Fishery wharf berth and refrigeration house scale determination method and system considering fishing flood change

Abstract

The invention provides a fishery wharf berth and refrigeration house scale determination method and system considering fishing flood changes, and belongs to the technical field of fishery resource development and fishery facility planning. Firstly, fishing ground position distribution and expected fish catch are obtained by analyzing fishing flood information; secondly, establishing a fishery fishing fleet configuration and scheduling model, solving the model to output an optimal fleet configuration scheme and ship arrival time data, and performing data statistics to form a fleet configuration data set and a ship arrival information data set; and thirdly, based on the fleet configuration data set, establishing a fishery wharf berth and warehouse scale simulation optimization model, solving the model, and outputting an optimal berth scale and warehouse scale configuration scheme. The system mainly comprises a processor, an input device, an output device and a memory. According to the invention, port constructors can be helped to determine the fishing port berth scale and the fishing port reservoir scale in planning and construction stages according to the local fish flood state, so that the cost of port parties and ship parties is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of fishery resource development and fishery facility planning, and relates to a method for determining the number of berths at a fishing terminal and the scale of cold storage by comprehensively considering information such as the time of the fishing season, changes in production, and the number, type, and fishing capacity of the fishing fleet. In particular, the present invention relates to a method for determining the scale of berths at a fishing terminal and cold storage by considering the fishing season and the configuration of the fishing fleet. Background Art

[0002] With rising living standards and the growth of global trade, demand for low-temperature frozen and fresh food is increasing. This has driven the continuous improvement and development of cold chain systems. Ports, as a crucial component of this system, are also undergoing continuous optimization and upgrading of their cold chain infrastructure. Against this backdrop, this paper optimizes berths and storage yards within port cold chain systems, taking into account fishing seasons and fleet configuration. Regarding fleet configuration, most research focuses on traditional transportation models, focusing on single-vessel operations, with limited research on long-term fleet planning. Regarding berth optimization, domestic and international scholars have proposed various berth planning and optimization methods. Port berth utilization, operational efficiency, and port and vessel costs are widely considered. For traditional cold chain systems, existing research has extensively examined route selection, transportation efficiency, storage location, and environmental protection. Optimization has been conducted on cold chain transportation networks and node facilities, with the goal of minimizing costs in determining cold storage size. The scale of storage facilities is also directly related to cold chain equipment utilization and service quality. Existing research has mostly used inventory control theory to determine storage yard size. Research on fleet configuration, cold chain, berth and warehouse size is now relatively mature, but there is less research on how the arrival pattern of fishing vessels affects the scale construction of fishing ports and the configuration decision of loading and unloading equipment. Summary of the Invention

[0003] To address the aforementioned issues with existing technologies, the present invention provides a method and system for determining the scale of fishing port berths and cold storage facilities, taking into account changes in fishing seasons. To promote the efficient utilization of fishery resources, the allocation of fishing fleets is studied, taking into account the seasonal and regional distribution of catches. Based on arrival information and catch data from fishing vessels, and with the goal of minimizing costs for both port and vessel owners, a theoretical method for optimizing the scale of cold chain berths and storage facilities at ports is proposed. This method then explores the scale of fishing port berth construction and cold storage facilities.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] On the one hand, the method for determining the scale of berths and cold storage at the fishing terminal is as follows: first, the location distribution of fishing grounds and the expected catch are obtained by analyzing the fishing season information; second, based on the fishing season information obtained in the first step, a fishing fleet configuration and scheduling model is established, and constraints such as fleet size, navigation route and operation sequence are clarified. Third, an adaptive search algorithm (ALNS) is used to solve the fishing fleet configuration and scheduling model established in the second step, and the optimal fleet configuration plan and ship arrival time data are output. Fourth, data statistics are performed on the fleet scheduling plan obtained in the third step, and the fleet ship type, ship arrival time, and actual catch information are summarized to form a fleet configuration data set and a ship arrival information data set. Fifth, based on the fleet configuration data set, a simulation optimization model for the scale of berths and storage yards at the fishing terminal is established. Sixth, simulation technology is used to solve the simulation optimization model for the scale of berths and storage yards at the fishing terminal, and the optimal berth scale and storage yard scale configuration plan is output. The specific steps are as follows:

[0006] Step 1: Analyze fishing information and collect vessel parameters

[0007] Based on historical fishing season information, the distribution of fishing grounds and expected catches are predicted. In addition to fishing season information, other required data include the vessel model, maximum cargo capacity, and maximum range. Fishing operations begin at a central port and begin fishing in several fishing grounds. After completing each fishing operation, the vessel returns to the central port.

[0008] Furthermore, the fishing season information includes the coordinates of the fish school, the time when the fish school appears, and the size of the fish school.

[0009] Step 2: Establish a fishing fleet configuration and scheduling model based on fishing season information;

[0010] Based on the fishing season parameters extracted in step 1, the model input variables and constraints are constructed, and the fleet net profit maximization is used as the objective function to establish a fishing fleet configuration and scheduling model. The fleet profit is the income from the catch. , the cost mainly includes the activation cost Fuel costs , fishing costs , refrigeration costs , the objective function is as follows:

[0011] (1)

[0012] Step 2.1, ship income;

[0013] The fleet's income mainly comes from the output of fish caught by the fleet. The overall income of the fleet is To express it, the expression is as follows:

[0014] (2)

[0015] in Represents the column vector of fishery value, m is the total number of fisheries, index Indicates that the fishing network node includes fishing port and fishing ground nodes, where 0 represents the fishing port node. represents the fishing ground node, is the total number of fisheries, represents the total number of fishing network nodes, so represents the unit catch value of fishery i. for Dimensional vessel-fishing ground catch matrix, is the total number of ships, index Represents the unique ship number, such as the matrix Elements in It represents the catch of the vessel numbered p in the fishing ground i, and it is a non-negative continuous variable, that is, Secondly, introduce , which is a P×1-dimensional all-one vector. Its function is to horizontally accumulate the catch in the ship dimension to obtain the total catch tonnage of each fishing ground.

