Parking scheduling method, system and equipment based on movable slipway

Abstract

The invention provides a docking scheduling method, system and equipment based on a movable slipway, and relates to the technical field of ship docking scheduling planning.The ship docking sequence and slipway distribution are optimized through a genetic algorithm, and the total operation time is minimized based on rapid matching of air gap space and docking height; according to the method, ship sequence and berth distribution are comprehensively considered based on double-coding chromosomes, an initial population is optimized with the assistance of a greedy algorithm, and the operation process of air gap lifting of ships entering and leaving a port and a movable slipway is reasonably dispatched; a plurality of factors such as ship arrival time, loading and unloading time, a docking height interval, a slipway berth number, a tidal parameter of a location, an air gap height interval and the like are considered, and through operations such as adaptive crossover, variation and the like on double-coding chromosomes in a genetic algorithm, the scheduling efficiency and the scheme quality are improved, the ship docking arrangement is optimized, and the port efficiency is improved. And waiting time and resource waste are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of ship berthing scheduling and planning, and in particular to a berthing scheduling method, system and equipment based on a movable slipway. Background Art

[0002] Ports are important nodes in the waterway transportation network, and the scheduling of ship berthing and loading and unloading at ports is the key to port operation and management. Port berth resources are limited, and how to reasonably arrange the order of ship berthing and loading and unloading operations, shorten the ship's stay time at the port, and improve the utilization rate of terminal berths are important issues facing ports.

[0003] Traditional ship scheduling mostly relies on manual experience and simple rules, which has low scheduling efficiency and cannot meet the requirements of current large-scale ships and automated terminals. In recent years, some scholars have proposed ship scheduling methods based on operations research optimization models, such as integer programming and heuristic algorithms, which have improved the efficiency of ship scheduling to a certain extent. However, these methods generally have disadvantages such as long calculation time, slow convergence speed, and easy to fall into local optimality, which makes it difficult to meet the needs of real-time scheduling.

[0004] With the technological breakthrough of movable slipways, mobile docks with movable and adjustable air gaps are gradually becoming a key role in shipping. The movable slipway can adjust the air gap height in real time according to the draft of the ship. Both the left and right sides of the slipway can be used for loading and unloading of incoming ships and can be loaded and unloaded at the same time. Therefore, the adjustment of the air gap height of the movable slipway is also an important factor affecting the loading and unloading efficiency. However, traditional methods often lack effective control means in air gap optimization and are difficult to adapt to dynamically changing operational needs. In addition, most of them treat the ship berthing sequence and berth allocation as independent optimization problems, ignoring the coupling relationship between the two, which makes it difficult to achieve optimal resource allocation and operational efficiency in practical applications.

[0005] Therefore, there is an urgent need for a method that can collaboratively optimize ship scheduling and berth air gaps, so as to comprehensively consider multiple factors such as ship sequence, berth allocation, and air gap adjustment, and improve the overall port operation efficiency and resource utilization. Summary of the invention

[0006] In view of this, the embodiment of the present application provides a method, system and storage medium for berthing scheduling based on a movable slipway, which comprehensively considers the ship sequence and berth allocation based on a dual-coding genetic algorithm, and optimizes the initial population with a greedy algorithm to reasonably schedule the operation process of ships entering and leaving the port and the air gap lifting of the movable slipway. The embodiment of the present application provides the following technical solutions:

[0007] On the one hand, an embodiment of the present application provides a docking scheduling method based on a movable berth, comprising the following steps:

[0008] S1. Obtaining scheduling parameters, including ship parameters and berth parameters, and calculating the expected berthing interval of the ship and the air gap height interval of the berth;

[0009] S2. generating an initial population, each individual being represented by a double-coding chromosome, the first-level coding of the double-coding chromosome referring to the ship, and the second-level coding referring to the slipway;

[0010] S3. Optimizing the initial population using a greedy algorithm to generate an optimized population, wherein the expected docking interval in the chromosome overlaps with the air gap height interval;

[0011] S4. Calculate the fitness value of each chromosome in the optimized population, sort and calculate the ranking according to the fitness value;

[0012] S5. Calculate the selection probability and randomly select the parent chromosome;

[0013] S6. randomly selecting two chromosomes from the parent chromosome to perform a crossover operation to generate a daughter chromosome;

[0014] S7. randomly extract individuals from the offspring chromosomes to perform mutation operations, replace the first layer code with other ship sequences, and then replace the second layer code with other ship slip numbers;

[0015] S8. Determine whether the number of iterations reaches the specified value. If so, stop the iteration and output the docking scheduling plan; if not, jump to S4.

[0016] Furthermore, in S1, the ship parameters include arrival time, loading and unloading time, ship leaving water interval and gangway overlap interval, and the slipway parameters include berth number, local water depth and air gap height interval. The local water depth is superimposed on the air gap height interval to obtain the slipway height interval.

[0017] Furthermore, based on the ship parameters, the calculated tidal difference is searched in the water depth at the location, and the expected berthing height range is calculated.

[0018] Furthermore, in S2, the first layer of coding of the dual-coding chromosome is sorted in ascending order based on the arrival time of the ship, and the second layer of coding of the dual-coding chromosome is based on the berth number of the ship randomly assigned to the ship of the first layer of coding and meets the docking condition, and the docking condition refers to the overlapping interval between the expected docking height interval and the air gap height interval.

