Flood season anchorage ground-wharf channel ship scheduling optimization method and system based on multi-population genetic algorithm

Abstract

The invention provides a flood season anchoring ground-wharf channel ship scheduling optimization method based on a multi-population genetic algorithm, and belongs to the field of ship scheduling, and the method comprises the steps: building a flood season anchoring ground-wharf channel ship scheduling model with the purpose of minimizing the waiting time of a ship at an anchoring ground and the departure delay time of the ship at a transfer wharf, and optimizing the ship scheduling model; establishing constraint conditions satisfied by ship scheduling of the anchorage ground-wharf channel in the flood season; respectively solving a flood season anchoring ground-wharf channel ship dispatching anchoring ground departure time decision problem, a water flow limiting channel distribution problem and a transfer wharf berth distribution problem based on the satisfied constraint conditions to obtain a flood season anchoring ground-wharf channel ship dispatching initial scheme; and based on a multi-population genetic algorithm with a hybrid coding strategy, an elitism strategy and a self-adaptive mechanism, carrying out iterative solution on a ship scheduling problem to obtain an optimized ship scheduling scheme. The ship transportation efficiency of a water-land combined transportation mode is improved, and the navigation pressure of the hydro-junction ship lock is relieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship scheduling, and more specifically, relates to a method and system for optimizing ship scheduling in an anchorage-wharf channel during flood season based on a multi-population genetic algorithm. Background Art

[0002] Inland waterway transportation has become an important mode of transportation for bulk commodities due to its good cost and environmental benefits. At the same time, inland waterways are usually dotted with numerous water conservancy projects and are designed with corresponding locks, which can help ships pass through dams and overcome the water level difference caused by dams. However, with the sharp increase in the demand for inland waterway transportation, the limited service capacity of locks may lead to congestion of ship navigation in water conservancy hubs, making the delay of ship passing through dams increasingly serious.

[0003] In order to deal with this problem, the dam management department has proposed three main measures: ① expand and renovate the existing lock facilities; ② improve the operating efficiency of the existing locks; ③ strengthen the application of the water-land transport mode. However, the expansion of the existing lock facilities is costly and time-consuming, and there is a limit to the improvement of the lock operation efficiency. Therefore, strengthening the application of the water-land transport mode is considered to be an effective way to relieve the pressure of ships passing the dam. Specifically, ships transport cargo from the waiting anchorage to the transfer terminal for unloading, and then transport it to the upstream destination by road or rail. This transfer mode allows ships to avoid waiting in line when the locks are congested, which can effectively alleviate the navigation congestion of the water conservancy hub and reduce the delay costs caused by congestion.

[0004] Ship scheduling is an important part of the water-land transport mode. This process mainly involves three important nodes: anchorages, waterways and berths. The coordinated scheduling of the three can effectively improve the efficiency of ship transportation. In addition, ship scheduling in the waterway is also affected by the water flow standards around the dam. Ship navigation must be carried out within the time window when the current waterway water flow does not exceed the maximum acceptable water flow of the ship. Based on this principle, when a ship misses the time period allowed for navigation, it needs to wait for a long time. Therefore, ship scheduling in water-land transport needs to consider the coordination of anchorages, waterways and berths, and evaluate the impact of waterway water flow on ship transportation efficiency, so as to make joint scheduling decisions. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention proposes a method and system for optimizing ship scheduling in the anchorage-wharf waterway during the flood season based on a multi-population genetic algorithm, which takes into account the coordination of anchorages, waterways and berths, and evaluates the impact of water flow in the waterway on the ship transportation efficiency, so as to make joint scheduling decisions.

[0006] To achieve the above object, according to one aspect of the present invention, a method for optimizing ship scheduling in anchorage-wharf channel during flood season based on a multi-population genetic algorithm is provided, comprising:

[0007] Obtain ship information, waterway information, berth information, navigation time information, and navigation characteristics during flood season at water transport hubs;

[0008] According to the ship information, channel information, berth information, navigation time information, and navigation characteristics of the water transport hub, the ship scheduling model of the anchorage-terminal channel in the flood season is constructed with the optimization goal of minimizing the waiting time of ships at the anchorage and the departure delay time of ships at the transfer terminal, and the constraint conditions satisfied by the ship scheduling of the anchorage-terminal channel in the flood season are established;

[0009] Based on the satisfied constraints, the three sub-problems of anchorage departure time decision problem, waterway allocation problem with water flow restriction, and berth allocation problem of transshipment terminal are solved respectively, and the initial plan of ship scheduling in anchorage-wharf channel during flood season is obtained.

