Ship berthing strategy determination method and device, medium, electronic equipment and program product

Abstract

The invention relates to the technical field of berth allocation, and provides a ship berthing strategy determination method and device, a medium, electronic equipment and a program product, and the method comprises the steps: determining a ship berthing model which at least comprises ship parameters, wharf parameters, inventory parameters, cargo allocation parameters and time parameters; solving the ship berthing model by taking a ship scheduling constraint condition, a berth physical constraint condition, an inventory constraint condition, a cargo allocation constraint condition, a time window constraint condition, a ship arrival time constraint condition and an inventory deduction constraint condition as target constraint conditions and aiming at minimizing the total waiting time of all ships to obtain a ship berthing strategy; the ship berthing strategy at least comprises a berth of ship berthing, a cargo allocation scheme and a berthing time period. According to the ship berthing strategy determination method, the ship waiting time can be effectively shortened, the berth utilization rate is improved, the cargo loading and unloading sequence is optimized, the time window is reasonably arranged, and the ship turnover efficiency and the wharf operation efficiency are remarkably improved.

Description

Technical Field

[0001] This disclosure relates to the field of berth allocation technology, specifically to a method, apparatus, medium, electronic equipment, and program product for determining ship berthing strategies. Background Technology

[0002] With the continuous development of global trade, ports, as vital hubs for cargo exchange, are becoming increasingly important in terms of efficiency and operational management. Ports face ever-increasing cargo throughput and ship arrivals, and traditional berth allocation methods can no longer meet the demands of efficient modern port operations. For example, many container ports are experiencing overloaded operations, with unreasonable berth allocation leading to low ship turnaround efficiency and hindered logistics operations.

[0003] The Berth Allocation Problem (BAP) involves allocating berths to ships to reduce overall service costs and waiting times. Like the Traveling Salesman Problem, the BAP is an NP-hard problem, and the quality of berth allocation directly impacts the efficiency of container port loading and unloading. To achieve optimal berth resource allocation, many scholars and researchers have developed various optimization models and algorithms. These models and algorithms are typically based on the theoretical foundations of operations research, systems control theory, and other disciplines, considering the multi-objective and dynamic characteristics of port resources. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, medium, electronic equipment, and program product for determining ship berthing strategies to solve the above-mentioned problems.

[0005] To achieve the above objectives, this disclosure provides a method for determining a ship berthing strategy, including: Determine the ship berthing model, wherein the ship berthing model includes at least ship parameters, terminal parameters, inventory parameters, cargo allocation parameters, and time parameters; Using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as objective constraints, and with the objective of minimizing the total waiting time of all ships, the ship berthing model is solved to obtain the ship berthing strategy. The ship berthing strategy includes at least the berth where the ship berths, the cargo allocation plan, and the berthing time period.

[0006] Optionally, the ship parameters include: ship set, total cargo demand of the ship, cargo type demand set of the ship, ship arrival time, and time required for loading and unloading cargo. The terminal parameters include: terminal set, berth set in the terminal, upper limit of berth tonnage, berth width limit, and berth loading and unloading efficiency. The inventory parameters include: the set of goods types and the available inventory of goods at the dock; The allocation parameters include: a set of allocation schemes, and the proportion of goods of type m in the allocation scheme; The time parameters include: the total planning period and the time step.

[0007] Optionally, the ship scheduling constraints include a first ship scheduling constraint and a second ship scheduling constraint. The first ship scheduling constraint characterizes a ship loading and unloading cargo at a berth in a wharf at a given time point. The second vessel scheduling constraint characterizes a vessel's commencement of cargo loading and unloading operations upon arrival at the berth, and the completion of such operations within a specified time. The physical constraints of the berths include the physical constraints of the first berth and the physical constraints of the second berth. The physical constraints of the first berth indicate that the total cargo mass of all ships on a berth does not exceed the tonnage limit of the berth. The second berth physical constraint condition indicates that the width of all ships on a berth is within the berth's width limit. Inventory constraints include the first inventory constraint and the second inventory constraint. The first inventory constraint condition indicates that the total weight of cargo retrieved by all ships from the terminal does not exceed the available inventory of cargo on the terminal. The second inventory constraint indicates that the weight of cargo retrieved by the vessel from the terminal does not exceed the remaining available quantity of cargo on the terminal. The loading constraints include the first loading constraint and the second loading constraint. The first cargo allocation constraint characterizes the proportion of different types of cargo obtained by the ship in the cargo allocation scheme. The second allocation constraint indicates that the total amount of goods of a certain type is equal to the total weight of goods allocated proportionally in the allocation scheme that includes the goods of that type. The time window constraint condition indicates that the time for loading and unloading operations of a ship at the berth is equal to the time required for loading and unloading cargo. The time constraint condition for a vessel to reach its berth indicates that the time a vessel occupies the berth does not exceed the time it takes for the vessel to reach the berth. The inventory deduction constraint indicates that the current inventory of a cargo type is equal to the previous inventory of the cargo type minus the total amount of cargo removed from the cargo type by all ships at the current time.

[0008] Optionally, the ship berthing model is solved with the objective constraints of ship scheduling, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints, aiming to minimize the total waiting time of all ships, to obtain the ship berthing strategy, including: Using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as objective constraints, and minimizing the total waiting time of all ships as the objective, the ship berthing model is solved using a genetic algorithm and a particle swarm optimization algorithm to obtain the ship berthing strategy.