[0019] Further, in S3, the greedy algorithm optimizes at least 5% of the double-coding chromosomes in the initial population:

[0020] Based on the order of the first layer encoding, the ship is preferentially allocated to a vacant berth that meets the berthing conditions; if there is no vacant berth, a reserve berth is sought, the air gap height interval of the reserve berth is closest to the expected berthing height interval of the current ship to be allocated, and the waiting time is recorded, the waiting time being the remaining loading and unloading time of the ship in the current berth plus the height adjustment time of the slipway; if there is no reserve berth, an available berth is selected, the remaining loading and unloading time of the ship in the available berth is the shortest.

[0021] Further, in S4, the fitness value is the inverse of the total operation time of the scheduling scheme, and the total operation time is the longest operation time of the slipway berth, and the operation time includes the height adjustment time of the slipway and the loading and unloading time of the ship.

[0022] Further, in S4, the calculation formula of the fitness value is total waiting time + α*slipway idle time + β*number of air gap adjustments + γ*total air gap adjustment amplitude, wherein α, β and γ are weight coefficients.

[0023] Furthermore, in S5, the calculation formula of the selection probability is: where r i is the calculated ranking, and P is the total number of the optimized population; in S8, a backtracking algorithm is also included. If the current scheduling scheme cannot meet the requirements of all ships, the backtracking algorithm is used to fall back to the optimized population of the previous generation.

[0024] On the other hand, an embodiment of the present application provides a docking scheduling system based on a movable slipway, including an electrically connected parameter acquisition module, a population generation module, a population optimization module, a calculation ranking module, a parent selection module, a crossover operation module, a mutation operation module and an iteration judgment module, wherein:

[0025] A parameter acquisition module is used to acquire scheduling parameters, including ship parameters and berth parameters, and calculate the expected berthing interval of the ship and the air gap height interval of the berth;

[0026] A population generation module, used for generating an initial population, each individual is represented by a double-coded chromosome, the first layer of the double-coded chromosome refers to the ship, and the second layer of the double-coded chromosome refers to the slipway;

[0027] A population optimization module, used for optimizing the initial population using a greedy algorithm to generate an optimized population, wherein the expected docking interval in the chromosome overlaps with the air gap height interval;

[0028] A ranking calculation module is used to calculate the fitness value of each chromosome in the optimized population, and to sort and calculate the ranking according to the fitness value;

[0029] The parent selection module is used to calculate the selection probability and randomly select the parent chromosome;

[0030] A crossover operation module, used to randomly select two chromosomes from the parent chromosome to perform a crossover operation to generate a daughter chromosome;

[0031] A mutation operation module, used for randomly extracting individuals from the offspring chromosomes to perform mutation operations, replacing the first layer codes with other ship sequences, and then replacing the second layer codes with other ship slip numbers;

[0032] The iteration judgment module is used to judge whether the number of iterations reaches the specified value. If it reaches the specified value, the iteration is stopped and the docking scheduling plan is output; if it does not reach the specified value, the module jumps to the calculation ranking module.

[0033] On the other hand, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the docking scheduling method based on a movable slipway as described in any one of the above items when executing the computer program.

[0034] Compared with the prior art, the at least one technical solution adopted in the embodiment of the present application can achieve the following beneficial effects:

[0035] 1. The method of the present invention realizes dynamic adjustment of ship berthing sequence and slipway air gap through optimization means such as genetic algorithms, thereby optimizing overall loading and unloading efficiency; the method of the present invention effectively solves the problems of low efficiency and unbalanced resource utilization of traditional scheduling methods when dealing with ship queuing, berth allocation and air gap adjustment in complex port environments. By constructing a complete scheduling framework based on genetic algorithms and performing iterative optimization, it is possible to find the optimal or approximately optimal ship berthing scheduling scheme under multiple constraints, which is more efficient than traditional heuristic algorithms and can handle more complex scheduling problems; based on the berthing scheduling of movable slipways, the calculation of air gap height intervals is considered to improve the accuracy and reliability of the scheduling scheme.

[0036] 2. The method of the present invention introduces a greedy algorithm in the population initialization stage to accelerate the convergence speed of the optimization process and improve the quality of the initial solution. The initial population is generated by sorting based on the arrival time, which improves the quality of the initial population, shortens the convergence time of the algorithm, and accelerates the speed of finding the optimal solution. In addition, the docking conditions must be met when randomly allocating berths to ensure the feasibility of the initial solution.

[0037] 3. The method of the present invention adopts a double-coded chromosome design for the ships to be docked and the available berths, respectively encoding the ship sequence and berth allocation information, more accurately describing the matching relationship between ships and berths, and more comprehensively expressing the solution space of the problem, allowing the algorithm to optimize these two key factors simultaneously.

