A berth allocation method for autonomous surface ships at sea in a mixed scenario

Abstract

The application provides a kind of mixed scene under offshore autonomous water surface ship berth allocation method, comprising: the berth type of port berthing is divided into A and B, wherein, A type berth refers to traditional berth-Only can satisfy the berthing of manned ship;B type berth refers to mixed berth-Simultaneously satisfy the berthing requirements of MASS and manned ship;At the same time, it can be foreseen that in a long period of time in the future, the number of manned ships is still far more than the number of MASS: the demand of A type berth is far more than B type berth;When the number of A type berth of port can completely satisfy the berthing use of manned ship, design MASS and manned ship separate berth allocation strategy;When the number of A type berth of port cannot satisfy the berthing use of manned ship, design MASS and manned ship mixed berth allocation strategy.The technical problem solved by the application is how to optimize the utilization efficiency of port berth and overall operation benefit by establishing effective berth allocation strategy in the mixed operation scene of MASS and manned ship.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of port management and maritime transportation, in particular, especially relates to a method for berth allocation of autonomous water surface ships in a mixed scenario. BACKGROUND

[0002] The current port berth allocation problem (BAP) has been studied more, especially in the optimization of berth allocation for manned ships and the scheduling of port resources. The traditional berth allocation strategy mostly adopts a fixed berth mode, that is, according to the type of the ship, a specific berth is allocated, which is not suitable for the mixed operation scenario of MASS and manned ships. In this case, the traditional berth allocation strategy cannot meet the demand of MASS and manned ships sharing berth resources, resulting in low utilization efficiency of port berths, increasing the waiting time and operating cost of ships.

[0003] The development of MASS has gradually increased the demand for autonomous berth allocation, but existing researches mostly focus on the autonomous navigation, obstacle avoidance and path optimization of MASS, and fail to explore the berth allocation strategy of MASS in depth. Few documents involve MASS berth allocation, such as optimizing berth allocation and speed to reduce fuel consumption and delay cost, but still fail to solve the problem of collaborative allocation of MASS and manned ships sharing berth resources.

[0004] Currently, a few ports try to use part of the manned ship berths to berth MASS, but there is no systematic research on the mixed berthing of MASS and manned ships, especially the lack of strategies and models that can adapt to different types of ships sharing berths. The existing berth scheduling model cannot meet the collaborative operation demand of MASS and manned ships. In the mixed scenario of MASS and manned ships, how to improve the utilization efficiency of port berths through reasonable allocation of berths is still a difficult problem to be solved.

[0005] In summary, the research on autonomous navigation and safety of MASS has made significant progress, especially in path planning and obstacle avoidance, which has shown good application prospects. However, the problem of berth allocation, especially in the mixed operation scenario of MASS and manned ships, still lacks sufficient research. SUMMARY

[0006] According to the above-mentioned MASS and manned ship mixed operation scene under the berth allocation technical problem, a kind of mixed scene under the sea autonomous water surface ship berth allocation method is provided to improve the utilization efficiency and overall operation benefit of port berth.The mixed integer programming model established by the present application can not only realize the optimal configuration of berth resources, reduce the operating cost of ship in port, but also promote the collaborative operation of MASS and manned ship through effective berth allocation strategy, improve the operation efficiency of port.In addition, through sensitivity analysis, the influence of different number of MASS special berths on the total cost of ship berthing can be evaluated, to provide scientific decision support for port when facing the changes of ship type, berth resources and operation demand.

[0007] The technical means adopted by the present application are as follows:

[0008] A kind of mixed scene under the sea autonomous water surface ship berth allocation method, comprising:

[0009] S1, the berth type parked in port is divided into A and B, wherein A type berth refers to traditional berth-only manned ship berthing can be met;B type berth refers to mixed berth-meet the berthing requirements of MASS and manned ship;At the same time, it can be foreseen that in a long period of time in the future, the number of manned ship is still far more than the number of MASS: the demand of A type berth is far more than B type berth;

[0010] S2, when the number of A type berth of port can completely meet the berthing use of manned ship, design MASS and manned ship separation type berth allocation strategy;

[0011] S3, when the number of A type berth of port cannot meet the berthing use of manned ship, design MASS and manned ship mixed type berth allocation strategy.

[0012] Further, the method further comprises the following setting steps:

[0013] Set that the communication between MASS and manned ship and between MASS and port management system is reliable, without packet loss, error code or delay;

[0014] Set that the port equipment and berth structure in MASS exclusive berth can adapt to the berthing and loading and unloading demand of manned ship;

[0015] Set that all sea ships, whether manned ship or MASS, strictly comply with all regulations and rules related to navigation and berthing;

[0016] Set the research object as discrete dynamic BAP, and the arrival time and loading and unloading time of all ships are known;

[0017] The physical factors of setting separate berths and mixed berths can meet and serve all MASS and passenger ships arriving at the port;

[0018] The setting of berth preferences is between A-type berths and B-type berths;

[0019] All ship loading and unloading operations are set to be performed immediately after the ship is docked, ignoring the time of part of the equipment adaptation process, and the cargo loading and unloading time of each ship is fixed;

[0020] Each ship is set to leave the port immediately after completing cargo loading and unloading;

[0021] It is set that during the execution of berth allocation, environmental factors such as wind speed, flow rate and other marine conditions are known and relatively stable.

