Multi-objective storage method for mixed cargo terminal based on cargo type and berth allocation

Abstract

The present application relates to a kind of based on the multi-objective piece bulk cargo terminal stacking method of cargo class and berth allocation, belong to stacking technical field, comprising the following steps: S1: parameter is defined and initialized;S2: the average turnover of each cargo is calculated by the annual throughput of cargo and stacking cycle;S3: with the overall shortest distance of various goods from berth to yard as optimization target, add constraint condition, define objective function;S4: the objective function is solved using multi-objective linear programming, and the berth of unloading of each cargo, the number and quantity of storage yard number, total storage capacity and the difference with average turnover are output.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of stacking, and relates to a multi-objective bulk cargo terminal stacking method based on cargo type and berth allocation. BACKGROUND

[0002] Port logistics management is a core component of modern logistics systems. As global trade continues to expand, ports are increasingly demanding efficiency and resource utilization. In this context, bulk cargo terminals, as a traditional but important port facility, not only have broad growth potential in market demand, but also exhibit significant cost advantage, becoming an indispensable link in the port logistics system. With the increasing annual throughput of port cargo, the stacking demand of bulk cargo terminals has increased dramatically, leading to increasingly tight stacking space, and existing stacking strategies have been difficult to meet the efficient demand of modern port operations.

[0003] In recent years, research on stacking strategies has mainly focused on the container field, and research on bulk cargo terminal stacking strategies, berth allocation, and transportation path optimization is gradually increasing. The research field focuses on reducing cargo transfer time in the yard, improving yard space utilization, and overall operational efficiency. However, current technologies still have limitations when dealing with multiple cargo types and multiple berth scenarios.

[0004] Current research on bulk cargo terminal stacking strategies mainly focuses on the following two aspects:

[0005] (1) Based on the existing conditions of the yard, data investigation and analysis are carried out from the perspective of cargo turnover, with the goal of minimizing the transportation distance from the berth to the yard. The yard cargo is redistributed, and the set cargo type is single, usually focusing on the characteristics of a single cargo. Through berth allocation optimization, the space utilization in the yard is improved, and the algorithm optimization is solved. However, these studies are mostly limited to a single berth, ignoring the importance of multiple berths in stacking strategies.

[0006] (2) Berth-related research focuses on optimizing ship plans to shorten ship time in port and berth allocation optimization, with limited consideration of yard factors. The complex interrelationships between cargo turnover cycle, berth distance, cargo characteristics, and yard capacity are not fully considered, making it difficult to fully realize the optimization effect.

[0007] Under the conditions of multiple cargo types and berth allocation, existing stacking strategies are difficult to achieve optimal allocation of overall port resources. SUMMARY

[0008] Therefore, the purpose of the present application is to provide a multi-objective bulk cargo terminal stacking method based on cargo type and berth allocation.

[0009] To achieve the above object, the present application provides the following technical solutions:

[0010] A multi-objective piece bulk cargo terminal stacking method based on cargo type and berth allocation, comprising the following steps:

[0011] S1: define and initialize parameters;

[0012] S2: calculate the average turnover of each cargo by the annual throughput of the cargo and the stacking period;

[0013] S3: add constraints and define the objective function with the shortest overall distance of various cargoes from the berth to the stacking yard as the optimization target;

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

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

[0016] Key parameters:

[0017] Cargo type N, represented by dictionary i values , including four cargoes, i.e. i=1, 2, 3, 4, wherein cargo 1 is wood, cargo 2 is steel plate, cargo 3 is steel coil, and cargo 4 is coil plate;

[0018] Stack yard number M, represented by list j values ;

[0019] Berth information, represented by dictionary k values , including two berths, i.e. k=1, 2;

[0020] Cargo characteristics:

[0021] Annual throughput w i : represents the processing amount of each cargo in a year;

[0022] Stacking period T i : represents the average time of each cargo in the stacking yard;

[0023] Stack yard capacity p j : defines the maximum storage capacity of each stacking yard;

[0024] Distance d kj : defines the distance from each berth to each stacking yard, used for calculating transportation cost;

[0025] Weight q i : defines the weight of a full pallet of various cargoes, used for calculating the objective function;

[0026] Stack yard characteristics:

[0027] Stack capacity p j : defines the tonnage of each stack fully loaded with goods, used to calculate the space utilization of the stack;

[0028] Area a j : defines the area of each stack, used to calculate the tonnage of goods per unit area that can be stored in the stack and the space utilization of the stack;

[0029] Used area of the stack r j : statistics the used area of each stack, used to calculate the space utilization;

[0030] Total available area of the stack R j : used to calculate the space utilization.

