CN115731748A - 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 approach 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; wherein, 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 the neighborhood constructed by the algorithm in the solution space can be increased, so that the total delay time of the obtained flight runway sequencing scheme is reduced, and the operation efficiency of flights can be increased 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, and the operation efficiency of the airport can be improved, so that the operation efficiency of a national airspace system can be improved on the whole. At present, as air traffic management methods in terminal areas and airspaces are continuously improved, airports have become bottlenecks that limit the improvement of air traffic operation efficiency. At many hub airports, the airport capacity is insufficient to meet the high air traffic demand, and the inefficient operation of the runway system has been identified as a major bottleneck limiting the efficiency of airport operation. When the runway system operates under the condition of unbalanced flight operation demand and capacity, the flight delay time is usually overlong, the airport operation is congested, adverse effects are caused to the environment, the traveling satisfaction of passengers is reduced, and extra cost is added to the operation of an airline company.
Large hub airports have taken a number of measures, such as increasing infrastructure investment to increase airport operating capacity and alleviate airport congestion.
However, although the capacity of the airport can be increased by building a new runway or expanding an existing runway, the investment cost is high and the effect is slow.
Therefore, how to balance flight operation demands with runway capacity of an airport without increasing infrastructure investment to improve flight operation efficiency within a specified runway capacity limit remains to be solved by those skilled in the art.
Disclosure of Invention
In view of this, embodiments of the present disclosure provide a flight runway sorting method and a storage medium, which can effectively improve the utilization efficiency of airport runways, balance flight operation requirements and runway capacity of airports, and thus improve the operation efficiency of flights within a specified runway capacity limit range.
An embodiment of the present specification provides a flight runway sequencing method, including:
constructing a multi-runway approach 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;
wherein, the large-scale neighborhood searching 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 includes:
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:
and dividing the multi-runway incoming and outgoing flight runway sequencing problem into a plurality of sub-problems.
Optionally, the constructing a multi-runway approach and departure flight runway sequencing model includes:
setting an objective function of a multi-runway approach and departure flight runway sequencing model;
setting a flight safety interval constraint condition;
setting a constraint condition of a runway time window for flight use;
and setting a constraint condition of the occupied time of the flight runway.
Optionally, the objective function includes: flight weighted total delay time is minimal; and
the flight safety interval constraints include:
the second flight should use the runway at least later than the first flight by the minimum 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 using the runway by any two flights is one and only one;
only one runway can be used by any flight; and
the flight usage runway time window constraints include:
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 departing flight must be within a time window defined by the expected 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 calculating of the flight weighted total delay time includes a delay cost coefficient of a flight, wherein the delay cost coefficient of a flight is reflected by a priority.
Optionally, the constructing a large-scale neighborhood search simulated annealing algorithm to solve the flight runway sequencing model includes:
initializing parameters of a simulated annealing algorithm;
generating a flight runway sequence initial solution of the multi-runway approach and departure flight runway sequencing model;
calculating an optimal objective function value of the flight runway sequence initial solution;
when the iteration times of the algorithm reach preset cycle times, destroying the flight runway sequence initial solution to obtain a flight runway sequence destruction solution;
performing flight recombination based on the flight runway sequence destruction solution to obtain a flight runway sequence recombination solution;
generating a neighborhood solution of the flight runway sequence based on the recombination solution;
obtaining an objective function value of a domain solution of the flight runway sequence;
confirming that the objective function value of the domain solution of the flight runway sequence meets the 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 flight runway order initial solution to obtain a flight runway order destruction solution includes at least one of:
adjacent flight removal;
maximum savings in flight removal;
random flight removal;
and removing a single point.
Optionally, the performing flight reorganization based on the flight runway order destruction solution to obtain a flight runway order reorganization solution includes:
and reinserting the removed flights in the damage solution into the damage solution in a greedy random insertion mode to carry out flight recombination and obtain a flight runway sequential recombination solution.
Optionally, the generating a neighborhood solution of the flight runway order based on the regrouping solution includes:
generating a neighborhood solution of a flight runway sequential recombination solution by adopting a local search mode, wherein the local search comprises the following steps:
carrying out two exchanges on flight queues of the same runway;
carrying out two-item exchange on flight queues of different runways;
changing the flight sequence of the same runway;
and changing the flight sequence of different runways.
