Method and device for generating a ship berthing plan, electronic equipment and storage medium

Abstract

The present disclosure provides a method and device for generating a ship berthing plan, an electronic device and a storage medium. Ship information and arrival information of a to-be-docked ship are obtained. A transfer plan model of the to-be-docked ship is constructed based on the ship information. The transfer plan model is optimized by using a constraint generation algorithm to obtain an optimal transfer plan. A berthing plan model supporting the optimal transfer plan is generated. The arrival state of the to-be-docked ship is determined based on the arrival information. According to the arrival state, a corresponding berthing planning algorithm is called to optimize the berthing plan model to obtain a final berthing plan. Compared with related technologies, the present disclosure generates and optimizes the transfer plan and the berthing plan by obtaining detailed ship information and docking information, can reasonably arrange the berthing operation sequence of the ship, improve the direct transfer volume and transfer efficiency of the port, and reduce the operating energy consumption cost.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for generating a ship berthing plan, an electronic device, and a storage medium. Background Art

[0002] In the global shipping sector, bulk carriers, transporting cargoes such as iron ore, coal, and grain, account for the largest proportion. With the rapid growth of international trade, the use of ultra-large ships is becoming increasingly widespread. However, these ships require longer and deeper berths, as well as matching loading and unloading capabilities and yard storage space. This makes it difficult for smaller terminals to meet their service needs, making transshipment operations a common practice. Hub bulk transshipment terminals have emerged, whose main purpose is to serve large ships and facilitate the transfer of cargo between large ships and smaller feeder ships. In some regions, the presence of a large number of steel mills has prompted the construction of hub bulk transshipment terminals in specific locations to handle imported goods such as iron ore.

[0003] While hub bulk transshipment terminals achieve economies of scale in ocean shipping, each visit slows down the delivery process and results in double handling of cargo. To improve transshipment efficiency, direct transshipment has become a common practice in the industry. In a typical bulk terminal, electricity costs play a significant role, with conveyors accounting for 50% to 70% of total electricity consumption. Due to the deep draft of large vessels, berths are often located offshore, requiring long conveyors to connect the berths to the yard. Furthermore, cargo transportation within the yard relies on multiple strip conveyors. Direct transshipment can significantly reduce the number or length of conveyors used, significantly lowering electricity consumption. This not only helps reduce operating costs, infrastructure, and maintenance costs, but also promotes more sustainable development by reducing the terminal's carbon footprint and environmental impact. However, hub bulk transshipment terminals currently lack the ability to generate efficient transshipment and berthing plans to guide the berthing of large vessels and feeder vessels, enabling more vessels to engage in direct transshipment and reducing the amount of indirect transshipment.

[0004] Therefore, how to generate scientific transshipment and berthing plans based on the cargo and arrival conditions of the ships, increase the number of direct transshipments, and reduce the power consumption of hub bulk transshipment terminals has become an urgent problem to be solved. Summary of the Invention

[0005] The present disclosure provides a method and device, electronic device and storage medium for generating a ship berthing plan, which is mainly intended to solve the current problem of a small number of direct transfers between giant ships and feeder ships and high power consumption.

[0006] According to a first aspect of the present disclosure, a method for generating a ship berthing plan is provided, comprising:

[0007] Acquiring ship information and arrival information of a vessel to be berthed, and constructing a transshipment plan model for the vessel to be berthed based on the ship information;

[0008] Applying a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed;

[0009] Based on the arrival information and the optimal transfer plan, a berthing plan model of the vessel to be berthed is constructed, a berthing planning algorithm is called, and a berthing optimization calculation is performed on the berthing plan model to obtain an optimal berthing plan.

[0010] In some embodiments, constructing a transshipment plan model for the vessel to be berthed based on the vessel information includes:

[0011] Determining the transshipment decision variables of the vessel to be berthed based on the vessel information, wherein the transshipment decision variables include: a transshipment mode decision variable and a direct transshipment quantity decision variable;

[0012] The transshipment planning model is constructed using the transshipment decision variables and transshipment objective function.

[0013] In some embodiments, applying a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed includes:

[0014] Applying a constraint generation algorithm to the transshipment planning model, dynamically adding constraints to accelerate the solution;

[0015] The transshipment plan model is optimized and solved to maximize the quantity of cargo directly transshipped between the giant ship and the feeder ship among the ships waiting to be berthed, thereby obtaining the optimal transshipment plan.

[0016] In some embodiments, the step of constructing a berthing plan model for the vessel to be berthed based on the arrival information and the optimal transfer plan, calling a berthing planning algorithm, and performing berthing optimization calculation on the berthing plan model to obtain an optimal berthing plan includes:

[0017] Obtaining the ships to be berthed that involve direct transshipment in the optimal transshipment plan, and rearranging the ship sequence in the corresponding optimal transshipment topology to obtain a transformation topology, wherein the direct transshipment links of the ships in the transformation topology do not cross, and the ship sequence corresponding to each variation of the topology represents a feasible berthing order;

[0018] If the arrival status of the vessel to be berthed is static arrival, calling a static berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan;

[0019] If the arrival status of the vessel to be berthed is dynamic arrival, a dynamic berthing algorithm is called to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan.

[0020] In some embodiments, calling a static berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan includes:

[0021] Analyzing the optimal transshipment plan to determine a first berthing constraint condition of the berthing plan model;

[0022] Based on the first algorithm and the first berthing constraint, performing berthing optimization calculations on the vessels waiting to be berthed that have direct transfer relationships in the conversion topology, so as to minimize the completion time of the vessels waiting to be berthed that are directly transferred, and obtaining a partial berthing plan;

[0023] Filling the remaining vessels waiting to berth for which no berthing plan has been generated into the berthing gaps in the partial berthing plan, and optimizing different filling methods based on the second algorithm to minimize the total completion time of all vessels waiting to berth, thereby obtaining the completion time of the berthing plan corresponding to the converted topology;

[0024] The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

[0025] In some embodiments, calling a dynamic berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan includes:

[0026] Analyzing the optimal transshipment plan to determine a second berthing constraint condition of the berthing plan model;

[0027] Inserting the remaining waiting vessels that do not participate in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence;

[0028] Based on the second berthing constraint, optimizing the berthing sequence to minimize the total completion time of all vessels to be berthed, and obtaining the completion time of the berthing plan corresponding to the conversion topology;

[0029] The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

[0030] According to a second aspect of the present disclosure, there is provided a device for generating a ship berthing plan, comprising:

[0031] a construction unit, configured to obtain ship information and arrival information of a vessel to be berthed, and to construct a transshipment plan model for the vessel to be berthed based on the ship information;

[0032] a generating unit, configured to apply a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed;

[0033] The optimization unit is used to construct a berthing plan model for the vessel to be berthed based on the arrival information and the optimal transfer plan, call a berthing planning algorithm, perform berthing optimization calculation on the berthing plan model, and obtain an optimal berthing plan.