[0031] Further, the average turnover quantity of each kind 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 kind of goods in the j-th stack, and is a continuous variable;

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

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

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

[0038] Total quantity of goods constraint: the total storage quantity of each kind of goods in the stack is equal to its average turnover quantity;

[0039] Stack capacity constraint: the storage quantity of each stack cannot exceed its maximum capacity;

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

[0041] Berth selection constraint: according to the characteristics of the goods and the distance, select the appropriate berth, limit the storage of specific goods to optimize the transportation efficiency; and berth 1 has a significant distance advantage, transportation efficiency advantage and yard utilization rate advantage compared to berth 2; by preferentially selecting berth 1 to unload and store high-turnover goods nearby, transportation costs and time can be significantly reduced, and space utilization and operational safety of the yard can be improved;

[0042] Goods separation constraint: the goods separation constraint effectively reduces the safety risk of yard operation by limiting the coexistence of wood, steel coils and coiled plates in the same yard. This constraint is realized in the optimization model through the logical restriction of binary variables, ensuring the reasonable allocation of dangerous goods;

[0043] Yard selection constraint: goods with a larger average turnover (such as goods 2 and goods 4, with average turnover of 56856 tons and 33860 tons respectively) usually have higher storage needs and frequent transportation needs; by preferentially allocating these goods to yards closer to berth 1, transportation distance can be significantly reduced, thereby reducing transportation costs and time;

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

[0045]

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

[0047]

[0048]

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

[0050]

[0051] For each kind of goods i and each yard j, according to the average turnover of the goods and its storage in each yard, multiply the distance from the berth to the yard, calculate the transportation distance from the berth to the yard, and add the berth selection constraint; when calculating the transportation distance, for each kind of goods i, if its average turnover Qi is greater than 32000 or is wood, i.e. i = 1, then preferentially use berth 1 to unload; otherwise, use berth 2 to unload. In the present application, the actual distribution range of the average turnover of goods Qi is 30066 tons to 33860 tons, and based on the comprehensive consideration of numerical approximation, model robustness, operational feasibility and calculation efficiency, it is set to 32000.

[0052] Further, in step S4, the space utilization of each yard and the overall space utilization are calculated, and the percentage of the actually used yard area to the total yard area is analyzed, and the specific mathematical formula is as follows:

[0053]

[0054] The beneficial effects of the present application are that the present application optimizes the distance of the overall cargo from the berth to the yard and the number of times of transportation of the trailer, the transportation mileage is reduced by more than 35% compared to other strategies, the average number of times of transportation of the trailer is reduced, the required time of the cargo in the transportation process is reduced, the transportation cost is reduced, the overall transportation efficiency of the terminal is improved, the logistics time is shortened, the overall space utilization of the terminal yard is increased to 8.7%, the amount of cargo accommodated in the yard space is expanded, and the storage cost per unit of cargo is also reduced accordingly.

[0055] Other advantages, objects, and features of the present application will be in part apparent and in part pointed out hereinafter in the specification, and will be observed by variations now known or hereafter devised, according to the principles of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the specification. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be combined with the drawings, in which:

[0057] Fig. 1 Flow chart of the multi-objective piece bulk cargo terminal stacking method based on cargo type and berth allocation;

[0058] Fig. 2 Flow chart of the piece bulk cargo terminal yard layout;

[0059] Fig. 3 Schematic diagram of the piece bulk cargo terminal yard layout;

[0060] Fig. 4 Schematic diagram of the yard layout under the cargo type stacking strategy;

[0061] Fig. 5 Schematic diagram of the yard layout under the berth allocation stacking strategy;

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

[0063] The present application is described in greater detail by way of specific embodiments as follows. Other advantages and benefits of the present application will become apparent to those skilled in the art upon consideration of the disclosure herein. The present application can be implemented or applied in other different embodiments and various modifications and changes can be made to the details of the application without departing from the spirit of the application. It should be noted that the drawings provided in the following embodiments only schematically illustrate the basic concepts of the present application, and the drawings only show the components related to the present application, rather than the number, shape and size of the components in actual implementation. The shapes, numbers and proportions of the components in actual implementation can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0064] It should be noted that the drawings provided in the following embodiments only schematically illustrate the basic concepts of the present application, and the drawings only show the components related to the present application, rather than the number, shape and size of the components in actual implementation. The shapes, numbers and proportions of the components in actual implementation can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0065] In the following description, a large number of details are discussed to provide a more thorough explanation of embodiments of the application, however, it will be apparent to those skilled in the art that the embodiments of the application can be implemented without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the application.