Embodiments of the present specification further provide a storage medium storing a computer program or instructions, which when executed, implement the method in any one of the above flight runway sequencing method embodiments.
By adopting the flight runway sequencing method in the embodiment of the specification, a multi-runway approach and departure flight runway sequencing model is constructed; 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 can improve the range of the neighborhood constructed by the algorithm in a solution space by constructing a domain solution of an initial solution of the simulated annealing algorithm and acquiring the flight runway sequencing scheme based on the domain solution, so that the total delay time of the acquired flight runway sequencing scheme is reduced, and the operation efficiency of flights 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 can improve the speed and the efficiency of solving the multi-runway approach and departure flight runway sequencing model by dividing the multi-runway approach and departure flight runway sequencing problem into a plurality of sub-problems, and has strong implementation.
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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 needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating steps of a flight runway sequencing method in an embodiment of the present specification;
FIG. 2 is a schematic diagram illustrating steps of constructing a large-scale neighborhood search simulated annealing algorithm according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating flight encoding results in an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating a workflow of a rolling horizon control strategy algorithm in an embodiment of the present disclosure;
fig. 5 is a schematic diagram illustrating iteration results of objective function values of a multi-runway approach and departure flight runway sequencing model in different algorithms in an embodiment of the present disclosure.
Detailed Description
As described in the background, when a runway system operates under unbalanced flight operating demand and capacity, it usually causes a flight delay time to be too long, an airport operation jam, and adverse effects on the environment, and reduces the passenger travel satisfaction, and adds extra cost to the operation of an airline company. In the prior art, the capacity of an airport is 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 takes effect slowly.
The inventor finds through research and experiments that the flight runway sequencing problem is the key point influencing flight operation efficiency, the flight runway sequencing problem is a (Non-deterministic polymeric-time hard, NP-hard) optimization problem, and the problem scale increases exponentially with the increase of the problem scale.
For the small-scale flight sequencing case, the existing mixed integer programming model and the meta-heuristic algorithm can provide a high-quality flight runway use scheme in a short time. However, for the flight sequencing problem of multiple runways, and when the planning time range is large, the performance of the existing solving algorithm is insufficient, and a high-quality runway operation scheme cannot be provided within the specified time range.
In order to solve the above problems, in the embodiments of the present specification, a multi-runway approach and departure flight runway sorting model is constructed; 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 can improve the range of the neighborhood constructed by the algorithm in a solution space by constructing a domain solution of an initial solution of the simulated annealing algorithm and acquiring the flight runway sequencing scheme based on the domain solution, so that the total delay time of the acquired flight runway sequencing scheme is reduced, and the operation efficiency of flights can be improved within a specified runway capacity limit range.
For a better understanding and appreciation of the illustrative embodiments by those skilled in the art, the concepts, schemes, principles, and advantages of the illustrative embodiments will be described in detail below with reference to the accompanying drawings, along with a specific application example.
First, an embodiment of the present disclosure provides a flight runway sequencing method, and referring to a schematic step diagram of a flight runway sequencing method shown in fig. 1, the flight runway sequencing method may be performed by adopting the following steps:
in order to make the embodiments of the present disclosure better understood and implemented by those skilled in the art, the model parameters used in the present disclosure are explained below, and particularly, refer to table 1.
Set of parameters for the model of Table 1
In particular implementations, to increase the speed of the calculations, one may want to
The collection is split into two collections, wherein
Representing an incoming flight in the set,
to represent
The departure flights in the set are selected,
step A: constructing a multi-runway approach and departure flight runway sequencing model;
in a particular implementation, the projected runway usage times for flights within the planned time horizon are ordered in ascending order, i.e., for
Flight i first compares it
(for incoming flights),
the ordering is performed (for outgoing flights). As a specific example, if
Then k > g. The invention is independent operation aiming at the runway operation modeThe mode is that the operation is carried out on a multi-runway system at the same time, the take-off and landing operations on the runways are not influenced mutually, and the take-off and landing operations can be carried out on a plurality of runways at the same time.
In the specific implementation, in order to ensure that the runway capacity is fully utilized, a multi-runway approach and departure flight runway sequencing model can be established by taking the minimum total flight weighted delay time as an objective function.
Step A1: setting an objective function of a multi-runway approach and departure flight runway sequencing model;
in a specific implementation, the minimum total flight weighted delay time can be used as an objective function.