[0010] Based on a multi-population genetic algorithm with a hybrid coding strategy, an elite retention strategy and an adaptive mechanism, the ship scheduling model of the anchorage-wharf channel in flood season is iteratively solved, and the initial ship scheduling plan is continuously updated to obtain the optimized ship scheduling plan.

[0011] In some optional implementation schemes, the ship information includes: the number of ships to be dispatched, the size of the ship, the maximum acceptable water flow, the sailing speed, the ship safety inspection time, and the estimated time for the ship to arrive at the anchorage and return to the anchorage;

[0012] The channel information includes: channel length, channel width, and channel water flow from the anchorage to the berth;

[0013] The berth information includes: the number of berths at the transshipment terminal and the distribution of cranes;

[0014] Said navigation time information includes: the start and end time of the scheduling period, the minimum time interval between adjacent ships and the scheduling time window considering water flow restrictions;

[0015] The navigation characteristics during the flood season include: when a ship is sailing, the current water flow standard of the waterway does not exceed the maximum acceptable value of the ship.

[0016] In some optional embodiments, Construct the ship dispatching model of anchorage-wharf channel in flood season, tb i is the actual time when ship i leaves the anchorage, ta i is the estimated time for ship i to arrive at the anchorage, td i is the actual time when vessel i leaves the berth, is the sailing time of ship i from berth q to anchorage, tf i is the estimated time for ship i to leave the berth, η iq It means that if ship i is assigned to berth q, it is 1, otherwise it is 0; N represents a set of ships, and Q represents a set of berths.

[0017] In some optional implementation schemes, the constraints include the following constraints for the anchorage departure time decision problem: Among them, T start is the start time of the scheduling period; Among them, TS i The safety inspection time of vessel i; tc i is the actual time for ship i to travel from anchorage to berth q, is the sailing time of ship i from anchorage to berth q, L iq is the sailing distance between the anchorage and berth q of ship i, is the average speed from the anchorage to berth q; Δt is the minimum time interval between ships, l i is the length of ship i, and r is the proportional coefficient set according to the length of the ship.

[0018] In some optional embodiments, the constraints include the following constraints describing the transshipment terminal berth allocation problem: Among them, d q represents the last ship in the ship sequence moored at berth q, ψ ijq ∈{0,1}, which means that if ship j berths at berth q immediately after ship i, it is 1, otherwise it is 0;

[0019]

[0020] Among them, q represents the first ship in the ship sequence moored at berth q; Where S represents a set of transshipment terminal crane resources; ξ is ∈{0,1}, which means that if the crane resource s of the transshipment terminal is allocated to ship i, it is 1, otherwise it is 0;

[0021] Among them, td i ≥0, indicating the actual time when ship i leaves the platform berth; u is is the processing time of ship i using the transshipment terminal crane resource s, and M is a constant.

[0022] In some optional implementation schemes, the constraints include the following constraints on the ship's sailing time: te i is the actual time when ship i leaves berth q and returns to the waiting area, is the sailing time of ship i leaving berth q and returning to the waiting area, is the average ship speed from berth q to the waiting area; T end The end time of the scheduling period.

[0023] In some optional embodiments, the constraints include constraints describing safe water flow limits of the waterway as follows: Among them, T t represents the interval length of time window t, and T represents a set of time windows; Among them, τ it ∈{0,1}, which means that if ship i passes through the channel within time window t, it is 1, otherwise it is 0; δ iqt ∈{0,1}, which means that if ship i passes through the channel and reaches berth q within time window t, it is 1, otherwise it is 0; w i is the maximum acceptable water flow, W qt is the actual waterway current; R is the total width of the channel, g i is the width of ship i.

[0024] In some optional embodiments, the multi-population genetic algorithm based on a hybrid coding strategy, an elite retention strategy and an adaptive mechanism is used to iteratively solve the ship scheduling problem, including:

[0025] Input ship information, channel information, berth information and sailing time information;

[0026] Set the fitness function of the chromosome, which consists of two parts: the objective function value and the penalty function value of the solution;

[0027] Encodes the chromosome structure, which is represented by x=(x 1 ,…,x N ,…,x mN ), where N is the number of ships and the first N dimensions represent the actual time when N ships leave the anchorage. The second dimension N represents the time when N ships leave the berth The remaining multiple N-dimensional genes represent binary variables (η iq , ψ ijq ,ξ is , τit , δ iqt );

[0028] Apply a multi-population genetic algorithm to randomly generate initial solutions and evaluate them through fitness functions;

[0029] The roulette wheel method is used to select the chromosomes of the next generation. The selection probability of each individual is calculated according to the fitness function, which is expressed as Among them, f i is the fitness value of individual i, S is the number of individuals in the population;

[0030] Two parent individuals are randomly selected in each population, and single-point crossover operations are performed on the continuous coding part and the binary coding part respectively, where the crossover rate p c =0.7+(0.9-0.7)·rand(MP,1), MP is the number of populations;

[0031] Perform mutation operator operation, use reversal operation and redistribute genes for continuous coding part, and randomly select and swap the value of binary coding part between 0 and 1, where the mutation rate p m =0.001+(0.05-0.001)·rand(MP,1);

[0032] The best individuals of a population are introduced into other populations through the immigration operator to replace the worst individuals in the target population;

[0033] In each generation of evolution, the elite individual operator selects the best individuals from all populations and puts them into the elite population. The individuals in the elite population remain unchanged during the evolution process.