[0016] Step 2.2, Fleet Activation Cost ;

[0017] The fleet activation cost includes equipment preparation costs, ship berthing costs, and crew wages. Its expression is:

[0018] (3)

[0019] in, A vector of activation cost coefficients representing the ship. , indicating whether the ship is enabled. If the element in the vector is 0, it means it is not enabled, and 1 means it is enabled. For example: Indicates that the ship p is not enabled. Indicates that the ship p is enabled.

[0020] Step 2.3, fuel costs ;

[0021] Ship fuel cost is one of the most important expenses in ship operating costs, and its expression is:

[0022] (4)

[0023] Fishing nodes on the network All feasible paths are numbered in order. but in , represents the distance from node i to node j in the fishing network. , which is used to mark the path of each ship, such as the matrix Elements in When , it means that the ship numbered p chooses path m, otherwise the value is 0. Represents the unit distance sailing cost of each ship.

[0024] Step 2.4, fishing costs ;

[0025] (5)

[0026] in, is the expected catch vector of the fishing ground (tons), m is the total number of fishing grounds, index Corresponding to the specific fishery number and, e.g. represents the expected catch of fishery i. Representing the fishery-vessel deployment matrix, for example, the matrix Elements in When , it means that the ship numbered p is dispatched to the fishing ground i for operation; otherwise, the value is 0. represents the unit fishing cost of the vessel, e.g. represents the unit weight fishing cost of vessel p.

[0027] Step 2.5, Refrigeration Cost ;

[0028] The costs associated with refrigerating or freezing fish after they are caught are It means that its expression is:

[0029] (6)

[0030] in, represents the cooling cost of the ship p. for dimensional transport time matrix, e.g. represents the travel time of ship p on path m. , which is used to mark the travel path of each ship. for Dimensional job time matrix, e.g. represents the operation time of vessel p at node i in the fishing network. represents the fishing ground-vessel deployment matrix. Introducing the all-one vector , its dimensions have been automatically matched according to the requirements of matrix operations to ensure that all related matrix operations can be carried out smoothly under the premise of complying with mathematical rules.

[0031] Step 2.6, set constraints for the model:

[0032] The constraints mainly include three aspects: flow balance constraints, resource allocation constraints, maximum capacity constraints and maximum range constraints.

[0033] Step 2.6.1, flow balance constraint:

[0034] (7)

[0035] This constraint states that the number of ships flowing into each fishing node is equal to the number of ships flowing out of the fishing node. ,matrix Elements in When , it means the starting point of path m is i. When , the end point of path m is i. In other cases . , which is used to mark the travel path of each ship. ,when When represents the fishing port node, hour, , indicating that it must start from the fishing ground node and return, hour, It means that the number of times each ship enters each fishing ground node is equal to the number of times it leaves each fishing ground node.

[0036] Step 2.6.2, resource allocation constraints:

[0037] (8)

[0038] This constraint means that there is only one ship assigned to each fishing ground node. Table represents the fishing ground-vessel deployment matrix. and is a vector of all 1s.

[0039] Step 2.6.3, Maximum Capacity Constraint:

[0040] (9)

[0041] This constraint states that the total catch of each vessel must be less than the vessel's hold capacity. , represents the fishing ground-vessel deployment matrix. , is the expected catch vector of the fishery (tons). ]

[0042] Step 2.6.4, Maximum range constraint:

[0043] (10)

[0044] This constraint means that the sailing distance of each ship is less than the ship's endurance. , which is used to mark the travel path of each ship. , ], which represents the maximum sailing distance of ship p.

[0045] Step three, use the adaptive large neighborhood search algorithm (ALNS) to solve the fishing fleet configuration and scheduling model established in step two.

[0046] The basic idea of ​​the adaptive large neighborhood algorithm is to iteratively destroy and repair the current solution to search for a better solution by designing a set of node deletion and insertion operations for a specific problem. This algorithm designs two destruction operators: , three repair operators The specific steps of the algorithm are as follows:

[0047] Step 3.1: Construct a preliminary feasible solution, generate an initial solution that satisfies the basic constraints by random sampling as the starting point of the iteration, and set the initial solution as the current solution and optimal solution , included in the feasible solution set .

[0048] Step 3.2, initialize parameters and set the maximum number of iterations , the current number of iterations is 1. Initialize the probability of the weights of the destruction and repair operators (all operators have equal weights), the scores of the destruction and repair operators (all scores are equal), set the temperature of the simulated annealing process , lower limit of temperature .

[0049] Step 3.3, termination condition judgment, if the current number of iterations , then terminate the process and output the optimal solution; otherwise, go to step 3.4.

[0050] Step 3.4: Update the operator weights. If the number of iterations is a multiple of 100, update the weights of the destruction operator and the repair operator and proceed to step 3.5. Otherwise, keep the weights unchanged and proceed directly to step 3.5.

[0051] The calculation method of the further update operator is:

[0052] (11)

[0053] in is the weight of the kth iteration of the nth destruction operator, represents the destruction operator, where . is the weight update coefficient, Destruction operator The cumulative score of Destruction operator The cumulative number of times used.

[0054] (12)

[0055] in is the weight of the k-th iteration of the n-th repair operator, represents the repair operator, where . is the weight update coefficient, Destruction operator The cumulative score of Destruction operator The cumulative number of times used.

[0056] Step 3.5, select a destruction operator and a repair operator through the roulette strategy, and Perform deletion (delete some fishing nodes) to generate partial solutions, and then generate candidate solutions through repair (restore some fishing nodes) operations , proceed to step 3.6.

