Ship berthing plan generation method and device, electronic equipment and storage medium

Abstract

The invention provides a ship berthing plan generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining ship information and arrival information of a to-be-berthed ship, building a transfer plan model of the to-be-berthed ship based on the ship information, carrying out the optimization processing of the transfer plan model through employing a constraint generation algorithm, and obtaining a ship berthing plan of the to-be-berthed ship. An optimal transfer plan is obtained; and a berthing plan model supporting the optimal transfer plan is generated, the arrival state of the ship to be berthed is determined based on the arrival information, a corresponding berthing planning algorithm is called according to the arrival state, optimization calculation is performed on the berthing plan model, and a final berthing plan is obtained. Compared with the prior art, the transfer plan and the berthing plan are generated and optimized by obtaining the detailed ship information and the berthing information, the berthing operation sequence of the ships can be reasonably arranged, the direct transfer amount and the transfer efficiency of a port are improved, and the operation energy consumption cost is reduced.

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 that transport goods 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 more and more widespread. However, such ships require longer and deeper berths, as well as matching loading and unloading capabilities and yard storage space, which makes it difficult for smaller terminals to meet their service needs, prompting transshipment operations to become a common practice. Hub bulk transshipment terminals came into being, with the main purpose of providing services for large ships and facilitating 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 distribution process and causes double handling of goods. To improve transshipment efficiency, direct transshipment has become a common practice in the industry. In a typical bulk terminal, electricity costs play an important role, with conveyor belts accounting for 50% to 70% of total electricity consumption. Due to the deep draft characteristics of large ships, berths are usually located at sea, requiring long conveyor belts to connect berths to yards, and cargo transportation in the yard also relies on multiple strip conveyors. Direct transshipment can reduce the number or length of conveyors used, thereby significantly reducing electricity consumption, which not only helps to reduce operating costs, save infrastructure and maintenance costs, but also promotes more sustainable development by reducing the carbon footprint and environmental impact of the terminal. However, hub bulk transshipment terminals are currently unable to generate efficient transshipment and berthing plans to guide the berthing of large ships and feeder ships, so that more ships can be directly transshipped and the amount of ore that is indirectly transshipped is reduced.

[0004] Therefore, how to generate scientific transshipment and berthing plans based on the cargo and arrival conditions of the ship, 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 invention provides a method and device for generating a ship berthing plan, an electronic device and a storage medium, and aims to solve the problem that the number of direct transfers between giant ships and feeder ships is small and the power consumption is large.

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

[0007] Acquire ship information and arrival information of the ship to be berthed, and construct a transshipment plan model for the ship 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 berthing optimization calculation is performed on the berthing plan model to obtain an optimal berthing plan.

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

[0011] Based on the ship information, determining the transshipment decision variables of the waiting ship, wherein the transshipment decision variables include: transshipment mode decision variables and direct transshipment quantity decision variables;

[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 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 ships and the feeder ships in the waiting ships, and to obtain the optimal transshipment plan.

[0016] In some embodiments, the berthing plan model of the vessel to be berthed is constructed based on the arrival information and the optimal transfer plan, a berthing planning algorithm is called, and berthing optimization calculation is performed on the berthing plan model to obtain the optimal berthing plan, including:

[0017] Obtaining the waiting ships for direct transshipment in the optimal transshipment plan, and rearranging the ship sequence in the corresponding optimal transshipment topology to obtain a conversion topology, wherein the ship direct transshipment links of the conversion topology do not cross, and the ship sequence corresponding to each change topology represents a feasible berthing order;

[0018] 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;

[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, the calling of 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 condition, a berthing optimization calculation is performed on the waiting-to-berth vessels with direct transfer relations in the conversion topology, so as to minimize the completion time of the waiting-to-berth vessels with direct transfer, and obtain a partial berthing plan;