[0034] Determine whether the number of generations of the currently saved optimal individuals is equal to Maxgen. If so, terminate and output the optimal ship scheduling plan; otherwise, return to the step of applying the roulette wheel method to select the chromosome of the next generation, and Maxgen is the preset maximum number of iterations.

[0035] In some optional embodiments, the penalty function value of the solution is: in, is the inequality constraint function, ε y (x) is the equality constraint function, α r and β y is a positive penalty factor, R and Y represent the number of inequality and equality restrictions, respectively;

[0036] The fitness function of the chromosome is: Among them, η iq is 1 if ship i is assigned to berth q, otherwise 0; is the sailing time of ship i from berth q to anchorage, tf i is the estimated time for vessel i to leave the berth.

[0037] According to another aspect of the present invention, a flood season anchorage-wharf channel ship scheduling optimization system based on a multi-population genetic algorithm is provided, comprising:

[0038] The acquisition module is used to obtain the ship information, channel information, berth information, navigation time information and navigation characteristics during the flood season of the water transport hub;

[0039] A construction module is used to construct a flood season anchorage-wharf channel ship scheduling model based on the ship information, channel information, berth information, navigation time information, and navigation characteristics of the water transport hub, with the goal of minimizing the waiting time of ships at the anchorage and the departure delay time of ships at the transfer terminal, and establish the constraints that the flood season anchorage-wharf channel ship scheduling meets;

[0040] The initial scheme establishment module is used to solve the three sub-problems of anchorage departure time decision problem, water flow restricted channel allocation problem and transfer terminal berth allocation problem based on the satisfied constraints, and obtain the initial scheme of ship scheduling in the anchorage-wharf channel during the flood season;

[0041] The optimization iteration module is used to iteratively solve the ship scheduling model of the anchorage-wharf channel during the flood season based on a multi-population genetic algorithm with a hybrid coding strategy, an elite retention strategy and an adaptive mechanism, and continuously update the initial ship scheduling plan to obtain the optimized ship scheduling plan.

[0042] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0043] (1) The present invention proposes a method for optimizing ship scheduling in anchorage-wharf waterways during flood season based on a multi-population genetic algorithm. The method constructs a scheduling model with the goal of minimizing the waiting time of ships at anchorages and the departure delay time at transfer terminals, so as to reasonably allocate waterway and berth resources for ships passing through the dam. In addition, the present invention takes into account the impact of changes in water flow in waterways around water conservancy hubs, which is conducive to modeling waterway ship scheduling problems under different scenarios, and helps ships make scientific and efficient navigation decisions under water flow restriction conditions.

[0044] (2) The multi-population genetic algorithm with hybrid coding strategy, elite retention strategy and adaptive mechanism proposed in the present invention solves the problem that traditional genetic algorithms are prone to fall into local optimal solutions, improves global search capabilities and solution efficiency, and can more effectively solve complex ship scheduling problems. The multi-population genetic algorithm designed by the present invention can efficiently solve the ship scheduling optimization model under different scenarios and provide hub managers with optimized ship scheduling solutions.

[0045] (3) The present invention improves the operating efficiency of the water-land transport mode by adopting an optimization method for ship scheduling in the anchorage-wharf channel during the flood season based on a multi-population genetic algorithm, which enables ships to choose more efficient transfer strategies when the locks are congested, thereby reducing the navigation pressure of the locks of water conservancy hubs and alleviating traffic congestion in inland waterways. It has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the steps of the method for optimizing ship scheduling in anchorage-wharf channel during flood season based on multi-population genetic algorithm proposed by the present invention;

[0047] Figure 2 It is a flow chart of the steps of the multi-population genetic algorithm proposed by the present invention;

[0048] Figure 3 It is a water flow data diagram of a waterway during a flood season provided by an embodiment of the present invention;

[0049] Figure 4 1 is a comparison diagram of algorithms for solving the ship scheduling problem provided by an embodiment of the present invention, wherein (a) is a normal situation, and (b) is a special situation (flood season);

[0050] Figure 5 is a comparison chart of departure times of ships at anchorages provided by an embodiment of the present invention;

[0051] Figure 6 It is a comparison chart of the departure times of ships at the transshipment terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will further describe the technical solutions in the embodiments of the present invention in detail in conjunction with the 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 are within the scope of protection of the present invention.

