when dealing with multiple cargo types and multi-berth scenarios.

[0004] The current research on storage strategies for breakbulk cargo terminals focuses on the following two aspects:

[0005] (1) Based on the current status of the yard, data survey and analysis are conducted. Starting from the perspective of cargo turnover, the cargo in the yard is redistributed with the goal of minimizing the transportation distance from the berth to the yard. The cargo type is set to be single, usually focusing on the characteristics of only one type of cargo. By optimizing the stacking allocation, the space utilization rate in the yard is improved, and the solution is obtained through algorithm optimization. However, these studies are mostly limited to a single berth, ignoring the importance of multiple berths in the storage strategy.

[0006] (2) Berth-related research has mostly focused on shortening the time ships spend in port by optimizing ship plans and optimizing berth allocation, while the consideration of yard factors is relatively limited. It has failed to systematically and comprehensively consider the complex relationships among cargo turnover cycle, berth distance, cargo characteristics, and yard capacity, resulting in the inability to fully realize the optimization effect.

[0007] Under the conditions of various cargo types and berth allocations, the existing storage strategies are difficult to achieve optimal allocation of the port's overall resources. Summary of the invention

[0008] In view of this, an object of the present invention is to provide a multi-objective general cargo terminal stockpiling method based on cargo type and berth allocation.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A multi-objective general cargo terminal stockpiling method based on cargo type and berth allocation comprises the following steps:

[0011] S1: define and initialize parameters;

[0012] S2: Calculate the average turnover of each type of goods through the annual throughput and storage period of the goods;

[0013] S3: Taking the overall shortest distance from the berth to the yard for various goods as the optimization goal, add constraints and define the objective function;

[0014] S4: Use multi-objective linear programming to solve the objective function and output the unloading berth, storage yard number and quantity, total storage volume and the difference from the average turnover volume for each cargo.

[0015] Further, the parameters defined in step S1 include:

[0016] Key parameters:

[0017] Goods type N, through dictionary i values Indicates that there are four types of goods, i.e., i = 1, 2, 3, 4, where Goods 1 is wood, Goods 2 is steel plate, Goods 3 is steel coil, and Goods 4 is coil;

[0018] The number of yards M, using list j values express;

[0019] Berth information, through the dictionary k values It means that it includes two berths, that is, k = 1, 2;

[0020] Cargo characteristics:

[0021] Annual throughput w i : Indicates the handling volume of each type of cargo in a year;

[0022] Storage cycle T i : Indicates the average storage time of each type of goods in the yard;

[0023] Yard capacity p j : Define the maximum storage capacity of each yard;

[0024] Distance d kj : Define the distance from each berth to each yard, which is used to calculate the transportation cost;

[0025] Weight i : Define the weight of a pallet fully loaded with various goods, which is used to calculate the objective function;

[0026] Yard characteristics:

[0027] Yard capacity p j : Define the tonnage of fully loaded cargo in each yard, which is used to calculate the space utilization rate of the yard;

[0028] Area j : Define the area of ​​each yard, which is used to calculate the tonnage of goods that can be stored per unit area of ​​the yard and the utilization rate of the yard space;

[0029] The usable area of ​​the yard j : Count the used area of ​​each yard to calculate the space utilization rate;

[0030] Total available area of ​​the yard R j : Used to calculate space utilization.

[0031] Further, the average turnover of each type of goods in step S2 is calculated as follows:

[0032]

[0033] Further, in step S3, an optimization model is first created, and the variables defined in the optimization model include:

[0034] Decision variable z[i,j]: represents the storage quantity of the i-th type of goods in the j-th yard, and is a continuous variable;

[0035] Decision variable x[i,j]: a binary variable indicating whether the i-th type of goods is stored in the j-th yard;

[0036] Slack variable slack[i]: used to calculate the difference between the total storage volume of goods and its average turnover. In the optimization model, the slack variable slack[i] is introduced to increase the flexibility of the model, allowing temporary tolerance of slight violations of constraints during the solution process. However, in the final optimal solution, the value of the slack variable is required to be zero, that is, the actual freight volume is strictly equal to the average turnover.