[0066] As shown in Figs. 1-3 , the present application optimizes the layout of the container yard of a piece cargo terminal through a multi-objective stacking 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 and increase the overall space utilization, while ensuring that the storage of goods in the yard meets certain constraints. The following is a detailed explanation of the process:

[0067] Step 1: Initialization and parameter definition

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

[0069] Cargo type (N): There are four types of cargo (wood, steel plate, steel coil, and coil plate) in this method, represented by the dictionary i values , i.e. 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 plate.

[0070] Number of yards (M): There are 13 yards, represented by the list j values .

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

[0072] Step 2: Define cargo and yard characteristics

[0073] Annual throughput (w i ): represents the handling volume of each cargo in a year (unit: ton).

[0074] Storage period (T i ): represents the average time of each cargo in the yard (unit: day).

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

[0076] Distance (d kj ): defines the distance from each berth to each yard, which is convenient for subsequent calculation of transportation cost (unit: meter).

[0077] Weight (q i ): defines the weight of various cargos on a pallet, which is used for subsequent calculation of the objective function (unit: ton).

[0078] Yard capacity (p j ): defines the tonnage of each yard full of cargo, which is used for subsequent calculation of the space utilization rate of the yard (unit: ton).

[0079] Area (a j ): defines the area of each yard, which is used for subsequent calculation of the tonnage of storable cargo per unit area and the space utilization rate of the yard (unit: square meter).

[0080] Used area of the yard r j : statistics of the used area of each yard, which is used for calculation of the space utilization rate.

[0081] Total available area of the yard R j : used for calculation of the space utilization rate.

[0082] Step 3: Calculate the average turnover

[0083] Calculate the average turnover (Q i ) of each cargo through the annual throughput and storage period of the cargo, as the storage time of the cargo in the bulk cargo terminal is longer, therefore it is very important to calculate the average turnover of the cargo, which reflects the flow frequency and warehouse turnover efficiency of the cargo in a year. 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 cargo in the j-th yard, which is a continuous variable.

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

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

[0091] Step 5: Add constraints

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

[0093] (1) Total cargo constraint: ensures that the total storage of each cargo in the yard is equal to its average turnover.

[0094] (2) Yard capacity constraint: the storage capacity of each yard cannot exceed its maximum capacity, ensuring that the use of the yard does not exceed the load.

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

[0096] (4) Berth selection constraint: according to the characteristics of the cargo and the distance, the appropriate berth is selected, limiting the storage of certain cargo to optimize transportation efficiency. And berth 1 has significant distance advantage, transportation efficiency advantage and yard utilization advantage compared to berth 2. By preferentially selecting berth 1 for unloading and storing high turnover cargo nearby, transportation cost and time can be significantly reduced, and space utilization and operation safety of the yard can be improved.

[0097] (5) Cargo separation constraint: in order to avoid the storage of certain cargoes (such as wood, steel coils and coils) in the same yard in large quantities, which may cause safety problems, the constraint is added to ensure that they do not occupy too much space and ensure the safety of personnel and cargo. The cargo separation constraint effectively reduces the safety risk of yard operation by limiting the coexistence of wood, steel coils and coils in the same yard. This constraint is realized in the optimization model through the logical restriction of binary variables, ensuring the reasonable distribution of dangerous goods.

[0098] (6) Yard selection constraint: When the average turnover of a cargo is large, a yard closer to the berth will be chosen for storage. Cargos with large average turnover (such as cargo 2 and cargo 4, with average turnovers of 56856 tons and 33860 tons, respectively) usually have high storage demand and frequent transportation demand. Prioritizing the allocation of these cargos to yards closer to berth 1 can significantly reduce transportation distance, thereby reducing transportation cost and time;

[0099] The specific mathematical model according to the above parameter definition and modeling idea 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 cargo i and each yard j, the average turnover of the cargo and its storage capacity in each yard are multiplied by the distance from the berth to the yard to calculate the transportation distance from the berth to the yard, and the berth selection constraint is added. When calculating the transportation distance, for each cargo i, if its average turnover Qi is greater than 32000 or it is wood, i.e. i = 1, then berth 1 is preferred for unloading; otherwise, berth 2 is used for unloading. In this invention, the actual distribution range of cargo average turnover Qi is 30066 tons to 33860 tons, and based on the comprehensive consideration of numerical approximation, model robustness, operation feasibility and calculation efficiency, it is set to 32000. 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, output the result; if there is no solution, output the infeasible subsystem information for debugging.

