CN115731748B - Flight runway sequencing method and storage medium - Google Patents
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Abstract
The invention provides a flight runway sequencing method, which comprises the following steps: constructing a multi-runway entrance and departure flight runway sequencing model; constructing a large-scale neighborhood search simulated annealing algorithm to solve the flight runway sequencing model so as to obtain a flight runway sequencing scheme; the large-scale neighborhood search simulated annealing algorithm comprises the following steps: constructing a domain solution of an initial solution of a simulated annealing algorithm; and acquiring a flight runway sequencing scheme based on the domain solution. By adopting the scheme, the range of constructing the neighborhood in the solution space by the algorithm can be improved, so that the total delay time of the acquired flight runway sequencing scheme is reduced, and the flight operation efficiency can be improved within the specified runway capacity limit range.
Description
Technical Field
The embodiment of the specification relates to the technical field of air traffic control decision support, in particular to a flight runway sequencing method and a storage medium.
Background
The airport is used as a key node of an air transportation network, so that the operation efficiency of the airport is improved, and the operation efficiency of a national airspace system can be improved as a whole. At present, as air traffic management methods in a terminal area and an airspace are continuously improved, airports have become bottlenecks for limiting the improvement of air traffic operation efficiency. In many hub airports, the capacity of the airport is insufficient to meet the high air traffic demand, and the inefficiency of the runway system has been determined to be a major bottleneck in limiting the airport operation efficiency. When the runway system runs under unbalanced flight running requirements and capacity, the runway system usually causes overlong flight delay time, airport running congestion and adverse influence on the environment, reduces the travel satisfaction of passengers and adds extra cost to the operation of airlines.
Large hub airports have taken a number of measures, such as increasing infrastructure investment to increase the operating capacity of the airport and to alleviate airport congestion.
However, while airport capacity can be increased by means such as building new runways or expanding existing runways, investment costs are high and slow.
Therefore, how to balance flight operation needs with runway capacity of an airport to improve flight operation efficiency within prescribed runway capacity limits without increasing infrastructure investment is to be addressed by those skilled in the art.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method and a storage medium for sorting flight runways, which can effectively improve the use efficiency of the runway of the airport, balance the flight operation requirement and the runway capacity of the airport, and thereby improve the flight operation efficiency within the specified runway capacity limit range.
The embodiment of the specification provides a flight runway sequencing method, which comprises the following steps:
constructing a multi-runway entrance and departure flight runway sequencing model;
constructing a large-scale neighborhood search simulated annealing algorithm to solve the flight runway sequencing model so as to obtain a flight runway sequencing scheme;
the large-scale neighborhood search simulated annealing algorithm comprises the following steps:
constructing a domain solution of an initial solution of a simulated annealing algorithm;
and acquiring a flight runway sequencing scheme based on the domain solution.
Optionally, the flight runway sequencing method further comprises:
constructing a rolling time domain control strategy algorithm, and solving the flight runway sequencing model to obtain a flight runway sequencing scheme;
wherein, the rolling time domain control strategy algorithm comprises:
the multi-runway approach departure flight runway ordering problem is divided into a plurality of sub-problems.
Optionally, the constructing a multi-runway inbound and outbound flight runway sequencing model includes:
setting an objective function of a multi-runway entrance and departure flight runway sequencing model;
setting a flight safety interval constraint condition;
setting a time window constraint condition of a flight using runway;
and setting a flight runway occupation time constraint condition.
Optionally, the objective function includes: the flight weighting total delay time is minimum; and
the flight safety interval constraint condition comprises:
the second flight uses the runway at least later than the first flight by the lowest runway safety interval, wherein the first flight and the second flight use the same runway, and the first flight uses the runway before the second flight;
the sequence of the use runways of any two flights is one;
only one runway can be used for any flight; and
the flight use runway time window constraint includes:
the landing time allocated for each incoming flight must be within a time window defined by the expected landing time and the acceptable maximum delay time;
the departure time allocated for each departure flight must be within a time window defined by the predicted departure time and the acceptable maximum delay time; and
the flight runway occupation time constraint conditions comprise:
each runway can only be occupied by one flight at a time.