[0037] Furthermore, the constraints added in step S3 include:

[0038] Total cargo quantity constraint: The total storage quantity of each cargo in the yard is equal to its average turnover;

[0039] Yard capacity constraint: The storage volume of each yard cannot exceed its maximum capacity;

[0040] Logical constraint: If z[i,j] is greater than 0, then x[i,j] is 1, ensuring that the yard is marked as in use only when there is cargo stored;

[0041] Berth selection constraints: Select appropriate berths based on cargo characteristics and distance, and limit the storage volume of specific cargo to optimize transportation efficiency. Berth 1 has significant advantages in distance, transportation efficiency, and yard utilization compared to berth 2. By giving priority to berth 1 for unloading and storing high-turnover cargo nearby, transportation costs and time can be significantly reduced, and the space utilization and operational safety of the yard can be improved.

[0042] Cargo separation constraint: Cargo separation constraint effectively reduces the safety risk of yard operations by limiting the coexistence of wood, steel coils and rolled plates in the same yard. This constraint is implemented in the optimization model through logical constraints of binary variables to ensure the reasonable allocation of dangerous goods;

[0043] Yard selection constraint: Cargoes with large average turnover (such as Cargo 2 and Cargo 4, whose average turnovers are 56,856 tons and 33,860 tons respectively) usually have high storage requirements and frequent transportation requirements; allocating these cargoes to the yard closer to berth 1 can significantly reduce the transportation distance, thereby reducing transportation costs and time;

[0044] According to the above parameter definitions and modeling ideas, the specific mathematical model is as follows:

[0045]

[0046] z ij ≤x ij G, G is a sufficiently large number (16)

[0047]

[0048]

[0049] Furthermore, the objective function defined in step S3 is:

[0050]

[0051] For each type of cargo i and each yard j, the transportation distance from the berth to the yard is calculated based on the average turnover of the cargo and its storage volume in each yard, multiplied by the distance from the berth to the yard, and the berth selection constraint is added; when calculating the transportation distance, for each type of cargo i, if its average turnover Qi is greater than 32,000 or is wood, that is, i=1, berth 1 is used for unloading first; otherwise, berth 2 is used for unloading. In the present invention, the actual distribution range of the average cargo turnover Qi is 30,066 tons to 33,860 tons, which is set to 32,000 based on the comprehensive consideration of numerical approximation, model robustness, operational feasibility and computational efficiency.

[0052] Furthermore, step S4 also includes calculating the space utilization rate of each yard and the overall yard, and analyzing the percentage of the actual yard area used to the total yard area. The specific mathematical formula is as follows:

[0053]

[0054] The beneficial effects of the present invention are as follows: the present invention achieves the best in terms of the distance from the berth to the yard and the number of trailer transportations. Compared with other strategies, the transportation mileage is reduced by more than 35%, the average number of trailer transportations is reduced, the time required for the transportation of goods is reduced, the transportation cost is reduced, the overall transportation efficiency of the terminal is improved, and the logistics time is shortened. The overall space utilization rate of the terminal yard is increased to 8.7%, the amount of goods that can be accommodated in the yard space is expanded, and the storage cost per unit of goods is also reduced accordingly.

[0055] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0057] Figure 1 A flow chart of a multi-objective general cargo terminal stockpiling method based on cargo type and berth allocation;

[0058] Figure 2 Flowchart for the layout of the breakbulk terminal yard;

[0059] Figure 3 This is a schematic diagram of the general cargo terminal yard layout;

[0060] Figure 4 This is a schematic diagram of the yard layout based on the cargo storage strategy;

[0061] Figure 5 It is a schematic diagram of the yard layout based on the berth allocation storage strategy;

[0062] Figure 6 Schematic diagram of the yard layout under the multi-objective stockpiling strategy based on cargo type and berth allocation. DETAILED DESCRIPTION

[0063] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0064] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0065] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0066] like Figure 1-3 As shown, the present invention optimizes the layout of the general cargo terminal yard through a multi-objective storage strategy based on cargo type and berth allocation, aiming to minimize the transportation distance from the berth to the yard, reduce the average number of trailer transportation times and increase the overall space utilization, while ensuring that the storage of goods in the yard meets specific constraints. The following is a detailed explanation of the process:

[0067] Step 1: Initialization and parameter definition

[0068] In the Gurobi library, a powerful optimization solver, key parameters are defined, including:

[0069] Goods type (N): There are four types of goods in this method (wood, steel plate, steel coil, coil), through the dictionary i values Indicates that i=1,2,3,4, where cargo 1 is wood, cargo 2 is steel plate, cargo 3 is steel coil, and cargo 4 is coil.

