Inland ship reservation lockage scheduling method in key reservation and declaration queuing mode

Abstract

The invention provides an inland ship reservation lockage scheduling method in a key reservation and declaration queuing mode, and the method comprises the steps: setting the ship priority according to the ship type, setting the priority of a common ship needing to be declared and queued to be the lowest, and enabling the priority of a key ship needing to be reserved to be higher than the priority of the common ship; constructing a cost function # imgabs0 # based on the ship priority, the ship waiting time and the ship navigation time; according to the ship lockage time difference, a queuing fairness function # imgabs1 # is constructed; establishing a comprehensive congestion function # imgabs2 through a complex network model; through one or more of a cost function # imgabs3 #, a fairness function # imgabs4 #, a comprehensive congestion function # imgabs5 #, a lock chamber utilization rate function # imgabs6 # and a gate discharge frequency function # imgabs7 #, an objective function is constructed through weighting, and constraint conditions are established according to actual passing requirements; and solving the target function and the constraint condition through an optimization algorithm to obtain a lockage scheduling scheme. The ship lock operation efficiency is effectively improved, and support is provided for scheduling of inland river shipping.

Description

Technical Field

[0001] The present invention relates to the technical field of ship scheduling, and in particular to a method for scheduling inland river ship lock passage reservations under key reservation and declaration queuing modes. Background Art

[0002] With the rapid growth of inland waterway shipping demand, the efficiency of ship lock dispatching has become an important factor restricting the overall performance of the shipping system. Although the existing arrival time queuing mode and declaration time queuing mode are relatively simple in operation, they have exposed a series of problems in a complex shipping environment: these modes lack effective management of ship priority needs, causing high-priority ships and ordinary ships to wait in the same queue, resulting in continued intensification of traffic pressure in anchorages and areas in front of the locks. Especially during peak traffic periods, the backlog of ships may spread to downstream sections, causing chain system congestion, which not only significantly reduces the utilization rate of the lock chamber and increases the number of lock discharges, but also directly affects the overall operational efficiency of the shipping system.

[0003] In recent years, appointment-based scheduling has gradually become an exploration direction for optimizing lock management. Appointment-based scheduling improves the controllability and management efficiency of scheduling to a certain extent by mastering ship information in advance, such as ship type, arrival time, cargo conditions, etc., and rationally planning the passage order. However, the scheduling model based entirely on appointments lacks flexibility in the face of uncertainties and emergencies in inland waterway shipping. For example, it is impossible to quickly adjust to the temporary increase of key ships, resulting in insufficient system flexibility and difficulty in meeting scheduling needs in complex shipping environments. Summary of the invention

[0004] The present invention proposes a method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing modes, so as to solve the technical problem that the existing scheduling scheme has insufficient system flexibility and is difficult to meet the scheduling needs in a complex shipping environment under the key reservation and declaration queuing modes.

[0005] In order to solve the above technical problems, the present invention provides a method for scheduling inland river vessel lock passage reservation under key reservation and declaration queue mode, comprising the following steps: Step S1: Set the ship priority according to the ship type, set the priority of ordinary ships that need to be queued to the lowest, and the priority of key ships that need to make an appointment to pass is higher than that of ordinary ships; construct a cost function based on the ship priority, ship waiting time and ship sailing time ; Step S2: Construct a queue fairness function based on the time difference of ships passing through the lock ; Step S3: Establish a comprehensive congestion function through a complex network model ; Step S4: Using the cost function And the fairness function , comprehensive congestion function , lock chamber utilization function And the number of gate discharge functions One or more of them, construct the objective function by weighting, and establish the constraint conditions according to the actual traffic requirements; Step S5: Solve the objective function and constraint conditions through an optimization algorithm to obtain a gate-passing scheduling plan.

[0006] Preferably, in step S1, the cost function The expression is: ; In the formula, Indicates the number of ships; P i For ships Priority; P max is the maximum value of the priority; Indicates ship Waiting time; For ships Sailing time; α Indicates the additional weight of ordinary ships; represents a binary variable, when For key ships, ,when For ordinary ships, .

[0007] Preferably, the queuing fairness function The expression is: ; In the formula, Indicates the number of ships; is the indicator function, if the ship Assigned to a queue j In =1, otherwise =0; Indicates the number of queues; For queue j The gate-crossing start time represents the first gate-crossing time assigned to queue j; For ships Arrival time.

[0008] Preferably, the comprehensive congestion function The expression is: ; ; ; ; In the formula, , and is the weight coefficient; represents the average transit time; represents the standard deviation of the passing time; represents the weighted average transit time; M Through the node Total number of ships; Indicates j Ships at the node The passage time of the ship j From the entry node The time to complete the gate crossing or passing; It is j The priority of the vessel.