[0038] 4. The method of the present invention clearly defines the fitness function, so that the algorithm can effectively evaluate the advantages and disadvantages of different scheduling schemes. First, the operation time of the longest berth is taken as the total operation time, which reflects the optimization of resource utilization; second, a more flexible fitness function is provided, which can balance the relationship between different objectives by adjusting the weight coefficient, for example, it can focus more on reducing waiting time or reducing the number of air gap adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a flow chart of a docking scheduling method based on a movable slipway of the present invention;

[0041] Figure 2 It is a schematic diagram of an implementation of a docking scheduling method based on a movable slipway of the present invention;

[0042] Figure 3 It is a chromosome schematic diagram of a docking scheduling method based on a movable slipway of the present invention;

[0043] Figure 4 It is a Gantt chart of a docking scheduling method based on a movable slipway of the present invention;

[0044] Figure 5 It is an algorithm process diagram of a docking scheduling method based on a movable berth of the present invention;

[0045] Figure 6 It is a comparison diagram before and after optimization of a docking scheduling method based on a movable slipway of the present invention;

[0046] Figure 7 It is a schematic diagram of a docking scheduling system based on a movable slipway of the present invention. DETAILED DESCRIPTION

[0047] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0049] The docking scheduling method based on a movable slipway provided in the embodiment of the present application can be applied to the scenario in the field of ship docking scheduling planning where it is necessary to coordinately optimize ship scheduling and slipway air gap. The ship docking scheduling problem aims to optimize the order and time of ships docking on a limited number of movable slipways, with the goal of minimizing the waiting time of ships and the idle time of slipways. The characteristic of the movable slipway is that its air gap height can be adjusted according to the draft and size of the ship, thereby improving the slipway utilization rate.

[0050] In the prior art, first-come-first-served optimization is usually only performed when the dock is fixed. Here, "fixed" means that the geographical location is fixed or the height of the dock from the bottom of the water is fixed. However, with the development of technology, movable slipways have gradually replaced the status of fixed docks in many key areas, such as semi-permanent docks in the military and temporary docks in the rescue field. These docks are flexible, and the height of the dock from the bottom of the water can be changed, which can ensure that the berth adjusts the air gap height according to the draft and size of the incoming ship to ensure adaptability. This is something that traditional fixed docks cannot do. Traditional berths need to pay attention to tidal changes to dock incoming ships. Generally, dock operations are concentrated at high tide. When it is low tide, the dock is much higher than the water level, and the gangway for docking ships cannot be connected in a safe position, so loading and unloading operations cannot be carried out. The movable slipway can adjust the air gap to ensure that the gangway can be safely connected, so its applicability exceeds that of traditional docks. Therefore, the traditional genetic algorithm intelligent scheduling method for loading and unloading ships based on fixed docks cannot solve the scheduling method of movable slipways.

[0051] At the same time, most existing technologies are optimized for first-come, first-served scheduling algorithms, but in fact, the scheduling scheme obtained from the perspective of first-come, first-served is not necessarily the optimal solution for terminal operation time. For example, a large ship with a deep draft arrives first, but during low tide, it cannot be loaded and unloaded immediately. At this time, a small ship with a draft that meets the requirements can be loaded and unloaded to save time. This is a blind spot of first-come, first-served. In addition, since a slipway provides two berths and shares the slipway height, this means that when ships on both sides of the slipway (two berths) come to unload at the same time, it is necessary to consider that the slipway height interval at the unloading time needs to partially overlap.

[0052] On the one hand, the embodiment of the present application provides a docking scheduling method based on a movable berth, such as Figure 1 The flowchart of the method for berthing and scheduling based on a movable berth of the present invention is shown, and the method comprises the following steps:

[0053] S1. Obtain the dispatching parameters, including ship parameters and berth parameters. The ship parameters provide the data basis for the first layer of the subsequent double coding, and the berth parameters provide the data basis for the second layer of the subsequent double coding. The dispatching parameters should also include the water area parameters used for constraint condition judgment.

[0054] Specifically, ship parameters include ship number, length, width, draft, arrival time, expected docking time, etc.; slipway parameters include the number of movable slipways, the carrying capacity of each slipway, berth number and location coordinates, water depth distribution map of the slipway area and tidal pattern table, which are used to determine whether the ship can dock at a specific location.

[0055] In some embodiments, during the S1 process, based on the ship parameters, the arrival time, loading and unloading time, ship leaving water interval and gangway connection interval of the ship are determined, and the expected berthing height interval is calculated; based on the slipway parameters, the berth number, local water depth and air gap height interval are determined, and the slipway height interval is obtained by superimposing the local water depth and the air gap height interval.

[0056] like Figure 2Shown is a schematic diagram of an implementation of a docking scheduling method based on a movable slipway of the present invention. The slipway in the figure can be moved on the sea surface and can be fixed according to the geological conditions of the seabed. For the convenience of calculation, in the description of this article, the distance between the sea surface and the upper surface of the slipway is referred to as the air gap height. The air gap height is crucial for the loading and unloading operations of the ship because it affects the height of the gangway overlapped between the ship and the slipway. The ideal air gap height can ensure that the ship can load and unload cargo and passengers safely and efficiently. In the method of the present invention, the slipway is not only a movable dock, but also its air gap height can be adjusted according to the draft depth and body size of different ships to meet the loading and unloading needs of different ships. By dynamically adjusting the air gap height of the slipway, the collaborative operation of the ship and the slipway can be optimized, the operating efficiency of the dock can be improved, and the safety of the operation can be ensured.