As a specific example, the objective function may be:
minΦ
wherein, the first and the second end of the pipe are connected with each other,
wherein mu i For the delay cost factor of flight i,
a weighted value for the difference between the actual runway usage time for the inbound flight and the target runway usage time,
a weighted value of the difference between the actual runway usage time and the earliest runway usage time for the departing flight.
In particular implementations, the priority may be used to reflect a deferral cost factor for the flight.
As a specific example, μ f For the delay cost coefficient of flight f, get mu f ＝G/P f ，P f For the priority of flight f, G takes the maximum value in the priority table. In particular, for flight f, its corresponding characteristic parameters, i.e. whether the flight is continuous, the type of flight and the incoming or outgoing flight, are denoted I respectively f 、R f And J f . Wherein
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 F ＝I f +R f +J f 。
Take light off-site flights of non-continuous voyages as an example, I f ＝2,R f ＝3,J f And (2). Priority P of the aircraft f P (2,3,2) =27, and the priorities of the respective aircrafts can be found as shown in table 2.
TABLE 2 flight priority parameter
Step A2: setting a flight safety interval constraint condition;
in a particular implementation, minimum separation requirements must be met for a continuous inbound flight or a continuous outbound flight to comply with safety regulations imposed by the Federal Aviation Administration (FAA) and the International Civil Aviation Organization (ICAO).
As a specific example, as shown in equation (1), if flight i, j uses the same runway and flight i uses the runway before flight j, then flight j should use the runway for a minimum time later than flight i by the minimum runway safety interval. The formula (2) limits the using sequence of the runway of any two flights to be one and only one. Equation (3) limits any flight to use only one runway.
Step A3: setting a constraint condition of a time window of a flight using runway;
specifically, 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, as shown in equation (4);
the departure time allocated for each departing flight must be within a time window defined by the expected departure time and the acceptable maximum delay time, as shown in equation (5).
Step A4: setting a constraint condition of the occupied time of the flight runway;
specifically, in order to make each runway occupied by only one aircraft at the same time, an approach flight runway occupation time constraint is set according to historical data of actual operation of the airport, as shown in formula (6), and an departure flight runway occupation time constraint is set according to formula (7).
And 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 specific implementation, the flight runway sequencing model can be solved by constructing a Large-scale Neighborhood Search Simulated Annealing algorithm (SALNS), so that a flight runway sequencing scheme can be obtained.
In specific implementation, a large-scale neighborhood searching method is designed to replace an original neighborhood construction method in a neighborhood construction process which is one of key steps in algorithm iteration based on a simulated annealing algorithm frame, and a new solution is formed by performing a destructive recombination process and a local searching process on a current solution.
In some embodiments of the present disclosure, referring to a schematic step diagram of constructing a large-scale neighborhood search simulated annealing algorithm shown in fig. 2, the large-scale neighborhood search simulated annealing algorithm SALNS may be constructed by the following steps:
step B1: initializing parameters of a simulated annealing algorithm;
specifically, the initialization algorithm initial temperature T = T 0 Algorithm end temperature T L Temperature reduction coefficient lambda, internal circulation number beta, circulated number sigma, non-update algebra mu =0, and maximum non-update algebra theta notimp 。
As a specific example, a real number encoding manner may be adopted, and the flight runway sequencing code includes c flights to be sequenced with numbers {1,2, …, c }, and s runways with numbers { c +1, c +2, …, c + s }, in the planning time period, where the flight number is the flight sequence sequenced in the ascending order of the expected runway usage time in step a. Referring to a schematic diagram of a flight code result shown in fig. 3, fig. 3 shows a schematic diagram of flight use sequence codes of 10 flights to be sequenced and 2 runways, where 11 and 12 respectively represent runway numbers 04 and 22L, and the flight use sequence of the flight runway sequencing code shown in fig. 2 can be converted into a flight use sequence of runway number 04: 1 → 2 → 4 → 6 → 8 → 5 → 7; the flight use order for track No. 22L is 3 → 10 → 9.
And step B2: and generating an initial solution of the multi-runway approach and departure flight runway sequencing model.
Specifically, the algorithm needs to generate an initial solution for flight runway sequencing during initialization.