[0009] Optionally, the step of solving the ship berthing model using a genetic algorithm and a particle swarm optimization algorithm to obtain the ship berthing strategy includes: The global optimal solution is continuously generated through iterative optimization, and the latest global optimal solution is used as the ship berthing strategy when the iteration stopping condition is met.

[0010] This disclosure also provides a vessel berthing strategy determination device, comprising: The first processing module is configured to determine the ship berthing model, wherein the ship berthing model includes at least ship parameters, dock parameters, inventory parameters, cargo allocation parameters, and time parameters. The second processing module is configured to solve the ship berthing model with the objectives of minimizing the total waiting time of all ships, using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as target constraints. The solution yields a ship berthing strategy, which includes at least the berth where the ship berths, the cargo allocation plan, and the berthing time period.

[0011] Optionally, the ship parameters include: ship set, total cargo demand of the ship, cargo type demand set of the ship, ship arrival time, and time required for loading and unloading cargo. The terminal parameters include: terminal set, berth set in the terminal, upper limit of berth tonnage, berth width limit, and berth loading and unloading efficiency. The inventory parameters include: the set of goods types and the available inventory of goods at the dock; The allocation parameters include: a set of allocation schemes, and the proportion of goods of type m in the allocation scheme; The time parameters include: the total planning period and the time step.

[0012] This disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described ship berthing strategy determination method.

[0013] This disclosure also provides an electronic device, including: A memory on which computer programs are stored; A processor is configured to execute the computer program in the memory to implement the steps of the above-described method for determining ship berthing strategies.

[0014] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described ship berthing strategy determination method.

[0015] Using the above technical solution, a ship berthing model is established, incorporating ship parameters, terminal parameters, inventory parameters, cargo allocation parameters, and time parameters. Then, with the objective of minimizing the total waiting time for all ships, the model is solved considering constraints such as ship scheduling, berth physics, inventory, cargo allocation, time windows, ship arrival times, and inventory deductions. The solution process can employ optimization algorithms such as genetic algorithms and particle swarm optimization. Through iterative optimization, new solutions are continuously generated and evaluated, and the global optimal solution is updated. Ultimately, a berthing strategy is obtained, including ship berthing locations, cargo allocation plans, and berthing times. This effectively reduces ship waiting time, improves berth utilization, optimizes cargo loading and unloading sequences, and rationally arranges time windows, thereby significantly improving ship turnaround efficiency and terminal operating efficiency.

[0016] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a method for determining a ship berthing strategy according to an exemplary embodiment.

[0018] Figure 2 This is a block diagram illustrating a ship berthing strategy determination device according to an exemplary embodiment.

[0019] Figure 3 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0020] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0022] With the continuous development of global trade, ports, as vital hubs for cargo exchange, are becoming increasingly important in terms of efficiency and operational management. Ports face ever-increasing cargo throughput and ship arrivals, and traditional berth allocation methods can no longer meet the demands of efficient modern port operations. For example, many container ports are experiencing overloaded operations, with unreasonable berth allocation leading to low ship turnaround efficiency and hindered logistics operations.

[0023] The berth allocation problem involves assigning berths to ships to reduce overall service costs and waiting times. Like the Traveling Salesman Problem (TSP), the Berth Allocation Problem (BAP) is an NP-hard problem, and the quality of berth allocation directly impacts the efficiency of container port loading and unloading. To achieve optimal berth resource allocation, many scholars and researchers have developed various optimization models and algorithms. These models and algorithms are typically based on the theoretical foundations of operations research, systems control theory, and other disciplines, considering the multi-objective and dynamic characteristics of port resources.

[0024] Ports are facing ever-increasing cargo throughput and ship arrivals, and traditional berth allocation methods can no longer meet the demands of efficient modern port operations. For example, many container ports are experiencing overloaded operations, with unreasonable berth allocation leading to low ship turnaround efficiency and hindered logistics operations. Existing technologies lack specific solutions for handling the unique operational rules of ports, resulting in poor performance in practical applications.

[0025] To address the aforementioned issues, a ship berthing model is established, incorporating parameters such as ship parameters, terminal parameters, inventory parameters, cargo allocation parameters, and time parameters. Then, with the objective of minimizing the total waiting time for all ships, the model is solved considering constraints such as ship scheduling, berth physics, inventory, cargo allocation, time windows, ship arrival times, and inventory deductions. The solution process employs optimization algorithms such as genetic algorithms and particle swarm optimization. Through iterative optimization, new solutions are continuously generated and evaluated, updating the global optimal solution. Ultimately, a berthing strategy including ship berthing locations and berthing times is obtained, effectively reducing ship waiting time, improving berth utilization, optimizing cargo loading and unloading sequences, and rationally arranging time windows, thereby significantly improving ship turnaround efficiency and terminal operating efficiency.

[0026] To improve port operational efficiency and reduce operating costs, this study aims to enhance port competitiveness by rationally allocating and scheduling resources to achieve rapid ship loading and unloading, timely cargo turnover, and improved port efficiency. In terms of solution methods, it draws on the ideas of optimization algorithms such as genetic algorithms and particle swarm optimization. Genetic algorithms possess powerful global search capabilities, finding optimal solutions in complex solution spaces, while particle swarm optimization is characterized by fast convergence. Combining and improving upon these two approaches results in a hybrid heuristic algorithm suitable for ship-cargo matching problems, improving both efficiency and accuracy. Based on fundamental constraints such as ship tonnage, width, and berthing time, as well as terminal inventory, specific conditions are further set according to the actual port's business rules, such as departure rules and berth operation intervals, making the model more closely aligned with real-world application scenarios.