[0057] Specifically, first calculate the expected docking height interval [G1, G2] of the ship, and obtain the expected arrival time, loading and unloading time, tide table, shock margin δ, ship water separation interval [B1, B2], ship gangway length l, gangway safe lowering angle a, gangway safe lifting angle b, so as to compare and judge whether the current air gap height interval [T1, T2] of the slipway overlaps with the expected docking height interval [G1, G2], and output the result of whether there is a suitable air gap interval or not. The following is the calculation process:

[0058] 1. Query the highest tide w in the corresponding tide table according to the estimated arrival time of the ship and the estimated operation time max and the lowest tide min .

[0059] 2. Use the oscillation margin δ to correct the values ​​of the highest and lowest tides. The new highest tide calculation method is as follows: max =w max +δ, the new minimum tide calculation method is as follows W min =w min -δ. Calculate the tidal difference W between the new highest and lowest tides diff =W max -W min .

[0060] 3. Calculate the springboard overlap interval [H1, H2] according to the springboard length l, the springboard safe lowering angle a and the springboard safe lifting angle b, where the overlapping lowering height H1 = cos(a)*l and the overlapping lifting height H2 = cos(b)*l.

[0061] 4. Consider two extreme cases of ship loading and unloading and tidal combinations:

[0062] The first is that the ship is unloaded and the tide is at its highest point. At this time, the tide has reached the highest tide in the operating range and will not rise any more. When the ship is unloaded, the waterline will not become smaller due to unloading, and the height of the ship's docking platform will not increase. Therefore, the calculation formula for the lower limit value G1 of the expected berthing is: G1 = W diff +B2-H1, where B2 is the height of the gangway joint from the water surface when the ship is unloaded, and H1 is the safe lowering height of the joint. Since the tide level will change periodically over time, the impact of tide level fluctuations on operations needs to be considered in actual operations.

[0063] The second is that the ship is fully loaded and the tide is at its lowest point. At this time, the tide has reached the lowest tide in the operating range and will not drop any more. When the ship is fully loaded, the waterline will not increase due to loading, and the height of the ship's gangway will not drop. Therefore, the upper limit value G2 of the expected berthing is calculated as follows: G2 = B1 + H2, where B1 is the height of the gangway's joint point from the water surface when the ship is fully loaded, and H2 is the safe lifting height of the joint.

[0064] 5. Judge the rationality of the boundary. If G1>G2, it means that the lower boundary is greater than the upper boundary. At this time, there is no suitable docking interval during the working time period, and it is necessary to consider changing the time period for operation. If G1<0, it means that the lower boundary is lower than the tide level and the dock is submerged. In this case, you only need to set G1 to a value greater than 0.

[0065] In summary, the estimated berthing height interval is based on the ship's arrival time, loading and unloading time, ship's departure interval and ramp connection interval, and the calculated tidal difference is found in the local tidal parameters to calculate the lower and upper limits. When the estimated berthing height interval overlaps with the air gap height interval of the current berth, the current berth can be matched.

[0066] S2. Based on the scheduling parameters, an initial population is generated. Each individual in the initial population is represented by a double-coded chromosome. The first layer of the double-coded chromosome refers to the ship, and the second layer of the code refers to the slipway.

[0067] like Figure 3 The figure shows a chromosome schematic diagram of a docking scheduling method based on a movable slipway of the present invention. In S2, the first layer of coding of the double-coded chromosome is sorted in ascending order based on the arrival time of the ship, and the second layer of coding of the double-coded chromosome is based on the berth number of the ship randomly assigned to the docking slipway based on the first layer of coding, and the docking condition is met. The docking condition refers to the overlapping interval between the docking height interval and the air gap height interval.

[0068] Specifically, the first level of coding (ship scheduling order) uses integers to encode ship numbers, indicating the order in which ships dock; the second level of coding (slip allocation) uses integers to encode berth numbers, indicating the slip number at which each ship docks. For example, if there are three ships (A, B, C), a possible coding is [2, 1, 3], indicating that ship B docks first, followed by ship A, and finally ship C; corresponding to the first level of coding [2, 1, 3], the second level of coding can be [1, 2, 1], indicating that ship B docks at slip 1, ship A docks at slip 2, and ship C docks at slip 1.

[0069] like Figure 4 The figure shows a scheme Gantt chart of a method for berthing scheduling based on a movable slipway of the present invention. The Gantt chart is based on the need to check whether there is a conflict in the second-layer coding when generating the initial population (i.e., the same berth is allocated to multiple ships at the same time). If there is a conflict, the individual chromosome is regenerated until all berths are occupied by only one ship in the same time period.

[0070] S3. Use the greedy algorithm to optimize the initial population, prioritize the ships, and generate the optimized population; the core idea of ​​the algorithm when using the improved greedy algorithm to optimize the initial population is: prioritize the ships to the earliest available slipway according to the order of arrival, and consider the matching of ship size and slipway carrying capacity, as well as water depth restrictions, to meet the docking conditions.

[0071] When allocating slipways, the greedy algorithm must check the availability of slipways to ensure that there are no conflicts. If a berth is already occupied, the algorithm needs to find the next available slipway. For each ship, a slipway is selected from the available berths that meets its draft and size requirements and has the shortest waiting time. If multiple slipways meet the conditions, one is selected randomly. For two berths on the same slipway, it is necessary to consider the overlap of the berthing height intervals of the ships docked on both sides, and during the loading and unloading process, the slipway's air gap height can be automatically adjusted according to the draft and size of the selected slipway and the ship to ensure the safe docking of the ship.