[0034] In some embodiments, the building blocks include:

[0035] A determination module is used to determine the transshipment decision variables of the vessel to be berthed based on the vessel information, wherein the transshipment decision variables include: a transshipment mode decision variable and a direct transshipment quantity decision variable;

[0036] A construction module is used to construct the transshipment planning model using the transshipment decision variables and the transshipment objective function.

[0037] In some embodiments, the generating unit includes:

[0038] A solution module, for applying a constraint generation algorithm to the transshipment planning model, dynamically adding constraints, and accelerating the solution;

[0039] A generation module is used to optimize and solve the transshipment plan model to maximize the amount of cargo directly transshipped between the giant ships and the feeder ships in the waiting ships, and obtain the optimal transshipment plan.

[0040] In some embodiments, the optimization unit includes:

[0041] an arrangement module, configured to obtain the vessels to be berthed involving direct transshipment in the optimal transshipment plan, and rearrange the ship sequences in the corresponding optimal transshipment topology to obtain a transformation topology, wherein the direct transshipment links of the ships in the transformation topology do not cross, and the ship sequence corresponding to each change topology represents a feasible berthing order;

[0042] a first optimization module, configured to, when the arrival status of the vessel to be berthed is static arrival, call a static berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan;

[0043] The second optimization module is used to call a dynamic berthing algorithm when the arrival status of the vessel to be berthed is dynamic arrival, perform berthing optimization calculation on the berthing plan model, and obtain the optimal berthing plan.

[0044] In some embodiments, the first optimization module is further configured to:

[0045] Analyzing the optimal transshipment plan to determine a first berthing constraint condition of the berthing plan model;

[0046] Based on the first algorithm and the first berthing constraint, performing berthing optimization calculations on the vessels waiting to be berthed that have direct transfer relationships in the conversion topology, so as to minimize the completion time of the vessels waiting to be berthed that are directly transferred, and obtaining a partial berthing plan;

[0047] Filling the remaining vessels waiting to berth for which no berthing plan has been generated into the berthing gaps in the partial berthing plan, and optimizing different filling methods based on the second algorithm to minimize the total completion time of all vessels waiting to berth, thereby obtaining the completion time of the berthing plan corresponding to the converted topology;

[0048] The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

[0049] In some embodiments, the second optimization module is further configured to:

[0050] Analyzing the optimal transshipment plan to determine a second berthing constraint condition of the berthing plan model;

[0051] Inserting the remaining waiting vessels that do not participate in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence;

[0052] Based on the second berthing constraint, optimizing the berthing sequence to minimize the total completion time of all vessels to be berthed, and obtaining the completion time of the berthing plan corresponding to the conversion topology;

[0053] The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

[0054] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0055] at least one processor; and

[0056] a memory communicatively connected to the at least one processor; wherein,

[0057] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0058] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0059] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0060] The present disclosure provides a method and device, electronic device, and storage medium for generating a ship berthing plan. The method obtains ship information and arrival information of a vessel to be berthed, and based on the ship information, constructs a transshipment plan model for the vessel to be berthed; applies a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed; and constructs a berthing plan model for the vessel to be berthed based on the arrival information and the optimal transshipment plan. A berthing planning algorithm is then called to perform berthing optimization calculations on the berthing plan model to obtain an optimal berthing plan. Compared to related technologies, the embodiments of the present disclosure generate and optimize transshipment plans and berthing plans by obtaining detailed ship information and berthing information, which can reasonably arrange the berthing operation sequence of ships, increase the direct transshipment volume and transshipment efficiency of the port, and reduce operating energy consumption costs.

[0061] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0063] Figure 1 A flow chart of a method for generating a ship berthing plan provided in an embodiment of the present disclosure;

[0064] Figure 2 A flow chart of a method for generating a ship berthing plan provided in an embodiment of the present disclosure;

[0065] Figure 3 A schematic diagram of a topological relationship of transshipment between ships provided in an embodiment of the present disclosure;

[0066] Figure 4 A flowchart of a method for generating a berthing plan using a static berthing algorithm provided in an embodiment of the present disclosure;

[0067] Figure 5 A schematic diagram of a transformation topology derivation process of an optimal transport topology provided in an embodiment of the present disclosure;

[0068] Figure 6 A schematic diagram of three types of berthing clearances in a partial berthing solution provided in an embodiment of the present disclosure;

[0069] Figure 7A flowchart of a method for generating a berthing plan using a dynamic berthing algorithm provided in an embodiment of the present disclosure;

[0070] Figure 8 A schematic structural diagram of a device for generating a ship berthing plan provided in an embodiment of the present disclosure;

[0071] Figure 9 A schematic structural diagram of another device for generating a ship berthing plan provided by an embodiment of the present disclosure;

[0072] Figure 10 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0073] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0074] Mega-vessels unload at hub bulk terminals, and their transshipment cargo can be transported to the transshipment yard or loaded directly onto feeder vessels. Meanwhile, feeder vessels load at the terminal, and their transshipment cargo can be transshipped from the transshipment yard or directly from the mega-vessels. With these vessels, terminal operators must decide how to berth the vessels to efficiently perform transshipment operations and maximize the share of direct transshipments. More specifically, terminal operators must determine the berthing time for each vessel, the transshipment method (direct or indirect), and the number of direct transshipments between each mega-vessel and feeder pair in order to achieve a greater amount of direct transshipments through a feasible berthing plan.

[0075] The following describes the method and device, electronic device, and storage medium for generating a ship berthing plan according to embodiments of the present disclosure with reference to the accompanying drawings.

[0076] Figure 1 A flowchart of a method for generating a ship berthing plan provided in an embodiment of the present disclosure.

[0077] like Figure 1 As shown, the method comprises the following steps:

[0078] Step 101: Obtain ship information and arrival information of a vessel to be berthed, and construct a transshipment plan model for the vessel to be berthed based on the ship information.

[0079] In the embodiments of the present disclosure, in order to efficiently and orderly arrange ship berthing and operations in port operations management, it is first necessary to obtain comprehensive and accurate detailed ship information of the ships to be berthed. This process usually involves communication with shipowners, agents, or relevant maritime agencies to ensure the real-time and accuracy of the data.