[0022] Further, step S2 specifically comprises:

[0023] S21, according to the scenario of separate berth allocation, a multi-objective mathematical model with cost and time as optimization objectives is constructed to optimize the allocation strategies of A-type and B-type berths respectively, and the multi-objective mathematical model is as follows:

[0024]

[0025] Wherein, C1 and C2 represent the two parts of the cost respectively, and are optimized as the main objectives; B represents a set of port berths, B = { B _1, B_ _2,... B_ _m}, B_ m represents the total number of port berths; represents a set of arriving ships in a port, = { _1, _2,... _m}, _m represents the total number of arriving ships; represents a set of arriving MASS in a port, I l = { I l _1, I l _2... I l _n}), I l _n represents the total number of arriving MASS; C i (op) represents a set of passenger ships i operating cost per unit time in the port, in ten thousand yuan;C i (run) MASS i Operating cost per unit time in port, in ten thousand yuan; C i (handling) MASS i Handling cost per unit time when handling at ordinary berth, in ten thousand yuan; C i (load) MASS i Handling cost per unit time when handling at MASS berth, in ten thousand yuan; h i Handling time of MASS at berth, in hours; y ir 0-1 variable, if the MASS i is berthed at the berth of the port, r then y ir =1, otherwise y ir =0; b ir Actual berthing start time of MASS i at the berth of the port; r a i Arrival time of MASS at the port;

[0026] S22, to improve the utilization rate of the berth, an improved optimization model is designed, a berth idle time target optimization function T3 is added in the objective function, to minimize the berth idle time, wherein the in-port time involved includes: berthing waiting time, anchoring time and handling time; the berth idle time target optimization function T3 is as follows:

[0027]

[0028] wherein, b jr Actual berthing start time of MASS j at the berth of the port; r Z i,j,r 0-1 variable, if the MASS i , j is berthed at the berth of the port, and r berths earlier than i , then j Z i,j,r =1, otherwise Z i,j,r =0; ​​​

[0029] S23, according to the above description and parameters and decision variables, the multi-objective optimization model is expressed as the following objective function:

[0030]

[0031] Wherein, the objective function Model 1 is used for minimizing the total cost of ship berthing of all ships within the planning period days;

[0032] S24, the constraint conditions are set for the objective function, as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] Constraint 1 is used to ensure that each ship is docked at a certain berth; constraint 2 is the main difference between the MASS special berth model and the traditional BAP model, and is used to ensure that MASS cannot be docked at other A-type berths, but only at the MASS special berth; constraint 3 is used to ensure that the manned ship cannot be docked at the MASS special berth, but only at the manned ship berth; constraint 4 is used to ensure that the port will be docked at the special berth if the special berth is set, so as to avoid waste of special berth resources; constraint 5 is used to calculate the number of MASS special berths of the port; constraint 6 is used to ensure that the number of MASS special berths of the port cannot exceed the upper limit; constraint 7 expresses the relationship between y ir and b ir ; constraint 8 is used to ensure that the ship docking time is not earlier than the arrival time; constraint 9 is used to ensure that the actual docking time of the ship is greater than or equal to the sum of the basic docking time and the anchoring time of the ship; constraint 10 and constraint 11 express the relationship between y ir and Z i,j,r , so as to ensure that the two ships docked at the same berth do not conflict in time; constraints 12-16 give the value range of the decision variable; M represents a large positive number; K r represents a 0-1 variable, if a port sets a berth r as a MASS special berth, then K r = 1, otherwise K r = 0; n (max) represents the maximum number of MASS special berths of a port; n represents the number of MASS special berths of a port.

[0050] Further, in step S24, constraints 2-6 are restriction conditions related to the MASS special berth, and other constraints are restriction conditions considered in the traditional BAP model; constraints 2 and 3 depict the influence of the MASS special berth on the port after the port is reconstructed; constraints 4, 5 and 6 depict the way of the MASS special berth and the restrictions thereof.

[0051] Further, in step S3, the method specifically comprises:

[0052] S31, based on the improved multi-objective optimization model, a mixed berth allocation mathematical model is constructed, as follows:

[0053]

[0054]

[0055] wherein, represents the loading and unloading time discount factor of the manned ship at the MASS exclusive berth;

[0056] S32, calculate the loading and unloading time discount factor of the manned ship at the MASS exclusive berth, the calculation formula is as follows:

[0057]

[0058] wherein, T MASS represents the loading and unloading time of the B-type berth; T prep represents the additional preparation time of the manned ship at the MASS berth, including the equipment debugging and personnel coordination time required when the manned ship is docked at the B-type berth; T man represents the loading and unloading time of the A-type berth; the calculation formula is as follows:

[0059]

[0060]

[0061] wherein, the loading and unloading time of the B-type berth T MASS is related to the automation equipment efficiency R MASS , the loading and unloading demand D MASS and the optimization of equipment quantity and scheduling E MASS ; the loading and unloading time of the A-type berth T man is related to the operation speed of the port loading and unloading equipment R man , the adjustment time of the crew operation A man and the loading and unloading demand D man ;

[0062] S33, add the berth idle time target optimization function T3 in the objective function, as follows:

[0063]

[0064] S34, according to the above description and parameters and decision variables, the multi-objective optimization model is represented as the following objective function:

[0065]

[0066] The objective function Model 2 is used to minimize the total cost of ship berthing of all ships in the planning period;

[0067] In step S35, constraints are set for the objective function as follows:

[0068]

[0069]

[0070] Constraint 18 guarantees that each ship is berthed at a certain berth; constraint 19 is the main difference between the model considering the MASS special berth and the traditional BAP model, and is used to guarantee that the MASS cannot be berthed at other A-type berths but only at the MASS special berth; constraint 20 is used to guarantee that the port will be berthed at the special berth if the MASS special berth is set, so as to avoid waste of special berth resources; constraint 21 is used to calculate the number of MASS special berths of the port after reconstruction; constraint 22 is used to guarantee that the number of MASS special berths of the port cannot exceed the upper limit; constraint 23 expresses the relationship between y ir and b ir ; constraint 24 is used to guarantee that the berthing time of the ship is not earlier than the arrival time; constraint 25 is used to guarantee that the actual berthing time of the ship is equal to the sum of the basic berthing time of the ship and the anchoring time; constraint 26 and constraint 27 express the relationship between y ir and Z i,j,r , so as to guarantee that two ships berthed at the same berth do not conflict in time; constraints 28-32 give the value range of the decision variable.

[0071] Further, in step S35, constraint 19-constraint 23 are the restriction conditions related to the MASS special berth, and the other constraints are the restriction conditions considered in the traditional BAP model; constraint 19 and constraint 20 depict the influence on the ship berthing berth selection after the port reconstructs the MASS special berth; constraint 21, constraint 22 and constraint 23 depict the way of the MASS special berth and the restriction thereof.