[0027] Figure 1 This is a flowchart illustrating a method for determining a ship berthing strategy according to an exemplary embodiment. This method can be applied to electronic devices; please refer to [link / reference needed]. Figure 1 The method for determining the berthing strategy of a ship may include steps S1 and S2.

[0028] Step S1: Determine the ship berthing model.

[0029] The ship berthing model includes at least ship parameters, dock parameters, inventory parameters, cargo allocation parameters, and time parameters.

[0030] Ship parameters may include: ship set Total cargo demand of ships The set of cargo types required by ships Time of arrival of the vessel and the time required for loading and unloading cargo on ships. .

[0031] The time required for loading and unloading cargo on a ship can be determined by the ship's total cargo demand and the berth's loading and unloading efficiency.

[0032] Dock parameters may include: dock set berths in the dock upper limit of berth tonnage The berth width limit and the berth loading and unloading efficiency. .

[0033] The relationship between a port and a terminal is one-to-many, and the relationship between a terminal and a berth is also one-to-many. Each terminal has its own inventory, and all berths under the terminal share the same inventory. Each berth has its own corresponding loading and unloading efficiency.

[0034] The berth width limit includes the maximum berth width limit. Minimum width of berth .

[0035] Inventory parameters may include: a set of product categories And the available inventory of goods at the dock.

[0036] For example, the available inventory of goods at a dock can be the available inventory of goods of type m at dock d at time t. .

[0037] Picking parameters may include: a set of picking schemes And the proportion of goods type m in the distribution plan. .

[0038] Time parameters may include: overall planning period and time step .

[0039] Step S2 involves solving the ship berthing model with the objectives of minimizing the total waiting time of all ships, using constraints such as ship scheduling, berth physical conditions, inventory, cargo allocation, time window, ship arrival time, and inventory deduction as the target constraints. This yields the ship berthing strategy.

[0040] Among them, the ship berthing strategy includes at least the berth where the ship berths, the cargo allocation plan, and the berthing time.

[0041] Minimizing the total waiting time for all ships can be expressed as:

[0042] in, This represents the actual docks and berths allocated to the vessel s.

[0043] Decision variables can be as follows: This characterizes whether ship s uses berth b at dock d at time t. This indicates that at time t, ship s is assigned to berth b at dock d for mooring. This indicates that at time t, the vessel s was not assigned to berth b at dock d.

[0044] It represents the amount of cargo that ship s acquires from cargo type m at time t; It represents the amount of cargo that ship s obtains from the cargo allocation scheme (consisting of cargo type m1 + cargo type m2) at time t.

[0045] In one possible implementation, the ship scheduling constraints may include a first ship scheduling constraint and a second ship scheduling constraint.

[0046] The first ship scheduling constraint characterizes a ship loading and unloading cargo at a berth in a wharf at a given time.

[0047] The first ship scheduling constraint can be expressed as:

[0048] That is, each ship can only be dispatched once, and for each ship, it is ensured that the ship is arranged to carry out loading and unloading operations at a specific time, at a specific berth, and at a specific dock.

[0049] The second vessel scheduling constraint characterizes a vessel's commencement of loading and unloading operations after arriving at its berth, and the completion of these operations within a specified time.

[0050] The second ship scheduling constraint can be expressed as:

[0051] That is, the ship must complete loading and unloading within a specified time after arrival: for each ship, the start time of loading and unloading operations shall not be earlier than its arrival time, and the entire loading and unloading process shall be completed within a specified time.

[0052] The physical constraints of the berths include the physical constraints of the first berth and the physical constraints of the second berth.

[0053] The physical constraints of the first berth indicate that the total mass of cargo carried by all ships at a berth does not exceed the berth's tonnage limit.

[0054] The physical constraints of the first berth can be expressed as follows:

[0055] That is, for each berth in all terminals, at any given time, the total mass of cargo loaded and unloaded by all ships through that berth cannot exceed the berth's tonnage limit.

[0056] The physical constraints of a second berth define that the width of all vessels at a berth must be within the berth's width limit.

[0057] The physical constraints of the second berth can be expressed as follows:

[0058] That is, for each berth in all terminals, at any given time, all vessels must remain within the minimum width limit of the berth. Maximum width of berth between.

[0059] The combined effect of the physical constraints of the first berth and the physical constraints of the second berth ensures both the physical load-bearing safety of the berth and the matching of the ship's size with the berth specifications, preventing overloading or size mismatch.

[0060] Inventory constraints include the first inventory constraint and the second inventory constraint.

[0061] The first inventory constraint indicates that the total weight of cargo retrieved by all ships from the dock does not exceed the available inventory of cargo at the dock.

[0062] The first inventory constraint can be expressed as:

[0063] That is, for all cargo types, terminals, and times, the weight of cargo that all ships acquire from a cargo type cannot exceed the available inventory of cargo type m at time t terminal d.

[0064] The second inventory constraint indicates that the weight of cargo retrieved by a ship from the dock does not exceed the remaining available quantity of cargo on the dock.

[0065] The second inventory constraint can be expressed as:

[0066] That is, for each ship, cargo type, dock, and time, the amount of cargo a ship obtains from a cargo type cannot exceed the remaining available amount of that cargo type at time t (after deducting the amount that has already been occupied).