[0080] The acquisition of ship information includes but is not limited to the following aspects: Basic ship data: such as ship name, call sign, IMO number, flag state, ship type (such as container ship, bulk carrier, tanker, etc.), length, width, draft, gross tonnage, net tonnage, etc. These data are crucial for evaluating the seaworthiness of the ship, selecting a suitable berth, and calculating berth occupancy time. Cargo information: including the type, quantity, packaging method, special requirements (such as dangerous goods, refrigerated goods, etc.) of cargo. This information not only affects the loading and unloading process of the ship, but is also directly related to the formulation of the transshipment plan, because different cargoes may require different processing methods and time. Crew and equipment status: Understanding the ship's crew configuration, health status, and the integrity of the onboard equipment will help predict possible operational delays and formulate emergency measures.

[0081] Arrival information acquisition focuses on the following aspects: Estimated Time of Arrival (ETA): This forecasts the specific time a ship will arrive at the port based on the vessel's current position, speed, weather conditions, and route planning. Arrival Window: This sets a reasonable arrival time range (the arrival window) for the ship, taking into account factors such as tides, channel congestion, and port operations. Communication and Navigation Equipment Status: This ensures that the vessel's communication and navigation equipment are functioning properly to ensure smooth communication with the port during navigation.

[0082] Based on the ship information and arrival information obtained above, a transshipment plan model for ships waiting to berth can be constructed. The scope of ships waiting to berth widely covers giant ships and feeder ships. The embodiment of the present disclosure specifically uses the transshipment of goods from giant ships to feeder ships as an example to illustrate. This is intended to clearly illustrate the transshipment process, rather than to impose any form of restriction on the actual transshipment method. It should be clear that whether the goods are transferred from giant ships to feeder ships, or from feeder ships to giant ships, the core lies in the design and implementation of the transshipment plan. The formulation principles, methods and optimization strategies of the plan maintain a high degree of consistency and applicability in both cases. Therefore, the embodiment of the present disclosure is proposed to provide a specific operating scenario for easy understanding and application, rather than to limit the only direction or mode of transshipment activities.

[0083] The transshipment planning model will comprehensively consider multiple factors such as the vessel's loading and unloading needs, berth resources, terminal operating capacity, cargo transshipment priority and sequence, and possible weather and tidal impacts, and optimize through algorithms to increase the amount of direct transshipment cargo. The transshipment planning model's goal is to focus on increasing the amount of direct transshipment cargo. It uses algorithms to analyze the destination and transshipment needs of the cargo carried by each ship to find the best cargo matching and transshipment path. This includes identifying which cargo can be directly transshipped from one large ship to another feeder ship without going through additional loading, unloading or storage processes.

[0084] Step 102: Apply a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed.

[0085] In the disclosed embodiments, based on the established transshipment planning model, in order to develop an optimal transshipment plan for vessels awaiting berthing that is both feasible and efficient, a comprehensive constrained optimization process must be performed on the model. This process involves multiple considerations and adjustments to ensure that the resulting transshipment plan can be successfully implemented in practice and achieve the desired results.

[0086] When constructing a transshipment plan, the core goal is not only to maximize the amount of cargo that can be directly transshipped to improve efficiency, but also to deeply consider the actual configuration of the port, including but not limited to berth capacity, loading and unloading equipment capabilities, channel navigation time restrictions, natural conditions such as weather and tidal influences, as well as factors such as the scheduling and scheduling of port operators. This process requires meticulous planning to ensure that the transshipment plan can be seamlessly connected and transformed into a practical berthing plan. Specifically, it must ultimately ensure that the transshipment plan can be implemented through a feasible berthing plan. Therefore, it is necessary to introduce a series of constraints to ensure that the transshipment plan can be implemented in the actual berthing arrangement, and ultimately achieve the goal of maximizing the number of direct transshipments.

[0087] Step 103: Based on the arrival information and the optimal transfer plan, a berthing plan model of the vessel to be berthed is constructed, a berthing planning algorithm is called, and berthing optimization calculation is performed on the berthing plan model to obtain an optimal berthing plan.

[0088] In the embodiments of the present disclosure, a berthing plan model for ships to be berthed is constructed based on the arrival information of the ships, including the specific arrival time of each ship, as well as the relevant regulations and characteristics in static or dynamic arrival situations, and combined with the optimal transfer plan, covering important information such as the transfer topology, the specific transfer relationship between ships, and the clear direct transfer quantity.

[0089] When constructing the model, for each ship waiting to berth, it is necessary to clearly define its arrival time and stipulate that they can berth at any time after arrival. The key decision variable is to determine the start and end time of berthing for each giant ship and feeder ship. Next, the berthing planning algorithm is called to perform berthing optimization calculations on the berthing planning model. Specifically, a mathematical programming model is first established, and the goal is to minimize the completion time of berthing, while meeting a series of constraints. For example, the berthing time must be later than the arrival time of the ship, the berthing time must be longer than the default berthing time, the berthing time of different ships cannot overlap with each other, and for pairs of ships involved in direct transshipment, the berthing time constraints between them must be considered, and the synchronized berthing time must be able to meet the needs of cargo transshipment. Then, different algorithms are used for optimization calculations according to the arrival of the ships. In the case of static arrival, a specific algorithm is used. The specific steps include converting the optimal transfer topology into multiple different topologies, determining the corresponding partial berthing plan for each topology, considering the impact of direct transfer operations on the optimal berthing time of the ship, and designing corresponding algorithms to determine these plans. At the same time, the berthing gaps must be filled, considering the impact of the filling methods of different types of gaps on the total time, and optimizing the filling method by establishing a mathematical model. Finally, the topology with the minimum total time and the corresponding berthing plan are selected from many options. In the case of dynamic arrival, the algorithm used includes converting the optimal transfer topology into multiple topologies, inserting the berthing sequence of ships that do not participate in direct transfer under each topology, determining the berthing plans of all ships under each retained topology, and finally selecting the topology with the minimum total time and the corresponding berthing plan.

[0090] The present disclosure provides a method for generating a ship berthing plan, which obtains ship information and arrival information of a vessel to be berthed, and based on the ship information, constructs a transshipment plan model for the vessel to be berthed; applies a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed; and constructs a berthing plan model for the vessel to be berthed based on the arrival information and the optimal transshipment plan. A berthing planning algorithm is then invoked to perform berthing optimization calculations on the berthing plan model to obtain an optimal berthing plan. Compared to related technologies, the present disclosure generates and optimizes transshipment and berthing plans by obtaining detailed ship information and berthing information, which can reasonably arrange the order of ship berthing operations, increase the port's direct transshipment volume and transshipment efficiency, and reduce operating energy costs.

[0091] In order to clearly illustrate the method of generating a berthing plan in the present disclosure, this embodiment provides a flowchart of another method for generating a ship berthing plan.

[0092] like Figure 2 As shown, the method comprises the following steps:

[0093] Step 201: Determine the transshipment decision variables of the vessel to be berthed based on the vessel information, wherein the transshipment decision variables include: transshipment mode decision variables and direct transshipment quantity decision variables.