Optionally, the calculation of the flight weighted total delay time includes a delay cost coefficient of a flight, wherein the delay cost coefficient of the flight is reacted by priority.
Optionally, the constructing a large-scale neighborhood search simulated annealing algorithm solves the flight runway sequencing model, including:
initializing parameters of a simulated annealing algorithm;
generating an initial solution of the flight runway sequence of the multi-runway entrance-departure flight runway sequencing model;
calculating an optimal objective function value of the initial solution of the flight runway sequence;
when the algorithm iteration number is confirmed to reach the preset cycle number, destroying the initial solution of the flight runway sequence to obtain a flight runway sequence destroy solution;
performing flight reorganization based on the flight runway sequence disruption solution to obtain a flight runway sequence reorganization solution;
generating a neighborhood solution of the flight runway sequence based on the reorganization solution;
acquiring an objective function value of a domain solution of the flight runway sequence;
confirming that an objective function value of a domain solution of the flight runway sequence meets a preset requirement;
confirming that the current temperature of the algorithm meets the algorithm termination requirement;
and acquiring a flight runway sequencing scheme.
Optionally, the destroying the initial solution of the flight runway sequence to obtain a solution of the flight runway sequence destruction, including at least one of the following:
removing adjacent flights;
maximum economy flight removal;
random flight removal;
single point removal.
Optionally, the performing flight reorganization based on the flight runway sequence disruption solution, to obtain a flight runway sequence reorganization solution, includes:
reinserting the removed flights in the ring-breaking solution into the ring-breaking solution in a greedy random insertion mode, and carrying out flight reorganization to obtain a flight runway sequence reorganization solution.
Optionally, the generating a neighborhood solution of the flight runway sequence based on the reorganization solution includes:
generating a neighborhood solution of the flight runway sequence reorganization solution in a local search mode, wherein the local search comprises the following steps:
two exchanges are carried out on the flight queues of the same runway;
performing two exchanges on flight queues of different runways;
changing the flight sequence of the same runway;
the flight sequence of different runways is changed.
The present description also provides a storage medium storing a computer program or instructions that, when executed, implement the method of any one of the above-described runway sequencing method embodiments.
By adopting the flight runway sequencing method in the embodiment of the specification, a multi-runway incoming and outgoing flight runway sequencing model is constructed; and constructing a large-scale neighborhood search simulated annealing algorithm to solve the flight runway sequencing model so as to obtain a flight runway sequencing scheme; the large-scale neighborhood search simulated annealing algorithm obtains a flight runway sequencing scheme based on a domain solution by constructing the domain solution of the initial solution of the simulated annealing algorithm, so that the range of constructing the neighborhood in a solution space by the algorithm can be improved, the total delay time of the obtained flight runway sequencing scheme is reduced, and the flight operation efficiency can be improved within a specified runway capacity limit range.
Further, solving the flight runway sequencing model by constructing a rolling time domain control strategy algorithm to obtain a flight runway sequencing scheme; the rolling time domain control strategy algorithm divides the multi-runway inbound and outbound flight runway sequencing problem into a plurality of subproblems, so that the speed and the efficiency of solving the multi-runway inbound and outbound flight runway sequencing model can be improved, and the implementation is strong.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic diagram showing steps of a method for sorting flights in an embodiment of the present disclosure;
FIG. 2 shows a schematic diagram of steps for constructing a large-scale neighborhood search simulated annealing algorithm in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram showing a flight code result in the embodiment of the present disclosure;
FIG. 4 shows a schematic workflow diagram of a rolling horizon control strategy algorithm in an embodiment of the present specification;
fig. 5 is a schematic diagram showing the iterative results of objective function values in different algorithms of a multi-runway inbound and outbound flight runway sorting model according to an embodiment of the present disclosure.