[0053] The embodiment of the present invention takes the Three Gorges Water Conservancy Project as an example, and considers two situations of ship scheduling in the Three Gorges anchorage-wharf channel: normal situation and special situation (flood season). Specifically, under normal circumstances, it is only necessary to reasonably arrange the time for the ship to leave the anchorage or wharf, without considering the influence of the change in water flow in the waterway. However, due to the water flow restriction of the anchorage-wharf channel during the flood season, the departure time of some ships may be restricted, and they can only wait for another appropriate time (i.e., an acceptable water flow time window). Based on this, the present invention proposes a ship scheduling method for the anchorage-wharf channel based on a multi-population genetic algorithm, which obtains ship information, channel information, berth information and navigation time information, constructs a scheduling model with the goal of minimizing the waiting time of the ship at the anchorage and the departure delay time of the transfer terminal, and sets corresponding constraints. Based on the satisfied constraints, the three sub-problems of anchorage departure time decision problem, water flow restriction channel allocation problem, and transfer terminal berth allocation problem of the anchorage-wharf channel ship scheduling during the flood season are solved respectively, and the initial plan for ship scheduling in the anchorage-wharf channel during the flood season is obtained. The optimized ship scheduling scheme is finally obtained through iterative solution of multi-population genetic algorithm with hybrid coding strategy, elite retention strategy and adaptive mechanism. Figure 1 As shown, the following steps are included:

[0054] S1. Obtaining ship information, waterway information, berth information, navigation time information, and navigation characteristics during the flood season of the water transport hub; the ship information includes: the number of ships to be dispatched, ship size, maximum acceptable water flow, navigation speed, ship safety inspection time, and the estimated time for the ship to arrive at the anchorage and return to the anchorage;

[0055] Channel information includes: channel length, channel width, and channel water flow from the anchorage to the berth; berth information includes: number of berths and crane distribution;

[0056] Navigation time information includes: the start and end time of the scheduling period, the minimum time interval between adjacent ships and the scheduling time window considering water flow restrictions; the navigation characteristics during the flood season include: when the ship is sailing, the current water flow standard of the waterway does not exceed the maximum acceptable value of the ship.

[0057] S2. Taking minimizing the waiting time of ships at anchorages and the departure delay time of ships at transshipment terminals as the optimization goal, a ship scheduling model for anchorage-terminal waterways during flood season is constructed, and the constraints satisfied by ship scheduling for anchorage-terminal waterways during flood season are established;

[0058] The constraints include: ship sorting and scheduling problems, berth allocation problems, ship sailing time and channel safety displacement restrictions.

[0059] It should be noted that the ship scheduling model from the anchorage to the wharf during the flood season uses the following objective function formula to describe the scheduling scheme optimization target:

[0060]

[0061] Among them, tb i is the actual time when ship i leaves the anchorage, ta i is the estimated time for ship i to arrive at the anchorage, td i The actual time when vessel i left the berth, is the sailing time of ship i from berth q to anchorage, tf i is the estimated time for ship i to leave the berth; η iq It means that if ship i is assigned to berth q, it is 1, otherwise it is 0; N represents a set of ships, and Q represents a set of berths.

[0062] The constraints of the ship scheduling model from the water conservancy hub anchorage to the wharf channel include the following constraints describing the ship sorting and scheduling problem:

[0063]

[0064] The above constraints ensure that each ship is scheduled within the scheduling period, where T start is the start time of the scheduling period;

[0065]

[0066] The above constraints ensure that each ship completes the safety inspection before leaving the anchorage, where TS i The safety inspection time of vessel i;

[0067]

[0068] The above constraints calculate the actual time tc of ship i from anchorage to berth q i and sailing time Among them, L iq is the sailing distance between the anchorage and berth q of ship i, is the average speed from the anchorage to berth q;

[0069]

[0070] The above constraints calculate the minimum time interval Δt between ships to ensure that the interval between ships i and j that pass through the channel continuously is maintained to avoid congestion and collision of ships in the channel; where l i is the length of ship i, r is the proportional coefficient set according to the length of the ship;

[0071] The constraints of the joint scheduling model for ships from anchorage to wharf during flood season include the following constraints describing the berth allocation problem:

[0072]

[0073] The above constraints ensure that each vessel occupies one berth;

[0074]