[0023] Fill the remaining waiting-to-berth vessels for which berthing plans have not been generated into the berthing gaps in the partial berthing plans, and optimize and calculate different filling methods based on the second algorithm so as to minimize the total completion time of all waiting-to-berth vessels, thereby obtaining the completion time of the berthing plan corresponding to the conversion 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, the calling of the dynamic docking algorithm to perform docking optimization calculation on the docking plan model to obtain the optimal docking 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-to-berth vessels not participating in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence;

[0028] Based on the second berthing constraint condition, optimizing the berthing sequence so as to minimize the total completion time of all the 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, used for acquiring ship information and arrival information of a ship to be berthed, and constructing a transshipment plan model of the ship to be berthed based on the ship information;

[0032] A generating unit, used for applying a constraint generating 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 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.

[0034] In some embodiments, the building block comprises:

[0035] A determination module, 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;

[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 comprises:

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

[0039] The generation module is used to optimize and solve the transshipment plan model so as to maximize the quantity 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 comprises:

[0041] An arrangement module is used to obtain the waiting ships for 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 ship direct transshipment links of the conversion topology do not cross, and the ship sequence corresponding to each change topology represents a feasible berthing order;

[0042] A first optimization module is used to call a static berthing algorithm when the arrival state of the vessel to be berthed is static arrival, perform berthing optimization calculation on the berthing plan model, and 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 condition, berthing optimization calculation is performed on the waiting-to-berth vessels with direct transfer relationship in the conversion topology, so as to minimize the completion time of the waiting-to-berth vessels with direct transfer, and obtain a partial berthing plan;

[0047] Fill the remaining waiting-to-berth vessels for which berthing plans have not been generated into the berthing gaps in the partial berthing plans, and optimize and calculate different filling methods based on the second algorithm so as to minimize the total completion time of all waiting-to-berth vessels, thereby obtaining the completion time of the berthing plan corresponding to the conversion 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-to-berth vessels not participating in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence;

[0052] Based on the second berthing constraint condition, optimizing the berthing sequence so as to minimize the total completion time of all the 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, and 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, which obtains the ship information and arrival information of the ship to be berthed, and constructs a transshipment plan model of the ship to be berthed based on the ship information; applies a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the ship to be berthed; constructs a berthing plan model for the ship to be berthed based on the arrival information and the optimal transshipment plan, calls a berthing planning algorithm, performs berthing optimization calculations on the berthing plan model, and obtains an optimal berthing plan. Compared with the related art, the embodiment of the present disclosure generates and optimizes the transshipment plan and berthing plan by obtaining detailed ship information and berthing information, which can reasonably arrange the berthing operation sequence of the ship, improve the direct transshipment volume and transshipment efficiency of the port, and reduce the operating energy consumption cost.

[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 used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0063] Figure 1 A schematic diagram of a process for generating a ship berthing plan provided in an embodiment of the present disclosure;

[0064] Figure 2 A schematic diagram of a process 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 flow chart 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 diagram of the structure of a device for generating a ship berthing plan provided in an embodiment of the present disclosure;

[0071] Fig. 9 A schematic diagram of the structure of another device for generating a ship berthing plan provided in an embodiment of the present disclosure;

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

[0073] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art 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-ships unload at hub bulk terminals, and their transshipment cargo can be transported to the transshipment yard or directly loaded onto feeder ships. Meanwhile, feeder ships load at the terminal, and their transshipment cargo can be transshipped from the transshipment yard or directly from the mega-ships. With these ships, terminal operators must decide how to berth the ships in order to efficiently carry out transshipment operations and maximize the share of direct transshipments. More specifically, terminal operators must determine the berthing time of each ship, the transshipment mode (direct or indirect), and the number of direct transshipments between each pair of mega-ships and feeder ships in order to achieve a greater number of direct transshipments through a feasible berthing plan.

[0075] The following describes the method and device for generating a ship berthing plan, an electronic device, and a storage medium according to an embodiment 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, obtaining the ship information and arrival information of the ship to be berthed, and constructing a transshipment plan model of the ship to be berthed based on the ship information.