[0072] Compared with the prior art, the present application has the following advantages:

[0073] 1. The application provides a mixed scenario autonomous water surface ship berth allocation method, which realizes efficient collaborative berth allocation of MASS and manned ships by constructing a mixed integer programming model, and reduces the idle time of the berth. Experimental results show that the mixed berth allocation strategy improves the berth utilization efficiency compared with the traditional berth allocation strategy in the case of tight berth resources in the port, which helps to fully utilize the existing berth resources and avoid the phenomenon of berth idling and resource waste.

[0074] 2. The mixed scenario autonomous water surface ship berth allocation method provided by the application optimizes berth scheduling and reduces ship waiting time, effectively reducing ship time in port and overall operating cost. In the case of limited berth resources, the mixed berth allocation strategy can shorten the ship time in port by about 7% compared with the traditional strategy, significantly reducing the operating cost of the port and improving the operating efficiency of the port.

[0075] 3. The mixed scenario autonomous water surface ship berth allocation method provided by the application introduces collaborative optimization in berth allocation, so that MASS and manned ships can efficiently coexist in the same port, solving the technical problem of mixed berth allocation of MASS and manned ships, and ensuring the smooth berthing and unloading of different types of ships. This strategy provides strong support for the efficient operation of MASS in the traditional port environment, and promotes the intelligent development of port operation and management.

[0076] 4. The mixed scenario autonomous water surface ship berth allocation method provided by the application realizes intelligent berth scheduling of MASS and manned ships under the condition of existing berth resources, which is suitable for the demand of future port intelligent management. This technology provides a new scheme for berth resource optimization for port managers, and helps to promote the progress of port in intelligent and automation, and supports the large-scale application of MASS in the port.

[0077] In summary, by optimizing the berth allocation strategy, MASS and manned ships can efficiently collaborate in the same port, not only improving the utilization rate of berth resources, but also effectively reducing the operating cost of the port, providing solid technical support for the development of future port intelligent management and autonomous maritime transportation system.

[0078] Based on the above reasons, the application can be widely promoted in the fields of port management and maritime transportation. BRIEF DESCRIPTION OF DRAWINGS

[0079] In order to make the technical scheme of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings described below are only some embodiments of the present application, and the ordinary skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0080] Figure 1 Flow chart of the method of the present application.

[0081] Figure 2 Separation berth allocation problem diagram provided by the embodiment of the present application.

[0082] Figure 3 Single-target separation berth allocation strategy provided by the embodiment of the present application.

[0083] Figure 4 Multi-target separation berth allocation strategy provided by the embodiment of the present application.

[0084] Figure 5 Mixed berth allocation problem diagram provided by the embodiment of the present application.

[0085] Figure 6 Mixed berth allocation strategy provided by the embodiment of the present application.

[0086] Figure 7 Comparison of the total cost of separation and mixed berthing provided by the embodiment of the present application.

[0087] Figure 8 Influence of the number of MASS berths on the total cost of ship berthing provided by the separation berth strategy of the embodiment of the present application.

[0088] Figure 9 Influence of the number of MASS berths on the total cost of ship berthing provided by the mixed berth strategy of the embodiment of the present application. DETAILED DESCRIPTION

[0089] In order to make the technical scheme of the embodiments of the present application, the technical scheme of the embodiments of the present application will be described clearly and completely below by combining the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by the ordinary skilled in the art without any creative effort should be within the scope of protection of the present application.

[0090] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and in the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0091] As Figure 1 indicated, the present application provides a mixed scenario autonomous water surface ship berth allocation method at sea, comprising:

[0092] S1, for the berth allocation problem of manned ships and MASS, the following practical application problems are faced: that is, the current existing manned ship berth is not suitable for MASS to stop the most realistic and feasible strategy is to modify the existing manned ship berth, so that it can adapt to the stop demand of manned ships and MASS. For the above case, the port berth type is divided into A and B types in the analysis of the present application, wherein the A type berth refers to the traditional berth-which can only meet the stop of manned ships; the B type berth refers to the mixed type berth-which can meet the stop requirements of MASS and manned ships at the same time; it can be foreseen that in a long period of time in the future, the number of manned ships is still far more than the number of MASS: the demand of A type berth is far more than that of B type berth; therefore, the present application discusses the berth allocation strategy in two scenarios;

[0093] S2, the first scenario: when the number of A type berths of the port can completely meet the stop use of manned ships, a separate berth allocation strategy of MASS and manned ships is designed; that is, the ships are strictly stopped in the corresponding berth according to the different ship types; for example, the manned ships are stopped in the A type berth, and the MASS are stopped in the B type berth, and the berth allocation of the manned ships and the MASS is optimized respectively through mathematical modeling; as Figure 2 indicated.

[0094] S3, the second scenario: when the number of A type berths of the port cannot meet the stop use of manned ships, a mixed berth allocation strategy of MASS and manned ships is designed. For example, when the manned ships and the MASS arrive at the port at the same time, the basic principle of stopping is that the MASS are stopped in the B type berth first, and the manned ships are stopped in the A type berth; if the A type berth is full, the manned ships can be flexibly adjusted to the B type berth; as Figure 5 indicated.

[0095] In specific implementation, as a preferred embodiment of the present application, the method further comprises the following setting steps:

[0096] The communication between the MASS and the manned ship and between the MASS and the port management system is reliable, without packet loss, bit error or delay;

[0097] The port equipment and berth structure in the MASS exclusive berth can adapt to the berthing and loading and unloading requirements of the manned ship;

[0098] All sea ships, whether manned ships or MASS, strictly comply with all regulations and rules related to navigation and berthing, such as navigation rules, safety distance, etc;

[0099] The research object is discrete dynamic BAP, and the arrival time and loading and unloading time of all ships are known;

[0100] The physical factors of the separated berth and the mixed berth can meet and serve all arriving MASS and manned ships;

[0101] The berth preference is between the A-type berth and the B-type berth;

[0102] The loading and unloading operation of all ships is performed immediately after the ship is berthed, ignoring the time of part of the equipment adaptation process, and the loading and unloading time of each ship is fixed;

[0103] Each ship leaves the port immediately after completing the loading and unloading of goods;

[0104] During the execution of the berth allocation, environmental factors such as wind speed, flow rate and other marine conditions are known and relatively stable.