[0057] Step 3.6, determine the new solution Is it better than the optimal solution? If yes, go to step 3.7, otherwise, use the simulated annealing criterion to determine whether to accept the candidate solution. If accepted, proceed to step 3.8, otherwise proceed to step 3.9.

[0058] Furthermore, simulated annealing accepts the probability Choose whether to accept,

[0059] Step 3.7, Check Is it already in the feasible solution set? ,like There is no feasible solution set , then the high-weight score of the destruction operator and the repair operator that generated this solution is accumulated , record the number of times the operator is used, and mark the candidate solution as the optimal solution And reset the current solution to the candidate solution , go to step 3.10. If Already exists in the feasible solution set , then the low-weight score of the destruction operator and the repair operator that generated this solution is accumulated , and record the number of times the operator is used, and go to step 3.10.

[0060] Step 3.8, Check Is it already in the feasible solution set? If it does not exist, add it to the feasible solution set and reset the current solution to the candidate solution , which accumulates a medium-weight score for the destruction operator and the repair operator that generated this solution , and record the number of times the operator is used, and go to step 3.10. is already present in the feasible solution set , then the low-weight score of the destruction operator and the repair operator that generated this solution is accumulated , and record the number of times the operator is used, and go to step 3.10.

[0061] Step 3.9: Accumulate low-weight scores for the destruction operator and repair operator that generate candidate solutions , and record the number of times the operator is used, and go to step 3.10.

[0062] Further .

[0063] Step 3.10, update the simulated annealing algorithm temperature , update the number of iterations , return to step 3.3.

[0064] After the solution is completed, the final output results include the optimal fishing fleet type, the optimal fishing route, the time when the fishing boat arrives at the fishing port, and the catch.

[0065] Step 4: Based on the results of step 3, ship arrival information and catch information are collected to form a fleet configuration dataset and a ship arrival dataset. The specific data are as follows:

[0066] Furthermore, the fishing fleet configuration dataset includes the ship type, ship tonnage, and maximum ship cargo capacity of each ship in the optimal fishing fleet calculated in step 3. The ship arrival information includes the ship arrival sequence, the amount of fish caught by the ship upon arrival, the type of fish caught, and the corresponding ship model information calculated in step 3.

[0067] Step 5: Based on the fleet configuration dataset and ship arrival dataset from step 4, a simulation optimization model for the berth and storage yard size of the fishing terminal is established.

[0068] In step 5.1, a simulation model is built to simulate the loading and unloading of fishing vessels at the fishing port. This model dynamically simulates the loading and unloading process. The fleet configuration and vessel arrival datasets generated in step 4 are input into the simulation model. Initial berth and storage capacity and various cost parameters are set.

[0069] Further costs include fixed costs , loading and unloading and transportation costs (port operating costs) 2. Operating costs of ships waiting in port , cold storage rental costs , cargo damage cost and penalty costs , as follows:

[0070] Step 5.1.1, Fixed costs. This mainly includes machinery depreciation costs and terminal construction costs. It is related to the number of berths at the terminal and can be expressed as:

[0071] (13)

[0072] in, Indicates the berth type number, Indicates the total number of berth types. represents fixed costs, represents the number of z-berths, The average annual construction cost of a Z-berth is Average annual depreciation cost of machinery on one z-berth.

[0073] Step 5.1.2, Loading, unloading and transportation costs (port operating costs). Different berth types are equipped with different amounts of machinery and transportation equipment, and the loading, unloading and transportation costs are also different. The expression is:

[0074] (14)

[0075] in, Indicates the berth type number of the ship, Indicates the total number of berth types, Indicates the number of z-berths, represents the daily operating cost of the Z-berth, and N represents the total operating time of the port in a year.

[0076] Step 5.1.3, operating costs of ships waiting in port The expression of the operating cost of a ship waiting in port is:

[0077] (15)

[0078] in, Indicates the type of ship, Indicates the total number of ship types, Indicates the ship q number, Represents the total number of ship type q.

[0079] For a ship of type q The departure time of For a ship of type q departure time; Indicates a ship of type q Operating costs in Hong Kong.

[0080] Step 5.1.4, cold storage rental cost. The freezer is considered in the form of direct rental. Different freezer areas need to be determined according to different fish species and their characteristics. Therefore, the cold storage rental cost expression is:

[0081] (16)

[0082] in, Indicates the cold storage type number, Indicates the total number of cold storage types. is the area of ​​the S-type cold storage. represents the rental cost of an S-type warehouse;

[0083] Step 5.1.5, cargo damage costs during transportation. This mainly refers to cargo damage caused by the loss of freshness due to exposure to normal temperature air during loading and unloading at the port, and cargo damage caused by temperature changes caused by opening the door of the refrigerated transport ship during unloading. The expression is:

[0084] (17)

[0085] in, Indicates the cold storage type number, represents the price per ton of type k fish; represents the loading and unloading time of type q ship; Indicates the spoilage rate of fresh products when the door is opened; represents the total catch of k type of fish; e is a natural constant.

[0086] Step 5.3.6, penalty cost. This mainly refers to the additional cost of shipping excess goods to a cold storage outside the port when the cold storage capacity is insufficient. The expression is:

[0087] (18)

[0088] in, is the penalty coefficient, is the cargo storage volume per unit area of ​​type j fish catch, For 0-1 variables:

[0089] (19)

[0090] is the penalty function, , is the total area of ​​the j-type fish warehouse (m 2), solved using JTS165-2013 "Seaport Overall Design Code" .

[0091] Step 5.2: Establish constraints to define the boundary conditions of the simulation model.