[0067] By using both the first and second inventory constraints, over-allocation is prevented. The allocation of goods is dynamic and real-time, taking into account previous allocations to avoid overselling and ensure sufficient inventory during actual operations.

[0068] The loading constraints include the first loading constraint and the second loading constraint.

[0069] The first cargo allocation constraint characterizes the proportion of different types of cargo acquired by the ship in the cargo allocation scheme.

[0070] The first order fulfillment constraint can be expressed as:

[0071] That is, for all cargo types m1 and m2 in all cargo allocation schemes, at any time t and ship s, the quantity of cargo allocated from cargo type m1 multiplied by the proportion of cargo type m1 in the cargo allocation scheme is equal to the quantity of cargo allocated from cargo type m2 multiplied by the proportion of cargo type m2 in the cargo allocation scheme.

[0072] The second allocation constraint indicates that the total amount of goods of a certain type is equal to the total weight of goods allocated proportionally in the allocation scheme that includes goods of that type.

[0073] The second order fulfillment constraint can be expressed as:

[0074] in, P(m) represents the proportion of each type of goods in a distribution plan. P(m) represents the set of distribution plans in which each type of goods m participates.

[0075] That is, for each ship s, each cargo type m, and time t, the amount of cargo directly allocated from a single cargo type m is equal to the total weight of cargo allocated proportionally in all allocation schemes that include cargo type m.

[0076] The first and second batching constraints work together to ensure that the proportions of each component are correct during batching and that the entire batching process meets the established formula requirements.

[0077] The time window constraint indicates that the time for a ship to load and unload cargo at the berth is equal to the time required for the ship to load and unload cargo.

[0078] The time window constraint can be expressed as:

[0079] That is, for each vessel s, each dock d, and each berth b, if vessel s uses berth b at time t, the sum of all time points (multiplied by the time step Δt) equals the time required for loading and unloading of that vessel, ensuring that the loading and unloading operations of the vessel are not interrupted and are completed within the specified time.

[0080] The time constraint condition indicates that the time a ship occupies the berth does not exceed the time it takes for the ship to arrive at the berth.

[0081] The time constraint condition for a ship to meet can be expressed as:

[0082] That is, for each ship s, each dock d, and each berth b, if ship s uses berth b at time t, then the sum of the numbers of these time points (i.e., the actual times) multiplied by the number of whether the berth is occupied is greater than or equal to the ship's arrival time.

[0083] The inventory deduction constraint indicates that the current inventory of a cargo type is equal to the previous inventory of the cargo type minus the total amount of cargo removed from the cargo type by all ships at the current time.

[0084] The inventory deduction constraint can be expressed as:

[0085] That is, for each type of cargo m, each terminal d, and time t (t>1), the inventory at the current time t is equal to the inventory at the previous time point t-1 minus the total amount of cargo taken from cargo type m by all ships s at that time t.

[0086] For example, the goods can be coal, and the type of goods can be a type of coal.

[0087] The inventory deduction constraint ensures that inventory changes are continuous, with inventory decreasing accordingly each time a ship picks up cargo.

[0088] The fundamental attributes of a ship order: A ship order contains key information such as the ship's tonnage, width requirements, and arrival time at the port. It forms the basis of the entire matching process and determines the ship's basic requirements for berths. For example, a large, deep-draft ship has a larger tonnage requirement and therefore a higher upper limit for the tonnage of the berth; while a wider ship can only berth in berths with sufficient width.

[0089] Port-level screening: When a vessel arrives at a specific port, the port will initially screen a range of potentially suitable berths based on its overall resources and vessel order information. Factors such as infrastructure development and berth distribution at different ports determine the potential berthing options they can offer. For example, some ports may have multiple specialized berths; for different types of cargo transportation, the cargo type information in the vessel order will guide the system to prioritize screening within the range of berths with the corresponding handling capacity.

[0090] Detailed matching of wharves and berths: Within the range of wharves selected by the port, further matching is performed based on the specific parameters of each berth within the wharf. Parameters such as the maximum tonnage and width restrictions of a berth are key factors determining whether a vessel can berth. By comparing these parameters with the tonnage and width requirements in the vessel's order, berths meeting the basic conditions are selected from the wharves. For example, if the maximum tonnage of a berth is lower than the tonnage requirement of the vessel, that berth will be excluded; similarly, if the width of a berth does not meet the vessel's width requirements, it cannot be considered as a candidate.

[0091] Correlation between cargo allocation demand and inventory: Cargo allocation parameters reflect the required proportion of different cargo types in a ship's cargo demand, necessitating a model that links ship orders with terminal inventory levels. Terminals need to check whether their inventory of various cargo types can meet the ship's cargo allocation requirements. For example, if a ship requires a specific ratio of high-calorific-value coal to low-calorific-value coal, but the terminal's high-calorific-value coal inventory is insufficient, the ship may not be able to berth smoothly at the terminal.

[0092] Cargo demand influences berth selection: The complexity of cargo allocation and the diversity of required cargo types also affect berth selection. Some berths may be equipped with more advanced cargo allocation equipment and facilities, enabling them to handle vessels with complex cargo allocation needs more efficiently. Therefore, during the matching process, the system comprehensively considers cargo allocation parameters and the relevant equipment capabilities of the berths, prioritizing the allocation of vessels with complex cargo allocation needs to berths with corresponding capabilities to improve cargo allocation efficiency and quality.