[0105] In specific implementation, as a preferred embodiment of the present application, in step S2, for the separated berth allocation scenario, a separated berth allocation mathematical model is constructed based on the improved multi-objective optimization model and is solved. The specific application scenario is described as follows: according to the historical record analysis of the port ship berthing, if the number of A-type berths of the port can meet the berthing use of the manned ship, the strategy of separate berthing of the MASS and the manned ship is given. For example: after the arrival of the manned ship 5 (such as Figure 2 ), the A-type berth must be selected for berthing. Even if the A-type berth is full, it still needs to wait for other manned ships to leave the released berth before berthing. At this time, even if there is an idle B-type berth, it cannot be berthed; similarly, the MASS can only select the B-type berth for berthing. Specifically, it includes:

[0106] S21, according to the scenario of separated berth allocation, a multi-objective mathematical model is constructed (in this embodiment, the target optimization modeling is based on time and cost. First, the main consideration for optimizing the berth is the cost. The berth cost mainly includes the operation cost related to the time in port of the ship and the handling cost related to the time of cargo loading and unloading, which are denoted as C1 and C2 respectively and optimized as the main target), to optimize the allocation strategy of the A-type and B-type berths respectively, the multi-objective mathematical model is as follows:

[0107]

[0108] wherein C1 and C2 respectively represent the two parts of cost and are optimized as the main target; B denotes the set of port berths, B = { B _1, B_ 2,... B_ m}, B_ m represents the total number of port berths; denotes the set of ships arriving at a port, = { _1, _2,... _m}, _m represents the total number of arriving ships; denotes the set of MASS arriving at a port, I l ={ I l _1, I l _2... I l _n}), I l _n represents the total number of arriving MASS; C i (op) denotes a manned ship i operation cost per unit time in port, with the unit of ten thousand yuan; C i (run) denotes a MASS i operation cost per unit time in port, with the unit of ten thousand yuan; C i (handling) denotes a manned ship i loading and unloading cost per unit time when the ship is at a normal berth, with the unit of ten thousand yuan; C i (load) denotes a MASS iThe unit time loading and unloading cost in the MASS exclusive berth, unit: ten thousand yuan; h i The loading and unloading time of the ship in the berth, unit: hour; y ir Indicates a 0-1 variable, if the ship i is berthed in a certain port berth r , then y ir =1, otherwise y ir =0; b ir Indicates the actual berthing start time of the ship i in a certain port berth r ; a i Indicates the arrival time of the ship;

[0109] S22, after solving the above berth allocation model, it is found that the utilization efficiency of the scheme is low. As mentioned in the example above (as shown in Figure 2 ): when the port appears A type idle berth, the No. 5 ship is still in the waiting state, and cannot enter the idle berth in time, causing waste of resources. In order to improve the utilization rate of the berth, an improved optimization model is designed, and a berth idle time target optimization function T3 is added in the objective function to minimize the berth idle time, wherein the in-port time includes: berthing waiting time, anchoring time and loading and unloading time; The berth idle time target optimization function T3 is as follows:

[0110]

[0111] Among them, b jr Indicates the actual berthing start time of the ship j in a certain port berth r ; Z i,j,r Indicates a 0-1 variable, if the ship i , j is berthed in a certain port berth r , and i is earlier than j berthing, then Z i,j,r =1, otherwise Z i,j,r =0;

[0112] S23, according to the above description and parameters and decision variables, the multi-objective optimization model is expressed as the following objective function:

[0113]

[0114] The objective function Model 1 is used to minimize the total cost of ship berthing of all ships in the planning period;

[0115] S24, setting constraints for the objective function, as follows:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] The constraint one is used to ensure that each ship is berthed at a certain berth; the constraint two is the main difference between the model considering the MASS special berth and the traditional BAP model, which is used to ensure that the MASS cannot be berthed at other A-type berths, but only at the MASS special berth; the constraint three is used to ensure that the manned ship cannot be berthed at the MASS special berth, but only at the manned ship berth; the constraint four is used to ensure that if the port sets the MASS special berth, it will be berthed at this berth to avoid waste of special berth resources; the constraint five is used to calculate the number of MASS special berths of the port reconstruction; the constraint six is used to ensure that the number of MASS special berths of the port cannot exceed the upper limit; the constraint seven expresses y ir andb ir Constraint 9 is used to ensure that the actual berthing time of the ship is not earlier than the arrival time; constraint 10 and constraint 11 express the relationship between the basic berthing time of the ship and the anchoring time, and are used to ensure that the actual berthing time of the ship is greater than or equal to the sum of the basic berthing time of the ship and the anchoring time; constraint 12-constraint 16 give the value range of the decision variable; y ir Constraint 9 is used to ensure that the actual berthing time of the ship is not earlier than the arrival time; constraint 10 and constraint 11 express the relationship between the basic berthing time of the ship and the anchoring time, and are used to ensure that the actual berthing time of the ship is greater than or equal to the sum of the basic berthing time of the ship and the anchoring time; constraint 12-constraint 16 give the value range of the decision variable; Z i,j,r Constraint 9 is used to ensure that the actual berthing time of the ship is not earlier than the arrival time; constraint 10 and constraint 11 express the relationship between the basic berthing time of the ship and the anchoring time, and are used to ensure that the actual berthing time of the ship is greater than or equal to the sum of the basic berthing time of the ship and the anchoring time; constraint 12-constraint 16 give the value range of the decision variable; M represents a large enough positive number; K r represents a 0-1 variable, if a port sets a berth r as a MASS special berth, then K r = 1, otherwise K r = 0; n (max) represents the maximum number of MASS special berths in a port; n represents the number of MASS special berths in a port.