[0094] Step 202: construct the transshipment planning model using the transshipment decision variables and transshipment objective function.

[0095] Step 203: Apply a constraint generation algorithm to the transshipment plan model, dynamically add constraints, and accelerate the solution.

[0096] Step 204 : Optimizing and solving the transshipment plan model to maximize the quantity of cargo directly transshipped between the mega-ships and the feeder ships among the vessels waiting to be berthed, thereby obtaining the optimal transshipment plan.

[0097] Based on the transshipment constraints, the transshipment plan model is optimized and solved to maximize the quantity of cargo directly transshipped between the giant ships and the feeder ships among the vessels to be berthed, thereby obtaining the optimal transshipment plan.

[0098] Specifically, in steps 201 to 204, the optimal transshipment plan is generated based on the ship information and the arrival information of the waiting ships. First, the transshipment plan is determined, and the decision-making includes determining the transshipment method and the number of direct transshipments between each pair of ships. Mega ships (also referred to as mega ships) and feeder ships (also referred to as feeder ships) are respectively denoted as and is the set of all ships that need to berth (i.e. ships waiting to berth). At the same time, the cargo type set is represented by K. For each giant ship The unloading amount of type k∈K is For each feeder ship The loading quantity of type k∈K is The efficiency of the conveyor is R0. If direct transfer is arranged between giant ship i and feeder ship j, let the binary variable x ij is equal to 1, is the number of cargoes k directly transferred between the pair of ships. In this step, we ignore the ship's berthing schedule and focus solely on the transshipment plan. However, we must ensure that the transshipment plan can ultimately be realized through a feasible berthing schedule. Therefore, to ensure the feasibility of the berthing time, necessary and sufficient conditions must be met. To address this issue, we introduce the concept of "transshipment topology," which represents the transshipment plan as a topological structure.

[0099] A transshipment topology is a topological representation of transshipment plans, where large vessels and feeder vessels are displayed in two columns, and each link between any two vessels represents a direct transshipment arrangement. The sequence of ships in a transshipment topology does not represent the order of berthing. A transshipment topology can have at least one feasible berthing plan if and only if both of the following conditions are met: 1. The transshipment topology has no loops. 2. For any vessel (vessel ), there are at most two feeder (mega) ships connected to ship i (ship k) that can be connected to All ships except i(k). Figure 3 Three examples of transshipment topologies are shown. According to the definition of a transshipment topology, only transshipment topology 1 can produce a feasible berthing plan. In short, the proof of sufficiency by contradiction shows that if a topology does not satisfy both conditions, then it cannot produce any feasible berthing plan. Since ships must be processed continuously, it is clear that if the sequence in a transshipment topology represents a berthing sequence, then if all links do not cross, then the topology can have at least one feasible berthing plan. Therefore, the proof of necessity is provided by a construction method that shows that if a given topology satisfies both conditions, it can be transformed into multiple topologies without any crossing links by switching the ship sequence.

[0100] Based on the constraints of the transshipment topology with feasible berthing plans, the following set of constraints can be established to obtain the optimal transshipment plan, which will be implemented in one or more feasible berthing plans. The transshipment topology with the optimal transshipment plan is called the optimal transshipment topology. Based on the ship information and arrival information, a series of transshipment constraints need to be established, including but not limited to:

[0101] The amount of cargo k directly loaded from the mega vessel onto the feeder vessel cannot exceed the total amount of cargo k on the mega vessel.

[0102]

[0103] The amount of cargo k loaded directly from the mega-vessel onto the feeder vessel cannot exceed the total loading capacity of cargo k on the feeder vessel.

[0104]

[0105] In this case, x is only valid if the amount of cargo transferred between megaship i and feeder ship j via direct transfer is greater than 0. ij = 1. Note that the following inequality allows x ij =1, The existence of avoids strict inequalities and does not affect the optimal solution. Constraints (4), (5), (7), and (8) follow the same adjustments. M is a very large integer.

[0106]

[0107] The transshipment topology must be loop-free. For any vessel (vessel ), there are at most two feeder (mega) ships connected to ship i (ship j) that can be connected to If the large (feeder) ship i(j) completes a direct transfer with at least one ship, then set the binary variable

[0108]

[0109]

[0110] The domains of the variables are listed below:

[0111]

[0112] With the above constraints, the transshipment plan is optimized, and the goal is to maximize the number of goods transshipped by direct transshipment, which is formulated as follows:

[0113]

[0114] Constraint (6) is similar to the commonly used subcircuit elimination constraint (SEC), which is called loop elimination constraint in the embodiment of the present application. The number of constraints contained in constraint (6) increases exponentially. Therefore, only when Only when the value of is small can it be possible to generate all the inequalities in advance and pass them to the model before starting the optimization. As a result, a separation algorithm is needed during the optimization process to identify violations of constraint (6) in the solution. A constraint generation process is developed and embedded in the branch and bound framework to ensure the optimal solution of the problem. Given constraints (1) to (5), (7) to (16) and (18), an initial linear programming (LP) relaxation is obtained. Constraint (17) is relaxed and constraint (6) is dynamically generated into the problem. At each node of the search tree with a feasible relaxed solution, the separation problem of integer and fractional solutions is solved simultaneously and all violated ring elimination constraints are added until the relaxed solution has no rings. The optimal relaxed solution violation Loop to eliminate constraints if:

[0115]

[0116] In order to express the separation problem, we introduce g i =1 binary variable If a large (feeder) ship i(j) is from the unknown set Select, and f ij With f ij =1 if The problem of the giant ship v is formulated as the following integer programming model (separation problem model):

[0117]

[0118] because The linear programming relaxation of the above integer programming has an optimal solution where Should be as large as possible. Assume g i ≤1, Then constraint (23) is automatically satisfied and can be discarded. Now, the remaining constraints ensure that the linear programming relaxation is completely unimodular and that the integer programming model always has an optimal integer solution. Since a loop always contains both megaships and feeder ships, the separation problem can be solved for megaships only and exactly by solving The maximum flow problem is solved because the constraint matrix in the dual of the linear programming relaxation is a node-arc incidence matrix. For the separation problem, any strictly positive feasible solution will result in a constraint violation. The constructed transshipment planning model is solved using optimization algorithms (such as linear programming, integer programming, heuristic algorithms, or metaheuristic algorithms). These algorithms can find an optimal or near-optimal solution that maximizes the transshipment objective function while satisfying all transshipment constraints.