[0093] The role of time parameters in the matching process The timeliness of vessel arrival time and berth allocation: Arrival time in a vessel order is a crucial time parameter. The model can rationally allocate berth usage based on vessel arrival times to ensure timely berthing and operations. During the matching process, priority is given to vessels arriving soon to avoid prolonged waiting times. For example, if multiple vessels are competing for a berth, those with earlier arrival times will be allocated priority.

[0094] Loading and unloading operation time and inventory dynamics: The loading and unloading operation time of ships is closely related to the dynamic changes in terminal inventory. When ships begin loading and unloading operations, they consume terminal inventory; conversely, after loading and unloading are completed, new cargo may be added to the inventory. Therefore, the impact of loading and unloading operation time scheduling on inventory needs to be considered during the matching process. For example, for cargo types with tight inventory, loading and unloading operation times can be rationally scheduled to avoid inventory depletion due to excessively long loading and unloading times, which could affect the cargo allocation needs of other ships. At the same time, by rationally scheduling the loading and unloading sequence and time, inventory turnover efficiency can be improved, ensuring the effective utilization of terminal resources.

[0095] The overall synergistic mechanism of ship orders, ports, terminals, berths and inventory Systemic Interaction in Integrated Decision-Making: The entire cargo-ship matching process is a comprehensive decision-making process involving interaction and collaborative decision-making among various factors. The model needs to comprehensively consider the demand for ship orders, the distribution of port resources, the facility conditions of the terminal, the parameter constraints of berths, and the dynamic situation of inventory. Through mathematical optimization models and solution algorithms, it finds the optimal matching solution. For example, based on meeting the requirements for ship tonnage and width, as well as inventory conditions, and according to loading and unloading operation time and berth availability, ships are rationally allocated to specific berths to maximize port operational efficiency.

[0096] Feedback and Adjustment Mechanism: In actual operation, a feedback and adjustment mechanism also exists between various elements. When a certain element changes, such as a sudden berth failure or an unexpected decrease in inventory, the model will readjust the matching scheme according to the new situation to ensure the stable operation of the entire system. For example, when a berth becomes unusable due to equipment failure, the model will quickly find other berths that meet the conditions and re-plan the operation time and sequence of relevant vessels, while considering the impact on inventory management and cargo allocation needs, achieving overall balance and optimization through adjustments.

[0097] In one possible implementation, step S4 may include: Using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as objective constraints, and minimizing the total waiting time of all ships as the objective, the ship berthing model is solved by genetic algorithm and particle swarm optimization algorithm to obtain the ship berthing strategy.

[0098] The goal of minimizing the total waiting time for all vessels is to reduce the total time vessels spend waiting for berthing and operations in port.

[0099] Objective constraints are used to impose restrictions from the following perspectives.

[0100] Ship scheduling constraints: scheduling rules such as berthing order and operation sequence of ships; Berth physical constraints: Physical limitations such as berth length and water depth determine which vessels can berth at which berths; Inventory constraints: Limitations on port cargo inventory affect loading and unloading capacity; Cargo allocation constraints: Rules and requirements for cargo allocation and loading; Time window constraints: Vessels must complete operations within a specific time frame; Vessel arrival time constraint: The time limit for the actual arrival of a vessel at the port; Inventory deduction constraints: Rules governing inventory changes during loading and unloading.

[0101] Genetic algorithms (GA) optimize solutions step by step through selection, crossover, and mutation operations by simulating the biological evolution process. They are suitable for discrete combinatorial optimization problems and can be designed with appropriate chromosome encoding methods to represent berthing schemes.

[0102] Particle Swarm Optimization (PSO) is a algorithm that simulates the social behavior of flocks of birds or schools of fish, where each particle represents a potential solution. The position is updated by tracking the optimal solutions of individuals and the group. It is suitable for continuous or discrete optimization problems.

[0103] In one possible implementation, step S4, which involves solving the ship berthing model using a genetic algorithm and a particle swarm optimization algorithm to obtain the ship berthing strategy, may include: The global optimal solution is continuously generated through iterative optimization, and the latest optimal solution is used as the ship berthing strategy when the iteration stopping condition is met. Iterative optimization is performed in the following way: The individual with the highest fitness was identified through fitness assessment; The global optimal solution is updated using the individual with the highest fitness.

[0104] This method utilizes the characteristics of genetic algorithms and particle swarm optimization to generate and optimize solutions. It eliminates the speed-related issues inherent in traditional particle swarm optimization, employing crossover and mutation methods from genetic algorithms to generate new solutions. Furthermore, it combines recording individual best (pbest) and global best (gbest) values ​​to improve the overall convergence and operational efficiency of the model.

[0105] The population consists of multiple lists of ships, each representing a possible ship scheduling scheme. The initial population is obtained by first optimizing the original ship lists. Subsequently, new solutions are generated through mutation operations.