[0133] In the specific implementation, as a preferred embodiment of the present application, in step S24, constraint 2-constraint 6 are restriction conditions related to the MASS special berth, and other constraints are restriction conditions considered in the traditional BAP model; constraint 2 and constraint 3 depict the influence on the berthing berth selection of the ship after the port is reconstructed as the MASS special berth; constraint 4, constraint 5 and constraint 6 depict the mode of the MASS special berth and the restriction thereof.

[0134] In the embodiment of the present application, a solving process of the separated berth allocation strategy model is also provided, as follows:

[0135] This embodiment is aimed at an example of how to berth 12 ships (9 of which are passenger ships and 3 of which are MASS) in a port with 6 berths. As shown in the table, the number of MASS berths (B type berths) is set to 2, which are 1# and 2# berths; the number of passenger ship berths (A type berths) is 4, which are 3#-6# berths. Thus, the feasibility of the model in practical application is verified. Figure 3

[0136] ​The running environment of the embodiment is as follows: based on the AIS ship information arriving at a certain port in a certain week in 2024, 12 ship arrival time information is randomly selected and applied to the above mathematical model; the operating system of the computer is Windows 11, the main frequency is 2.6GHz, and the running memory is 8G; the algorithm program is compiled by Matlab 2021a to solve the model; the berth allocation diagrams obtained by using single-objective and multi-objective separated berth strategies are shown in Figure 3 and 4 .

[0137] The model is solved by using the Gurobi solver, and the simulation analysis of the solution result is shown in Figure 3 and Figure 4 . In the figure, the horizontal axis represents the berth number, and the vertical axis represents the time. As shown in Figure 3 and Figure 4 , the ship time in the port is shortened by about 12% by using the multi-objective optimization strategy compared with the single-objective optimization strategy. This shows that the reasonable introduction of the objective function to minimize the sum of idle time can improve the utilization rate of port resources while optimizing the ship scheduling in the port.

[0138] In the specific implementation, as a preferred embodiment of the present application, in step S3, according to the historical record analysis of the port ship berthing, if the number of A-type berths of the port cannot meet the berthing use of the passenger ship, at this time, a mixed berth allocation strategy is provided. The basic principle of the mixed berth allocation is that the passenger ship can choose the B-type berth for berthing when the A-type berths are all occupied, and the MASS is not allowed to choose the A-type berth for berthing. As shown in Figure 5 , if the passenger ship 5 is originally planned to berth in the A-type berth, but the A-type berth is full, at this time, the planned berthing in the B-type berth can only be changed. To solve the above mixed berth allocation problem, specifically includes:

[0139] S31, based on the improved multi-objective optimization model (in this embodiment, a multi-objective optimization model is established based on cost and time, the cost still mainly includes the operating cost and cargo loading and unloading cost of the ship in the port, and the time refers to the berth idle time. The change of the mixed strategy model compared with the separated strategy model is mainly the ship time in the port. The ship time in the port is mainly composed of berthing waiting time, anchoring time and loading and unloading time, as shown in the red box in Figure 6 . When the passenger ship can berth in the B-type berth, since the B-type berth belongs to the newly transformed MASS berth, the degree of automation and intelligence is higher than that of the A-type berth, resulting in the shortening of the loading and unloading time of the passenger ship in the B-type berth. Therefore, in the mixed berth allocation model, the loading and unloading time discount coefficient of the passenger ship in the B-type berth is increased, and the mixed berth allocation mathematical model is constructed as follows:

[0140]

[0141]

[0142] wherein, represents the loading and unloading time discount factor of the manned ship at the MASS exclusive berth;

[0143] S32, calculate the loading and unloading time discount factor of the manned ship at the MASS exclusive berth, the calculation formula is as follows:

[0144]

[0145] wherein, T MASS represents the loading and unloading time of the B-type berth; T prep represents the additional preparation time of the manned ship at the MASS berth, including the equipment debugging and personnel coordination time required when the manned ship is at the B-type berth; T man represents the loading and unloading time of the A-type berth; the calculation formula is as follows:

[0146]

[0147]

[0148] wherein, the loading and unloading time of the B-type berth T MASS and the efficiency of the automated equipment R MASS , the loading and unloading demand D MASS and the optimization of equipment quantity and scheduling E MASS are related; the loading and unloading time of the A-type berth T man is related to the operation speed of the port loading and unloading equipment R man , the adjustment time of the crew operation A man and the loading and unloading demand D man ;

[0149] S33, add the berth idle time target optimization function T3 in the objective function, as follows:

[0150]

[0151] S34, according to the above description and parameters and decision variables, the multi-objective optimization model is represented as the following objective function:

[0152]

[0153] wherein the objective function Model 2 is used for minimizing the total cost of ship berthing of all ships in the planning period;

[0154] S35, setting a constraint condition for the objective function, as follows:

[0155]

[0156]

[0157] wherein constraint eighteen guarantees that each ship is berthed at a certain berth; constraint nineteen is the main difference between the model considering the MASS special berth and the traditional BAP model, and is used for guaranteeing that the MASS cannot be berthed at other A-type berths but only at the MASS special berth; constraint twenty is used for guaranteeing that if the MASS special berth is set, the port will be berthed at this berth, so as to avoid waste of the special berth resources; constraint twenty-one is used for calculating the number of MASS special berths of the port; constraint twenty-two is used for guaranteeing that the number of MASS special berths of the port cannot exceed the upper limit; constraint twenty-three expresses the relationship between y ir and b ir ; constraint twenty-four is used for guaranteeing that the berthing time of the ship is not earlier than the arrival time; constraint twenty-five is used for guaranteeing that the actual berthing time of the ship is equal to the sum of the basic berthing time and the anchoring time of the ship; constraint twenty-six and constraint twenty-seven express the relationship between y ir and Z i,j,r , so as to guarantee that two ships berthed at the same berth do not collide in time; constraints twenty-eight to thirty-two give the value range of the decision variable.

[0158] In the embodiment of the present application, a mixed berth allocation model solving and example analysis process is also provided, as follows:

[0159] Since the objective function of the mixed berth allocation strategy model Model 2 contains a nonlinear part , the Model 2 is first converted into a linear model for solving.