[0119] Step 205: Obtain the ships to be berthed that involve direct transshipment in the optimal transshipment plan, and rearrange the ship sequence in the corresponding optimal transshipment topology to obtain a conversion topology, wherein the direct transshipment links of the ships in the conversion topology do not cross, and the ship sequence corresponding to each change topology represents a feasible berthing order.

[0120] Step 206: If the arrival status of the vessel to be berthed is static arrival, a static berthing algorithm is called to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan.

[0121] Step 207: If the arrival status of the vessel to be berthed is dynamic arrival, a dynamic berthing algorithm is called to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan.

[0122] Specifically, in steps 205 to 207, after obtaining the optimal transshipment plan, the system further focuses on the vessels waiting to berth that are involved in direct transshipment. Direct transshipment is a key component in improving port operational efficiency because it reduces the number of transit times and storage time for cargo. To optimize direct transshipment, the system analyzes the ship sequence in the optimal transshipment plan and considers the direct transshipment relationships between them. In this step, the system first identifies all vessel pairs involved in direct transshipment and records the transshipment links between them.

[0123] To construct the transfer topology, an optimization algorithm is used to rearrange the ship sequence while maintaining the continuity and efficiency of direct transfer links. Each transfer topology represents a feasible berthing sequence in which direct transfers between ships are optimized.

[0124] If the arrival status of waiting vessels is static—meaning the arrival times and order of all vessels are fixed and unchanging—the system can use a static berthing algorithm to optimize the berthing schedule. This algorithm typically calculates an optimal berthing sequence based on known vessel arrival information, taking into account factors such as berth capacity, loading and unloading efficiency, and resource constraints.

[0125] During the optimization process, the algorithm tries different combinations of berthing sequences and evaluates the impact of each on transshipment efficiency, costs, and the environment. By comparing the pros and cons of different combinations, the algorithm ultimately determines an optimal berthing schedule that maximizes port operational efficiency while satisfying all constraints.

[0126] Unlike static arrivals, the arrival times and order of ships in dynamic arrival scenarios are constantly changing. To address this uncertainty, the system requires a dynamic berthing algorithm to optimize berthing plans in real time. Dynamic berthing algorithms typically offer greater flexibility and adaptability, dynamically adjusting berthing plans based on real-time arrival information. As the algorithm operates, the system continuously receives new ship arrival information and uses this information to reevaluate and optimize berthing plans.

[0127] In order to clearly illustrate the static docking algorithm in the present disclosure, this embodiment provides a flow chart of a static docking algorithm.

[0128] like Figure 4 As shown, the method comprises the following steps:

[0129] Step 301: Analyze the optimal transshipment plan to determine the first berthing constraint of the berthing plan model.

[0130] Step 302 : Based on the first algorithm and the first berthing constraint, berthing optimization calculation is performed on the vessels waiting to be berthed in the conversion topology that have direct transfer relationships, so as to minimize the completion time of the vessels waiting to be berthed in the direct transfer relationship, and obtain a partial berthing plan.

[0131] In step 303, the remaining vessels waiting to berth for which no berthing plan has been generated are filled into the berthing gaps in the partial berthing plan. Different filling methods are optimized based on the second algorithm to minimize the total completion time of all vessels waiting to berth, thereby obtaining the completion time of the berthing plan corresponding to the converted topology.

[0132] Step 304 : Select the berthing plan corresponding to the conversion topology with the shortest completion time as the optimal berthing plan.

[0133] Specifically, in steps 301 to 304, the optimal transshipment plan obtained in steps 201 to 204 is used to complete the detailed berthing plan for all ships. Each ship i(j) has an arrival time It can berth at any time after its arrival. Under static arrival, the arrival time Set to 0, the key decision variables are the start and end of the berthing time of each large (feeder) ship

[0134] set up q is the optimal transfer plan ij The solution, is a set of (i, j) such that x in the optimal transport plan ij = 1. Assume that the berthing time of giant ship i∈M starts at the berthing time of giant ship i Completed before, then set the binary decision variable y ii′ = 1. Further, if the feeder ship The berthing time of feeder ship j starts at the berthing time of feeder ship j Completed before, then set z jj′ = 1. If the berthing time of the giant ship i∈M is Completed before the end of the berthing time, let e ij = 1, if the berthing time of the giant ship i∈M is Starts before the berthing time begins, let s ij = 1. From these instructions, we can derive the following set of constraints:

[0135] The maximum voyage is the latest end time of the berthing time of mega ships and feeder ships

[0136]

[0137] The start berthing time of each vessel should be later than its arrival time

[0138]

[0139] Each vessel's berthing time should be longer than its default berthing time

[0140]

[0141] Vessel berthing times cannot overlap

[0142]

[0143] For ships The constraints for the end of berthing time are as follows:

[0144]

[0145] For ships The constraints on the start of the berthing time are given as follows:

[0146]

[0147] The simultaneous berthing time should be long enough to allow cargo to be transshipped via direct transshipment

[0148]

[0149] The domain of the variable is as follows

[0150]

[0151] The goal is to minimize the maximum time of parking

[0152]

[0153] Note the intermediate variable y ii′ 、z jj′ The number of and the corresponding constraints (31) to (34) is a power function of the problem size. The above programming model grows rapidly when the problem size increases, so it is necessary to design tailored algorithms for static and dynamic reach without relying on commercial solvers.

[0154] Under static arrival conditions, vessels can be stranded at anchor for extended periods due to extreme weather, potentially impacting future operations. Therefore, a berthing schedule with minimal optimal gaps is used to address this issue. In contrast, with dynamic arrivals, the requirement to minimize completion times is less stringent than with static arrivals. Therefore, terminal operators can choose between shorter runs with higher optimal gaps and longer runs with smaller optimal gaps.

[0155] Assuming that direct transfer operations are not performed between mega-ships and feeder ships, the berthing schedules of mega-ships and feeder ships can be determined separately according to the mature single-machine scheduling algorithm. The difficulty of scheduling lies in the coordination problem between the ships performing direct transfer operations. Since direct transfer operations require synchronized berthing times, this limits the The berthing sequence and berthing time of the vessels are determined, so the static berthing algorithm in the embodiment of the present disclosure provides a method for arranging the remaining vessels. Arrange in advance Before introducing the static berthing algorithm, we first need to define a concept called "transformation topology", which is the topology transformed from the optimal transit topology and represents the berthing order.

[0156] Convert topology: By switching the order of ships, The topology derived from the optimal transshipment topology. The ship sequence in the transshipment topology represents the berthing sequence. All links in the transshipment topology do not cross.

[0157] The static docking algorithm works as follows: (1) The optimal transport topology is transformed into multiple transformed topologies; (2) given each transformed topology, determine a topology consisting only of (3) the berthing intervals (i.e., idle time between ships) of the partial berthing plan are divided into the berthing intervals (i.e., idle time between ships) of the remaining ships. Fill, determine the berthing scheme of these ships, and adjust as needed based on the topology after each transformation (4) Select the transformed topology and the corresponding berthing plan with the maximum range.