[0009] Preferably, the constraints set in step S4 include: 1) The waiting time for ordinary ships is less than 60 hours; 2) The impact of the passage cost of the reserved ships on ordinary ships shall not exceed the threshold: ; In the formula, is the balance threshold of the impact of scheduled and non-scheduled ship costs, Indicates ship Waiting time; For ships Sailing time; 3) The difference in waiting time for ordinary ships is not greater than the set threshold: ; In the formula, and Represents any two adjacent ordinary ships; is the maximum acceptable waiting time difference.

[0010] Preferably, in step S4, the objective function The expression is: ; In the formula, is the weight parameter, In order to control the influence of lock chamber utilization, It is the proportion of the number of gate discharges in the total target; It is to balance the fairness of the queues of ordinary ships; It is to quantify the impact of the congestion level of complex networks on the objective function.

[0011] Preferably, the cost function in step S1 is Penalty weights are added to avoid long waiting times for common ships. The expression is: ; In the formula, Indicates the number of ships; is the penalty coefficient; is the waiting time threshold, Indicates ship Waiting time; The updated cost function The expression is: .

[0012] Preferably, the optimization algorithm in step S5 adopts an improved dung beetle optimization algorithm, in which fractional-order derivatives are introduced for path search, including five stages: initial population generation, fractional-order update, dynamic queuing and boundary adjustment, multi-objective optimization, local optimization and global search.

[0013] Preferably, the fractional order updating method comprises: The formula for fractional derivative is as follows: ; In the formula, L ( t +1) is the position of the next generation solution; and is the adjustment coefficient; represents fractional derivative; represents the current global optimal solution; X ( t )and X ( t -1) indicates the solution of the current generation and the previous generation; During the updating process, the weight parameters of different sub-goals are dynamically adjusted using the following formula: ; ; In the formula, For the The next step value of the sub-goal weight, is the adjustment coefficient, For the The current deviation value of the subgoal.

[0014] Preferably, the improved dung beetle optimization algorithm updates the upper and lower boundaries by the following expressions: ; ; In the formula, and Represents the upper and lower bounds of the next generation search area; R represents the iteration ratio.

[0015] The beneficial effects of the present invention include at least: the present invention proposes a key reservation + declaration queuing mode. This mode achieves an organic combination of precision and flexibility by giving priority to the management of key ships and flexibly dispatching ordinary ships. Under this mode, key ships such as earthquake relief ships, passenger ships and fresh cargo ships plan their passage plans in advance through the reservation mechanism to ensure that they have priority to pass through the locks; ordinary ships queue up in the order of declaration time, which not only ensures the fairness of scheduling, but also significantly improves the efficiency of lock operation. At the same time, the key reservation + declaration queuing mode shows higher adaptability in dealing with emergencies and uncertainties, and provides an innovative solution for inland waterway shipping scheduling.

[0016] In order to comprehensively evaluate the effects of different scheduling modes, this paper constructs an evaluation function based on multi-objective optimization, covering five core indicators: passage cost, lock chamber utilization, number of lock discharges, queue fairness, and congestion level in complex network models. This evaluation function comprehensively quantifies the operation status of the ship lock from three aspects: resource utilization, scheduling efficiency, and traffic mobility, providing a scientific quantitative basis for optimizing ship lock scheduling.

[0017] It effectively improves the operating efficiency of the locks, reduces waiting time, reduces the number of lock openings, improves the system's ability to cope with uncertainty and emergencies, and provides advanced technical support for the intelligent scheduling of inland waterway shipping. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention; Figure 2 A schematic diagram of a declaration / reservation process in a key reservation and declaration queuing mode according to an embodiment of the present invention; Figure 3 Schematic diagram of the FODBO algorithm flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0020] Aiming at the outstanding problems of inefficient ship lock dispatching, waste of resources and traffic congestion in the inland waterway navigation system, the present invention proposes a ship lock dispatching mode of key reservation + declaration queue, such as Figure 2 As shown, it aims to optimize the ship passing process, improve the utilization efficiency of the lock, and effectively alleviate the traffic congestion problem in inland waterway shipping. Under this mode, the process of ship passing the lock includes full-line declaration, joint control, anchorage security inspection and planned passing. According to the navigation scheduling regulations, key ships need to submit the dam passing information through the remote declaration system and obtain confirmation feedback. After entering the linkage control waters, if the dam passing information changes, the ship needs to confirm the adjustment plan through the system again, and then enter the core waters according to the scheduling instructions to complete the security inspection and departure operations. The lock passing process of ordinary scheduled ships is similar to that of key ships, but their lock clearance priority is lower.