[0160] Model linearization: the embodiment introduces 0-1 auxiliary variables . If a port is reconstructed at a berth , and the ship is berthed at the berth, then = 1, otherwise, = 0. In addition, when the ship is not berthed at the berth ,y lr = 0, the ship in the berth time b ir Also 0. At this time, the objective function can be represented as:

[0161]

[0162] At the same time, the following constraints also need to be introduced:

[0163]

[0164]

[0165] For the above proposed Model 2, the gurobi solver is directly used for solving.

[0166] Example analysis: this embodiment still aims at the example of 6 berths (4 A type berths, 2 B type berths) and 12 ships stopping, analyzes the mixed berth allocation strategy, as shown in Figure 6 , the specific mixed berth strategy of 12 ships is obtained, verifying the effectiveness of the model. By comparing the separate berth allocation strategy and the mixed berth allocation strategy Figure 4 and Figure 6 , it is found that under the same conditions, the mixed berth allocation strategy can save at least 7% of the ship in port time (see the red marked part in the figure).

[0167] Embodiment

[0168] This embodiment mainly includes total ship stopping cost analysis under two kinds of berth strategies and MASS berth (B type berth) number influence analysis on total ship stopping cost under two kinds of berth strategies.

[0169] Total ship stopping cost analysis of two kinds of berth allocation strategies:

[0170] This embodiment first estimates the total stopping cost of manned ships and MASS ships respectively, then calculates the loading and unloading time discount factor of manned ships in B type berths , and finally compares and analyzes the separate berth and mixed berth strategies. The total ship stopping cost is composed of ship operation cost and loading and unloading cost. Therefore, it is necessary to estimate the ship operation cost and loading and unloading cost first. Because MASS and manned ships have significant differences in operation process, equipment demand and human resources, leading to the difficulty of direct comparison of their operation cost and loading and unloading cost in practical application, the scientificity and rationality of the estimation of MASS operation cost and loading and unloading cost are the difficulties of this paper.

[0171] The total cost estimation process of the manned ship docking is as follows:

[0172] The study divides the arriving ships into 4 groups, each group with 3 ships, as shown in Table 1. The total cost of ship docking is composed of ship operation cost and loading and unloading cost. Among them, the loading and unloading cost = unit loading and unloading cost x loading and unloading time. Among them, the loading and unloading time is generated randomly for each group of corresponding ship loading and unloading time with the average loading and unloading time as the expected uniform distribution As shown in Table 1.

[0173]

[0174] The unit time loading and unloading cost of manned ship is calculated as C i (handling) =1047.57( )CNY / hour.

[0175] Table 1 Ship loading and unloading time information table

[0176]

[0177] The ship operation cost = ship time in port x unit operation cost, wherein the ship time in port is obtained from the AIS data, as shown in Table 2.

[0178]

[0179] The unit time operation cost of manned ship is calculated as C i (op) =2095.14( )CNY / hour;

[0180] Table 2 Example AIS data

[0181]

[0182] The total cost estimation process of the MASS ship docking is as follows:

[0183] The unit time operation cost of MASS is set as:

[0184]

[0185] Where, assuming that the maintenance of MASS is mainly concentrated on the monitoring of the shore control center and the technical support of port maintenance. Then the maintenance expenditure of the shore maintenance team is about 135,000 yuan per year; due to the risk uncertainty of autonomous ships, the insurance cost estimate may be slightly higher than that of traditional ships. Assuming that the insurance cost accounts for about 1% of the total operating cost of traditional ships, it is estimated that the insurance cost of autonomous ships per hour is about 20 CNY; the human cost of the shore control center is about 33,000 yuan per year per ship; other operating costs: including communication, port docking and other costs, which are expected to increase by about 10%. After calculation, the operating cost of MASS per unit time is C i (run) 1746.09( )CNY / hour;

[0186] Calculate the unit time handling cost of MASS:

[0187]

[0188] It is reasonable to speculate that the technical cost of autonomous handling equipment is in the range of hundreds of yuan per hour. And considering the high investment of automated handling equipment, the lease fee is set at 800 CNY / hour, which can reflect the advantages of automated handling to a certain extent, while also maintaining moderate cost control. Therefore, it is assumed that the lease fee of autonomous handling equipment is about 800 CNY / hour. According to the actual situation of domestic ports, the average hourly wage of loading and unloading positions is usually between 50 and 80 CNY, depending on the skill requirements of the region and the job. If we take into account the overtime pay, night shift allowance and the need for technical support, especially the special skill requirements for assisting autonomous equipment operation, the actual hourly wage may be higher. Therefore, at a rate of 100 CNY / hour per person, it is assumed that 4-5 workers are needed to assist, resulting in a total labor cost of about 478 CNY / hour. Setting the labor assistance fee at 478 CNY / hour can also cover some labor costs of technical personnel; after substituting the above equipment lease fee and labor and auxiliary fees, the unit time handling cost of MASS is C i (load) =1278.04( )CNY / hour;

[0189] Handling time discount factor of manned ships in B-type berth The calculation process is as follows:

[0190] Assumed parameters: MASS automated handling rate R MASS =30 standard containers / hour, number of available equipment per MASS E MASS=2. Loading and unloading rate of manned vessels R man =20 standard containers / hour, adjustment time per operation A man =1 hour. Assume loading and unloading demand D. MASS =120 and D man =120 standard containers, then:

[0191]

[0192] Additional preparation time T prep Considering that the additional preparation time for manned vessels to adapt to the MASS automated berth may include equipment debugging, personnel coordination, etc., we assume it is 1 hour.

[0193] Based on the above rough calculations, we obtain... =0.57 (rounded to two decimal places). Maximum number of dedicated berths. =[|B|] (B is the number of berths).

[0194] On a computer with Windows 11 operating system, a main frequency of 2.6GHz, and 8GB of RAM, the algorithm program was compiled using Python and Gurobi 9.5.2 was used to solve the model in this paper, with a maximum solution time of 1 hour.