[0158] In order to complete the above steps effectively, in step (1), analyze (i) from The exact number of transfer topologies derived from the optimal transfer topology of , and (ii) the dominance rules for these transfer topologies so that a complete enumeration can be avoided. In steps (2) and (3), we analyze (i) how the direct transfer operation affects the optimal berthing duration of the ship given the transfer topology. And (ii) how the gap filling method affects the maximum voyage. By determining A partial docking plan (given each preserved transition topology) is then optimally inserted in the docking gaps

[0159] The topology is analyzed to derive a transfer topology from the optimal transshipment topology. A "complete subtopology" concept is clearly defined. A complete subtopology is a subset of the transshipment topology in which any two ships in the topology are connected by one or more links. Ships in a complete subtopology are connected only to other ships within the same complete subtopology and not to any other ships outside the complete subtopology.

[0160] set up for The set of all complete subtopologies of the optimal transport topology. Note that the union of all complete subtopologies is The optimal transport topology of V single for The set of ships connected to only one ship in V multi for The collection of ships connected to more than one ship in set up V multi (V single ) in the complete subtopology p). If the complete subtopology p contains There are multiple ships in the p is equal to 2, otherwise it is equal to 1. multi Each ship v in Defined as The set of ships linked to v in . Based on this, the following proposition is proposed.

[0161] Proposition 1: Optimal Transshipment Topology Can be transformed into Convert topology.

[0162] In short, the first element in the expression shows the number of possible sequences of complete subtopologies. The second element represents V multi The third element represents the number of possible sequences of V single Connect to V multi The number of possible sequences of blood vessels in . Figure 5 Given For an example of the derivation of all conversion topologies of the optimal transport topology, see Proposition 1. The optimal transshipment topology has two complete sub-topologies. The first one consists of mega-ships 1, 2, 4 and feeder ships 1, 2, 3, and the second one consists of mega-ship 3 and feeder ship 4. In the first topology, V multi Includes Mega Vessel 1 and Feeder Vessels 2 and 3, each connected to V single A ship in the second topology includes V single Therefore, the optimal transshipment topology can be transformed into (2!)*(2*1)*(1!*1!(*1!)=4 conversion topologies.

[0163] Before introducing the advantage rules of these conversion topologies, we must first analyze how the converted topology (i.e., docking sequence) affects Part of the berthing plan. The following concepts are defined:

[0164] Tight direct transfer duration: The shortest berthing duration required for a vessel to perform a direct transfer operation given the converted topology.

[0165] Close berthing duration: The maximum value of a vessel's default berthing duration and its close direct transit duration, given the converted topology.

[0166] Below, Proposition 2 shows that given each conversion topology, the Optimal berthing duration.

[0167] Proposition 2: Given every transition topology under static arrival, The optimal berthing time is equal to the close berthing time of the ship.

[0168] The duration of tight direct transshipment between mega vessels and feeder vessels is defined as The close berthing duration is Based on Proposition 2, a partial docking planning algorithm is proposed, which minimizes the The maximum completion time. If Then the algorithm minimizes the maximum completion time of all ships. The algorithm shows code; The codes for differ only in their sign and are therefore omitted for simplicity. This algorithm ensures optimality by making the berthing duration of each ship equal to its immediate berthing duration and allowing ships to berth at the earliest feasible time. At the same time, it also minimizes the total berthing time. To minimize Maximum range is the target.

[0169] Based on Proposition 2 and the partial berthing planning algorithm, Proposition 3 is proposed to determine The order of the containers in the equation is the main factor that determines all quantities. According to Proposition 1, Using Proposition 3, we can only Steps (2) and (3) are performed on the preserved transformation topology from and V multi sequence.

[0170] Proposition 3: Given a static arrival V multi In the case of sequence, The optimal sequence of ships in is all possible sequences that minimize the close berthing duration of v and allow the earliest feasible start time of berthing for v.

[0171] For the optimal transport topology There is only one complete subtopology in the unique case where all ships perform direct transshipment operations. Proposition 4 is introduced to prove that V multi The sequence does not affect the maximum range. In this special case, Propositions 3 and 4 can be used to determine V single and V multi The sequence of , and there is only one complete subtopological sequence, is very efficient in determining the exact optimality of the berthing plan.

[0172] Proposition 4: Sequence There is no impact on the maximum completion time of a single complete subtopology p under static arrival conditions.

[0173] Insert the remaining vessels In some berthing planning algorithms There are three types of berthing clearances in the partial berthing scheme, as follows: Figure 6 shown.

[0174] Mega (Feeder) gap type I: The berthing gap in the local berthing plan of a mega (feeder) ship within a complete sub-topology, that is, the berthing gap between two ships within the same complete sub-topology.

[0175] Mega (Feeder) gap type II: berthing gap of a mega (feeder) ship in a local berthing plan before or after completing a sub-topology.

[0176] Bottom clearance of giant (feeder) vessel: The bottom of the partial berthing plane of giant (feeder) vessel. For the sake of simplicity, it is called bottom clearance.

[0177] definition As Mega gap type I, Mega gap type II, Feeder gap type I, Feeder gap type II, Mega bottom gap, Feeder bottom gap. is the length of the gap g, The collection of medium and large ships and feeder ships are respectively denoted as and If a giant (feeder) ship i(j) is inserted into the gap g, then the binary decision variable τ ig , is equal to 1. Obviously, it is obvious that any ship in and The optimal berthing time (or tight berthing time) of a vessel is equal to its default berthing time. The sum of the tight berthing time of all mega vessels and feeder vessels is defined as The final maximum completion time of the mega-ship and feeder ship is expressed as S M and S F To solve this gap filling problem, all possible gap filling methods are analyzed, and how different methods determine the final maximum completion time. In short, the final maximum range of each type of ship after step (3) is Extension of unfilled gaps and extension of current berthing duration in the final berthing plan.

[0178] In the case of static arrival, Insufficient filling The length is insufficient, and any gaps are overfilled. The length is too large. The bottom gap filling does not extend and Therefore, a simple 0-1 variable integer programming model (gap-filling model) can be established, which can be efficiently solved by the solver to optimally fill the gaps and minimize the completion time. This produces the following set of constraints (the first berthing constraint), and the gap-filling model is used to fill the existing gaps so that an optimal berthing plan that minimizes the completion time can be obtained.