[0075] The above constraints establish the relationship between ship sequencing and berth allocation to ensure correct sequencing; among them, d q represents the last ship in the ship sequence moored at berth q; ψ ijq ∈{0,1}, which means that if ship j berths at berth q immediately after ship i, it is 1, otherwise it is 0;

[0076]

[0077] The above constraints ensure that there is a unique starting ship and ending ship in the ship sequence of each berth, and the ship sequence is continuous, where o q represents the first ship in the ship sequence moored at berth q;

[0078]

[0079] The above constraints ensure that each ship is assigned a certain number of crane resources, where S represents a set of transshipment terminal crane resources; ξ is ∈{0,1}, which means that if the crane resource s of the transshipment terminal is allocated to ship i, it is 1, otherwise it is 0;

[0080]

[0081]

[0082] The above constraints ensure that the joint scheduling of ships between anchorages and berths is reasonable, where td i ≥0, indicating the actual time when ship i leaves the berth; u is is the processing time of ship i using the crane resource s at the transshipment terminal, and M is a sufficiently large constant;

[0083] The constraints of the ship scheduling model from the anchorage to the wharf during the flood season include the following description of the ship sailing time constraints:

[0084]

[0085] The above constraints define the actual time te for ship i to leave berth q and return to the anchorage. iand sailing time in, is the average speed from berth q to the anchorage;

[0086]

[0087] The above constraints ensure that there is sufficient time interval between consecutive ships returning to the anchorage;

[0088]

[0089] The above constraints ensure that all ships are dispatched at the end of the scheduling period T end Before the scheduling is completed.

[0090] The constraints of the joint scheduling model for ships from the anchorage to the wharf during the flood season, including the waterway safety water flow constraints, are as follows:

[0091]

[0092] The above constraints ensure that ship i completes the entire process of transshipment within the time window t as planned, where T t represents the interval length of time window t, and T represents a set of time windows;

[0093]

[0094] The above constraints relate the time window t to the allocation of berth q, where τ it ∈{0,1}, which means that if ship i passes through the channel within time window t, it is 1, otherwise it is 0; δ iqt ∈{0,1}, which means that if ship i passes through the channel and reaches berth q within time window t, it is 1, otherwise it is 0;

[0095]

[0096] The above constraints ensure that the maximum acceptable water flow w for a ship selecting berth q within time window t is i Must be greater than or equal to the actual water flow W of the channel at this time qt ;

[0097]

[0098] The above constraints ensure that in any time window t, the sum of the widths of all ships selecting berth q cannot exceed the total width of the channel R, g i is the width of ship i.

[0099] It should be noted that for the ship scheduling model from the anchorage to the wharf during the flood season, when the constraints of the safe water flow requirements for ships in the channel are not considered, the model will be transformed into a ship scheduling optimization model under normal circumstances.

[0100] S3. Based on the satisfied constraints, the three sub-problems of anchorage departure time decision problem, water flow restricted channel allocation problem and transshipment terminal berth allocation problem are solved respectively to obtain the initial plan for ship scheduling in the anchorage-wharf channel during the flood season;

[0101] S4. Based on a multi-population genetic algorithm with a hybrid coding strategy, an elite retention strategy and an adaptive mechanism, the ship scheduling model of the anchorage-wharf channel in the flood season is iteratively solved, and the initial ship scheduling plan is continuously updated to obtain the optimal ship scheduling plan; Figure 2 As shown, the following steps are included:

[0102] S41. Input ship information, channel information, berth information and navigation time information;

[0103] S42. Setting the fitness function of the chromosome, wherein the fitness function is composed of two parts: an objective function value and a penalty function value of a solution;

[0104] Specifically, the penalty function value of the solution can be expressed as: in, is the inequality constraint function, ε y (x) is the equality constraint function, α r and β y is a positive penalty factor, R and Y represent the number of inequality and equality constraints respectively; the fitness function of the chromosome can be expressed as:

[0105] S43. Encoding chromosome structure, the chromosome structure can be expressed as x=(x 1 ,…,x N ,…,x mN ), where N is the number of ships, and the first N dimensions can represent the actual time when N ships leave the anchorage. The second dimension N represents the time t when N ships leave the berth di , the remaining multiple N-dimensional genes can represent binary variables (η iq , ψ ijq ,ξ is , τ it , δ iqt );

[0106] S44. Applying a multi-population genetic algorithm (MPGA) to randomly generate initial solutions and evaluate them using a fitness function;

[0107] S45. The roulette wheel method is used to select chromosomes for the next generation. The selection probability of each individual is calculated based on the fitness function, which can be expressed as Among them, f i is the fitness value of individual i, S is the number of individuals in the population;