[0195] The comparative analysis process of separate berth and mixed berth strategies is as follows:

[0196] In this embodiment, the study compared and analyzed the impact of separate and hybrid berth allocation strategies on the total cost of ship berthing. The study showed that the hybrid berth allocation strategy has significant advantages in terms of berth utilization and operating costs. The total cost of ship berthing under different numbers of dedicated MASS berths for the two berth allocation strategies was quantified, and the configuration with the lowest cost is summarized in Table 3.

[0197] Table 3 Comparison of berth planning effectiveness under different MASS dedicated berth usage scenarios.

[0198]

[0199] Figure 7 The cost performance of the separate berth allocation strategy and the mixed berth allocation strategy is represented by yellow and blue lines, respectively. The horizontal axis in the figure represents the number of dedicated berths for MASS (Main Service Assigned Berths), and the vertical axis represents the total cost of vessel berthing (unit: RMB 10,000). This embodiment compares and analyzes the impact of MASS-dedicated berths and manned vessel berth allocation methods on the total cost of vessel berthing through multiple sets of repeated experiments. Figure 7The influence of the number of special berths on the total cost of ship berthing under different numbers of berths is shown. As can be seen from the figure, with the increase of the number of special berths of MASS, the total cost of ship berthing under the two berth strategies both shows a downward trend. However, the cost of the mixed berth allocation strategy is significantly lower than that of the separated berth strategy. Specifically, only when the number of special berths is 1, the cost of the separated berth strategy is slightly lower than that of the mixed berth allocation strategy, in which the total cost of ship berthing under the separated berth strategy is about 550,000 yuan, while the total cost of ship berthing under the mixed berth is close to 600,000 yuan. However, after the number of special berths increases to 2, the cost of the mixed berth allocation strategy begins to be lower than that of the separated berth strategy. When the number of special berths increases to 3, the cost of the mixed berth allocation strategy tends to be stable, maintaining at about 400,000 yuan. The cost of the separated berth strategy also tends to be stable when the number of special berths reaches 3, maintaining at about 475,000 yuan. As can be clearly seen from the figure, under the same number of special berths of MASS, the cost saving effect of the mixed berth allocation strategy is more significant than that of the separated berth strategy. In addition, from the data in Table 3 and Figure 7 , it can be concluded that the mixed berth allocation strategy can significantly reduce the total cost of ship berthing by at least 17% compared with the separated berth strategy. This comparison shows that the mixed berth configuration can more effectively reduce the waiting time of ships in the port and improve the utilization efficiency of berth resources, thereby reducing the overall operating cost of the port.

[0200] Analysis of the influence of the number of MASS berths on the total cost of ship berthing under the two berth strategies:

[0201] This embodiment mainly includes the analysis of the influence of the number of MASS berths on the total cost of ship berthing under the two berth strategies.

[0202] Influence of the number of MASS berths on the total cost of ship berthing under the separated berth strategy: This embodiment analyzes the influence of the number of special berths of MASS on the total cost of ship berthing under the separated berth strategy. By controlling the increase and decrease of the number of MASS berths, the total cost of ship berthing can be significantly affected. When the number of MASS berths is small, the total cost of ship berthing is high; with the increase of the number of berths, the addition of special berths helps to optimize the berthing efficiency, reduce the waiting time of ships, and thus reduce the overall operating cost. The results show that the reasonable configuration of the number of special berths of MASS has a significant effect on the economic benefit of the separated berth strategy. As shown in Table 4, the influence of the increase or decrease of the number of different types of berths on the total cost of ship berthing is analyzed:

[0203] Table 4 Influence of the number of MASS berths on the total cost of ship berthing under the separated berth strategy

[0204] Figure 8The middle horizontal coordinate is the number of special berths for the port, the left vertical coordinate is the total number of berths of the port, and the right vertical coordinate is the total cost of ship berthing. With the increase of the number of MASS special berths, the total cost of ship berthing under the mixed strategy is significantly reduced. When the number of MASS special berths is low, the total cost of ship berthing is high; but when the number of MASS special berths is moderately increased, the berth resources are fully utilized, the waiting time of the ship is reduced and the utilization efficiency of the berth is optimized, so the overall operating cost is significantly reduced.

[0205] The influence of the number of MASS berths on the total cost of ship berthing under the mixed berth strategy is shown in Table 5:

[0206] Table 5 Influence of the number of MASS berths on the total cost of ship berthing under the mixed berth strategy

[0207]

[0208] Figure 9 The middle horizontal coordinate is the number of special berths for the port, the left vertical coordinate is the total number of berths of the port, and the right vertical coordinate is the total cost of ship berthing. With the increase of the number of MASS special berths, the total cost of ship berthing under the mixed strategy is significantly reduced. When the number of MASS special berths is low, the total cost of ship berthing is high; but when the number of MASS special berths is moderately increased, the berth resources are fully utilized, the waiting time of the ship is reduced and the utilization efficiency of the berth is optimized, so the overall operating cost is significantly reduced.

[0209] This embodiment analyzes the influence of the number of MASS special berths on the total cost of ship berthing under different berth strategies. The research shows that in the separate berth strategy, increasing the number of MASS special berths can effectively reduce the competition for berths between manned ships and MASS ships, thereby improving the utilization efficiency of berth resources, reducing the waiting time and operating cost of ships, and making the total cost of ship berthing show a downward trend. In the mixed berth strategy, the influence of the number of MASS berths on the total cost is particularly significant: when the number of MASS berths is small, the total cost of ship berthing is high; with the moderate increase of the number of MASS berths, the total cost of ship berthing is significantly reduced. This strategy arranges berths flexibly, so that manned ships can use MASS berths when necessary, further improving the utilization efficiency of berths and the flexibility of scheduling, thereby significantly reducing the overall operating cost. In summary, reasonable allocation of the number of MASS berths, especially under the mixed berth strategy, can optimize the utilization efficiency of berth resources and significantly reduce the total cost of ship berthing, providing a useful reference for the allocation and management of port berth resources.