Detailed Description
As described in the background art, when the runway system operates under unbalanced flight operation requirements and capacity, the flight delay time is usually too long, the airport operation is congested, the environment is adversely affected, the travel satisfaction of passengers is reduced, and additional cost is added to the operation of airlines. In the prior art, airport capacity is generally increased by building a new runway or expanding an existing runway to improve the operation efficiency of flights, but the method has high investment cost and slow effect.
The inventor finds that the flight runway sequencing problem is a key point affecting the flight operation efficiency through researches and experiments, the flight runway sequencing problem is a (Non-deterministic polynomial-time hard, NP-hard) optimization problem, and the problem scale increases exponentially along with the increase of the problem scale.
For small-scale flight sequencing cases, the existing mixed integer programming model and meta-heuristic algorithm can give a high-quality flight runway use scheme in a short time. However, for the problem of multi-runway flight sequencing, and when the planning time range is large, the existing solving algorithm is insufficient in performance, and a high-quality runway operation scheme cannot be given in a specified time range.
In view of the above problems, in the embodiments of the present disclosure, a multi-runway inbound/outbound flight runway sorting model is constructed; and constructing a large-scale neighborhood search simulated annealing algorithm to solve the flight runway sequencing model so as to obtain a flight runway sequencing scheme; the large-scale neighborhood search simulated annealing algorithm obtains a flight runway sequencing scheme based on a domain solution by constructing the domain solution of the initial solution of the simulated annealing algorithm, so that the range of constructing the neighborhood in a solution space by the algorithm can be improved, the total delay time of the obtained flight runway sequencing scheme is reduced, and the flight operation efficiency can be improved within a specified runway capacity limit range.
For a better understanding and to be obtained by anyone skilled in the art to practice the embodiments of the present description, the following detailed description is of the concepts, solutions, principles and advantages of the embodiments of the present description, etc. with reference to the drawings, by way of specific examples of application.
First, the embodiment of the present disclosure provides a method for sorting a flight runway, referring to a step schematic diagram of a method for sorting a flight runway shown in fig. 1, the following steps may be adopted to sort the flight runway:
for a better understanding of one skilled in the art and to practice the examples herein, the model parameters used in the present description are explained below, with particular reference to table 1.
Table 1 parameter sets of models
In particular implementations, to increase the speed of the computation, one canThe set is split into two sets, wherein +.>Representing incoming flights in a collection, +.>Representation->Departure flight in the collection, +.>
Step A: constructing a multi-runway entrance and departure flight runway sequencing model;
in particular implementations, the estimated runway usage times for flights within the planned time range are ordered in ascending order, i.e., forFlight i first compares it +.>(for incoming flights),>ordering (for off-the-road flights). As a specific example, if->K > g. The invention aims at the runway operation mode to be an independent operation mode, namely, the runway system is operated simultaneously, the take-off and landing operations on the runways are not mutually influenced, and the runways can take-off and landing operations simultaneously.
In a specific implementation, in order to ensure that the runway capacity is fully utilized, a multi-runway inbound and outbound flight runway sequencing model can be established by taking the minimum flight weighted total delay time as an objective function.
Step A1: setting an objective function of a multi-runway entrance and departure flight runway sequencing model;
in implementations, the minimum flight weighted total delay time may be used as an objective function.
As a specific example, the objective function may be:
minΦ
wherein,wherein mu i For the delay cost coefficient of flight i, +.>Weight for the difference between the actual runway time of the incoming flight and the target runway time, +.>A weight of the difference between the actual runway usage time and the earliest runway usage time for the departure flight.
In implementations, a delay cost factor for priority reacting flights may be employed.
Mu, as a specific example f Mu is taken as the delay cost coefficient of the flight f f ＝G/P f ，P f For the priority of flight f, G takes the maximum value in the priority table. Specifically, for flight f, its corresponding characteristic parameters, i.e., whether the flight is continuous, the type of flight, and inbound or outbound flights, are denoted as I f 、R f J f . Wherein the method comprises the steps of
Thus, priority P of aircraft f f The following can be calculated:
P f ＝P(I f ,R f ,J f )
＝(P F -1)(P F -2)(P F -3)/6+(2P F -I f -2)(I f -1)/2+J f
wherein P is F ＝I f +R f +J f 。
Taking a light departure flight of discontinuous course as an example, i.e f ＝2,R f ＝3,J f =2. Priority P of the aircraft f By the same token, P (2, 3, 2) =27, the priorities of the individual aircraft can be found as shown in table 2.