[0079] In the embodiments of the present disclosure, in order to efficiently and orderly arrange the berthing and operation of ships in port operation management, it is first necessary to comprehensively and accurately obtain detailed ship information of ships to be berthed. This process usually involves communication with ship owners, agents or relevant maritime agencies to ensure the real-time and accuracy of 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 essential for evaluating the seaworthiness of the ship, selecting a suitable berth, and calculating the berth occupancy time. Cargo information: including the type, quantity, packaging method, special requirements (such as dangerous goods, refrigerated goods, etc.) of the 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 crew configuration, health status, and integrity of the ship's equipment will help predict possible operational delays and formulate emergency measures.

[0081] The acquisition of arrival information mainly focuses on the following aspects: Estimated time of arrival (ETA): Based on the current position, speed, weather conditions and route planning of the ship, the specific time of arrival of the ship at the port is predicted. Arrival window: Considering factors such as tides, channel congestion, port operation arrangements, etc., a reasonable arrival time range is set for the ship, namely the arrival window. Communication and navigation equipment status: Confirm that the ship's communication and navigation equipment are working properly to ensure smooth contact with the port during the voyage.

[0082] Based on the ship information and arrival information obtained above, a transshipment plan model for ships to be berthed can be constructed. The scope of ships to be berthed widely covers giant ships and feeder ships. The embodiment of the present disclosure specifically uses the example of transshipping cargo from giant ships to feeder ships to illustrate the transshipment process clearly, rather than imposing any form of restriction on the actual transshipment method. It should be clear that whether the cargo is transferred from a giant ship to a feeder ship, or from a feeder ship to a giant ship, the core lies in the design and execution 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 intended 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 the loading and unloading needs of ships, berth resources, terminal operating capacity, cargo transshipment priority and order, and possible weather and tidal effects, and optimize through algorithms to increase the number of directly transshipped goods. The goal of the transshipment planning model is to focus on increasing the number of directly transshipped goods, and to find the best cargo matching and transshipment routes through algorithms to analyze the destination and transshipment needs of the goods carried by each ship. This includes identifying which goods can be directly transferred from a giant ship to another feeder ship without going through additional loading and unloading or storage processes.

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

[0085] In the embodiments of the present disclosure, based on the construction of the transshipment plan model, in order to formulate an optimal transshipment plan for the vessels to be berthed that is both feasible and efficient, the model must be subjected to in-depth constraint optimization processing. This process involves considerations and adjustments in multiple aspects to ensure that the transshipment plan finally generated can be smoothly implemented in actual operations and achieve the expected goals.

[0086] When constructing a transshipment plan, the core goal is not only to maximize the amount of cargo 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 capacity, channel navigation time restrictions, natural conditions such as weather and tidal effects, and factors such as the scheduling and scheduling of port operators. This process requires detailed 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, based on the arrival information of the ships, including the specific arrival time of each ship, and the relevant regulations and characteristics in the static or dynamic arrival situations, and combined with the optimal transfer plan, covering the topological structure of transfer, the specific transfer relationship between ships, and the clear direct transfer quantity and other important information, a berthing plan model for the ships to be berthed is constructed.

[0089] When constructing the model, for each ship to be berthed, it is necessary to clarify 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, call the berthing planning algorithm to perform berthing optimization calculations on the berthing planning model. Specifically, first establish a mathematical programming model, set the goal to minimize the completion time of berthing, and at the same time meet a series of constraints, such as the berthing time must be later than the arrival time of the ship, the berthing time must be greater 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 synchronous berthing time must be able to meet the needs of cargo transshipment. Then, according to the arrival of the ship, different algorithms are used for optimization calculations. 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, which requires 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 filling methods of different types of gaps on the total time, and optimizing the filling method by establishing a mathematical model, and finally selecting the topology with the smallest total time and the corresponding berthing plan 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 plan of all ships under each retained topology, and finally selecting the topology with the smallest total time and the corresponding berthing plan.