[0021] Through the key reservation + declaration queuing mode, the dispatching system can grasp the information such as ship type, quantity and expected arrival time in advance, and scientifically arrange the passing order and time of the ships.

[0022] Example 1 Therefore, this embodiment provides a method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queue mode, such as Figure 1 As shown in the figure, while ensuring that key ships have priority in passing through the lock, ordinary ships are flexibly dispatched through a dynamic queuing mechanism, achieving high efficiency and responsiveness in lock dispatching. The specific steps include: Step S1: Set the ship priority according to the ship type, set the priority of ordinary ships that need to be queued to the lowest, and the priority of key ships that need to make an appointment to pass is higher than that of ordinary ships; construct a cost function based on ship priority, ship waiting time and ship sailing time .

[0023] Specifically, this embodiment establishes the priority of all ship types according to the importance of different tasks and ship types. P i as follows: 1) The priority of special mission ships is 9; 2) Passenger ship priority is 8; 3) The priority for fresh cargo ships and key urgent material ships is 7; 4) The priority for commercial vehicle carriers and container ships is 6; 5) Other ship types have a priority of 1.

[0024] The priority setting ensures that ships with high mission requirements have priority in the order of passage, especially special mission ships with "absolute priority" such as earthquake relief ships, which are directly inserted into the head of the dispatch team to ensure their rapid passage.

[0025] The passage cost of ships is one of the core parts of optimizing the "key appointment + declaration queue" model. It directly reflects the time and resource costs consumed by ships in the process of passing through the lock. Due to the different importance of ship types and tasks, their priorities are also different. Therefore, in the optimization process, it is necessary to focus on the scheduling priority of high-priority ships. At the same time, in order to take into account the fairness of scheduling of ordinary ships, the model needs to dynamically balance the passage costs of high-priority ships and ordinary ships. Special mission ships, passenger ships, fresh cargo ships, etc. have high requirements for time and tasks. It is necessary to minimize their waiting time and sailing time to ensure scheduling efficiency and the completion of transportation tasks. In particular, special mission ships such as earthquake relief ships have "absolute priority" and need to be directly inserted into the head of the scheduling team to ensure their rapid passage. The scheduling of ordinary ships needs to be carried out with a suboptimal strategy, but it is still necessary to avoid extreme delays of ordinary ships due to excessive priority of key ships. For high-priority ships ( ), by normalizing the priority weight Amplify its impact on the passage cost and make it dominate the total cost. By making advance reservations for high-priority ships, such as special mission ships, passenger ships, etc., they will get higher priority and be assigned a larger weight in the objective function. Bi =1 indicates that other ships that have not been booked are added with additional weights. α Control its contribution to the toll cost through a binary variable Bi =0. Based on the above logic, the toll cost formula is designed as: ; In the formula, Indicates the number of ships; P i For ships The priority is determined by the ship type and the value range is [1,9]. The higher the priority, the higher the weight of the passage cost. P max It is the maximum value of the priority and is used for normalization processing. In this embodiment, the value is 9, which is the priority of the special mission ship. T w,i For ships The waiting time depends on the queue order and gate room allocation. T t,i For ships The sailing time is related to the ship scheduling plan. α It is the additional weight of ordinary ships, used to reduce the impact of ordinary ships on the total cost.

[0026] Step S2: Construct a queue fairness function based on the time difference of ships passing through the lock .

[0027] Specifically, the goal of the queue fairness part is to optimize the scheduling experience of ordinary ships, reduce the excessive crowding of ordinary ships by key ships, and ensure that the waiting time of ordinary ships is within an acceptable range. This part focuses on the time fairness in queue scheduling, and optimizes the scheduling scheme by quantifying the differences in the waiting time of ships in the queue. By quantifying the waiting time differences of ordinary ships, the ratio of the longest waiting time to the average waiting time in the queue is limited, so as to achieve the following goals: control the waiting time differences of ordinary ships so that the waiting time of ordinary ships is close to the scheduling average; impose higher penalty weights on ordinary ships with too long waiting time; dynamically optimize the scheduling scheme to reduce delays of ordinary ships without affecting the efficiency of high-priority ships. To achieve the above goals, the queue cost formula is introduced. : ; In the formula, Indicates the number of ships; is the indicator function, if the ship Assigned to a queue j In =1, otherwise =0; Indicates the number of queues; For queue j The gate-crossing start time represents the first gate-crossing time assigned to queue j; For ships Arrival time.

[0028] In order to further quantify fairness in this embodiment, the following threshold standards are set: The ratio is between 1.0 and 1.2: The difference between the maximum waiting time and the average waiting time in the queue is small, within the normal range, and fairness is good.