[0108] S46. Randomly select two parent individuals in each population and perform single-point crossover operations on the continuous coding part and the binary coding part respectively, where the crossover rate p c =0.7+(0.9-0.7)·rand(MP,1), MP is the number of populations;

[0109] S47. Perform mutation operator operation, use reversal operation and redistribute genes for the continuous coding part, and randomly select and swap the values ​​0-1 for the binary coding part, where the mutation rate p m =0.001+(0.05-0.001)·rand(MP,1);

[0110] S48. The best individuals of a population are introduced into other populations through the immigration operator to replace the worst individuals in the target population;

[0111] S49. In each generation of evolution, the elite individual operator selects the best individuals from all populations and puts them into the elite population, and the individuals in the elite population remain unchanged during the evolution process;

[0112] S410. Determine whether the number of generations of the optimal individuals currently saved by the algorithm is equal to the preset maximum number of iterations Maxgen. If so, the algorithm terminates and outputs the optimal ship scheduling plan; otherwise, return to step S45.

[0113] For further explanation, the embodiment of the present invention constructs a case with the actual ship navigation data of the Three Gorges hub. The scheduling time range selects the 24-hour waiting ship data on August 15, 2023. During the scheduling period, the length of each time window interval is set to 1 hour (ie 60 minutes), and there are a total of 24 time windows during the scheduling period. The number of ships visiting the transshipment terminal in one day is approximately 20. These ships will first leave the anchorage and enter the waterway. The cargo will be transshipped at the terminal, and then the ships will return to the anchorage through the waterway. The minimum time interval between adjacent ships is set to 0.3 hours, and the ship safety inspection time is approximately 1 hour. Since the ships are moored at different locations at the anchorage and the terminal, the waterway distance of each ship follows a uniform distribution, which can be expressed as L iq ~Uniform(17,20). Speed ​​of ship i Follow v~Uniform(20,30), and when the ship returns to the anchorage after unloading, its speed must meet In addition, there is one channel and two transfer terminals in the section from the anchorage to the transfer terminal in the Three Gorges Dam area. Each terminal has three berths, and each berth has two dock cranes. The utilization rate of the berths and cranes is about 70-75%. The channel flow data during the flood season is as follows: Figure 3 shown.

[0114] Figure 4 A comparison diagram of algorithms for solving the ship scheduling problem provided by an embodiment of the present invention; Figure 4 From the results shown, in normal circumstances or special circumstances (such as flood season), the genetic algorithm will fluctuate during the optimization process, and the calculation results will fall into the local optimal solution, making it difficult to obtain the global optimal solution. Compared with the genetic algorithm, the multi-population genetic algorithm proposed in the present invention can jump out of the local optimal solution and obtain a better solution than the genetic algorithm. In addition, compared with the genetic algorithm, the calculation time required for using the multi-population genetic algorithm in normal circumstances or flood season is reduced by 3.54s and 3.44s respectively. It can be seen that the multi-population genetic algorithm has better optimization ability than the traditional genetic algorithm, and can effectively solve the problem of joint scheduling of ships from the anchorage to the wharf section of the navigation hub.

[0115] The optimal scheduling scheme and the original scheduling scheme under different scenarios are as follows: Figure 5 and Figure 6 shown in Figure 5 It can be seen that the optimized ship scheduling scheme significantly improves the efficiency of ships leaving the anchorage. Compared with the original scheduling scheme, the optimized scheme can significantly advance the departure time of most ships, allowing some ships to leave the anchorage as soon as possible. Figure 6 The optimized ship scheduling scheme for the transshipment terminal is presented. The optimized scheme can significantly reduce the departure delay of the ship at the transshipment terminal, and in most cases, it can slightly adjust the departure time of the ship at the terminal to catch up with the current displacement window, avoiding unnecessary waiting. It can be seen that by fine-tuning the departure time of the ship at the anchorage or terminal, the optimization model effectively assists the ship to match the appropriate displacement time window, thereby improving shipping efficiency and ensuring the navigation safety of the ship. Especially in the flood season, although some ships need to adjust their departure time due to changes in the waterway displacement level, resulting in slight delays, the optimized scheme still shows higher efficiency and safety than the original scheme as a whole, from about 106.35 hours in the original scheme to 49.41 hours (normal situation) or 68.61 hours (flood season), effectively shortening the waiting time of ships at anchorages and terminals. Therefore, the optimized ship scheduling scheme not only significantly improves the departure efficiency of ships at anchorages and terminals, but also improves the safety and efficiency of the entire shipping process by ensuring that ships leave the port at a safe water level. Such interrelated decisions are crucial to solving the ship scheduling problem in the anchorage-to-pier channel, especially when considering the ship scheduling strategies under different time periods.