[0090] The present disclosure provides a method for generating a ship berthing plan, which obtains the ship information and arrival information of the ship to be berthed, and based on the ship information, constructs a transshipment plan model of the ship to be berthed; applies a constraint generation algorithm to the transshipment plan model to generate an optimal transshipment plan for the ship to be berthed; based on the arrival information and the optimal transshipment plan, constructs a berthing plan model for the ship to be berthed, calls a berthing planning algorithm, performs berthing optimization calculations on the berthing plan model, and obtains an optimal berthing plan. Compared with the related art, the embodiment of the present disclosure generates and optimizes the transshipment plan and berthing plan by obtaining detailed ship information and berthing information, which can reasonably arrange the berthing operation sequence of the ship, improve the direct transshipment volume and transshipment efficiency of the port, and reduce the operating energy consumption cost.

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

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

[0093] Step 201, based on the ship information, determining the transshipment decision variables of the waiting ship, wherein the transshipment decision variables include: transshipment mode decision variables, direct transshipment quantity decision variables.

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

[0095] Step 203, applying a constraint generation algorithm to the transfer planning model, dynamically adding constraints, and accelerating the solution.

[0096] Step 204, optimizing and solving the transshipment plan model, so as to maximize the quantity of cargo directly transshipped between the giant ship and the feeder ship in the waiting ships, and obtain 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 giant ships and feeder ships among the ships to be berthed, thereby obtaining the optimal transshipment plan.

[0098] Specifically, in step 201 to step 204, the optimal transfer plan is generated according to the ship information and the arrival information of the waiting ship. First, the transfer plan is determined, and the decision-making includes determining the transfer method and the number of direct transfers between each ship pair. 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 quantity of type k∈K is For each feeder ship The loading quantity of type k∈K is The conveyor efficiency 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 cargo k directly transferred between the pair of ships. In this step, the berthing plan of the ship is not considered, only the transshipment plan is concerned. Nevertheless, it must be ensured that the transshipment plan can eventually be realized through a feasible berthing plan. Therefore, to ensure the feasibility of the berthing time, necessary and sufficient conditions need to be met. In order to solve this problem, the concept of "transshipment topology" is introduced to represent the transshipment plan as a topological structure.

[0099] A transshipment topology is a transshipment plan displayed in a topological structure, where mega ships and feeder ships are displayed in two columns, and each link between any two ships indicates a direct transshipment arrangement. The sequence of ships in a transshipment topology does not represent the berthing order. 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 ship (vessel ), there are at most two feeder (mega) ships connected to ship i (ship k) that can connect to All ships except i(k) in the . Figure 3 As examples of three transshipment topologies, according to the definition of transshipment topology, only transshipment topology 1 can produce a feasible berthing plan. In short, the sufficiency is proved by contradiction. If a topology does not meet these two conditions, then the topology cannot produce any feasible berthing plan. Since ships must be processed without interruption, it is obvious that if the sequence in the transshipment topology represents a berthing sequence, then this topology can have at least one feasible berthing plan if all links do not cross. Therefore, the necessity is proved by a construction method. If a given topology satisfies both of the above conditions, it can be converted into multiple topologies without any crossed links by switching the ship sequence.

[0100] According to 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 of 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 directly loaded from the mega-ship 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 mega ship i and feeder ship j by direct transfer is greater than 0. ij equals 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 transfer topology must be loop-free. (vessel ), there are at most two feeder (mega) ships connected to ship i (ship j) that can be connected to If the giant (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] Through 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:

[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 inequalities in advance and pass them to the model before starting the optimization. As a result, a separation algorithm is required 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 from, 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 that 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 feasible solution with strictly positive values ​​will return a constraint violation. The constructed transfer planning model is solved by using optimization algorithms such as linear programming, integer programming, heuristic algorithms, or meta-heuristic algorithms. These algorithms can find the optimal solution or approximate optimal solution that maximizes the transfer objective function while satisfying all transfer constraints.