[0106] def calc2(self): best_fitness_score = -1000000000000 best_ship_list = None # During the first optimization, vessel orders that were not scheduled or allocated within 24 hours will enter the second optimization phase for re-matching. For ship in self.ship_list: if ship.is_satisfied is False or ship.start_load_windows - ship.ship_arrive_time>24: ship_bak = copy.deepcopy(ship) ship_bak.clear_result() # Clear the results of the first optimization ship_bak.create_new_demand() # Generates new demand coal types based on the candidate list. self.second_ship_list.append(ship_bak) else: self.first_arranged_ship_list.append(ship) last_fitness_score = self._calc_fitness(self.second_ship_list) if last_fitness_score>= best_fitness_score: best_fitness_score = last_fitness_score best_ship_list = self.second_ship_list print('last_fitness_score:{}'.format(last_fitness_score)) # Subtract the resources (berths, inventory) already allocated to ships in the first optimization from self.port_second. for ship in self.first_arranged_ship_list: for dock in self.port_second.dock_list: # in stock if ship.load_dock.dock_code == dock.dock_code: for inv in dock.inv_list: inv_m_code = inv.m_code # Finished product for material in ship.list_real_material: if material[0] == inv_m_code: for i in the range(len(inv.list_inv_able)): if i >= ship.start_load_windows: inv.list_inv_able[i] -= material[1] # Coal blending 1 for material in ship.list_blend_material_a: if material[0] == inv_m_code: for i in the range(len(inv.list_inv_able)): if i >= ship.start_load_windows: inv.list_inv_able[i] -= material[1] # Coal blending 2 for material in ship.list_blend_material_b: if material[0] == inv_m_code: for i in the range(len(inv.list_inv_able)): if i >= ship.start_load_windows: inv.list_inv_able[i] -= material[1] # Berth for berth in dock.berth_list: if ship.load_berth.berth_code == berth.berth_code: for i in the range(len(berth.is_used)): if ship.end_load_windows >= i >= ship.start_load_windows: berth.is_used[i] = 1 population_ship_list = [] population_fitness_score = [] for i in range(50): # Generate 50 individuals sub_ship_list = [] for ship in self.second_ship_list: ship_bak = copy.deepcopy(ship) ship_bak.clear_result() # Clears the results of the first optimization ship_bak.create_new_demand() # Generates new demand cargo types based on the candidate list. sub_ship_list.append(ship_bak) port_ = copy.deepcopy(self.port_second) self._calc(port_, sub_ship_list) fitness_score = self._calc_fitness(sub_ship_list) population_ship_list.append(sub_ship_list) population_fitness_score.append(fitness_score) # Find the individual with the highest fitness max_value_index = population_fitness_score.index(max(population_fitness_score)) print(max_value_index, max(population_fitness_score)) The fitness function is used to evaluate the merits of each individual (ship scheduling scheme).

[0107] Unmet requirements: score -1000, indicating extremely poor fitness.

[0108] For vessels that meet the requirements: the score is the negative of their waiting time (i.e., the shorter the waiting time, the higher the score).

[0109] def _calc_fitness(self, ship_list): ''' Ships not selected -1000 points The waiting time for the boats to be boarded is -1 * waiting time. ''' sorce_list = [] for ship in ship_list: if ship.is_satisfied is False: fitness = -1e10 else: # fitness = -1 * (ship.start_load_windows - ship.ship_arrive_time) fitness = -1 * ship.calc_demurrage_charge() fitness_list.append(fitness) total_score = sum(sorce_list) return total_score A full replacement strategy is adopted, which directly performs crossover and mutation operations on the entire population. That is, each iteration generates a new population based on the current population and selects the individual with the highest fitness.

[0110] ''choose''' # Initialize the indices of the maximum and second largest values max_index = -1 second_max_index = -1 # Initialize the maximum and second largest values max_value = float('-inf') second_max_value = float('-inf') # Iterate through population_fitness_score to find the indices of the maximum and second-largest values. for index, value in enumerate(population_fitness_score): if value > max_value: second_max_value = max_value second_max_index = max_index max_value = value max_index = index elif value > second_max_value: second_max_value = value second_max_index = index if last_fitness_score>= best_fitness_score: best_fitness_score = population_fitness_score[max_index] best_ship_list = population_ship_list[max_index] Cross-operations are achieved by exchanging the cargo needs of ships. This is used to combine the genes of two parent individuals to generate new offspring individuals.

[0111] '''cross''' for ship in population_ship_list[max_index]: ship.clear_result() for ship in population_ship_list[second_max_index]: ship.clear_result() for i in range(len(population_ship_list[max_index])): ship1 = population_ship_list[max_index][i] ship2 = population_ship_list[second_max_index][i] # First cargo requirement for exchanging ships ship_code = ship1.list_material[0].m_code ship1.list_material[0].m_code = ship2.list_material[0].m_code ship2.list_material[0].m_code = ship_code # Exchange sales price (assuming it is related to the demand for goods) price = ship1.list_material[0].sale_price ship1.list_material[0].sale_price = ship2.list_material[0].sale_price ship2.list_material[0].sale_price = price Mutation operations are achieved by randomly changing the cargo requirements of ships, which can be used to randomly alter the genes of individuals and increase the diversity of the population.

[0112] '''Mutations''' mu_ship_list = copy.deepcopy(population_ship_list[max_index]) for ship in mu_ship_list: ship.clear_result() # Randomly change the demand for goods ship.list_material[0].random_new_material_from_bak() port_ = copy.deepcopy(self.port_second) self._calc(port_, mu_ship_list) mu_score = self._calc_fitness(mu_ship_list) if mu_score>= best_fitness_score: best_fitness_score = mu_score best_ship_list = mu_ship_list print('mu_score:', mu_score) Solution generation and evaluation: New solutions are continuously generated and evaluated through iterative optimization. In each iteration, the individual with the highest fitness is selected based on fitness evaluation. By comparing the best individual in each iteration, the global optimum is updated to ensure that new solutions continuously improve towards the global optimum.