[0210] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent substitutions for part or all of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for allocating berths for autonomous surface vessels in a hybrid scenario, characterized in that, include: S1. Port berths are classified into Class A and Class B. Class A berths are traditional berths that can only accommodate passenger vessels; Class B berths are mixed berths that can accommodate both passenger and passenger vessels. S2. When the number of Class A berths in a port is sufficient to meet the berthing needs of passenger vessels, design a separate berth allocation strategy for passenger vessels and passenger ships. Passenger vessels will berth in Class A berths, while passenger ships will berth in Class B berths. The allocation strategy should be optimized based on operating costs, loading and unloading costs, and berth idle time. Specifically, this includes: S21. Based on the scenario of separate berth allocation, construct a multi-objective mathematical model with cost and time as optimization objectives to optimize the allocation strategies for Class A and Class B berths respectively. The multi-objective mathematical model is as follows: Here, C1 and C2 represent these two cost components, respectively, and are optimized as the primary objectives; B Indicates a collection of port berths. B = { B _1, B_ 2,... B_ m}, B_ m represents the total number of port berths; This indicates the assembly of ships arriving at a certain port. = { _1, _2,... _m}, _m represents the total number of ships arriving at the port; This represents the set of arrivals at a certain port. I l ={ I l _1, I l _2... I l _n}), I l _n represents the total number of arriving MASSes; C i (op) Indicates a manned vessel i Operating cost per unit time in Hong Kong, in RMB 10,000; C i (run) MASS i Operating cost per unit time in Hong Kong, in RMB 10,000; C i (handling) Indicates a manned vessel i The unit time cost of loading and unloading operations at ordinary berths, expressed in ten thousand yuan. C i (load) MASS i The unit time cost of loading and unloading operations at the MASS dedicated berth is expressed in ten thousand yuan. h i This indicates the loading and unloading time of a ship at its berth, in hours. y ir Represents a 0-1 variable, if the ship i berth at a port r Mooring, y ir =1, otherwise y ir =0; b ir Indicates a ship i berth at a port r The actual start time of berthing; a i Indicates the ship's arrival time; S22. To improve berth utilization, an improved optimization model is designed, adding a berth idle time objective optimization function T3 to the objective function to minimize berth idle time. The port time involved includes: berthing waiting time, anchorage time, and loading / unloading time. The berth idle time objective optimization function T3 is as follows: in, b jr Indicates a ship j berth at a port r The actual start time of berthing; Z i,j,r Represents a 0-1 variable, if the ship i , j berth at a port r Mooring, and i Earlier j Mooring, Z i,j,r =1, otherwise Z i,j,r =0; S23. Based on the above description and the parameters and decision variables, the multi-objective optimization model can be expressed as the following objective function: The objective function Model 1 is used to minimize the total cost of berthing for all ships within the planning period. S3. When the number of Class A berths in a port cannot meet the berthing needs of passenger vessels, a mixed berth allocation strategy for passenger vessels and MASS (Mass Assault Vessels) is designed. When passenger vessels and MASS arrive at the port at the same time, MASS berths in Class B berths, while passenger vessels berth in Class A berths. If Class A berths are full, passenger vessels are flexibly moved to Class B berths. The allocation strategy is optimized with operating costs, loading and unloading costs, and berth idle time as optimization objectives.

2. The method for allocating berths for autonomous surface vessels in a hybrid scenario according to claim 1, characterized in that, The method also includes a defined step, specifically defined as follows: The communication between MASS and manned vessels, as well as with the port management system, is set to be reliable, with no packet loss, bit errors, or delays. The port facilities and berth structures in the dedicated MASS berths are designed to accommodate the berthing and loading / unloading needs of manned vessels. All vessels at sea, whether manned or MASS, must strictly comply with all laws and regulations related to navigation and berthing. The research object is set as discrete dynamic BAP, and the arrival time and loading / unloading time of all ships are known; The physical factors for setting up separate and mixed berths can meet and serve all arriving MASS and manned vessels; The preference for berths is reflected between Class A berths and Class B berths; All loading and unloading operations are scheduled to be performed immediately after the ships berth, ignoring the time required for some equipment adaptation processes, and the cargo loading and unloading time for each ship is fixed. Each vessel is required to leave port immediately after completing cargo loading and unloading; During the berth allocation process, the environmental factors of wind speed and current velocity are assumed to be known and relatively stable.

3. The method for allocating berths for autonomous surface vessels in a hybrid scenario according to claim 1, characterized in that, Step S2 also includes: S24. Set constraints for the objective function as follows: Among these constraints, constraint 1 ensures that each ship berths at a specific berth; constraint 2, the main difference between the model considering dedicated MASS berths and the traditional BAP model, ensures that MASS vessels cannot berth at other Class A berths and can only berth at dedicated MASS berths; constraint 3 ensures that passenger vessels cannot berth at dedicated MASS berths and can only berth at passenger vessel berths; constraint 4 ensures that if a port has dedicated MASS berths, berthing will always occur at that berth, avoiding waste of dedicated berth resources; constraint 5 calculates the number of dedicated MASS berths required for port renovation; constraint 6 ensures that the number of dedicated MASS berths in the port cannot exceed the upper limit; and constraint 7 expresses... y ir and b ir The relationships between constraints; constraint eight is used to ensure that the berthing time of a vessel is not earlier than the arrival time; constraint nine is used to ensure that the actual berthing time of a vessel is greater than or equal to the sum of the basic berthing time and the anchorage time; constraints ten and eleven express... y ir and Z i,j,r The relationship between the constraints is used to ensure that two ships docked at the same berth do not conflict in time; constraints 12-16 give the range of values ​​for the decision variables; M Represents a sufficiently large positive number; K r This represents a 0-1 variable; if a port has berths... r As a dedicated berth for MASS, K r =1, otherwise K r =0; n (max) This indicates the maximum number of dedicated MASS berths at a given port. n This indicates the number of dedicated MASS berths at a certain port.

4. The method for allocating berths for autonomous surface vessels in a hybrid scenario according to claim 3, characterized in that, In step S24, constraints 2-6 are restrictions related to the MASS dedicated berth, while the other constraints are restrictions considered in the traditional BAP model; constraints 2 and 3 characterize the impact of port renovation to MASS dedicated berths on the selection of ship berths; constraints 4, 5, and 6 characterize the method of MASS dedicated berths and the restrictions they are subject to.

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