TABLE 2 flight priority parameters
Step A2: setting a flight safety interval constraint condition;
in practice, minimum interval requirements must be met for either a continuous inbound flight or a continuous outbound flight to comply with the safety regulations imposed by the federal aviation administration (Federal Aviation Administration, FAA) and the international civil aviation organization (International Civil Aviation Organization, ICAO).
As a specific example, if flight i, j uses the same runway and flight i uses the runway before flight j, the time that flight j uses the runway should be at least the lowest runway safety interval later than flight i, as shown in equation (1). The formula (2) limits the sequence of using runways of any two flights to one. Equation (3) limits the use of only one runway for any one flight.
Step A3: setting a time window constraint condition of a flight using runway;
specifically, the landing time allocated for each incoming flight must be within a time window defined by the predicted landing time and the acceptable maximum delay time, as shown in equation (4);
the departure time allocated for each departure flight must be within a time window defined by the predicted departure time and the acceptable maximum delay time, as shown in equation (5).
Step A4: setting a flight runway occupation time constraint condition;
specifically, in order to enable each runway to be occupied by only one aircraft at the same time, an inbound flight runway occupation time constraint is set according to the history data of the actual operation of the airport, as shown in formula (6), and an outbound flight runway occupation time constraint is set, as shown in formula (7).
And (B) step (B): constructing a large-scale neighborhood search simulated annealing algorithm to solve the flight runway sequencing model so as to obtain a flight runway sequencing scheme;
in particular implementations, the flight runway ordering model may be solved by constructing a large-scale neighborhood search simulated annealing algorithm (Simulated Annealing Large Neighborhood Search, SALNS) to enable a flight runway ordering scheme to be obtained.
In specific implementation, a large-scale neighborhood searching method is designed to replace an original neighborhood constructing method in the process of constructing a neighborhood in one of key steps in algorithm iteration based on a simulated annealing algorithm framework, and a new solution is formed by carrying out a destructive recombination process and a local searching process on a current solution.
In some embodiments of the present disclosure, referring to a schematic diagram of steps for constructing a large-scale neighborhood search simulated annealing algorithm shown in fig. 2, a large-scale neighborhood search simulated annealing algorithm SALNS may be constructed by:
step B1: initializing parameters of a simulated annealing algorithm;
specifically, the initialization algorithm initial temperature t=t 0 Algorithm termination temperature T L The cooling coefficient lambda, the internal circulation times beta, the circulation times sigma, the non-updated algebra mu=0, the maximum non-updated algebra theta notimp 。
As a specific example, the real number encoding method may be adopted to encode the flight runway in the planning period, where the flight runway sorting encoding includes c flights to be sorted with numbers {1,2, …, c } and s runways with numbers { c+1, c+2, …, c+s }, where the flight numbers are the flight sequence sorted according to the expected time ascending sequence of the runways in step a. Referring to a flight code result schematic diagram shown in fig. 3, fig. 3 shows a flight use sequence code schematic diagram of 10 flights to be sequenced and 2 runways, wherein 11 and 12 respectively represent the number 04 and 22L runways, and the flight runway sequencing code shown in fig. 2 can be converted into the number 04 runway flight use sequence as follows: 1- & gt 2- & gt 4- & gt 6- & gt 8- & gt 5- & gt 7; the flight use sequence of the 22L runway is 3-10-9.
Step B2: an initial solution of the multi-runway inbound and outbound flight runway sequencing model is generated.
Specifically, the algorithm needs to generate an initial solution for the flight runway sequencing during the initialization process.