[0070] Number of yards (M): There are 13 yards in total, using list j values express.

[0071] Berth information: There are two berths (berth 1 and berth 2), through the dictionary k values express.

[0072] Step 2: Define cargo and yard characteristics

[0073] Annual throughput (w i ): Indicates the handling volume of each type of cargo in one year (unit: tons).

[0074] Storage cycle (T i ): Indicates the average storage time of each type of goods in the yard (unit: day).

[0075] Yard capacity (p j ): Define the maximum storage capacity of each yard (unit: tons).

[0076] Distance (d kj ): Define the distance from each berth to each yard to facilitate the subsequent calculation of transportation costs (unit: meter).

[0077] Weight (q i ): Defines the weight of a pallet fully loaded with various goods, which is subsequently used to calculate the objective function (unit: tons).

[0078] Yard capacity (p j ): Defines the tonnage of fully loaded cargo in each yard, which is subsequently used to calculate the yard's space utilization rate (unit: tons).

[0079] Area (a j ): Defines the area of ​​each yard, which is subsequently used to calculate the tonnage of goods that can be stored per unit area of ​​the yard and the yard space utilization rate (unit: square meters).

[0080] The usable area of ​​the yard j :Count the used area of ​​each yard to calculate space utilization.

[0081] Total available area of ​​the yard R j : Used to calculate space utilization.

[0082] Step 3: Calculate average turnover

[0083] The average turnover of each type of goods (Q i ), since the storage time of goods at the general cargo terminal is relatively long, it is very important to calculate the average turnover of goods, which reflects the flow frequency of goods in a year and the warehouse turnover efficiency. The calculation formula is as follows:

[0084]

[0085] Step 4: Create an optimization model, define decision variables and slack variables

[0086] Create an optimization model named "YardLayoutOptimization".

[0087] The variables defined in the model are as follows:

[0088] (1) Decision variable z[i,j]: represents the storage quantity of the i-th type of goods in the j-th yard, and is a continuous variable.

[0089] (2) Decision variable x[i,j]: a binary variable indicating whether the i-th type of goods is stored in the j-th yard.

[0090] (3) Slack variable slack[i]: used to calculate the difference between the total cargo storage volume and its average turnover volume. In the optimization model, the slack variable slack[i] is introduced to increase the flexibility of the model, allowing temporary tolerance of minor violations of constraints during the solution process. However, in the final optimal solution, the value of the slack variable is required to be zero, that is, the actual cargo volume is strictly equal to the average turnover volume.

[0091] Step 5: Add constraints

[0092] The constraints added at this stage are as follows:

[0093] (1) Constraint on total cargo volume: Ensure that the total storage volume of each type of cargo in the yard is equal to its average turnover volume.

[0094] (2) Yard capacity constraint: The storage volume of each yard cannot exceed its maximum capacity to ensure that the yard is not overloaded.

[0095] (3) Logical constraint: If z[i,j] is greater than 0, then x[i,j] is 1, ensuring that the yard is marked as in use only when there is cargo stored.

[0096] (4) Berth selection constraints: Select appropriate berths based on cargo characteristics and distance, and limit the storage volume of specific cargo to optimize transportation efficiency. Berth 1 has significant advantages in distance, transportation efficiency, and yard utilization compared to berth 2. By giving priority to berth 1 for unloading and storing high-turnover cargo nearby, transportation costs and time can be significantly reduced, and the space utilization and operational safety of the yard can be improved.

[0097] (5) Cargo separation constraint: In order to avoid the storage of certain goods (such as wood, steel coils and coils) in large quantities in the same yard, placing wood, steel coils and coils together will cause safety problems. Constraints are added to ensure that they do not take up too much space and ensure the safety of personnel and goods. The cargo separation constraint effectively reduces the safety risk of yard operations by limiting the coexistence of wood, steel coils and coils in the same yard. This constraint is implemented in the optimization model through logical restrictions on binary variables, ensuring the reasonable allocation of dangerous goods.