[0179] The final completion deadlines for the mega (feeder) vessels are as follows:

[0180]

[0181] Inserting a giant (feeder) ship into a viable slot is accomplished as follows:

[0182]

[0183] The domain of the variable is as follows:

[0184]

[0185] The goal is to minimize the maximum completion time

[0186]

[0187] The approximate solution works by minimizing the maximum completion time of the giant ship and then gradually By moving from the bottom gap into the gap between Type I and Type II, the feeder vessel's completion time is brought close to that of the large vessel. This process is then repeated, this time minimizing the feeder vessel's completion time and bringing the large vessel's maximum completion time close to that of the feeder vessel. Finally, the berthing solution with the lowest completion time is selected. Each subproblem in this approach can be solved using a polynomial-time algorithm, and approximate solutions can account for optimality gaps.

[0188] It should be noted that the partial berthing plan obtained in step (2) is only the minimization The partial berthing algorithm allows ships to begin berthing at their earliest feasible time, but also allows some ships to begin berthing after their earliest feasible time, still resulting in the minimum maximum maketime. This "sliding" of ships allows the length of the berthing gaps around them to be adjusted, but if the gaps are treated as fixed, it may lead to unnecessary extension of the maximum voyage distance.

[0189] In order to clearly illustrate the dynamic docking algorithm in the present disclosure, this embodiment provides a flowchart of a method for generating a docking plan using the dynamic docking algorithm.

[0190] like Figure 7 As shown, the method comprises the following steps:

[0191] Step 401: Analyze the optimal transshipment plan to determine the second berthing constraint of the berthing plan model.

[0192] Step 402: insert the remaining waiting vessels that do not participate in direct transshipment into the berthing sequence of the conversion topology to obtain a berthing sequence.

[0193] Step 403 : Based on the second berthing constraint, the berthing sequence is optimized to minimize the total completion time of all vessels to be berthed, and the completion time of the berthing plan corresponding to the conversion topology is obtained.

[0194] Step 404 : Select the berthing plan corresponding to the conversion topology with the shortest completion time as the optimal berthing plan.

[0195] Specifically, in steps 401 to 404, under dynamic arrival, the berthing time of the ship performing direct transshipment depends not only on the berthing time, but also on the arrival time of the ships participating in the transshipment operation. In this case, the berthing plan obtained from the static situation may not be feasible. Given the same v berthing sequence, one way to obtain the completion time under dynamic arrival is to first arrange the berthing plan for static arrival. If the arrival time of a ship is later than the berthing start time under static arrival, the entire berthing plan must be postponed until the ship arrives at the port. Therefore, the maximum difference between the arrival time of the ship and the optimal berthing start time under static arrival must be reduced. For the relevant content of the dynamic berthing algorithm, please refer to the relevant description of the static berthing algorithm mentioned above, and this embodiment will not be repeated one by one.

[0196] The dynamic berthing algorithm is designed as follows: (1) The optimal transport topology is transformed into multiple conversion topologies, and the docking sequence under each conversion topology is inserted (2) Determine the berthing plan for all ships under each retained transformation topology; (3) Select the berthing plan with the minimum transformed topology and the corresponding maximum completion time.

[0197] Given each preserved transition topology, define the set of ships that are inserted after the complete subtopology p for The set of ships inserted before the first complete sub-topology is exist The set of ships inserted at the position is By minimizing The completion time of , and insert in step (2) The docking order under each transformation topology is given.

[0198] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers do not limit the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0199] Corresponding to the above-mentioned method for generating a ship berthing plan, the present invention also provides a device for generating a ship berthing plan. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment and will not be further described in the present invention.

[0200] Figure 8 A schematic diagram of a device for generating a ship berthing plan according to an embodiment of the present disclosure is shown in FIG. Figure 8 Shown, including:

[0201] A construction unit 51 is configured to obtain ship information and arrival information of a vessel to be berthed, and to construct a transshipment plan model for the vessel to be berthed based on the ship information;

[0202] A generating unit 52 is configured to apply a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed;

[0203] The optimization unit 53 is configured to construct a berthing plan model for the vessel to be berthed based on the arrival information and the optimal transfer plan, call a berthing planning algorithm, perform berthing optimization calculation on the berthing plan model, and obtain an optimal berthing plan.

[0204] The present disclosure provides a device for generating a ship berthing plan. The device obtains ship information and arrival information of a vessel to be berthed, constructs a transshipment plan model for the vessel to be berthed based on the ship information, applies a constraint generation algorithm to the transshipment plan model, and generates an optimal transshipment plan for the vessel to be berthed. Based on the arrival information and the optimal transshipment plan, the device constructs a berthing plan model for the vessel to be berthed, invokes a berthing planning algorithm, and performs berthing optimization calculations on the berthing plan model to obtain an optimal berthing plan. Compared with related technologies, the embodiments of the present disclosure generate and optimize transshipment and berthing plans by obtaining detailed ship information and berthing information, which can reasonably arrange the order of ship berthing operations, increase the port's direct transshipment volume and transshipment efficiency, and reduce operating energy consumption costs.

[0205] Furthermore, in a possible implementation of this embodiment, as Figure 9 As shown, the construction unit 51 includes:

[0206] The determination module 511 is configured to determine the transshipment decision variables of the vessel to be berthed based on the vessel information, wherein the transshipment decision variables include: transshipment mode decision variables and direct transshipment quantity decision variables;

[0207] The construction module 512 is used to construct the transshipment planning model using the transshipment decision variables and the transshipment objective function.

[0208] Furthermore, in a possible implementation of this embodiment, as Figure 9 As shown, the generating unit 52 includes:

[0209] A solution module 521 is used to apply a constraint generation algorithm to the transshipment plan model, dynamically add constraints, and accelerate the solution;

[0210] The generation module 522 is used to optimize and solve the transshipment plan model to maximize the amount of cargo directly transshipped between the mega-ships and the feeder ships in the waiting ships, and obtain the optimal transshipment plan.

[0211] Furthermore, in a possible implementation of this embodiment, as Figure 9 As shown, the optimization unit 53 includes:

[0212] The arrangement module 531 is configured to obtain the vessels to be berthed that involve direct transshipment in the optimal transshipment plan, and rearrange the ship sequences in the corresponding optimal transshipment topology to obtain a transformed topology, wherein the direct transshipment links of the ships in the transformed topology do not cross, and the ship sequence corresponding to each changed topology represents a feasible berthing order;

[0213] The first optimization module 532 is configured to, when the arrival status of the vessel to be berthed is static arrival, call a static berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan;

[0214] The second optimization module 533 is configured to call a dynamic berthing algorithm when the arrival status of the vessel to be berthed is dynamic arrival, perform berthing optimization calculation on the berthing plan model, and obtain the optimal berthing plan.