[0092] Furthermore, the constraint condition established is: the total berth length is greater than zero and less than the upper limit of the total planned length, and the expression is:

[0093] (20)

[0094] in, Indicates the berth type number of the ship, Indicates the total number of berth types, Indicates the number of z-berths, is the length of the Z-berth, is the total length of the berth.

[0095] Step 5.3: Establish a simulation optimization model to solve the target.

[0096] The goal is to minimize the port and ship costs, and each cost item includes fixed costs , loading and unloading and transportation costs (port operating costs) 2. Operating costs of ships waiting in port , cold storage rental costs , cargo damage cost and penalty costs ,Right now:

[0097] (twenty one)

[0098] Step 5.4: Run the simulation model. This model is run based on the ship arrival time series data. The simulation dynamically adjusts the berth and storage capacity through simulation. While satisfying the constraints, it outputs the optimal number of berths and cold storage capacity, minimizing terminal operating costs and optimizing resource utilization.

[0099] In another aspect, the present invention provides a system for determining the size of berths and cold storage facilities at a fishing terminal that takes into account changes in fishing seasons. This system primarily comprises a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected, wherein the memory is configured to store a computer program comprising program instructions, and the processor is configured to invoke the program instructions to execute the method for determining the size of berths and cold storage facilities at a fishing terminal that takes into account changes in fishing seasons described in the first aspect of the present invention.

[0100] Beneficial effects of the present invention:

[0101] This invention provides a method and system for determining the size of fishing port berths and cold storage facilities, taking into account changes in fishing seasons. By comprehensively analyzing changes in fishing seasons and the seasonal and regional distribution of fish catches, this method enables precise allocation of berth and cold storage facilities, effectively avoiding resource waste and supply shortages. This method helps port builders scientifically determine the appropriate size of fishing port berths and cold storage facilities during the planning and construction phases, based on actual local fishing season conditions. This reduces overall costs for both port and ship owners. Furthermore, port construction can better adapt to the changing needs of fishery production, increasing the service life of infrastructure and the return on investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 It is the basic flow chart of the present invention.

[0103] Figure 2 This is the flow chart of the adaptive search algorithm (ALNS) for solving the fishery fleet configuration and scheduling model.

[0104] Figure 3 It is a flow chart for solving the simulation optimization model of fishing port berths and storage yard scale.

[0105] Figure 4 It is the distribution of fish catch arriving at the port and vessels over time within a year.

[0106] Figure 5 It is the proportion of various costs of the fishing port.

[0107] Figure 6 This is a system structure diagram for determining the scale of fishing terminal berths and cold storage taking into account changes in fishing seasons. DETAILED DESCRIPTION

[0108] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0109] This embodiment is based on the fishing ground location distribution and expected catch data predicted by a shipping company based on the fishing conditions over the years, and selects two predicted fishing ground distribution scenario data as the research basis.

[0110] Step 1: Analyze fishing information and collect vessel parameters

[0111] Based on historical fishing season information, the distribution of fishing grounds and expected catches are predicted. In addition to fishing season information, other required data include the vessel model, maximum cargo capacity, and maximum range. Fishing operations begin at a central port and begin fishing in several fishing grounds. After completing each fishing operation, the vessel returns to the central port.

[0112] Further information on the catch volume of ships arriving at the port in a year and the distribution of ships over time is as follows: Figure 4shown.

[0113] Step 2: Establish a fishing fleet configuration and scheduling model based on fishing season information;

[0114] Based on the fishing season parameters extracted in step 1, the model input variables and constraints are constructed, and the fleet net profit maximization is used as the objective function to establish a fishing fleet configuration and scheduling model. The fleet profit is the income from the catch. , the cost mainly includes the activation cost Fuel costs , fishing costs , refrigeration costs , the objective function is as follows:

[0115] (1)

[0116] In this example, four fleet configuration schemes are set up for comparative study. Configuration schemes one, two, and three are all composed of single-type ships of v3, v2, and v1 respectively. Configuration scheme four is composed of a combination of three ship types.

[0117] Step 2.1, ship income;

[0118] The fleet's income mainly comes from the output of fish caught by the fleet. The overall income of the fleet is To express it, the expression is as follows:

[0119] (2)

[0120] in Represents the column vector of fishery value, m is the total number of fisheries, index Indicates that the fishing network node includes fishing port and fishing ground nodes, where 0 represents the fishing port node. represents the fishing ground node, is the total number of fisheries, represents the total number of fishing network nodes, so represents the unit catch value of fishery i. for Dimensional vessel-fishing ground catch matrix, is the total number of ships, index Represents the unique ship number, such as the matrix Elements in It represents the catch of the vessel numbered p in the fishing ground i, and it is a non-negative continuous variable, that is, Secondly, introduce , which is a P×1-dimensional all-one vector. Its function is to horizontally accumulate the catch in the ship dimension to obtain the total catch tonnage of each fishing ground.

[0121] Step 2.2, Fleet Activation Cost ;

[0122] The fleet activation cost includes equipment preparation costs, ship berthing costs, and crew wages. Its expression is:

[0123] (3)

[0124] in, A vector of activation cost coefficients representing the ship. , indicating whether the ship is enabled. If the element in the vector is 0, it means it is not enabled, and 1 means it is enabled. For example: Indicates that the ship p is not enabled. Indicates that the ship p is enabled.

[0125] Step 2.3, fuel costs ;

[0126] Ship fuel cost is one of the most important expenses in ship operating costs, and its expression is:

[0127] (4)

[0128] Fishing nodes on the network All feasible paths are numbered in order. but in , represents the distance from node i to node j in the fishing network. , which is used to mark the path of each ship, such as the matrix Elements in When , it means that the ship numbered p chooses path m, otherwise the value is 0. Represents the unit distance sailing cost of each ship.