In implementations, a first-come-first-serve random initialization method may be employed. The large-scale neighborhood searching method provided by the invention has high efficiency in the solving process, so that in the initializing process, a simpler greedy random initializing method of first-come first-serve is adopted, the diversity of initial solutions can be ensured, the initializing complexity can be reduced, and the algorithm efficiency can be further improved in the solving process.
As a specific example, greedy random initialization may take the following steps:
step B21: randomly and uniformly distributing flights to all runways;
step B22: based on the allocation result of the step B21, carrying out first-come first-serve initialization on the flight use sequence of each runway;
specifically, taking one runway as an example, the smallest estimated runway use time in the flight queue of the runway is selected as the first flight using the runway.
Step B23: of flights assigned to the runway, a flight whose use time of the runway is estimated to be closest to the last assigned flight and which is not scheduled is repeated until all flights assigned to the runway are scheduled as the next flight using the runway.
Step B24: and C, distributing the runway service time of the flights according to the constraint conditions of the runway time window of the flights in the step A3 according to the generated runway service sequence of the flights.
In implementations, the expected runway usage time for each flight may be specified and the runway usage order may be adjusted between flights within five minutes of each flight.
Step B3: and calculating the optimal objective function value of the initial solution.
In a specific implementation, σ=0 may be calculated, and the objective function value Φ (X) of the current initial solution X is calculated, and the optimal solution b=x may be obtained.
Step B4: and confirming that the algorithm iteration times reach preset cycle times.
In a specific implementation, if σ < β, step B5 is performed, otherwise let σ=0, and step B10 is performed.
Step B5: and destroying the initial solution to obtain a destroyed solution.
In a specific implementation, the disruption process of the initial solution of the flight ordering includes four types of disruption methods, BP respectively 1 Removing BP for neighboring flights 2 Maximum economy flight removal, BP 3 Random flightsRemoval and BP 4 Single point removal. When the disruption process is executed, the 4 disruption methods are executed on the initial solution of the flight runway sequencing in turn, and deleted flights are stored in an Insert array for use by the reassembly process.
As a specific example, the adjacent flight removal method may be implemented by:
step B511: randomly selecting one flight as the removal root flight;
step B512: calculating the maximum removed number of flights U 1 ，Wherein->Representing an upward rounding, P 1 The probabilities are removed for neighboring points. F is the number of flights of the selected runway;
step B513: randomly selecting the number of flights Q 1 ，Q 1 Is [0,U ] 1 ]Random numbers in between;
step B514: root flight and Q nearest to the root flight 1 The 1 flight is removed from the original ordering and added to the Insert array.
As another specific example, the maximum economy flight removal method may be implemented by:
step B521: calculating the cost delta of saving the flight delay time after each flight j is removed from the flight queue j Wherein, the incoming flights save the cost of the flight delay timeSaving flight delay time cost of departure flight +.>
Step B522: calculating the maximum removed number of flights U 2 ，Wherein->Representing an upward rounding. P (P) 2 Removing probability for the most saved flight;
step B523: randomly selecting the number of removed flights Q 2 ，Q 2 Is [0,U ] 2 ]Random numbers in between;
step B524: randomly selecting Q according to roulette method 2 Flights on shelves, wherein delta j The larger flights have the more opportunity to be selected for removal, the selected flights are removed from the original flight queue and added to the Insert array.
As yet another specific example, the random flight removal method may be implemented by:
step B531: calculating the maximum removed number of flights U 3 ，Wherein->Representing an upward rounding, P 3 Removing probabilities for random flights;
step B532: randomly selecting the number of removed flights Q 3 ，Q 3 Is [0,U ] 3 ]Random numbers in between;
step B533: random selection of Q 3 The flights are set up, all flights selected are removed, and added to the Insert array.
As yet another specific example, the single point removal method may be implemented by:
step B541: a random number θ between 0,1 is generated.
Step B542: if θ < P 4 One flight in the flight queue is randomly removed and all removed flights are added to the Insert array. Wherein P is 4 The probabilities are removed for single point flights.