[0029] The ratio is between 1.2 and 1.5: the waiting time of some ships in the queue varies greatly, which may affect the scheduling efficiency, but it is still acceptable.

[0030] Ratio greater than 1.5: The maximum waiting time in the queue is much greater than the average waiting time, the queue is unfair and may cause serious delays for ordinary ships.

[0031] Step S3: Establish a comprehensive congestion function through a complex network model .

[0032] Specifically, in order to effectively identify traffic congestion problems that may occur during ship scheduling, by modeling the inland waterway shipping system as a complex network, the traffic flow of each section can be evaluated in real time, the congestion level can be quantified, and potential traffic bottleneck areas can be predicted, providing basic data support for optimizing scheduling. The nodes in the complex network represent key locations in the shipping system, such as ports, sections, and locks. In order to effectively reflect the traffic conditions of ships passing through key nodes, the nodes themselves do not directly represent the passage time of the ships, but indirectly reflect the traffic characteristics of the location through the attributes of the nodes. These attributes include the flow of ships passing through the node and the average passage time of the ships. Through these indicators, the traffic efficiency of the nodes can be comprehensively evaluated, and possible congestion can be identified. In order to fully reflect the traffic characteristics of ships passing through nodes, the node attributes are composed of 3 indicators: 1) Average transit time Indicates that all ships pass through the node i Average time spent ; Indicates j Ships at the node The transit time at the node is The time to complete the gate crossing or passing; M Through the node The total number of ships. Reflection Node overall traffic efficiency.

[0033] 2) Standard deviation of passing time It represents the volatility through time, and its formula is: ; The larger the standard deviation, the more unstable the traffic flow at the node is, and there may be greater bottleneck pressure.

[0034] 3) Weighted average transit time , combined with ship priorities , reflecting the passage of high-priority ships through the node: ; In the formula It is j The weighted average time reflects the priority of the ships and reflects the passing efficiency of the high-priority ships, which is particularly important for nodes with dense high-priority ships.

[0035] The overall congestion level of the node The weighted combination of the three indicators yields:

[0036] In the formula , and is the weight coefficient used to balance the impact of each attribute. Comprehensive congestion level It is a key indicator reflecting the traffic pressure at a node. The higher the value, the more congested the node is.

[0037] In this embodiment, in order to further quantify and identify nodes of different congestion levels, the following congestion level standards are set: No congestion: ∈[0,0.3]——The node traffic efficiency is high and there is almost no congestion problem; Mild congestion: ∈(0.3,0.6]——The traffic efficiency of the node is slightly affected, and there is a certain degree of waiting; Moderate congestion: ∈(0.6,0.8]——There is an obvious bottleneck at the node, and the efficiency of gate passage is significantly reduced; Severe congestion: ∈(0.8,1.0]——The node is severely congested, causing large-scale traffic delays and affecting the overall scheduling efficiency.

[0038] Step S4: Through the cost function And the fairness function , comprehensive congestion function , lock chamber utilization function And the number of gate discharge functions One or more of them are used to construct the objective function through weighting, and the constraints are established according to the actual traffic requirements.

[0039] Specifically, the lock chamber area utilization rate refers to the ratio of the projected area of ​​ships passing through the lock on the horizontal plane of the lock to the effective area of ​​the lock. The lock chamber area utilization rate is a comprehensive reflection of the combination of ships passing through the lock and the level of lock scheduling. In theory, the lock chamber area utilization rate is between 0 and 1.0; the greater the demand for ships passing through the lock and the higher the degree of ship standardization, the greater the corresponding lock chamber area utilization rate, indicating that the higher the economic benefits created by the lock. When the demand for ships passing through the lock increases and the number of ships passing through the lock increases, the lock chamber area utilization rate will increase accordingly. The lock chamber area utilization rate can also reflect the service quality of the lock to a certain extent, and the lock chamber utilization rate should be maximized as much as possible. The calculation method is as follows: ; In the formula, is the area of ​​the lock chamber occupied by a ship in a lock, is the area of ​​a certain lock. When calculating the lock utilization rate, there are the following constraints: the sum of the areas of ships entering the lock chamber does not exceed the area of ​​the lock chamber; the length and width of the lock chamber are limited; the ships entering the lock chamber cannot overlap each other, etc.

[0040] The number of locks refers to the minimum number of locks required for a ship to pass through a lock under the premise of meeting the demand for cargo transportation. The more locks are required, the more likely it is to cause ship congestion when the maximum number of locks is exceeded. The lock number calculation method based on the ship arrival rate and service rate is a method borrowed from the queuing theory. The lock is regarded as a service node, and the process of ship passing through the lock can be regarded as a service process. The ship arrival rate is , the service rate of the lock , then in the queuing system, the average number of gate discharges can be estimated using the following formula Nm : ; In the formula, The ship arrival rate is calculated based on the actual shipping situation to calculate the number of ships arriving at the lock per unit time. The lock service rate indicates the number of lock services that can be completed by the lock in unit time, which depends on the design capacity of the lock and the actual scheduling efficiency.