[0215] Furthermore, in a possible implementation of this embodiment, the first optimization module 532 is further configured to:

[0216] Analyzing the optimal transshipment plan to determine a first berthing constraint condition of the berthing plan model;

[0217] Based on the first algorithm and the first berthing constraint, performing berthing optimization calculations on the vessels waiting to be berthed that have direct transfer relationships in the conversion topology, so as to minimize the completion time of the vessels waiting to be berthed that are directly transferred, and obtaining a partial berthing plan;

[0218] Filling the remaining vessels waiting to berth for which no berthing plan has been generated into the berthing gaps in the partial berthing plan, and optimizing different filling methods based on the second algorithm to minimize the total completion time of all vessels waiting to berth, thereby obtaining the completion time of the berthing plan corresponding to the converted topology;

[0219] The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

[0220] Furthermore, in a possible implementation of this embodiment, the second optimization module 533 is further configured to:

[0221] Analyzing the optimal transshipment plan to determine a second berthing constraint condition of the berthing plan model;

[0222] Inserting the remaining waiting vessels that do not participate in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence;

[0223] Based on the second berthing constraint, optimizing the berthing sequence to minimize the total completion time of all vessels to be berthed, and obtaining the completion time of the berthing plan corresponding to the conversion topology;

[0224] The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

[0225] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principles are the same, which is not limited in this embodiment.

[0226] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0227] Figure 10 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0228] like Figure 10 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 into a RAM (Random Access Memory) 603. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0229] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0230] Computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various specialized AI (Artificial Intelligence) computing chips, various computing units that run machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable processor, controller, microcontroller, etc. Computing unit 601 performs the various methods and processes described above, such as the method for generating a ship berthing plan. For example, in some embodiments, the method for generating a ship berthing plan can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by computing unit 601, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the aforementioned method for generating a ship berthing plan in any other appropriate manner (for example, by means of firmware).

[0231] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System on Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0232] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0233] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0234] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0235] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0236] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0237] It's important to note that artificial intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). This encompasses both hardware and software technologies. AI hardware technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily encompass computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graphs.

[0238] The various numerical numbers such as first and second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, and also indicate the order of precedence.

[0239] The at least one in the present disclosure can also be described as one or more, and the multiple can be two, three, four or more, which is not limited in the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", and there is no order of precedence or size between the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0240] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0241] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for generating a ship berthing plan, characterized in that: include: Obtaining ship information and arrival information of a vessel to be berthed, and constructing a transshipment planning model for the vessel to be berthed based on the ship information, including: determining transshipment decision variables for the vessel to be berthed based on the ship information, wherein the transshipment decision variables include: a transshipment mode decision variable and a direct transshipment quantity decision variable; and constructing the transshipment planning model using the transshipment decision variables and a transshipment objective function; Applying a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed; Based on the arrival information and the optimal transfer plan, a berthing plan model of the waiting ship is constructed, a berthing planning algorithm is called, and a berthing optimization calculation is performed on the berthing plan model to obtain an optimal berthing plan, including: obtaining the waiting ship involved in direct transfer in the optimal transfer plan, and rearranging the ship sequence in the corresponding optimal transfer topology to obtain a conversion topology, wherein the ship direct transfer links of the conversion topology do not cross, and the ship sequence corresponding to each conversion topology represents a feasible berthing order; if the arrival status of the waiting ship is static arrival, the static berthing algorithm is called, and the berthing optimization calculation is performed on the berthing plan model to obtain the optimal berthing plan; if the arrival status of the waiting ship is dynamic arrival, the dynamic berthing algorithm is called, and the berthing optimization calculation is performed on the berthing plan model to obtain the optimal berthing plan.

2. The method according to claim 1, characterized in that The applying a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed comprises: Applying a constraint generation algorithm to the transshipment planning model, dynamically adding constraints to accelerate the solution; The transshipment plan model is optimized and solved to maximize the quantity of cargo directly transshipped between the giant ship and the feeder ship among the ships waiting to be berthed, thereby obtaining the optimal transshipment plan.

3. The method according to claim 1, characterized in that The calling of the static berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan includes: Analyzing the optimal transshipment plan to determine a first berthing constraint condition of the berthing plan model; Based on the first algorithm and the first berthing constraint, performing berthing optimization calculations on the vessels waiting to be berthed that have direct transfer relationships in the conversion topology, so as to minimize the completion time of the vessels waiting to be berthed that are directly transferred, and obtaining a partial berthing plan; Filling the remaining vessels waiting to berth for which no berthing plan has been generated into the berthing gaps in the partial berthing plan, and optimizing different filling methods based on the second algorithm to minimize the total completion time of all vessels waiting to berth, thereby obtaining the completion time of the berthing plan corresponding to the converted topology; The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

4. The method according to claim 1, wherein The calling of the dynamic berthing algorithm to perform berthing optimization calculation on the berthing plan model to obtain the optimal berthing plan includes: Analyzing the optimal transshipment plan to determine a second berthing constraint condition of the berthing plan model; Inserting the remaining waiting vessels that do not participate in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence; Based on the second berthing constraint, optimizing the berthing sequence to minimize the total completion time of all vessels to be berthed, and obtaining the completion time of the berthing plan corresponding to the conversion topology; The berthing plan corresponding to the conversion topology with the shortest completion time is selected as the optimal berthing plan.

5. A device for generating a ship berthing plan, characterized in that: include: a construction unit, configured to obtain ship information and arrival information of a vessel to be berthed, and construct a transshipment planning model for the vessel to be berthed based on the ship information, comprising: determining transshipment decision variables for the vessel to be berthed based on the ship information, wherein the transshipment decision variables include: a transshipment mode decision variable and a direct transshipment quantity decision variable; and constructing the transshipment planning model using the transshipment decision variables and a transshipment objective function; a generating unit, configured to apply a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed; an optimization unit, configured to construct a berthing plan model of the vessel to be berthed based on the arrival information and the optimal transfer plan, call a berthing planning algorithm, perform berthing optimization calculation on the berthing plan model, and obtain an optimal berthing plan, comprising: obtaining the vessel to be berthed that involves direct transfer in the optimal transfer plan, and rearranging the ship sequence in the corresponding optimal transfer topology to obtain a conversion topology, wherein the direct transfer links of the ships in the conversion topology do not cross, and the ship sequence corresponding to each conversion topology represents a feasible berthing order; if the arrival status of the vessel to be berthed is static arrival, calling a static berthing algorithm, performing berthing optimization calculation on the berthing plan model, and obtaining the optimal berthing plan; if the arrival status of the vessel to be berthed is dynamic arrival, calling a dynamic berthing algorithm, performing berthing optimization calculation on the berthing plan model, and obtaining the optimal berthing plan.

6. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

8. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 4.

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