Step B6: flight reorganization is carried out based on the destructive solution, and a reorganization solution is obtained;
in a specific implementation, all removed flights may be reinserted into the solution of the sorting of the flight tracks after the disruption in the step B5 in the reassembly process, where the flights to be inserted are randomly inserted into the positions available for insertion according to a random sequence, and greedy random insertion is performed, where the positions available for insertion are located between any two flights.
As a specific example, flight reorganization may be performed by:
step B61: randomly disturbing the inserting sequence of flights to be inserted in the Insert array;
step B62: if the point to be inserted does not exist, ending, otherwise, searching for a flight position available for insertion on each runway according to the flight runway using time window constraint condition in the step A3;
step B63: the post-insertion added cost for all positions of all available inserted flights is calculated, the position with the least cost increase for the flight delay time or the second smallest is randomly selected, and the process returns to step B62.
Step B7: generating a neighborhood solution of the flight runway sequence reorganization solution;
in an implementation, a neighborhood solution of the flight runway sequence reorganization solution may be generated by performing a local search.
As a specific example, the local search may be divided into four local search processes, respectively: NS (NS) 1 、NS 2 、NS 3 、NS 4 The four processes are as follows:
NS 1 and C, carrying out two exchanges on the flight queues of the same runway, randomly selecting one runway, selecting two flights in the flight queues on the premise of meeting the constraint condition in the step A, exchanging the flight sequence between the two flights in the flight queues under the precursor meeting the constraint condition, checking all possible exchanges between the pair of flights, generating a new flight runway queue, calculating a new objective function value, and if the objective function value is reduced, retaining the current operation.
NS 2 Two exchanges are carried out on the flight queues of different runways, one runway is randomly selected, and the runways are selected on the premise of meeting constraint conditionsSelecting two flights in the flight queues, exchanging the flights between the two flights in the flight queues with the most recently separated flight queues when the flights are used on another runway and the two runways are used, generating a new flight runway queue, calculating a new objective function value, and retaining the current operation if the objective function value is reduced.
NS 3 The method comprises the steps of changing the flight sequence of the same runway, randomly selecting one runway, checking all flights on the runway on the premise of meeting constraint conditions, respectively inserting the flights before or after the flights which are spaced nearest to the runway in use, generating a new flight runway queue, calculating a new objective function value, and reserving current operation if the objective function value is reduced.
NS 4 The method comprises the steps of changing the flight sequence of different runways, randomly selecting one runway, checking all flights on the one runway on the premise of meeting constraint conditions, respectively inserting the flights into the different runways before or after the flights which are most distant from the runways when the runways are used, generating a new flight runway queue, calculating a new objective function value, and retaining the current operation if the objective function value is reduced.
Wherein NS is 1 、NS 3 Attempting to change runway usage time of flights, NS 2 、NS 4 And attempting to change the using runway of the flight, sequentially executing four local searching processes after the sequence solution of the flight runway is destroyed and recombined, and repeating the current local searching method when a better solution appears.
Step B8: acquiring an objective function value of the domain solution;
specifically, the objective function value Φ (X ') of the neighborhood solution X' is calculated, accepting the neighborhood solution according to the Metropolis criterion, σ=σ+1.
Step B9: confirming that the objective function value of the domain solution meets the preset requirement;
specifically, if Φ (X ') < Φ (B), let b=x ', Φ (B) =Φ (X '), μ=0.
In a specific implementation, it is also possible to return to step C after step B9.
Step B10: confirming the current temperature of the algorithm;
specifically, t=λt. If the current temperature optimal solution value is the same as the last temperature optimal solution value, μ=μ+1.
Step B11: confirming that the current temperature of the algorithm meets the algorithm termination requirement;
specifically, if T < T L Or μ=θ notimp And (3) stopping the algorithm, outputting an optimal solution B and an optimal objective function value phi (B), and otherwise, returning to the step B4.