[0119] Step 205, obtaining the waiting ships for direct transfer involved 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 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 needs to further focus on the waiting ships involved in direct transshipment. Direct transshipment is a key link in improving port operation efficiency because it can reduce the number of transshipments and storage time of goods. In order to optimize the direct transshipment process, the system needs to analyze the ship sequence in the optimal transshipment plan and consider the direct transshipment relationship between them. In this step, the system first identifies all the ship 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 the 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 the waiting ships is static, that is, the arrival time and order of all ships are determined and will not change, then the system can call the static berthing algorithm to optimize the berthing plan. The static berthing algorithm is usually based on the known ship arrival information, and calculates an optimal berthing order by considering factors such as berth capacity, loading and unloading efficiency, and resource constraints.

[0125] During the optimization process, the algorithm will try different combinations of berthing sequences and evaluate the impact of each combination on transshipment efficiency, cost and environment. By comparing the pros and cons of different combinations, the algorithm will eventually determine an optimal berthing plan that maximizes port operation efficiency while meeting all constraints.

[0126] Unlike static arrival, the arrival time and order of ships in dynamic arrival are constantly changing. In order to cope with this uncertainty, the system needs to call the dynamic berthing algorithm to optimize the berthing plan in real time. The dynamic berthing algorithm is usually more flexible and adaptable, and can dynamically adjust the berthing plan according to the real-time arrival information. During the operation of the algorithm, the system will continuously receive new ship arrival information, and re-evaluate and optimize the berthing plan based on this information.

[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 condition, berthing optimization calculation is performed on the waiting-to-berth vessels with direct transfer relationship in the conversion topology, so as to minimize the completion time of the waiting-to-berth vessels with direct transfer, and obtain a partial berthing plan.

[0131] Step 303, fill the remaining waiting ships for berthing that have not generated berthing plans into the berthing gaps in the partial berthing plan, and optimize and calculate different filling methods based on the second algorithm to minimize the total completion time of all waiting ships, and obtain the completion time of the berthing plan corresponding to the converted topology.

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

[0133] Specifically, in step 301 to step 304, the optimal transfer plan obtained in step 201 to step 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 mega (feeder) ship

[0134] set up is the optimal transport plan ij The solution, is a set of (i, j) such that x ij = 1. Assume that the berthing time of the giant ship i∈M starts at the berthing time of the giant ship i Before completion, let the binary decision variable y ii′ = 1. Further, if the feeder ship The berthing time of the feeder ship j starts at the berthing time of the feeder ship j If completed before, then z jj′ = 1. If the berthing time of the giant ship i∈M is is completed before the end of the berthing time, assuming e ij = 1, if the berthing time of the giant ship i∈M is within the feeder ship starts before the berthing time of ij = 1. From these instructions, we can get 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 ship 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 the 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 by 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 tailor-made algorithms for both static and dynamic reachability without relying on commercial solvers.

[0154] Under static arrival conditions, vessels are stranded at anchorages for long periods of time due to extreme weather, and such delays may affect future operations. Therefore, this problem is addressed with a berthing schedule with minimum optimal gap. In contrast, for dynamic arrivals, the requirement to minimize completion time is not as strict as for static arrivals. Therefore, terminal operators may be allowed to 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 ships performing direct transfer operations. Since direct transfer operations require synchronized berthing times, this limits Therefore, the static berthing algorithm in the embodiment of the present disclosure provides a method for arranging the remaining ships. Arrange before Before introducing the static berthing algorithm, we first clarify a concept called "conversion topology", which is the topology transformed from the optimal transit topology and represents the berthing order.