[0041] The final objective function integrates the travel cost, lock chamber utilization, gate discharge times and queue fairness, and the expression is as follows: ; In the formula, , , and is the weight parameter, In order to control the influence of lock chamber utilization, It is the proportion of the number of gate discharges in the total target; It is to balance the fairness of the queues of ordinary ships; It is to quantify the impact of the congestion level of complex networks on the objective function. When the impact of any one or more objectives is not considered, the corresponding weight parameter can be set to 0.

[0042] In this embodiment, in order to ensure fairness and efficiency, constraints are added to the travel cost: 1) The waiting time for ordinary ships shall not exceed 60 hours to ensure fairness and efficiency.

[0043] 2) The impact of the passage costs of scheduled ships on non-scheduled ships shall not exceed a certain proportion: ; in The threshold for balancing the impact of scheduled and non-scheduled ship costs is set at 1.8 in this embodiment to avoid serious unfairness.

[0044] 3) The waiting time difference of ordinary ships should not be too large to avoid extreme delays of ordinary ships due to the impact of high-priority ships:

[0045] in and represents any two adjacent ordinary ships, For the maximum acceptable waiting time difference, this embodiment sets For 12 hours.

[0046] Step S5: Solve the objective function and constraint conditions through the optimization algorithm to obtain the gate-passing scheduling plan.

[0047] By adopting the optimization algorithm and solving the above objective function under the constraints, the gate-passing dispatching plan can be obtained.

[0048] Example 2 Based on Example 1, this embodiment adopts an improved dung beetle optimization algorithm to replace the traditional optimization algorithm in Example 1, and proposes a fractional-order dung beetle optimization algorithm FODBO.

[0049] FODBO is an improved version of the traditional dung beetle optimization algorithm (DBO). The algorithm flow is as follows: Figure 3 As shown in the figure, the global search capability and dynamic adaptability of the algorithm are enhanced by combining biological behavior simulation with fractional calculus. By introducing fractional derivatives, FODBO uses historical information to optimize the search path and avoid local optimality, which is particularly suitable for multi-objective optimization scenarios. Based on the construction of the objective function, the algorithm comprehensively considers the travel cost, lock chamber utilization, number of gate trips, queue fairness and complex network congestion level, and realizes multi-objective collaborative optimization through dynamic weight adjustment.

[0050] The FODBO algorithm includes the following five stages: initial population generation, fractional order update mechanism, dynamic queuing and boundary adjustment, multi-objective optimization, local optimization and global search.

[0051] Step 1: Initialization FODBO first generates an initial solution set through the Good Points Set strategy to ensure the diversity of solutions. Each individual in the population represents a scheduling scheme, including the queuing order of high-priority ships, the dynamic allocation of ordinary ships, the calculation of lock utilization, etc. Traditional random initialization may lead to uneven distribution of solutions and easy to fall into local optimality, while the Good Points Set strategy can generate an evenly distributed initial population and improve the global search ability of the algorithm. The generation formula of the initial population is: ; in, n is the population size,X 0 is the initial solution set, ensuring that the algorithm can widely distribute solutions in the entire solution space. Each solution includes two information: high-priority ships are directly assigned to the priority position of the initial scheduling queue and ordinary ships are dynamically assigned to the remaining queue according to arrival time and remaining capacity. Calculate the comprehensive objective function of each solution C t , and record the current global optimal solution .

[0052] Step 2: Fractional Order Update Mechanism During the optimization process, the solution of each dung beetle individual is updated according to its historical position and current state. FODBO uses fractional calculus and introduces the memory of historical solutions to make path adjustment more flexible. The formula for fractional derivatives is as follows: ; in, L ( t +1) is the position of the next generation solution, To adjust the coefficient, balance global search and local development. It is a fractional derivative, and the path is adjusted dynamically based on the historical solution information. is the current global optimal solution, X ( t )and X ( t -1) represents the solution of the previous generation and the current generation. This path update method allows the algorithm to jump out of the local optimal solution area and improve the convergence speed of the algorithm. changes, yes and Make dynamic adjustments to improve algorithm performance.