[0072] like Figure 5 The figure shows an algorithm process diagram of a berthing scheduling method based on a movable slipway of the present invention. In S3, the greedy algorithm optimizes at least 5% of the double-coded chromosomes in the initial population: based on the order of the first-layer coding, the ship is preferentially assigned to the idle berths that meet the berthing conditions; if there is no idle berth, a reserve berth is found, and the air gap height interval of the reserve berth is closest to the berthing height interval of the current ship to be assigned, and the waiting time is recorded. The waiting time is the remaining loading and unloading time of the ship in the current berth plus the height adjustment time of the slipway; if there is no reserve berth, an available berth is selected, and the remaining loading and unloading time of the ship in the available berth is the shortest.

[0073] Specifically, first input the information of the ship to be allocated, check whether there is an idle berth that meets the docking conditions, if there is, directly allocate the ship to the idle berth; if not, proceed to the next step, find the reserve berth, check all berths, compare the difference between the air gap height interval of the slipway and the docking height interval of the ship to be allocated, and select the berth with the smallest height interval difference as the reserve berth. If a reserve berth is found, calculate the waiting time = the remaining loading and unloading time of the current ship + the slipway height adjustment time, and record the waiting time; if there is no reserve berth, proceed to the next step, select an available berth, check all berths, compare the remaining loading and unloading time of the current ship at each berth, select the berth with the shortest remaining loading and unloading time, and complete the allocation. That is, for ship allocation, idle berths are given priority, followed by the reserve berth with the closest height interval, and the available berth with the shortest remaining loading and unloading time is placed last.

[0074] S4. Calculate the fitness value of each chromosome in the optimized population, sort and rank according to the fitness value; calculate the fitness value of each chromosome in the optimized population. The fitness value can be calculated based on the total docking time, the moving distance of the slipway, and the penalty item for violating the constraint conditions. The lower the fitness value, the better the scheduling plan. Sort the chromosomes according to the fitness value and calculate the ranking.

[0075] In some embodiments, in S4, the fitness value is the inverse of the total operation time of the scheduling scheme, and the total operation time is the operation time of the longest slipway berth, and the operation time includes the slipway height adjustment time and the ship loading and unloading time.

[0076] Specifically, I represents the total number of ships, i represents a specific ship, for example, i=1 represents the ship numbered 1. J represents the total number of berths, j represents a specific berth, for example, j=1 represents the berth numbered 1. T represents the total time spent on dock operations, t ij It represents the loading and unloading time of a ship numbered i at j. The optimization objective is argmin∑ i,j)∈ u t ij , where u represents a set of different reasonable combinations of i and j. The fitness calculation is set to The inverse of fitness is the total time spent on terminal operations under the scheduling scheme.

[0077] In some embodiments, in S4, the fitness value is calculated as total waiting time + α* slipway idle time + β* number of air gap adjustments + γ* total amplitude of air gap adjustment, where α, β and γ are weight coefficients. The total waiting time represents the total waiting time of all ships before entering the slipway; the slipway idle time represents the time when the slipway is idle and not occupied by any ship; the number of air gap adjustments represents the number of times the air gap is adjusted during the use of the slipway; the total amplitude of the air gap adjustment represents the total amount of all air gap adjustments or the sum of the amplitudes of the adjustments. The relationship between different goals can be balanced by adjusting the weight coefficients, for example, more emphasis can be placed on reducing waiting time or reducing the number of air gap adjustments. If you want to reduce slipway idle time and improve slipway utilization, you can increase the value of α; if the cost of the air gap adjustment operation is high and you want to minimize the number of adjustments, you can increase the value of β; if the amplitude of the air gap adjustment has a greater impact on system stability, you can increase the value of γ. By optimizing multiple dimensions at the same time, you can avoid local optimal solutions and find a more balanced and comprehensive solution.

[0078] S5. Calculate the selection probability, and extract the parent chromosome from the optimized population according to the selection probability; calculate the selection probability of each chromosome according to the ranking of the chromosome, and then select the parent chromosome according to the probability.

[0079] In some embodiments, in S5, the calculation formula for the selection probability is: Where ri is the calculated ranking and P is the total number of optimized populations

[0080] S6. Randomly select two individuals from the parent chromosome without duplication and exchange them. After crossing the first layer of coding, adjust the second layer of coding accordingly to generate daughter chromosomes; the crossover operation needs to act on both layers of the double coding at the same time.

[0081] Specifically, a crossover point is selected randomly or according to a certain strategy, which will be applied to both the first-level coding (arrival order) and the second-level coding (specified berth); with the selected crossover point as the boundary, the partial gene segments of the two parent individuals (assuming P1 and P2) after the crossover point (or before, depending on the selection) are exchanged. After the crossover, the first-level coding of the offspring individuals (assuming C1 and C2) is checked to ensure that the arrival order is not repeated.