[0129] Step 2.4, fishing costs ;

[0130] (5)

[0131] in, is the expected catch vector of the fishing ground (tons), m is the total number of fishing grounds, index Corresponding to the specific fishery number and, e.g. represents the expected catch of fishery i. Representing the fishery-vessel deployment matrix, for example, the matrix Elements in When , it means that the ship numbered p is dispatched to the fishing ground i for operation; otherwise, the value is 0. represents the unit fishing cost of the vessel, e.g. represents the unit weight fishing cost of vessel p.

[0132] Step 2.5, Refrigeration Cost ;

[0133] The costs associated with refrigerating or freezing fish after they are caught are It means that its expression is:

[0134] (6)

[0135] in, represents the cooling cost of the ship p. for dimensional transport time matrix, e.g. represents the travel time of ship p on path m. , which is used to mark the travel path of each ship. for Dimensional job time matrix, e.g. represents the operation time of vessel p at node i in the fishing network. represents the fishing ground-vessel deployment matrix. Introducing the all-one vector , its dimensions have been automatically matched according to the requirements of matrix operations to ensure that all related matrix operations can be carried out smoothly under the premise of complying with mathematical rules.

[0136] Step 2.6, set constraints for the model:

[0137] The constraints mainly include three aspects: flow balance constraints, resource allocation constraints, maximum capacity constraints and maximum range constraints.

[0138] Step 2.6.1, flow balance constraint:

[0139] (7)

[0140] This constraint states that the number of ships flowing into each fishing node is equal to the number of ships flowing out of the fishing node. ,matrix Elements in When , it means the starting point of path m is i. When , it means the end point of path m is i. In other cases . , which is used to mark the travel path of each ship. ,when When represents the fishing port node, hour, , indicating that it must start from the fishing ground node and return, hour, It means that the number of times each ship enters each fishing ground node is equal to the number of times it leaves each fishing ground node.

[0141] Step 2.6.2, resource allocation constraints:

[0142] (8)

[0143] This constraint means that there is only one ship assigned to each fishing ground node. Table represents the fishing ground-vessel deployment matrix. and is a vector of all 1s.

[0144] Step 2.6.3, Maximum Capacity Constraint:

[0145] (9)

[0146] This constraint states that the total catch of each vessel must be less than the vessel's hold capacity. , represents the fishing ground-vessel deployment matrix. , is the expected catch vector of the fishery (tons). ]

[0147] Step 2.6.4, Maximum range constraint:

[0148] (10)

[0149] This constraint means that the sailing distance of each ship is less than the ship's endurance. , which is used to mark the travel path of each ship. , ], which represents the maximum sailing distance of ship p.

[0150] Step three, use the adaptive large neighborhood search algorithm (ALNS) to solve the fishing fleet configuration and scheduling model established in step two.

[0151] The basic idea of ​​the adaptive large neighborhood algorithm is to iteratively destroy and repair the current solution to search for a better solution by designing a set of node deletion and insertion operations for a specific problem. This algorithm designs two destruction operators: , three repair operators The specific steps of the algorithm are as follows:

[0152] Step 3.1: Construct a preliminary feasible solution, generate an initial solution that satisfies the basic constraints by random sampling as the starting point of the iteration, and set the initial solution as the current solution and optimal solution , included in the feasible solution set .

[0153] Step 3.2, initialize parameters and set the maximum number of iterations , the current number of iterations is 1. Initialize the probability of the weights of the destruction and repair operators (all operators have equal weights), the scores of the destruction and repair operators (all scores are equal), set the temperature of the simulated annealing process , lower limit of temperature .

[0154] Step 3.3, determine the termination condition. If the current number of iterations is {k>N}_{max}, terminate the process and output the optimal solution; otherwise, proceed to step 3.4.

[0155] Step 3.4: Update the operator weights. If the number of iterations is a multiple of 100, update the weights of the destruction operator and the repair operator and proceed to step 3.5. Otherwise, keep the weights unchanged and proceed directly to step 3.5.

[0156] The calculation method of the further update operator is:

[0157] (11)

[0158] in is the weight of the kth iteration of the nth destruction operator, represents the destruction operator, where . is the weight update coefficient, Destruction operator The cumulative score of Destruction operator The cumulative number of times used.

[0159] (12)

[0160] in is the weight of the k-th iteration of the n-th repair operator, represents the repair operator, where . is the weight update coefficient, Destruction operator The cumulative score of Destruction operator The cumulative number of times used.

[0161] Step 3.5, select a destruction operator and a repair operator through the roulette strategy, and Perform deletion (delete some fishing nodes) to generate partial solutions, and then generate candidate solutions through repair (restore some fishing nodes) operations , proceed to step 3.6.

[0162] Step 3.6, determine the new solution Is it better than the optimal solution? If yes, go to step 3.7, otherwise, use the simulated annealing criterion to determine whether to accept the candidate solution. If accepted, proceed to step 3.8, otherwise proceed to step 3.9.

[0163] Furthermore, simulated annealing accepts the probability Choose whether to accept,

[0164] Step 3.7, Check Is it already in the feasible solution set? ,like There is no feasible solution set , then the high-weight score of the destruction operator and the repair operator that generated this solution is accumulated , record the number of times the operator is used, and mark the candidate solution as the optimal solution And reset the current solution to the candidate solution , go to step 3.10. If Already exists in the feasible solution set , then the low-weight score of the destruction operator and the repair operator that generated this solution is accumulated , and record the number of times the operator is used, and go to step 3.10.

[0165] Step 3.8, Check Is it already in the feasible solution set? If it does not exist, add it to the feasible solution set and reset the current solution to the candidate solution , which accumulates a medium-weight score for the destruction operator and the repair operator that generated this solution , and record the number of times the operator is used, and go to step 3.10. is already present in the feasible solution set , then the low-weight score of the destruction operator and the repair operator that generated this solution is accumulated , and record the number of times the operator is used, and go to step 3.10.