[0098] (6) Yard selection constraint: When the average turnover of goods is large, the yard closer to the berth will be selected for storage. Goods with large average turnover (such as goods 2 and goods 4, whose average turnovers are 56,856 tons and 33,860 tons respectively) usually have high storage requirements and frequent transportation requirements. Allocating these goods to the yard closer to berth 1 can significantly reduce the transportation distance, thereby reducing transportation costs and time;

[0099] According to the above parameter definitions and modeling ideas, the specific mathematical model is as follows:

[0100]

[0101] z ij ≤x ij G, G is a sufficiently large number (26)

[0102]

[0103] Step 6: Define the objective function

[0104] In the objective function, for each type of cargo i and each yard j, the transportation distance from the berth to the yard is calculated according to the average turnover of the cargo and its storage volume in each yard, multiplied by the distance from the berth to the yard, and the berth selection constraint is added; when calculating the transportation distance, for each type of cargo i, if its average turnover Qi is greater than 32,000 or it is wood, that is, i=1, berth 1 is used for unloading first; otherwise, berth 2 is used for unloading. In the present invention, the actual distribution range of the average cargo turnover Qi is 30,066 tons to 33,860 tons, which is set to 32,000 based on the comprehensive consideration of numerical approximation, model robustness, operational feasibility and computational efficiency. The objective function formula is as follows:

[0105]

[0106] Step 7: Solve the model

[0107] Use the optimizer to solve. If the model finds a solution, the result is output; if there is no solution, the infeasible subsystem information is output for easy debugging.

[0108] Step 8: Output the results

[0109] If the model has a solution, output the unloading berth for each cargo, the yard number and quantity, the total storage volume, and the difference from the average turnover volume. And calculate the space utilization rate of each yard and the overall. Through the yard utilization rate, analyze the percentage of the actual yard area to the total yard area. This helps to understand the execution effect of the model and provide guidance for practical application. The specific mathematical formula is as follows:

[0110]

[0111] If the model has no solution, use model.computeIIS() to identify the constraints that lead to no solution and generate an infeasible subsystem file for debugging. Ensure that the storage of each type of goods meets the constraints, and minimize transportation costs through optimization to improve logistics efficiency.

[0112] The present invention adopts three different stockpiling strategies, namely, a stockpiling strategy based on cargo type, a stockpiling strategy based on berth allocation, and a multi-objective stockpiling strategy based on cargo type and unloading berth allocation, to conduct a comparative analysis on the yard layout problem (hereinafter referred to as Strategy 1, Strategy 2, and Strategy 3).

[0113] Table 1 shows the comparison of various indicators under the three strategies. In Table 1, strategy 1 for a single berth is compared with strategies 2 and 3 for multiple berths. The indicators of strategy 1 are not effectively optimized. The transportation mileage of strategies 2 and 3 is 8.53×10 5 m and 6.28×10 5 meters, and the average number of trailer transportations is 632 and 277 respectively. Strategy 3 is better than Strategy 2. Figure 4-6 The results show that strategies 1 and 2 may result in a single yard storing multiple goods or low overall space utilization, which may increase safety hazards. In strategy 3, the above problems are effectively solved.

[0114] Table 2 shows the comparison of the tonnage of cargo stored in each yard and the utilization rate of the yard under the three strategies. The comparison shows that in the case of multiple berths, the distance of cargo transportation from the berth to the yard is effectively shortened by 35% or more, and the frequency of trailer transportation required for transportation is also effectively reduced to 277 times, which saves manpower and material resources, and the overall space utilization rate of the terminal yard is effectively increased to 8.7%. The ratio of available space (total space minus utilized space) to total space quantifies the overall space utilization rate. The larger the ratio, the higher the space utilization rate. This data can also be used to measure the remaining available yard area. The data shows that there are empty yards, which provide buffer space for the terminal to respond to emergencies and reserve potential optimization space for expanding the terminal's throughput capacity in the future.

[0115] Table 1

[0116]

[0117] Table 2

[0118]

[0119] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0120] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0121] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0122] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0123] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0124] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0125] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0126] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0127] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0128] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0129] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A multi-objective general cargo terminal storage method based on cargo type and berth allocation, characterized by: The following steps are involved: S1: define and initialize parameters; S2: Calculate the average turnover of each type of goods through the annual throughput and storage period of the goods; S3: Taking the overall shortest distance from the berth to the yard for various goods as the optimization goal, add constraints and define the objective function; S4: Use multi-objective linear programming to solve the objective function and output the unloading berth, storage yard number and quantity, total storage volume and the difference from the average turnover volume for each cargo.