[0116] In addition, an embodiment of the present invention also provides a ship dispatching system from anchorage to wharf channel based on a multi-population genetic algorithm, comprising the following modules:

[0117] Acquisition module: used to obtain ship information, channel information, berth information, navigation time information, and navigation characteristics during flood season of water transport hubs;

[0118] Construction module: Taking minimizing the waiting time of ships at anchorages and the departure delay time of ships at transshipment terminals as the optimization goal, a ship scheduling model for anchorage-terminal waterways during flood season is constructed, and the constraints satisfied by ship scheduling for anchorage-terminal waterways during flood season are established;

[0119] Initial plan establishment module: Based on the satisfied constraints, the three sub-problems of anchorage departure time decision problem, water flow restricted channel allocation problem, and transfer terminal berth allocation problem are solved to obtain the initial plan for ship scheduling in the anchorage-wharf channel during the flood season;

[0120] Optimization iteration module: Based on a multi-population genetic algorithm with a hybrid coding strategy, an elite retention strategy and an adaptive mechanism, the ship scheduling model of the anchorage-wharf channel during the flood season is iteratively solved, and the initial ship scheduling plan is continuously updated to obtain the optimized ship scheduling plan.

[0121] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0122] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing ship scheduling in anchorage-wharf channel during flood season based on multi-population genetic algorithm, characterized in that: include: Obtain ship information, waterway information, berth information, navigation time information, and navigation characteristics during flood season at water transport hubs; According to the ship information, channel information, berth information, navigation time information, and navigation characteristics of the water transport hub, the ship scheduling model of the anchorage-terminal channel in the flood season is constructed with the optimization goal of minimizing the waiting time of ships at the anchorage and the departure delay time of ships at the transfer terminal, and the constraint conditions satisfied by the ship scheduling of the anchorage-terminal channel in the flood season are established; Based on the satisfied constraints, the three sub-problems of anchorage departure time decision problem, waterway allocation problem with water flow restriction, and berth allocation problem of transshipment terminal are solved respectively, and the initial plan of ship scheduling in anchorage-wharf channel during flood season is obtained. Based on a multi-population genetic algorithm with a hybrid coding strategy, an elite retention strategy and an adaptive mechanism, the ship scheduling model of the anchorage-wharf channel in flood season is iteratively solved, and the initial ship scheduling plan is continuously updated to obtain the optimized ship scheduling plan.

2. The method according to claim 1, characterized in that The ship information includes: the number of ships to be dispatched, the size of the ships, the maximum acceptable water flow, the sailing speed, the ship safety inspection time, and the estimated time for the ships to arrive at the anchorage and return to the anchorage; The channel information includes: channel length, channel width, and channel water flow from the anchorage to the berth; The berth information includes: the number of berths at the transshipment terminal and the distribution of cranes; Said navigation time information includes: the start and end time of the scheduling period, the minimum time interval between adjacent ships and the scheduling time window considering water flow restrictions; The navigation characteristics during the flood season include: when a ship is sailing, the current water flow standard of the waterway does not exceed the maximum acceptable value of the ship.

3. The method according to claim 1 or 2, characterized in that: Depend on Construct the ship dispatching model of anchorage-wharf channel in flood season, tb i is the actual time when ship i leaves the waiting area, ta i is the estimated time for ship i to arrive at the waiting area, td i is the actual time when vessel i leaves the platform berth, is the sailing time of ship i from berth q to anchorage, tf i is the estimated time of departure of vessel i from the berth; η iq It means that if ship i is assigned to berth q, it is 1, otherwise it is 0; N represents a set of ships, and Q represents a set of berths.

4. The method according to claim 3, characterized in that The constraints include the following constraints for the anchorage departure time decision problem: Among them, T start is the start time of the scheduling period; Among them, TS i The safety inspection time of vessel i; tc i is the actual time for ship i to travel from anchorage to berth q, is the sailing time of ship i from anchorage to berth q, L iq is the sailing distance between the anchorage and berth q of ship i, is the average speed from the anchorage to berth q; Δt is the minimum time interval between ships, l i is the length of ship i, and r is the proportional coefficient set according to the length of the ship.

5. The method according to claim 4, characterized in that The constraints include the following constraints describing the transshipment terminal berth allocation problem: Among them, d q represents the last ship in the ship sequence moored at berth q, ψ ijq ∈{0, 1}, which means that if ship j berths at berth q immediately after ship i, it is 1, otherwise it is 0; Among them, q represents the first ship in the ship sequence moored at berth q; Where S represents a set of transshipment terminal crane resources; ξ is ∈{0, 1}, which means that if the crane resource s of the transshipment terminal is allocated to ship i, it is 1, otherwise it is 0; Among them, td i ≥0, indicating the actual time when ship i leaves the platform berth; u is is the processing time of ship i using the transshipment terminal crane resource s, and M is a constant.