[0166] Step 3.9: Accumulate low-weight scores for the destruction operator and repair operator that generate candidate solutions , and record the number of times the operator is used, and go to step 3.10.

[0167] Further .

[0168] Step 3.10, update the simulated annealing algorithm temperature , update the number of iterations , return to step 3.3.

[0169] After the solution is completed, the final output results include the optimal fishing fleet type, the optimal fishing route, the time when the fishing boat arrives at the fishing port, and the catch.

[0170] Step 4: Based on the results of step 3, ship arrival information and catch information are collected to form a fleet configuration dataset and a ship arrival dataset. The specific data are as follows:

[0171] Furthermore, the fishing fleet configuration dataset includes the ship type, ship tonnage, and maximum ship cargo capacity of each ship in the optimal fishing fleet calculated in step 3. The ship arrival information includes the ship arrival sequence, the amount of fish caught by the ship upon arrival, the type of fish caught, and the corresponding ship model information calculated in step 3.

[0172] Step 5: Based on the fleet configuration dataset and ship arrival dataset from step 4, a simulation optimization model for the berth and storage yard size of the fishing terminal is established.

[0173] In step 5.1, a simulation model is built to simulate the loading and unloading of fishing vessels at the fishing port. This model dynamically simulates the loading and unloading process. The fleet configuration and vessel arrival datasets generated in step 4 are input into the simulation model. Initial berth and storage capacity and various cost parameters are set.

[0174] Further costs include fixed costs , loading and unloading and transportation costs (port operating costs) 2. Operating costs of ships waiting in port , cold storage rental costs , cargo damage cost and penalty costs , as follows:

[0175] Step 5.1.1, Fixed costs. This mainly includes machinery depreciation costs and terminal construction costs. It is related to the number of berths at the terminal and can be expressed as:

[0176] (13)

[0177] in, Indicates the berth type number, Indicates the total number of berth types. represents fixed costs, represents the number of z-berths, The average annual construction cost of a Z-berth is Average annual depreciation cost of machinery on one z-berth.

[0178] Step 5.1.2, Loading, unloading and transportation costs (port operating costs). Different berth types are equipped with different amounts of machinery and transportation equipment, and the loading, unloading and transportation costs are also different. The expression is:

[0179] (14)

[0180] in, Indicates the berth type number of the ship, Indicates the total number of berth types, Indicates the number of z-berths, represents the daily operating cost of the Z-berth, and N represents the total operating time of the port in a year.

[0181] Step 5.1.3, operating costs of ships waiting in port The expression of the operating cost of a ship waiting in port is:

[0182] (15)

[0183] in, Indicates the type of ship, Indicates the total number of ship types, Indicates the ship q number, Represents the total number of ship type q.

[0184] For a ship of type q The departure time of For a ship of type q departure time; Indicates a ship of type q Operating costs in Hong Kong.

[0185] Step 5.1.4, cold storage rental cost. The freezer is considered in the form of direct rental. Different freezer areas need to be determined according to different fish species and their characteristics. Therefore, the cold storage rental cost expression is:

[0186] (16)

[0187] in, Indicates the cold storage type number, Indicates the total number of cold storage types. is the area of ​​the S-type cold storage. represents the rental cost of an S-type warehouse;

[0188] Step 5.1.5, cargo damage costs during transportation. This mainly refers to cargo damage caused by the loss of freshness due to exposure to normal temperature air during loading and unloading at the port, and cargo damage caused by temperature changes caused by opening the door of the refrigerated transport ship during unloading. The expression is:

[0189] (17)

[0190] in, Indicates the cold storage type number, represents the price per ton of type k fish; represents the loading and unloading time of type q vessel; Indicates the spoilage rate of fresh products when the door is opened; represents the total catch of k types of fish; e is a natural constant. Take it as 0.005.

[0191] Step 5.3.6, penalty cost. This mainly refers to the additional cost of shipping excess goods to a cold storage outside the port when the cold storage capacity is insufficient. The expression is:

[0192] (18)

[0193] in, is the penalty coefficient. In this example, the unit ton transportation cost is considered to be 10% of the unit ton warehouse rental cost. is the cargo storage volume per unit area of ​​type j fish catch, For 0-1 variables:

[0194] (19)

[0195] is the penalty function, , is the total area of ​​the j-type fish warehouse (m 2 ), solved using JTS165-2013 "Seaport Overall Design Code" .

[0196] Step 5.2: Establish constraints to define the boundary conditions of the simulation model.

[0197] Furthermore, the constraint condition established is: the total berth length is greater than zero and less than the upper limit of the total planned length, and the expression is:

[0198] (20)

[0199] in, Indicates the berth type number of the ship, Indicates the total number of berth types, Indicates the number of z-berths, is the length of the Z-berth, is the total length of the berth.

[0200] Step 5.3: Establish a simulation optimization model to solve the target.

[0201] The goal is to minimize the port and ship costs, and each cost item includes fixed costs , loading and unloading and transportation costs (port operating costs) 2. Operating costs of ships waiting in port , cold storage rental costs , cargo damage cost and penalty costs ,Right now:

[0202] (twenty one)

[0203] Step 5.4: Run the simulation model. This model is run based on the ship arrival time series data. The simulation dynamically adjusts the berth and storage capacity through simulation. While satisfying the constraints, it outputs the optimal number of berths and cold storage capacity, minimizing terminal operating costs and optimizing resource utilization.

[0204] In another aspect, the present invention provides a system for determining the size of berths and cold storage facilities at a fishing terminal that takes into account changes in fishing seasons. This system primarily comprises a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected, wherein the memory is configured to store a computer program comprising program instructions, and the processor is configured to invoke the program instructions to execute the method for determining the size of berths and cold storage facilities at a fishing terminal that takes into account changes in fishing seasons described in the first aspect of the present invention.