[0053] During the updating process, different sub-goals in the objective function are mutually restrained. For example: passage cost and lock chamber utilization rate - increasing ship density to improve lock chamber utilization rate may increase ship waiting time, thereby increasing passage cost. Queue fairness and scheduling of high-priority ships - giving priority to ensuring the rapid passage of high-priority ships may occupy the queue resources of ordinary ships and affect the fairness of ordinary ships. Congestion relief in complex networks - giving priority to alleviating high-congestion nodes may lead to a short-term reduction in the overall efficiency of the system. In order to reduce conflicts between sub-goals, the dynamic adjustment of weights is used to dynamically change the importance of sub-goals according to the current system status. When the lock chamber utilization rate is less than 50%, increase value to improve the priority of resource utilization. When it is greater than 1.5, the maximum waiting time in the queue is much longer than the average waiting time, and the queue is unfair, which may cause serious delays for ordinary ships and increase The value of emphasizes fairness optimization and avoids extreme waiting for ordinary ships. When the value is higher than 0.6, it increases The value of is used to prioritize the alleviation of traffic pressure at high congestion nodes, and the dynamic weight adjustment formula is designed as follows: ; ; in, For the The next step value of the sub-goal weight, is the adjustment coefficient, For the The current deviation value of the subgoal.

[0054] Step 3: Dynamic Queuing and Boundary Adjustment In each iteration, FODBO dynamically adjusts the boundaries of the search area according to the current solution position and the objective function value. Dynamic queuing of high-priority ships: Dynamically optimize the time schedule of high-priority ships according to the appointment time; optimize the queue order to make the waiting time of high-priority ships T w The queue optimization of ordinary ships is as follows: ordinary ships are dynamically assigned to the remaining queues according to their arrival time. If the waiting time of ordinary ships exceeds 60 hours, constrained optimization is applied to limit the queue priority of overdue ships. If the ratio of the maximum waiting time to the average waiting time of ordinary ships exceeds 1.5, the penalty function Optimize to reduce their waiting time, thereby reducing the queuing delays of this part of ships. Establish a dynamic boundary adjustment mechanism to adjust the upper and lower bounds of the search area according to the current solution position: ; ; in, and Represents the upper and lower bounds of the next generation search area. R is the iteration ratio, which is used to dynamically adjust the search area. It is the lower bound of the search area, representing the current minimum possible solution space boundary. It is the upper bound of the search area, representing the maximum possible solution space limit at the moment. Dynamic boundary adjustment ensures that the algorithm can focus on mining in high-potential areas in each generation, while retaining a certain amount of exploration space to avoid falling into local optimality.

[0055] Step 4: Multi-objective optimization Comprehensive objective function It consists of sub-goals such as passage cost, lock chamber utilization, number of lock openings, queue fairness and complex network congestion level, strictly limits the maximum waiting time of ordinary ships, and limits the cost of scheduled and non-scheduled ships. Less than 1.8 and the waiting time difference limit of adjacent ordinary ships In less than 12 hours, by optimizing five objective functions, FODBO ensures a balance between efficiency, fairness, and resource utilization in the scheduling scheme.

[0056] Step 5: Local optimization and global search In the FODBO algorithm, local optimization and global search are the core parts of the optimization process. The two work together to achieve comprehensive optimization of the objective function. Local optimization focuses on fine optimization of the current solution, improves the quality of the solution by deeply exploring the potential of the local solution space, and optimizes five sub-goals including travel cost, lock chamber utilization, number of gate discharges, queue fairness, and complex network congestion level. Global search is committed to expanding the solution space, exploring possible global optimal solutions, and jumping out of the local optimum through dynamic boundary adjustment and complex network path optimization.

[0057] In the local optimization stage, the algorithm optimizes the passage cost of the objective function by adjusting the scheduling order of high-priority ships and ordinary ships based on the current global optimal solution. Specifically, the algorithm adjusts the position of high-priority ships through a dynamic insertion mechanism according to the priority of the ship, such as special mission ships, passenger ships, etc., to ensure that they have priority to pass through the locks, minimize waiting time and sailing time, and thus optimize the overall passage cost. In this process, the scheduling order of ordinary ships will also be adjusted to ensure that their waiting time is not too long. For example, if the appointment time of a high-priority ship conflicts with the current queue, the local optimization will adjust the position of ordinary ships, or re-plan the order of the locks to ensure that the appointment needs of high-priority ships are met while minimizing delays for ordinary ships.

[0058] At the same time, local optimization also focuses on the fairness of scheduling of ordinary ships to avoid excessive crowding of ordinary ships by high-priority ships. By introducing the penalty function, the waiting time of ordinary ships is dynamically adjusted to reduce the phenomenon of excessive waiting time. For example, when the waiting time of ordinary ships exceeds the maximum acceptable threshold, the algorithm will impose a penalty on its waiting time, thereby giving priority to reducing the waiting time of such ships during the optimization process. In addition, in areas with high node congestion levels, local optimization will give priority to optimizing node traffic pressure and reduce the ship flow at high-congestion nodes by adjusting ship allocation and queuing order.