2. The multi-objective general cargo terminal storage method based on cargo type and berth allocation according to claim 1 is characterized by: The parameters defined in step S1 include: Key parameters: Goods type N, through dictionary i values Indicates that there are four types of goods, i.e., i = 1, 2, 3, 4, where Goods 1 is wood, Goods 2 is steel plate, Goods 3 is steel coil, and Goods 4 is coil; The number of yards M, using list j values express; Berth information, through the dictionary k values It means that it includes two berths, that is, k = 1, 2; Cargo characteristics: Annual throughput w i : Indicates the handling volume of each type of cargo in a year; Storage cycle T i : Indicates the average storage time of each type of goods in the yard; Yard capacity p j : Define the maximum storage capacity of each yard; Distance d kj : Define the distance from each berth to each yard, which is used to calculate the transportation cost; Weight i : Define the weight of a pallet fully loaded with various goods, which is used to calculate the objective function; Yard characteristics: Yard capacity p j : Define the tonnage of fully loaded cargo in each yard, which is used to calculate the space utilization rate of the yard; Area j : Define the area of ​​each yard, which is used to calculate the tonnage of goods that can be stored per unit area of ​​the yard and the utilization rate of the yard space; The usable area of ​​the yard j : Count the used area of ​​each yard, which can be used to calculate the space utilization rate; Total available area of ​​the yard R j : Used to calculate space utilization.

3. The multi-objective general cargo terminal storage method based on cargo type and berth allocation according to claim 1 is characterized by: The average turnover of each type of goods in step S2 is calculated as follows:

4. The multi-objective general cargo terminal storage method based on cargo type and berth allocation according to claim 2 is characterized by: In step S3, an optimization model is first created. The variables defined in the optimization model include: Decision variable z[i,j]: represents the storage quantity of the i-th type of goods in the j-th yard, and is a continuous variable; Decision variable x[i,j]: a binary variable indicating whether the i-th type of goods is stored in the j-th yard; Slack variable slack[i]: It is used to calculate the difference between the total cargo storage volume and its average turnover volume. In the final optimal solution, the value of the slack variable is required to be zero, that is, the actual cargo volume is strictly equal to the average turnover volume.

5. The multi-objective general cargo terminal storage method based on cargo type and berth allocation according to claim 2 is characterized by: The constraints added in step S3 include: Total cargo quantity constraint: The total storage quantity of each cargo in the yard is equal to its average turnover; Yard capacity constraint: The storage volume of each yard cannot exceed its maximum capacity; Logical constraint: If z[i,j] is greater than 0, then x[i,j] is 1, ensuring that the yard is marked as in use only when there is cargo stored; Berth selection constraints: select suitable berths according to cargo characteristics and distance, and limit the storage volume of specific cargo to optimize transportation efficiency; Cargo separation constraint: Cargo separation constraint is implemented through logical constraints of binary variables in the optimization model by limiting the coexistence of wood, steel coils and coils in the same yard; Yard selection constraint: cargo with large average turnover volume is preferentially allocated to the yard closest to berth 1; According to the above parameter definitions and modeling ideas, the specific mathematical model is as follows: z ij ≤x ij ·G,G is a sufficiently large number (6) 6. The multi-objective general cargo terminal storage method based on cargo type and berth allocation according to claim 2 is characterized by: The objective function defined in step S3 is: For each type of cargo i and each yard j, the transportation distance from the berth to the yard is calculated based on the average turnover of the cargo and its storage volume in each yard, multiplied by the distance from the berth to the yard, and the berth selection constraint is added to it; When calculating the transport distance, for each cargo i, if its average turnover volume Qi is greater than 32,000 or it is wood, that is, i=1, berth 1 is used for unloading first; otherwise, berth 2 is used for unloading.

7. The multi-objective general cargo terminal storage method based on cargo type and berth allocation according to claim 1 is characterized by: Step S4 also includes calculating the space utilization rate of each yard and the overall yard, and analyzing the percentage of the actual yard area used to the total yard area. The specific mathematical formula is as follows:

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