[0205] 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 method for determining the scale of fishing berths and cold storage considering changes in fishing seasons, characterized in that: The method for determining the scale of the fishing terminal berth and cold storage comprises the following steps: Step 1: Analyze fishing season information to obtain the distribution of fishing grounds and expected catches, and collect vessel parameters; Step 2: Establish a fishing fleet configuration and scheduling model based on fishing season information and clarify various constraints; Step 3: Adaptive large neighborhood search algorithm (ALNS) is used to solve the fishing fleet configuration and scheduling model established in step 2, and output the optimal fleet configuration plan and ship arrival time data; Step 4: Based on the results of step 3, ship arrival information and catch information are collected, and fleet ship type, ship arrival time, and actual catch information are summarized to form a fleet configuration dataset and a ship arrival dataset; Step 5: Based on the fleet configuration dataset and ship arrival dataset from step 4, a simulation optimization model for the berth and storage scale of the fishing terminal is established. Simulation technology is used to solve the simulation optimization model for the berth and storage scale of the fishing terminal, and the optimal berth and storage scale configuration plan is output.

2. A method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 1, characterized in that: The step 1 is specifically as follows: based on the fishing season information of previous years, the distribution of fishing grounds and the expected catch are predicted, and the ship parameters are collected; the fishing season information includes the coordinates of the fish school, the time when the fish school appears, and the size of the fish school.

3. The method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 1, characterized in that: The step 2 is specifically as follows: Based on the fishing season parameters extracted in step 1, the model input variables and constraints are constructed, and the fleet net profit maximization is used as the objective function to establish a fishing fleet configuration and scheduling model; the fleet profit is the income from the catch. , the cost mainly includes the activation cost Fuel costs , fishing costs , refrigeration costs , the objective function is as follows: (1), step 2.1, ship income, use To express it, the expression is as follows: (2), where Represents the column vector of fishery value, m is the total number of fisheries, index Indicates that the fishing network node includes fishing port and fishing ground nodes, where 0 represents the fishing port node. represents the fishing ground node, is the total number of fisheries, represents the total number of fishing network nodes, so represents the unit catch value of fishery i; for Dimensional vessel-fishing ground catch matrix, is the total number of ships, index Indicates the unique number of the ship; , is a P × 1-dimensional all-one vector, and the total catch tonnage of each fishing ground is obtained; Step 2.2, Fleet Activation Cost , whose expression is: (3), where represents the activation cost coefficient vector of the ship; , indicating whether the ship is enabled. If the element in the vector is 0, it means it is not enabled, and 1 means it is enabled; Step 2.3, fuel costs , whose expression is: (4) The network fishing nodes All feasible paths are numbered in order. but in , represents the distance from node i to node j in the fishing network; , which is used to mark the path of each ship, such as the matrix Elements in When , it means that the ship numbered p chooses path m, otherwise the value is 0; Indicates the unit distance sailing cost of each ship; Step 2.4, fishing costs ; (5), where is the expected catch vector of the fishing ground (tons), m is the total number of fishing grounds, index Corresponding to the specific fishery number; represents the unit fishing cost of the vessel; Step 2.5, Refrigeration Cost ,use It means that its expression is: (6), where represents the cooling cost of the ship p; for dimensional transport time matrix; , its function is to mark the path of each ship; for Dimensional operation time matrix; Represents the fishing ground-vessel deployment matrix; introduces the all-one vector ; Step 2.6, set constraints, including three aspects: flow balance constraint, resource allocation constraint, maximum capacity constraint and maximum range constraint.

4. A method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 3, characterized in that: In step 2.6, the constraints are: Step 2.6.1, flow balance constraint: (7), where ,matrix Elements in When , it means the starting point of path m is i. When , it means the end point of path m is i. In other cases , marking the path of each ship; ,when When represents the fishing port node, hour, , indicating that it must start from the fishing ground node and return, hour, It means that the number of times each ship enters each fishing ground node is equal to the number of times it leaves each fishing ground node; Step 2.6.2, resource allocation constraints: (8), where Table represents the fishing ground-vessel deployment matrix; and is a vector of all 1s; Step 2.6.3, Maximum Capacity Constraint: (9), where , represents the fishing ground-vessel deployment matrix; is the expected catch vector of the fishery (tons); ] Step 2.6.4, Maximum range constraint: (10), where , its function is to mark the path of each ship; , ], which represents the maximum sailing distance of ship p.

5. The method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 1, characterized in that: The step three is specifically as follows: Step 3.1: Construct a preliminary feasible solution, generate an initial solution that satisfies the basic constraints by random sampling as the starting point of the iteration, and set the initial solution as the current solution and optimal solution , included in the feasible solution set ; Step 3.2, initialize parameters and set the maximum number of iterations , the current number of iterations is 1; initialize the probability of the weights of the destruction and repair operators, the scores of the destruction and repair operators, and set the temperature of the simulated annealing process , lower limit of temperature ; Among them, all operators have equal weights and all scores are equal; Step 3.3, termination condition judgment, if the current number of iterations , then terminate the process and output the optimal solution; otherwise, go to step 3.4; Step 3.4: Update the operator weights. If the number of iterations is a multiple of 100, update the weights of the destruction operator and the repair operator and proceed to step 3.