In some embodiments of the present description, the method may further comprise:
step C: constructing a rolling time domain control strategy algorithm frame;
in particular, the flight runway sequencing optimization problem may be partitioned into a number of sub-problems in a rolling horizon control (Receding Horizon Control, RHC) policy framework, thereby increasing the problem solving rate.
In a specific implementation, the RHC policy may be used to make a relevant decision to look forward for C time slots, i.e., when optimizing the current kth rolling time slot, the optimization schedule rolls forward the outbound flight information over C time slots, but only implements the flight scheduling result in the kth rolling time slot, and repeats the same process on the next time slot. When the rolling time domain control strategy adopted in the embodiment of the present disclosure optimizes the flight information on each time domain, the SALNS algorithm in step B in the foregoing embodiment is adopted to solve, so as to form an RHC-SALNS algorithm, which can further improve the efficiency of solving the multi-runway entrance and departure flight runway sequencing model, and the workflow of the RHC-SALNS algorithm is not repeated herein, and referring to fig. 4, and includes:
inputting algorithm parameters;
reading flight information on the current rolling time domain;
executing an SALNS algorithm to obtain an optimal individual in the current time domain;
only executing the decision on the first time window of the current time domain, and discarding the decisions on other time windows on the rolling time domain;
determining whether the current time domain reaches the end time;
and if the end time is reached, outputting the flight runway sequencing information, otherwise, backward scrolling a time sequence, and continuously executing the steps of reading the flight information on the current scrolling time domain and the follow-up steps until the end time is reached.
In order for those skilled in the art to better understand and practice the embodiments of the present invention, the following description of the course sequencing process is provided in a specific application scenario.
As a specific example, the present embodiment uses actual operational data of the martial arts airport 2020 as a benchmark example of the ordering of flight runways.
In implementations, examples of different scales may be divided by planning period and number of flights.
As a specific example, the present embodiment divides 13 benchmark test cases in total, referring specifically to the flight sequencing case shown in table 3, as shown in table 3, the first column indicates the case names, the second column indicates the number of aircrafts (n) in each case, and the third column indicates the case planning cycle time length (T). In these examples, the number of aircraft is from 10 to 500 frames. Each instance has a different number of flights to be planned, and examples 1 to 8 are considered as small-scale instances. Examples 9 to 13 are considered as large scale examples.
TABLE 3 flight ordering example
In a specific implementation, the MATLAB R2016a environment may be used for programming, and the code running environment is a computer with Intel i7-9700KF8C8T processor, 3.60GHz, and 16.0GB RAM.
In specific implementation, the algorithm parameters in the embodiments of the present disclosure may be specifically designed according to the example parameters shown in table 4, and specifically are as follows:
TABLE 4 example parameters
As a specific example, referring to table 5, the embodiment of the present specification shows comparison of the conventional simulated annealing algorithm (SA), genetic Algorithm (GA) solutions, and the algorithm solutions proposed by the present invention, from which it can be derived that the RHC-SALNS algorithm has good performance in the sorting of the inbound and outbound flight runways.
Table 5 algorithm solution results comparison
As another specific example, in order to evaluate the performance of the RHC-SALNS algorithm provided by the present invention, in this embodiment, real operation data of a martial river in 2020 is selected for analysis, an airport operation peak operation period is selected, the number of flights per hour is 30, and the RHC-SALNS algorithm solving process is compared with the conventional SA algorithm through algorithm sorting, so as to obtain an iterative change curve of the objective function value along with the algorithm as shown in fig. 5. As can be seen from fig. 5, the RHC-SALNS algorithm is far superior to SA in convergence speed, the SALNS is significantly superior to SA in iterative optimization, and SALNS completes convergence at the 43 rd generation, while SA completes convergence at the 148 th generation. In addition, the SALNS also has better convergence effect than SA, which can eventually converge to the best solution known to the computing world.
It should be understood that the data shown in the embodiments of the present specification and the parameters are merely exemplary data, and the algorithm shown in the embodiments of the present specification does not limit the usage data and parameters specifically.
The present description also provides a storage medium storing a computer program or instructions that, when executed, implement the method of any one of the above-described runway sequencing method embodiments.