[0059] Local optimization not only optimizes ship scheduling, but also further improves lock chamber utilization and reduces the number of lock openings. In each iteration, the algorithm dynamically optimizes the lock chamber allocation strategy to ensure that the projected area of ​​the ship maximizes the use of the effective area of ​​the lock, thereby improving the lock chamber utilization. At the same time, local optimization reduces the number of lock openings and reduces system operating costs by merging the lock opening plans of ordinary ships.

[0060] Global search further expands the solution space based on local optimization, and introduces random perturbations and dynamic boundary adjustment mechanisms to find potential global optimal solutions, thereby avoiding falling into local optimality. In order to jump out of the local optimality, global search performs a Introduce random perturbations to generate new candidate solutions: ; in is a random perturbation, which is used to expand the search range and enhance the diversity of solutions. Better than the current global optimal solution, update the global optimal solution: ; Otherwise, keep the current solution and continue to optimize. Based on local optimization, global search further expands the solution space through a random perturbation mechanism to explore possible global optimal solutions. For the current global optimal solution, the algorithm generates new candidate solutions through random perturbations to expand the diversity of solutions. In order to further expand the search range, the algorithm dynamically adjusts the boundaries of the search area, concentrates on optimizing in low-congestion areas, and improves overall scheduling efficiency.

[0061] Local optimization achieves high-quality initial solution optimization by fine-tuning the current solution, while global search jumps out of the local optimum through dynamic exploration to ensure the globality and comprehensiveness of the solution. In this process, the five sub-goals in the objective function are fully reflected through the collaborative optimization of the algorithm: the optimization of the passage cost ensures the rapid passage of high-priority ships; the improvement of the lock chamber utilization rate improves the resource utilization efficiency; the optimization of the number of lock discharges reduces the system operation cost; the optimization of queuing fairness improves the scheduling experience of ordinary ships; the dynamic adjustment of the complex network congestion level alleviates the traffic bottleneck in the system.

[0062] When the algorithm reaches the maximum number of iterations or the comprehensive objective function value of several consecutive generations When there is no significant improvement, the algorithm determines that it has converged to the optimal solution and the optimization process ends. The final output optimization results include the queue order of high-priority ships, the time arrangement of lock passage, and the dynamic allocation and lock discharge plan of ordinary ships. These solutions not only effectively balance the passage cost, lock chamber utilization and lock discharge times through comprehensive optimization of the comprehensive objective function, but also dynamically adjust the queue fairness and congestion level of the complex network. In addition, the algorithm also records key performance indicators, such as the optimized comprehensive objective function value , Lock chamber utilization rate A , number of gate discharges and node congestion level , providing a detailed quantitative basis for evaluating the efficiency and fairness of scheduling schemes.

[0063] By introducing the fractional-order dung beetle optimization algorithm FODBO, this embodiment achieves a systematic and efficient optimization of the lock scheduling problem. FODBO fully demonstrates the advantages of the "key reservation + declaration queue" model in the optimization process by combining the historical memory characteristics of fractional-order calculus and the dynamic adaptive mechanism. The algorithm not only ensures the rapid passage of high-priority ships, but also performs multi-objective collaborative optimization on the scheduling fairness, lock chamber utilization and lock discharge times of ordinary ships. In addition, the introduction of complex networks significantly improves the ability to identify and alleviate traffic bottlenecks, thereby optimizing overall traffic fluidity.

[0064] The multi-stage optimization process of the FODBO algorithm includes initialization, fractional-order update mechanism, dynamic boundary adjustment, local optimization and global search. Each stage cooperates with each other to ensure the global search capability and local optimization accuracy of the algorithm. This multi-objective optimization strategy enables the scheduling scheme to achieve a high balance between efficiency, fairness and resource utilization. By rationally planning lock resources, efficiently adjusting the lock discharge plan and dynamically optimizing the queue order, FODBO significantly reduces the operating cost of the scheduling system and improves the overall scheduling efficiency and fairness.

[0065] Example 3 This embodiment is based on the embodiment 1, and the cost function Add penalty weights. During the dispatch process, the priority passage of high-priority ships may cause the waiting time of ordinary ships to increase significantly. To avoid the waiting time of ordinary ships being too long, the waiting time is adjusted. Set up constraints: , is the maximum acceptable waiting time for ordinary ships, which is adjusted dynamically according to the scheduling target. A penalty weight is imposed on ships with too long waiting time: ; In the formula, is the penalty coefficient; is the threshold of the waiting time, which is set to 36 hours in this embodiment based on the empirical value of the design service capacity of the lock. The penalty function can limit the excessively long waiting time of ordinary ships while ensuring the efficiency of high-priority ships.