5. Otherwise, keep the weights unchanged and proceed directly to step 3.

5. The calculation method for updating the operator is: (11) , where is the weight of the kth iteration of the nth destruction operator, represents the destruction operator, where is the weight update coefficient, Destruction operator The cumulative score of Destruction operator The cumulative number of times used; (12) , where is the weight of the k-th iteration of the n-th repair operator, represents the repair operator, where is the weight update coefficient, Destruction operator The cumulative score of Destruction operator The cumulative number of times used; Step 3.5, select a destruction operator and a repair operator through the roulette strategy, and Perform deletion (generate partial solutions, then generate candidate solutions through repair operations) , go to step 3.6; Step 3.6, determine the new solution Is it better than the optimal solution? If yes, go to step 3.7, otherwise, use the simulated annealing criterion to determine whether to accept the candidate solution. If accepted, proceed to step 3.8, otherwise proceed to step 3.9; Step 3.7, Check Is it already in the feasible solution set? ,like There is no feasible solution set , then the high-weight score of the destruction operator and the repair operator that generated this solution is accumulated , record the number of times the operator is used, and mark the candidate solution as the optimal solution And reset the current solution to the candidate solution , go to step 3.10; if Already exists in the feasible solution set , then the low-weight score of the destruction operator and the repair operator that generated this solution is accumulated , and record the number of times the operator is used, and go to step 3.10; Step 3.8, Check Is it already in the feasible solution set? If it does not exist, add it to the feasible solution set and reset the current solution to the candidate solution , which accumulates the medium-weighted scores of the destruction operator and the repair operator that generated this solution , and record the number of times the operator is used, and go to step 3.10; if is already present in the feasible solution set , then the low-weight score of the destruction operator and the repair operator that generated this solution is accumulated , and record the number of times the operator is used, and go to step 3.10; Step 3.9: Accumulate low-weight scores for the destruction operator and repair operator that generate candidate solutions , and record the number of times the operator is used, and go to step 3.10; ; Step 3.10, update the simulated annealing algorithm temperature , update the number of iterations , return to step 3.3; After the solution is completed, the final output results include the optimal fishing fleet type, the optimal fishing route, the time when the fishing boat arrives at the fishing port, and the catch.

6. The method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 1, characterized in that: Specifically, step four includes: the fishing fleet configuration data set includes the ship type, ship tonnage, and maximum ship storage capacity of each ship in the optimal fishing fleet calculated in step three; the ship arrival information includes the ship arrival timing, the catch carried by the ship at the port, the type of catch, and the model information of the corresponding ship calculated in step three.

7. The method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 1, characterized in that: The step five is specifically as follows: Step 5.1: Build a simulation model that can simulate the loading and unloading of a fleet at a fishing port to dynamically simulate the loading and unloading process of fishing vessels. Input the fleet configuration dataset and the ship arrival dataset generated in Step 4 into the simulation model, and set the initial capacity of the berths and storage area, as well as various cost parameters. All costs include fixed costs , loading and unloading and transportation costs 2. Operating costs of ships waiting in port , cold storage rental costs , cargo damage cost and penalty costs ; Step 5.2, establish constraints to define the boundary conditions of the simulation model; The constraint condition is: the total berth length is greater than zero and less than the upper limit of the total planned length. The expression is: (18), among which, Indicates the berth type number of the ship, Indicates the total number of berth types, Indicates the number of z-berths, is the length of the Z-berth, is the total length of the berth; Step 5.3, establish a simulation optimization model to solve the target; The goal is to minimize the costs of the port and the ship, namely: (19), step 5.4, run the simulation model; run the simulation model based on the time series data of ship arrival at the port, dynamically adjust the scale of berths and storage yards through simulation, and output the optimal number of berths and cold storage scale while meeting the constraints, so as to minimize the terminal operating costs and optimize resource utilization.

8. The method for determining the scale of fishing berths and cold storage considering changes in fishing seasons according to claim 1, characterized in that: The step 5.1 is specifically as follows: Step 5.1.1, Fixed cost; the expression is: (11), where Indicates the berth type number, Indicates the total number of berth types; represents fixed costs, represents the number of z-berths, The average annual construction cost of a Z-berth is Average annual machinery depreciation cost on one z-berth; Step 5.1.2, The loading and unloading and transportation costs are expressed as: (12), where Indicates the berth type number of the ship, Indicates the total number of berth types, Indicates the number of z-berths, represents the daily operating cost of the z-berth, and N represents the total operating time of the port in a year; Step 5.1.3, operating costs of ships waiting in port ; The expression is: (13), among which, Indicates the type of ship, Indicates the total number of ship types, Indicates the ship q number, represents the total number of ship type q; For a ship of type q The departure time of For a ship of type q departure time; Indicates a ship of type q Operating costs in Hong Kong; Step 5.1.4, cold storage rental cost; the expression is: (14), among which, Indicates the cold storage type number, Indicates the total number of cold storage types; is the area of ​​the S-type cold storage; represents the rental cost of an S-type warehouse; Step 5.1.5, the cost of cargo damage during transportation; the expression is: (15), among which, Indicates the cold storage type number, represents the price per ton of type k fish; represents the loading and unloading time of type q vessel; Indicates the spoilage rate of fresh products when the door is opened; represents the total catch of k type of fish; e is a natural constant; Step 5.3.6, penalty cost; the expression is: (16), among which, is the penalty coefficient, is the cargo storage volume per unit area of ​​type j fish catch, For 0-1 variables: (17), is the penalty function, , is the total area of ​​the j-type fish warehouse m 2 .

9. A system for determining the size of fishing berths and cold storage facilities taking into account changes in fishing seasons, characterized in that: The method for determining the scale of fishing wharf berths and cold storage taking into account changes in fishing seasons as described in any one of claims 1-8 is implemented by the fishing wharf berths and cold storage scale determination system.

10. A system for determining the scale of fishing berths and cold storages taking into account changes in fishing seasons according to claim 9, characterized in that: The system for determining the scale of berths and cold storage at a fishing terminal includes a processor, an input device, an output device, and a memory; the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method for determining the scale of berths and cold storage at a fishing terminal as described in any one of claims 1-8.

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