[0082] If duplication is found, one of the following strategies can be used to handle it:

[0083] Reordering: reorder the repeated parts to ensure that each ship appears only once in the sequence, and the sorted order still meets the constraints of the problem; Replace repeats: select a number from another non-repeated part to replace the repeated number, while ensuring that the replaced order is still valid; Partial crossover: if the duplication problem is serious, you can consider exchanging only part of the gene segment instead of the entire gene segment to reduce the possibility of duplication and balance the reduction of the duplication rate and the maintenance of solution diversity.

[0084] While dealing with the duplication problem, it is necessary to ensure the consistency of the second-level coding with the first-level coding. That is, if a certain ship arrival sequence in the first-level coding changes, the corresponding berth designation in the second-level coding also needs to be adjusted accordingly. In addition, the offspring individuals after the crossover are verified to ensure that they meet all the constraints of the problem (such as berth capacity, time window, etc.). If the constraints are not met, appropriate adjustments can be made, such as reallocating berths or further adjusting the ship arrival sequence. The verified and adjusted offspring individuals will continue to participate in the evolution process of the genetic algorithm as part of the new generation population.

[0085] At the same time, due to the complexity of the ship scheduling problem, a lot of verification and adjustment work may be required after the crossover operation. Therefore, when designing the crossover operation, it is necessary to fully consider the characteristics and constraints of the problem to ensure that the generated new individuals are effective and feasible. In order to improve the efficiency of the algorithm, some heuristic rules or optimization strategies can be used to guide the crossover operation, such as similarity-based crossover, adaptive crossover, etc. These strategies can find high-quality solutions faster and improve the overall performance of the algorithm.

[0086] S7. Randomly extract individuals from the offspring chromosomes for mutation operations, replace the first-layer code with other ship sequences, and then replace the second-layer code with other dock numbers; randomly extract 5% of individuals from the offspring chromosomes for mutation operations, randomly change some gene values ​​in the individual code to generate new individuals, increase the diversity of the population, and prevent the algorithm from converging to the local optimal solution too early. It should be understood that the specific value of the percentage of extracted individuals can be adjusted according to the complexity of the problem and the performance of the algorithm.

[0087] Specifically, one or more mutation points are randomly selected in the first layer of encoding. The selection of mutation points can be continuous or scattered. The gene value at the mutation point (that is, the number of the order of incoming ships) is replaced with other non-repeating numbers. This can be achieved by randomly selecting a new number (real-valued mutation) and ensuring that the number does not appear in the current encoding. If the mutation operation causes the order of incoming ships to be repeated, the mutation operation needs to be performed again until the conditions are met. The order of incoming ships after mutation needs to remain valid, that is, each incoming ship appears only once in the order, and the order meets the constraints of the problem.

[0088] The second-layer coding mutation is similar to the first-layer coding. One or more mutation points in the second-layer coding are randomly selected. The berth designation numbers at the mutation points are replaced with other feasible berth numbers. This needs to take into account the berth capacity, location and other constraints. If the berth designation after mutation causes the berth capacity to be overloaded or other constraints are not met, the mutation operation needs to be repeated.

[0089] S8. Determine whether the current number of iterations reaches the specified number of iterations. If so, stop the iteration and output the docking scheduling plan; if not, jump to S4.

[0090] In S8, a backtracking algorithm is also included. If the current scheduling plan cannot meet the requirements of all ships, the backtracking algorithm is used to fall back to the optimized population of the previous generation.

[0091] like Figure 6 The comparison diagram before and after the optimization of a docking scheduling method based on a movable slipway of the present invention is shown in the figure, the vertical axis is the total operation time (hours), and the horizontal axis is the number of iterations. The experiment is assumed to be carried out at a seaside dock, the tidal change does not exceed 3 meters, the sea depth does not exceed 12 meters, the dock consists of 4 movable slipways, and provides 8 berths. The case of incoming ships is 20 passenger and vehicle ferries of different sizes, draft depths, tonnages, and container quantities.

[0092] The optimized algorithm calculated using the greedy algorithm has found an excellent solution in the 10th iteration, while the unoptimized algorithm only reaches the effect of the 10th generation of the optimized algorithm in the 31st generation. Therefore, it can be seen that the genetic algorithm optimized by the greedy algorithm can converge faster and improve efficiency. Since the greedy algorithm uses the idea of ​​first-come, first-served in time to initialize the population, the total operation time in the first generation is the first-come, first-served operation time, and the subsequent chromosome evolution based on this is an improvement on the first-come, first-served scheme. Therefore, it is proved that the first-come, first-served scheme is not necessarily the global optimal solution.

[0093] On the other hand, Figure 7 As shown in a schematic diagram of a docking scheduling system based on a movable slipway of the present invention, an embodiment of the present application provides a docking scheduling system based on a movable slipway, including an electrically connected parameter acquisition module, a population generation module, a population optimization module, a calculation ranking module, a parent selection module, a crossover operation module, a mutation operation module and an iteration judgment module, wherein:

[0094] The parameter acquisition module is used to obtain the scheduling parameters, including the ship parameters and the berth parameters, generate the expected berthing interval and the air gap height interval according to the ship parameters and the berth parameters, and pass the obtained scheduling parameters to the population generation module;

[0095] In some embodiments, the ship parameters in the parameter acquisition module include arrival time, loading and unloading time, ship leaving water interval and gangway overlap interval, and the slipway parameters include berth number, local water depth and air gap height interval. The local water depth and the air gap height interval are superimposed to obtain the slipway height interval; further, based on the ship parameters, the calculated tidal difference is searched in the local water depth, and the expected berthing height interval is calculated.