[0108] Step 8: Output the results

[0109] If the model has a solution, output the unloading berth of each cargo, the yard number and quantity of storage, the total storage capacity, and the difference from the average turnover. Calculate the space utilization rate of each yard and the total. Through the yard utilization rate, analyze the percentage of the actual used yard area to the total yard area. This helps to understand the execution effect of the model and provides 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 cause the infeasibility and generate an infeasibility subsystem file for debugging. Ensure that the storage of each kind of goods meets various constraints, and minimize transportation costs through optimization to improve logistics efficiency.

[0112] The present application adopts three different stacking strategies, i.e., a stacking strategy based on cargo types, a stacking strategy based on berth allocation, and a multi-objective stacking strategy based on cargo types and berth allocation (hereinafter referred to as strategy 1, strategy 2, and strategy 3).

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

[0114] Table 2 presents the comparison of the tonnage of goods stacked in each yard and the utilization rate of the yard under the three strategies. It is found that in the case of multiple berths, the distance of goods transportation from the berth to the yard is effectively shortened by more than 35%, the frequency of trailer transportation required for transportation is effectively reduced to 277 times, manpower and material resources are saved, and the overall space utilization rate of the yard is effectively improved 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 is empty yard, which provides buffer space for the port to deal with emergency situations, and reserves potential optimization space for future expansion of the port's throughput capacity.

[0115] Table 1

[0116]

[0117] Table 2

[0118]

[0119] In the above embodiments, references to “the present embodiment” in the specification indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments, of the application. Multiple occurrences of “the present embodiment” do not necessarily all refer to the same embodiment.

[0120] In the above embodiments, although the application has been described in conjunction with specific embodiments thereof, numerous alternatives, modifications, and variations will be readily apparent to those of ordinary skill in the art in light of the foregoing descriptions. For example, other storage structures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed. The embodiments of this application are intended to cover all such alternatives, modifications, and variations as come within the scope of the broadest possible interpretation of the appended claims.

[0121] The embodiments also provide a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements any of the methods in the embodiments.

[0122] The embodiments also provide an electronic terminal, comprising: a processor and a memory.

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

[0124] Those skilled in the art can understand that the computer readable storage medium in the embodiments can realize all or part of the steps of the above-mentioned method embodiments by using the hardware of the computer program. The foregoing computer program can be stored in a computer readable storage medium. The program, when executed, executes the steps of the above-mentioned method embodiments; and the foregoing storage medium includes ROM, RAM, magnetic disk or optical disk and various media that can store program codes.

[0125] The electronic terminal provided by the embodiments includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected with the processor and the transceiver and complete communication between each other. The memory is configured to store a computer program, the communication interface is configured to communicate, and the processor and the transceiver are configured to run the computer program, so that the electronic terminal executes each step of the above method.

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

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

[0128] The present application can be applied in numerous general or special computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, etc.

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

[0130] Finally, it is to be noted that the above embodiments are merely used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A multi-objective general cargo terminal stockpiling method based on cargo type and berth allocation, characterized by: Including the following step: S1: Define and initialize parameters; the defined parameters 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 coiled plate; The number of yards M, using list j values express; Berth information, through the dictionary k values Indicates that it includes two berths, i.e. k=1,2; Cargo characteristics: Annual throughput w i : Indicates the handling volume of each type of cargo in one year; Storage cycle T i : Indicates the average storage time of each type of cargo 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 for calculating transportation costs; Weight q i : Define the weight of a pallet fully loaded with various goods for calculating 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 a 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 yard space utilization rate; The usable area of ​​the yard r j : Statistics on the used area of ​​each yard can be used to calculate space utilization; Total available area of ​​the yard R j : Used to calculate space utilization; S2: Calculate the average turnover of each type of cargo based on the annual cargo throughput and storage period; S3: Taking the overall shortest distance from the berth to the yard for various cargoes as the optimization goal, add constraints and define the objective function. In step S3, first create an optimization model. 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]: 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; The constraints added are: Total cargo volume constraint: The total storage volume of each cargo in the yard is equal to its average turnover volume; 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 based on cargo characteristics and distance, and limit the storage volume of specific cargo to optimize transportation efficiency; Cargo separation constraint: Cargo separation constraint is implemented by limiting the coexistence of wood, steel coils and coils in the same yard through logical constraints of binary variables in the optimization model; 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: The objective function is defined as: For each cargo i and each yard j, the transport distance from the berth to the yard is calculated by multiplying the cargo's average turnover and its storage capacity at each yard by the distance from the berth to the yard, and the berth selection constraint is incorporated into the calculation. When calculating the transport distance, for each cargo i, if its average turnover Qi is greater than 32,000 or it is timber, that is, i=1, berth 1 is preferentially used for unloading; otherwise, berth 2 is used for unloading. 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 bulk 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: 。 3. The multi-objective bulk 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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