[0113] # In the main loop of the genetic algorithm (pseudocode representation) for generation in range(max_generations): # Generate a new population (crossover and mutation) new_population = crossover_and_mutate(population) # Calculate fitness fitness_scores = [self._calc_fitness(individual) for individual innew_population] # Update the global optimal solution current_best_fitness = max(fitness_scores) if current_best_fitness>global_best_fitness: global_best_fitness = current_best_fitness global_best_individual = new_population[fitness_scores.index(current_best_fitness)] # Replace the old population population = new_population The ship berthing strategy determination method provided by this invention has at least the following advantages: First, by using the ship-cargo matching algorithm, the berth allocation for ships that have not yet berthed can be optimized, thus achieving automatic arrangement of ship berthing plans and maximizing the port's inventory turnover efficiency.

[0114] Second, compared to traditional berth allocation algorithms, this algorithm employs a hybrid heuristic and simplifies the particle swarm optimization algorithm, eliminating the concept of velocity and directly updating solutions through crossover and mutation. Simultaneously, it records the individual best solution (pbest) and the global best solution (gbest), considering this information when calculating the acceptance probability to guide the iterative process towards the optimal solution. This improvement significantly enhances both the convergence speed and the quality of the solutions.

[0115] Third, it fully considers the actual business rules of different ports and sets different berth operation intervals and optimization cycles for different ports.

[0116] Fourth, the model design incorporates multi-layered elements, such as ports, wharves, berths, and inventory, forming a complete resource allocation and utilization strategy. This multi-layered structure allows for a more comprehensive consideration of various factors in port operations, enabling refined resource management.

[0117] Based on the same inventive concept, in order to realize the above-described method for determining ship berthing strategies, this disclosure also provides a device for determining ship berthing strategies. Please refer to [link to relevant documentation]. Figure 2 The vessel berthing strategy determination device 600 may include: The first processing module 601 is configured to determine the ship berthing model, wherein the ship berthing model includes at least ship parameters, dock parameters, inventory parameters, cargo allocation parameters and time parameters; The second processing module 602 is configured to solve the ship berthing model with the objectives of minimizing the total waiting time of all ships, using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as target constraints. The goal is to obtain the ship berthing strategy. The ship berthing strategy includes at least the berth where the ship berths, the cargo allocation plan, and the berthing time period.

[0118] Optionally, the ship parameters include: ship set, total cargo demand of the ship, cargo type demand set of the ship, ship arrival time, and time required for loading and unloading cargo. Terminal parameters include: terminal set, berth set within the terminal, maximum tonnage of berths, maximum width of berths, and loading / unloading efficiency of berths. Inventory parameters include: the set of goods types and the available inventory of goods at the dock; The loading parameters include: the set of loading schemes, and the proportion of goods type m in the loading scheme; The time parameters include: the total planning period and the time step.

[0119] Optionally, the ship scheduling constraints include a first ship scheduling constraint and a second ship scheduling constraint. The first ship scheduling constraint characterizes a ship loading and unloading cargo at a berth in a wharf at a certain time. The second vessel scheduling constraint characterizes a vessel's commencement of loading and unloading operations after arriving at the berth, and the completion of these operations within a specified time. The physical constraints of the berths include the physical constraints of the first berth and the physical constraints of the second berth. The physical constraints of the first berth indicate that the total mass of cargo carried by all ships at a berth does not exceed the berth's tonnage limit. The physical constraints of the second berth indicate that the width of all ships on a berth is within the width limit of the berth. Inventory constraints include the first inventory constraint and the second inventory constraint. The first inventory constraint condition indicates that the total weight of cargo retrieved by all ships from the terminal does not exceed the available inventory of cargo on the terminal. The second inventory constraint indicates that the weight of cargo retrieved by the ship from the dock does not exceed the remaining available quantity of cargo on the dock. The loading constraints include the first loading constraint and the second loading constraint. The first cargo allocation constraint characterizes the proportion of different types of cargo acquired by the ship in the cargo allocation plan. The second allocation constraint indicates that the total amount of goods of a certain type is equal to the total weight of goods allocated proportionally in the allocation scheme that includes goods of that type. The time window constraint indicates that the time for a ship to load and unload cargo at the berth is equal to the time required for the ship to load and unload cargo. The time constraint condition indicates that the time a vessel occupies the berth does not exceed the time it takes for the vessel to arrive at the berth. The inventory deduction constraint indicates that the current inventory of a cargo type is equal to the previous inventory of the cargo type minus the total amount of cargo removed from the cargo type by all ships at the current time.

[0120] Optionally, the second processing module 602 may include: The first sub-processing module is configured to use ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as objective constraints, with the goal of minimizing the total waiting time of all ships. It solves the ship berthing model using genetic algorithms and particle swarm optimization algorithms to obtain the ship berthing strategy.

[0121] Optionally, the first sub-processing module is specifically configured as follows: The global optimal solution is continuously generated through iterative optimization, and the latest global optimal solution is used as the ship berthing strategy when the iteration stopping condition is met.

[0122] Regarding the ship berthing strategy determination device in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the ship berthing strategy determination method, and will not be elaborated here.