[0066] Replace the cost function with: .

[0067] To ensure the dispatching experience of ordinary ships, it is necessary to optimize the fairness of their waiting time and reduce extreme waiting phenomena.

[0068] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0069] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for scheduling inland river vessel lock passage reservation under key reservation and declaration queuing mode, characterized by: The following steps are involved: Step S1: Set the ship priority according to the ship type, set the priority of ordinary ships that need to be queued to the lowest, and the priority of key ships that need to make an appointment to pass is higher than that of ordinary ships; construct a cost function based on the ship priority, ship waiting time and ship sailing time ; Step S2: Construct a queue fairness function based on the time difference of ships passing through the lock ; Step S3: Establish a comprehensive congestion function through a complex network model ; Step S4: Using the cost function And the fairness function , comprehensive congestion function , lock chamber utilization function And the number of gate discharge functions One or more of them, construct the objective function by weighting, and establish the constraint conditions according to the actual traffic requirements; Step S5: Solve the objective function and constraint conditions through an optimization algorithm to obtain a gate-passing scheduling plan.

2. According to the method of claim 1, the method is characterized by: In step S1, the cost function The expression is: ； In the formula, Indicates the number of ships; P i For ships Priority; P max is the maximum value of the priority; Indicates ship Waiting time; For ships Sailing time; α Indicates the additional weight of ordinary ships; represents a binary variable, when For key ships, ,when For ordinary ships, .

3. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing mode according to claim 1 is characterized by: The queue fairness function The expression is: ； In the formula, Indicates the number of ships; is the indicator function, if the ship Assigned to a queue j In =1, otherwise =0; Indicates the number of queues; For queue j The gate-crossing start time represents the first gate-crossing time assigned to queue j; For ships Arrival time.

4. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing mode according to claim 1 is characterized by: The comprehensive congestion function The expression is: ； ； ； ； In the formula, , and is the weight coefficient; represents the average transit time; represents the standard deviation of the passing time; represents the weighted average transit time; M Through the node Total number of ships; Indicates j Ships at the node The passage time of the ship j From the entry node The time to complete the gate crossing or passing; It is j The priority of the vessel.

5. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing mode according to claim 1 is characterized by: The constraints set in step S4 include: 1) The waiting time for ordinary ships is less than 60 hours; 2) The impact of the passage cost of the reserved ships on ordinary ships shall not exceed the threshold: ； In the formula, is the balance threshold of the impact of scheduled and non-scheduled ship costs, Indicates ship Waiting time; For ships Sailing time; 3) The difference in waiting time for ordinary ships is not greater than the set threshold: ； In the formula, and Represents any two adjacent ordinary ships; is the maximum acceptable waiting time difference.

6. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queue mode according to claim 1 is characterized by: In step S4, the objective function The expression is: ； In the formula, , , and is the weight parameter, In order to control the influence of lock chamber utilization, It is the proportion of the number of gate discharges in the total target; It is to balance the fairness of the queues of ordinary ships; It is to quantify the impact of the congestion level of complex networks on the objective function.

7. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing mode according to claim 1 is characterized by: The cost function in step S1 Penalty weights are added to avoid long waiting times for common ships. The expression is: ； In the formula, Indicates the number of ships; is the penalty coefficient; is the waiting time threshold, Indicates ship Waiting time; The updated cost function The expression is: 。 8. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queue mode according to claim 1 is characterized by: The optimization algorithm in step S5 adopts an improved dung beetle optimization algorithm, in which fractional-order derivatives are introduced for path search, including five stages: initial population generation, fractional-order update, dynamic queuing and boundary adjustment, multi-objective optimization, local optimization and global search.

9. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing mode according to claim 8 is characterized by: The fractional order updating method comprises: The formula for fractional derivative is as follows: ； In the formula, L ( t +1) is the position of the next generation solution; and is the adjustment coefficient; represents fractional derivative; represents the current global optimal solution; X ( t )and X ( t -1) indicates the solution of the current generation and the previous generation; During the updating process, the weight parameters of different sub-goals are dynamically adjusted using the following formula: ； ； In the formula, For the The next step value of the sub-goal weight, is the adjustment coefficient, For the The current deviation value of the subgoal.

10. The method for scheduling inland river vessel lock passage reservation under the key reservation and declaration queuing mode according to claim 9 is characterized in that: The improved dung beetle optimization algorithm updates the upper and lower bounds by the following expressions: ； ； In the formula, and Represents the upper and lower bounds of the next generation search area; R represents the iteration ratio.

Images