Although the embodiments of the present specification are disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Claims (8)
1. A method of ordering a flight runway, comprising:
the method for constructing the multi-runway entrance and departure flight runway sequencing model comprises the following steps of:
setting an objective function of a multi-runway entrance and departure flight runway sequencing model;
setting a flight safety interval constraint condition;
setting a time window constraint condition of a flight using runway;
setting a flight runway occupation time constraint condition;
the large-scale neighborhood search simulated annealing algorithm is constructed to solve the flight runway sequencing model to obtain a flight runway sequencing scheme, and the method comprises the following steps:
initializing parameters of a simulated annealing algorithm;
generating an initial solution of the flight runway sequence of the multi-runway entrance-departure flight runway sequencing model;
calculating an optimal objective function value of the initial solution of the flight runway sequence;
when the iterative times of the algorithm reach the preset cyclic times, destroying the initial solution of the sequence of the flight runway to obtain the solution of the sequence destruction of the flight runway;
performing flight reorganization based on the flight runway sequence disruption solution to obtain a flight runway sequence reorganization solution;
generating a neighborhood solution of the flight runway sequence based on the reorganization solution;
acquiring an objective function value of a domain solution of the flight runway sequence;
confirming that an objective function value of a domain solution of the flight runway sequence meets a preset requirement;
confirming that the current temperature of the algorithm meets the algorithm termination requirement;
acquiring a flight runway sequencing scheme;
the large-scale neighborhood search simulated annealing algorithm comprises the following steps:
constructing a domain solution of an initial solution of a simulated annealing algorithm;
and acquiring a flight runway sequencing scheme based on the domain solution.
2. The method as recited in claim 1, further comprising:
constructing a rolling time domain control strategy algorithm, and solving the flight runway sequencing model to obtain a flight runway sequencing scheme;
wherein, the rolling time domain control strategy algorithm comprises:
the multi-runway approach departure flight runway ordering problem is divided into a plurality of sub-problems.
3. The method of claim 2, wherein the objective function comprises: the flight weighting total delay time is minimum; and
the flight safety interval constraint condition comprises:
the second flight uses the runway at least later than the first flight by the lowest runway safety interval, wherein the first flight and the second flight use the same runway, and the first flight uses the runway before the second flight;
the sequence of the use runways of any two flights is one;
only one runway can be used for any flight; and
the flight use runway time window constraint includes:
the landing time allocated for each incoming flight must be within a time window defined by the expected landing time and the acceptable maximum delay time;
the departure time allocated for each departure flight must be within a time window defined by the predicted departure time and the acceptable maximum delay time; and
the flight runway occupation time constraint conditions comprise:
each runway can only be occupied by one flight at a time.
4. A method according to claim 3, wherein the calculation of the flight weighted total delay time comprises a delay cost factor for a flight, wherein the delay cost factor for a flight is reacted by priority.
5. The method of claim 4, wherein said destroying the initial solution of the airline runway sequence to obtain a solution of the airline runway sequence destruction comprises at least one of:
removing adjacent flights;
maximum economy flight removal;
random flight removal;
single point removal.
6. The method of claim 5, wherein the performing flight reorganization based on the flight runway sequence disruption solution to obtain a flight runway sequence reorganization solution comprises:
reinserting the removed flights in the ring-breaking solution into the ring-breaking solution in a greedy random insertion mode, and carrying out flight reorganization to obtain a flight runway sequence reorganization solution.
7. The method of claim 6, wherein generating a neighborhood solution for the flight runway order based on the reorganization solution comprises:
generating a neighborhood solution of the flight runway sequence reorganization solution in a local search mode, wherein the local search comprises the following steps:
two exchanges are carried out on the flight queues of the same runway;
performing two exchanges on flight queues of different runways;
changing the flight sequence of the same runway;
the flight sequence of different runways is changed.
8. A storage medium storing a computer program or instructions which, when executed, implement the method of any one of claims 1 to 7.
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