[0096] The population generation module is used to generate the initial population according to the scheduling parameters. Each individual is represented by a double-coded chromosome. The first layer of the double-coded chromosome refers to the ship, and the second layer of the code refers to the slipway. The generated initial population is passed to the population optimization module.

[0097] In some embodiments, the first layer of coding of the dual-coding chromosome in the population generation module is sorted in ascending order based on the arrival time of the ship, and the second layer of coding of the dual-coding chromosome is based on the berth number of the ship randomly assigned by the first layer of coding and meets the docking conditions, and the docking conditions refer to the overlapping interval between the expected docking height interval and the air gap height interval.

[0098] The population optimization module is used to optimize the initial population using a greedy algorithm, generate an optimized population, prioritize ships, and pass the optimized population to the calculation ranking module;

[0099] In some embodiments, the greedy algorithm in the population optimization module optimizes at least 5% of the double-coding chromosomes in the initial population: based on the order of the first layer of coding, the ship is preferentially assigned to the vacant berths that meet the docking conditions; if there are no vacant berths, a reserve berth is found, and the air gap height interval of the reserve berth is closest to the estimated berthing height interval of the current ship to be assigned, and the waiting time is recorded, and the waiting time is the remaining loading and unloading time of the ship in the current berth plus the height adjustment time of the slipway; if there are no reserve berths, an available berth is selected, and the remaining loading and unloading time of the ship in the available berth is the shortest.

[0100] A ranking calculation module is used to calculate the fitness value of each chromosome in the optimized population, sort and calculate the ranking according to the fitness value, and pass the fitness value, sorting and ranking to the parent selection module;

[0101] In some embodiments, the fitness value in the calculation ranking module is the inverse of the total operation time of the scheduling scheme, and the total operation time is the operation time of the longest slipway berth, and the operation time includes the slipway height adjustment time and the ship loading and unloading time;

[0102] At the same time, in some embodiments, the calculation formula for the fitness value in the ranking module is total waiting time + α*slipway idle time + β*number of air gap adjustments + γ*total air gap adjustment amplitude, where α, β and γ are weight coefficients.

[0103] The parent selection module is used to calculate the selection probability and randomly select the parent chromosome; the selection probability is determined based on the fitness value, sorting and ranking, and the parent chromosome is extracted from the optimized population based on the selection probability, and the parent chromosome is passed to the crossover operation module;

[0104] In some embodiments, the calculation formula for the selection probability in the parent selection module is: Where ri is the calculated ranking and P is the total number of optimized populations.

[0105] The crossover operation module is used to randomly select two bodies from the parent chromosome for crossover operation to generate offspring chromosomes, randomly extract two bodies from the parent chromosome without duplication to exchange, generate offspring chromosomes, and pass the offspring chromosomes to the mutation operation module;

[0106] The mutation operation module is used to randomly select individuals from the offspring chromosomes for mutation operation, replace the first-layer code with other ship sequences, and then replace the second-layer code with other berth numbers, and pass the mutated offspring chromosomes to the iterative judgment module;

[0107] The iteration judgment module is used to judge whether the number of iterations reaches the specified value. If it reaches the specified value, the iteration is stopped and the docking scheduling plan is output; if it does not reach the specified value, the module jumps to the calculation ranking module to continue the iteration.

[0108] In some embodiments, the iterative judgment module also includes a backtracking algorithm. If the current scheduling solution cannot meet the requirements of all ships, the backtracking algorithm is used to fall back to the optimized population of the previous generation.

[0109] On the other hand, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the docking scheduling method based on a movable slipway as described above are implemented.

[0110] The emergency terminal plan information software will first collect information about the next batch of incoming ships, such as the estimated arrival time, draft, tonnage, various sizes of ships, quantity and type of cargo. Through these parameters, the information software will call the loading and unloading time algorithm and the safe air gap interval algorithm to calculate the safe loading and unloading air gap interval in the future, and then pass the corresponding information into the algorithm, and output the results back to the information software. In a simulated environment, traditional manpower scheduling takes half an hour to three hours. Using this algorithm, it only takes 5 seconds to 5 minutes to complete the scheduling plan. Due to the fast calculation, when something unexpected happens, the plan can be quickly updated to fit the actual situation.

[0111] Through the lifting mechanism, the slipway can be raised when necessary to increase the air gap below, effectively responding to water level changes or tidal influences. In addition, the slipway design cleverly utilizes the space on its left and right sides as temporary berths for other ships, greatly improving the port's capacity and rescue efficiency in emergency situations. In the emergency terminal solution, information management software is used to manage incoming ship dispatch, slipway air gap, and monitor and predict tidal conditions.

[0112] In summary, the emergency terminal solution that adopts the docking scheduling method based on the movable slipway has become an ideal choice for quickly restoring water transportation and rescue operations after disasters due to its high flexibility, rapid deployment capability and wide adaptability to changeable sea conditions and geological conditions.

[0113] The steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that shown here.

[0114] In this specification, the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and the relevant parts can be referred to the partial description of the previous embodiments.