6. The method according to claim 5, characterized in that The constraints include the following constraints on the ship's sailing time: te i is the actual time when ship i leaves berth q and returns to the waiting area, is the sailing time of ship i leaving berth q and returning to the waiting area, is the average ship speed from berth q to the waiting area; T end The end time of the scheduling period.

7. The method according to claim 6, characterized in that The constraints include the following constraints describing the safe water flow limit of the waterway: Among them, T t represents the interval length of time window t, and T represents a set of time windows; Among them, τ it ∈{0, 1}, which means that if ship i passes through the channel within time window t, it is 1, otherwise it is 0; δ iqt ∈{0, 1}, which means that if ship i passes through the channel and reaches berth q within time window t, it is 1, otherwise it is 0; w i is the maximum acceptable water flow, W qt is the actual waterway current; R is the total width of the channel, g i is the width of ship i.

8. The method according to claim 7, characterized in that The multi-population genetic algorithm based on the hybrid coding strategy, the elite retention strategy and the adaptive mechanism is used to iteratively solve the ship scheduling problem and obtain an optimized ship scheduling solution, including: Input ship information, channel information, berth information and sailing time information; Set the fitness function of the chromosome, which consists of two parts: the objective function value and the penalty function value of the solution; Encode the chromosome structure, which is represented by x=(x1, ..., x N , ..., x mN ), where N is the number of ships and the first N dimensions represent the actual time when N ships leave the anchorage. The second dimension N represents the time when N ships leave the berth The remaining multiple N-dimensional genes represent binary variables (η iq , ψ ijq ,ξ is , τ it , δ iqt ); Apply a multi-population genetic algorithm to randomly generate initial solutions and evaluate them through fitness functions; The roulette wheel method is used to select the chromosomes of the next generation. The selection probability of each individual is calculated according to the fitness function, which is expressed as Among them, f i is the fitness value of individual i, S is the number of individuals in the population; Two parent individuals are randomly selected in each population, and single-point crossover operations are performed on the continuous coding part and the binary coding part respectively, where the crossover rate p c =0.7+(0.9-0.7)·rand(MP,1), MP is the number of populations; Perform mutation operator operation, use reversal operation and redistribute genes for continuous coding part, and randomly select and swap the value of binary coding part between 0 and 1, where the mutation rate p m =0.001+(0.05-0.001)·rand(MP, 1); The best individuals of a population are introduced into other populations through the immigration operator to replace the worst individuals in the target population; In each generation of evolution, the elite individual operator selects the best individuals from all populations and puts them into the elite population. The individuals in the elite population remain unchanged during the evolution process. Determine whether the number of generations of the currently saved optimal individuals is equal to Maxgen. If so, terminate and output the optimal ship scheduling plan; otherwise, return to the step of applying the roulette wheel method to select the chromosome of the next generation, and Maxgen is the preset maximum number of iterations.

9. The method according to claim 8, characterized in that The penalty function value of the solution is: in, is the inequality constraint function, ε y (x) is the equality constraint function, α r and β y is a positive penalty factor, R and Y represent the number of inequality and equality restrictions, respectively; The fitness function of the chromosome is: Among them, η iq is 1 if ship i is assigned to berth q, otherwise 0; is the sailing time of ship i from berth q to anchorage, tf i is the estimated time for vessel i to leave the berth.

10. A flood season anchorage-wharf channel ship scheduling optimization system based on multi-population genetic algorithm, characterized in that: include: The acquisition module is used to obtain the ship information, channel information, berth information, navigation time information and navigation characteristics during the flood season of the water transport hub; A construction module is used to construct a flood season anchorage-wharf channel ship scheduling model based on the ship information, channel information, berth information, navigation time information, and navigation characteristics of the water transport hub, with the goal of minimizing the waiting time of ships at the anchorage and the departure delay time of ships at the transfer terminal, and establish the constraints that the flood season anchorage-wharf channel ship scheduling meets; The initial scheme establishment module is used to solve the three sub-problems of anchorage departure time decision problem, water flow restricted channel allocation problem and transfer terminal berth allocation problem based on the satisfied constraints, and obtain the initial scheme of ship scheduling in the anchorage-wharf channel during the flood season; The optimization iteration module is used to iteratively solve the ship scheduling model of the anchorage-wharf channel during the flood season based on a multi-population genetic algorithm with a hybrid coding strategy, an elite retention strategy and an adaptive mechanism, and continuously update the initial ship scheduling plan to obtain the optimized ship scheduling plan.

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