[0156] Conversion topology: By switching the order of ships, The topology derived from the optimal transfer topology of . The ship sequence in the transfer topology represents the berthing sequence. All links in the transfer 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 replaced by the remaining ships. Fill, determine the berthing scheme for these ships, and adjust as needed based on the topology after each transformation (4) selecting 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 and the conversion topology is derived from the optimal transshipment topology. A concept of "complete subtopology" is clearly defined. Complete subtopology: A subset of the transshipment topology where any two ships are connected in the topology through one or more links. Ships in a complete subtopology are only connected to other ships in 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 single for The set of ships that are connected to only one ship in V multi for A collection of ships connected to more than one ship, such that set up V multi (V single ) in the complete subtopology p). If the complete subtopology p contains If there are multiple ships in 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 for these conversion topologies, we must first analyze how the converted topology (i.e., docking sequence) affects 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 of a vessel's default berthing duration and its close direct transit duration, given the converted topology.

[0166] Next, 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. Given each transition topology in step (2), the algorithm minimizes The maximum completion time. If Then the algorithm minimizes the maximum completion time of all ships. The algorithm shows The code; The codes of differ only in sign and are therefore omitted for simplicity. The 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 takes the shortest total berthing time To minimize Maximum range is the goal.

[0169] Based on Proposition 2 and the partial berthing plan 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 The remaining transformation topologies perform steps (2) and (3) and are derived from and V multi sequence.

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

[0173] Insert the remaining vessels In the part of the berthing plan algorithm 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: The berthing gap of a mega (feeder) ship in the local berthing plan before or after completing the sub-topology.

[0176] Bottom clearance of giant (feeder) ship: The bottom of the partial berthing plan of giant (feeder) ship. 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 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 In order 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 voyage of each type of ship after step (3) is extension of unfilled gaps and the extension of the 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 as follows, 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 the optimal berthing plan that minimizes the completion time can be obtained.

[0179] The final completion deadline for the mega (feeder) vessel is as follows:

[0180]

[0181] Inserting a giant (feeder) ship into a feasible gap 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 moving the ship Move from the bottom gap into the gap between Type I and Type II, bringing the completion time of the feeder close to that of the mega ship. Then, repeat the process, this time minimizing the completion time of the feeder first and bringing the maximum completion time of the mega ship close to that of the feeder. Finally, choose the berthing solution with the minimum completion time. Each subproblem in this approach can be solved by a polynomial time algorithm, and the approximate solution can account for the optimality gap.

[0188] It should be noted that the partial berthing plan obtained in step (2) is only to minimize The partial berthing algorithm allows ships to start berthing at their earliest feasible time, but also allows some ships to start berthing after their earliest feasible time, which still leads to the minimum maximum completion time. This "slidable" ship allows the length of the berthing gap around it to be adjusted, but if the gap is considered fixed, it may lead to unnecessary extension of the maximum voyage.

[0189] In order to clearly illustrate the dynamic docking algorithm in the present disclosure, this embodiment provides a flow chart 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, analyzing the optimal transshipment plan to determine a second berthing constraint condition of the berthing plan model.

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

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

[0194] Step 404 : Selecting 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 a ship performing direct transshipment depends not only on the berthing time, but also on the arrival time of the ships involved 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 a 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. The relevant content of the dynamic berthing algorithm can refer to the relevant description of the above-mentioned static berthing algorithm, 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 smallest transformed topology and 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 Given the docking order for each transformation topology.

[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 repeated in the present invention.

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

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

[0202] A generating unit 52, used for applying a constraint generating algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed;

[0203] The optimization unit 53 is used to construct a berthing plan model of the waiting vessel 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, which obtains the ship information and arrival information of the ship to be berthed, and constructs a transfer plan model of the ship to be berthed based on the ship information; applies a constraint generation algorithm to the transfer plan model to generate an optimal transfer plan for the ship to be berthed; constructs a berthing plan model for the ship to be berthed based on the arrival information and the optimal transfer plan, calls a berthing planning algorithm, performs berthing optimization calculation on the berthing plan model, and obtains an optimal berthing plan. Compared with the related art, the embodiment of the present disclosure generates and optimizes the transfer plan and berthing plan by obtaining detailed ship information and berthing information, which 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.