[0115] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A docking scheduling method based on a movable slipway, characterized in that: The following steps are involved: S1. Obtaining scheduling parameters, including ship parameters and berth parameters, and calculating the expected berthing interval of the ship and the air gap height interval of the berth; S2. generating an initial population, wherein each individual is represented by a double-coding chromosome, wherein the first-level coding of the double-coding chromosome refers to the ship, and the second-level coding refers to the slipway; S3. Optimizing the initial population using a greedy algorithm to generate an optimized population, wherein the expected docking interval in the chromosome overlaps with the air gap height interval; S4. Calculate the fitness value of each chromosome in the optimized population, sort and calculate the ranking according to the fitness value; S5. Calculate the selection probability and randomly select the parent chromosome; S6. Randomly select two chromosomes from the parent chromosome to perform a crossover operation to generate a daughter chromosome; S7. randomly extract individuals from the offspring chromosomes to perform mutation operations, replace the first layer code with other ship sequences, and then replace the second layer code with other ship slip numbers; S8. Determine whether the number of iterations reaches the specified value. If so, stop the iteration and output the docking scheduling plan; if not, jump to S4.

2. The method for berthing and dispatching based on a movable berth according to claim 1, characterized in that: In S1, the ship parameters include arrival time, loading and unloading time, ship leaving water interval and gangway connection interval, and the slipway parameters include berth number, local water depth and air gap height interval. The local water depth is superimposed on the air gap height interval to obtain the slipway height interval.

3. The method for berthing and scheduling based on a movable berth according to claim 2 is characterized in that: Based on the ship parameters, the calculated tidal difference is searched in the water depth at the location, and the expected berthing height range is calculated.

4. The method for berthing and dispatching based on a movable berth according to claim 2, characterized in that: In S2, the first layer of coding of the dual-coding chromosome is sorted in ascending order based on the arrival time of the ship, and the second layer of coding of the dual-coding chromosome is based on the berth number of the ship randomly assigned to the ship of the first layer of coding and meeting the docking condition, and the docking condition refers to the overlapping interval between the expected docking height interval and the air gap height interval.

5. The method for berthing and scheduling based on a movable berth according to claim 4 is characterized in that: In S3, the greedy algorithm optimizes at least 5% of the double-coding chromosomes in the initial population: Based on the order of the first layer coding, the ship is preferentially assigned to an idle berth that meets the berthing conditions; if there is no idle berth, a reserve berth is searched, the air gap height interval of the reserve berth is closest to the expected berthing height interval of the current ship to be assigned, and the waiting time is recorded, the waiting time being the remaining loading and unloading time of the ship in the current berth plus the height adjustment time of the slipway; If there is no such prepared berth, an available berth is selected, wherein the remaining loading and unloading time of the vessel is the shortest.

6. The method for berthing and dispatching based on a movable berth according to claim 1, characterized in that: In S4, the fitness value is the inverse of the total operation time of the scheduling scheme, and the total operation time is the longest operation time of the slipway berth, and the operation time includes the height adjustment time of the slipway and the loading and unloading time of the ship.

7. The method for berthing and dispatching based on a movable berth according to claim 1, characterized in that: In S4, the calculation formula of the fitness value is total waiting time + α*slipway idle time + β*number of air gap adjustments + γ*total air gap adjustment amplitude, wherein α, β and γ are weight coefficients.

8. The method for berthing and scheduling based on a movable berth according to claim 1, characterized in that: In S5, the calculation formula of the selection probability is: where r i is the calculated ranking, and P is the total number of the optimized population; in S8, a backtracking algorithm is also included. If the current scheduling scheme cannot meet the requirements of all ships, the backtracking algorithm is used to fall back to the optimized population of the previous generation.

9. A docking scheduling system based on a movable slipway, characterized in that: It includes an electrically connected parameter acquisition module, a population generation module, a population optimization module, a calculation ranking module, a parent selection module, a crossover operation module, a mutation operation module and an iterative judgment module, wherein: A parameter acquisition module is used to acquire scheduling parameters, including ship parameters and berth parameters, and calculate the expected berthing interval of the ship and the air gap height interval of the berth; A population generation module, used for generating an initial population, each individual is represented by a double-coded chromosome, the first layer of the double-coded chromosome refers to the ship, and the second layer of the double-coded chromosome refers to the slipway; A population optimization module, used for optimizing the initial population using a greedy algorithm to generate an optimized population, wherein the expected docking interval in the chromosome overlaps with the air gap height interval; A ranking calculation module is used to calculate the fitness value of each chromosome in the optimized population, and to sort and calculate the ranking according to the fitness value; The parent selection module is used to calculate the selection probability and randomly select the parent chromosome; A crossover operation module, used to randomly select two chromosomes from the parent chromosome to perform a crossover operation to generate a daughter chromosome; A mutation operation module, used for randomly extracting individuals from the offspring chromosomes to perform mutation operations, replacing the first layer codes with other ship sequences, and then replacing the second layer codes with other ship slip numbers; The iteration judgment module is used to judge whether the number of iterations reaches the specified value. If it reaches the specified value, the iteration is stopped and the docking scheduling plan is output; if it does not reach the specified value, the module jumps to the calculation ranking module.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the docking scheduling method based on the movable berth as described in any one of claims 1 to 8 are implemented.

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