[0123] Figure 3 This is a block diagram illustrating an electronic device 700 according to an exemplary embodiment. For example... Figure 3 As shown, the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0124] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the aforementioned ship berthing strategy determination method. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0125] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described ship berthing strategy determination method.

[0126] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the ship berthing strategy determination method described above. For example, the computer-readable storage medium may be the memory 702 including program instructions, which may be executed by the processor 701 of the electronic device 700 to complete the ship berthing strategy determination method described above.

[0127] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0128] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0129] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for determining a ship berthing strategy, characterized in that, include: Determine the ship berthing model, wherein the ship berthing model includes at least ship parameters, terminal parameters, inventory parameters, cargo allocation parameters, and time parameters; Using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as objective constraints, and with the objective of minimizing the total waiting time of all ships, the ship berthing model is solved to obtain the ship berthing strategy. The ship berthing strategy includes at least the berth where the ship berths, the cargo allocation plan, and the berthing time period.

2. The method for determining ship berthing strategy according to claim 1, characterized in that, The ship parameters include: ship set, total cargo demand of the ships, cargo type demand set of the ships, ship arrival time, and time required for loading and unloading cargo. The terminal parameters include: terminal set, berth set in the terminal, upper limit of berth tonnage, berth width limit, and berth loading and unloading efficiency. The inventory parameters include: the set of goods types and the available inventory of goods at the dock; The allocation parameters include: a set of allocation schemes, and the proportion of goods of type m in the allocation scheme; The time parameters include: the total planning period and the time step.

3. The method for determining ship berthing strategy according to claim 1, characterized in that, The ship scheduling constraints include a first ship scheduling constraint and a second ship scheduling constraint. The first ship scheduling constraint characterizes a ship loading and unloading cargo at a berth in a wharf at a given time point. The second vessel scheduling constraint characterizes a vessel's commencement of cargo loading and unloading operations upon arrival at the berth, and the completion of such operations within a specified time. The physical constraints of the berths include the physical constraints of the first berth and the physical constraints of the second berth. The physical constraints of the first berth indicate that the total cargo mass of all ships on a berth does not exceed the tonnage limit of the berth. The second berth physical constraint condition indicates that the width of all ships on a berth is within the berth's width limit. Inventory constraints include the first inventory constraint and the second inventory constraint. The first inventory constraint condition indicates that the total weight of cargo retrieved by all ships from the terminal does not exceed the available inventory of cargo on the terminal. The second inventory constraint indicates that the weight of cargo retrieved by the vessel from the terminal does not exceed the remaining available quantity of cargo on the terminal. The loading constraints include the first loading constraint and the second loading constraint. The first cargo allocation constraint characterizes the proportion of different types of cargo obtained by the ship in the cargo allocation scheme. The second allocation constraint indicates that the total amount of goods of a certain type is equal to the total weight of goods allocated proportionally in the allocation scheme that includes the goods of that type. The time window constraint condition indicates that the time for loading and unloading operations of a ship at the berth is equal to the time required for loading and unloading cargo. The time constraint condition for a vessel to reach its berth indicates that the time a vessel occupies the berth does not exceed the time it takes for the vessel to reach the berth. The inventory deduction constraint indicates that the current inventory of a cargo type is equal to the previous inventory of the cargo type minus the total amount of cargo removed from the cargo type by all ships at the current time.

4. The method for determining ship berthing strategy according to claim 1, characterized in that, The ship berthing model is solved with the following objective constraints: ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints. The objective is to minimize the total waiting time of all ships, thereby obtaining the ship berthing strategy, including: Using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as objective constraints, and minimizing the total waiting time of all ships as the objective, the ship berthing model is solved using a genetic algorithm and a particle swarm optimization algorithm to obtain the ship berthing strategy.

5. The method for determining ship berthing strategy according to claim 4, characterized in that, The process of solving the ship berthing model using genetic algorithms and particle swarm optimization to obtain the ship berthing strategy includes: The global optimal solution is continuously generated through iterative optimization, and the latest global optimal solution is used as the ship berthing strategy when the iteration stopping condition is met.

6. A device for determining a ship berthing strategy, characterized in that, include: The first processing module is configured to determine the ship berthing model, wherein the ship berthing model includes at least ship parameters, dock parameters, inventory parameters, cargo allocation parameters, and time parameters. The second processing module is configured to solve the ship berthing model with the objectives of minimizing the total waiting time of all ships, using ship scheduling constraints, berth physical constraints, inventory constraints, cargo allocation constraints, time window constraints, ship arrival time constraints, and inventory deduction constraints as target constraints. The solution yields a ship berthing strategy, which includes at least the berth where the ship berths, the cargo allocation plan, and the berthing time period.

7. The ship berthing strategy determination device according to claim 6, characterized in that, The ship parameters include: ship set, total cargo demand of the ships, cargo type demand set of the ships, ship arrival time, and time required for loading and unloading cargo. The terminal parameters include: terminal set, berth set in the terminal, upper limit of berth tonnage, berth width limit, and berth loading and unloading efficiency. The inventory parameters include: the set of goods types and the available inventory of goods at the dock; The allocation parameters include: a set of allocation schemes, and the proportion of goods of type m in the allocation scheme; The time parameters include: the total planning period and the time step.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the ship berthing strategy determination method according to any one of claims 1-5.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the ship berthing strategy determination method according to any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the ship berthing strategy determination method according to any one of claims 1-5.

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