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

[0206] The determination module 511 is used to determine the transshipment decision variables of the waiting vessel 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 Fig. 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 constraint conditions, and accelerate the solution;

[0210] The generation module 522 is used to optimize and solve the transshipment plan model so as to maximize the quantity of cargo directly transshipped between the giant 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 Fig. 9 As shown, the optimization unit 53 includes:

[0212] The arrangement module 531 is used to obtain the waiting ships for 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;

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

[0214] The second optimization module 533 is used to call the 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 condition, berthing optimization calculation is performed on the waiting-to-berth vessels with direct transfer relationship in the conversion topology, so as to minimize the completion time of the waiting-to-berth vessels with direct transfer, and obtain a partial berthing plan;

[0218] Fill the remaining waiting-to-berth vessels for which berthing plans have not been generated into the berthing gaps in the partial berthing plans, and optimize and calculate different filling methods based on the second algorithm so as to minimize the total completion time of all waiting-to-berth vessels, thereby obtaining the completion time of the berthing plan corresponding to the conversion 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-to-berth vessels not participating in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence;

[0223] Based on the second berthing constraint condition, optimizing the berthing sequence so as to minimize the total completion time of all the 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 principle is 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] Fig.10 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0228] like Fig.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 to a RAM (Random Access Memory) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. 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] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

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

[0231] Various embodiments of the systems and techniques described above herein may 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 may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may 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 may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, 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 equipment. 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 device, 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 lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc 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 a pointing device (e.g., a mouse or a 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 may 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 a 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 may 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 the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0237] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[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, but also indicate the order of precedence.

[0239] At least one in the present disclosure may also be described as one or more, and a plurality may 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", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0240] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0241] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for generating a ship berthing plan, characterized in that: include: Acquire ship information and arrival information of the ship to be berthed, and construct a transshipment plan model for the ship to be berthed based on the ship information; 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 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.

2. The method according to claim 1, characterized in that The step of constructing a transshipment plan model for the vessel to be berthed based on the vessel information includes: Based on the ship information, determining the transshipment decision variables of the waiting ship, wherein the transshipment decision variables include: transshipment mode decision variables and direct transshipment quantity decision variables; The transshipment planning model is constructed using the transshipment decision variables and transshipment objective function.

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

4. The method according to claim 1, characterized in that The method 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, performing berthing optimization calculation on the berthing plan model, and obtaining an optimal berthing plan includes: Obtaining the waiting ships for direct transshipment in the optimal transshipment plan, and rearranging the ship sequence in the corresponding optimal transshipment topology to obtain a conversion topology, wherein the ship direct transshipment 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 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; 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.

5. The method according to claim 4, 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 condition, a berthing optimization calculation is performed on the waiting-to-berth vessels with direct transfer relations in the conversion topology, so as to minimize the completion time of the waiting-to-berth vessels with direct transfer, and obtain a partial berthing plan; Fill the remaining waiting-to-berth vessels for which berthing plans have not been generated into the berthing gaps in the partial berthing plans, and optimize and calculate different filling methods based on the second algorithm so as to minimize the total completion time of all waiting-to-berth vessels, thereby 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.

6. The method according to claim 4, characterized in that 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-to-berth vessels not participating in direct transfer into the berthing sequence of the conversion topology to obtain a berthing sequence; Based on the second berthing constraint condition, optimizing the berthing sequence so as to minimize the total completion time of all the 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.

7. A device for generating a ship berthing plan, characterized in that: include: A construction unit, used for acquiring ship information and arrival information of a ship to be berthed, and constructing a transshipment plan model of the ship to be berthed based on the ship information; A generating unit, used for applying a constraint generating algorithm to the transshipment plan model to generate an optimal transshipment plan for the vessel to be berthed; The optimization unit is used 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.

8. 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 6.

9. 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 1-6.

10. 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 6.

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