In particular implementations, a first come first serve random initialization method may be employed. Because the efficiency of the large-scale neighborhood searching method provided by the invention in the solving process is very high, in the initialization process, a simple first-come-first-serve greedy random initialization method is adopted, so that the diversity of the initial solution can be ensured, the initialization 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: initializing the flight use sequence of each runway in a first-come first-serve mode based on the distribution result of the step B21;
specifically, taking one runway as an example, the smallest expected runway use time in the runway flight queue is selected as the first flight using the runway.
Step B23: and taking the flight which is not scheduled and is predicted to use the closest flight to the last allocated flight in the runway as the next flight using the runway, and repeating the operation until all the flights allocated to the runway are scheduled.
Step B24: and B, according to the generated using sequence of the flight runway, according to the constraint condition of the runway time window used by the flight in the step A3, allocating the runway using time of the flight according to the constraint.
In a particular implementation, it may be provided that the order of use of the runways may be adjusted between flights having an estimated time to use of the runways within five minutes of each other.
And step B3: and calculating the optimal objective function value of the initial solution.
In a specific implementation, let σ =0, calculate the objective function value Φ (X) of the current initial solution X, and let the optimal solution B = X, may obtain the optimal solution objective function value Φ (B).
And step B4: and confirming that the iteration times of the algorithm reach the preset cycle times.
In a specific implementation, if σ < β, step B5 is performed, otherwise let σ =0, and step B10 is performed.
And step B5: and destroying the initial solution to obtain a destroyed solution.
In the specific implementation, the destruction process of the flight sequencing initial solution comprises four types of destruction methods, namely BP 1 Adjacent flight removal BP 2 Maximum savings flight removal, BP 3 Random flight removal and BP 4 Single point removal. When the destruction process is executed, the 4 destruction methods are executed for sequencing the flight runway in sequence, and the deleted flights are stored in the Insert array for the recombination process.
As a specific example, the adjacent flight removal method can be implemented by the following steps:
step B511: randomly selecting one flight as a removal root flight;
step B512: calculating the maximum number of removed flights U 1 ，
Wherein
Denotes rounding up, P 1 The probabilities are removed for neighboring points. F is the number of flights for the selected runway;
step B513: randomly selecting flight number Q 1 ，Q 1 Is [0,U 1 ]A random number in between;
step B514: the root flight and the Q nearest to the root flight are set 1 -1 gantryShifts are removed from the original order and added to the Insert array.
As another specific example, the maximum savings flight removal method may be implemented by:
step B521: calculating the cost delta of saving flight delay time after each flight j is removed from the flight queue j Wherein the cost of saving flight delay time for incoming flights
Cost of saving flight delay time of departing flights
Step B522: calculating the maximum number of removed flights U 2 ，
Wherein
Indicating rounding up. P 2 Flight removal probability is saved to the maximum;
step B523: randomly selecting the number of removed flights Q 2 ，Q 2 Is [0,U 2 ]A random number in between;
step B524: randomly selecting Q according to roulette method 2 Overhead flight of which Δ j The larger flights have the opportunity to be selected for removal, 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 number of removed flights U 3 ，
Wherein
Denotes rounding up, P 3 Removing probabilities for random flights;
step B532: randomly selecting the number of removed flights Q 3 ，Q 3 Is [0,U 3 ]A random number in between;
step B533: randomly selecting Q 3 Rack flights, remove all flights selected, and add to the Insert array.
As another specific example, the single-point removal method may be implemented by:
step B541: a random number theta between 0,1 is generated.
Step B542: if theta < P 4 Then one flight in the flight queue is randomly removed and all removed flights are added to the Insert array. Wherein, P 4 The probabilities are removed for single points of flight.
Step B6: flight recombination is carried out based on the destruction solution, and a recombination solution is obtained;
in a specific implementation, all the removed flights may be reinserted into the solution scheme of the flight runway sort damaged in step B5 in the reorganization process, and the flights to be inserted are randomly inserted into the positions available for insertion according to a random order and greedy, where any two flights are located at the positions available for insertion.
As a specific example, flight reorganization may be performed by:
step B61: randomly disordering the insertion sequence of the flights to be inserted in the Insert array;
step B62: if the point to be inserted does not exist, ending, otherwise, searching flight positions available for insertion on each runway according to the constraint condition of the flight runway service time window in the step A3;
step B63: and calculating the added cost after all the positions for inserting the flight are inserted, randomly selecting the position with the minimum or the second smallest added cost of the flight delay time, and returning to the step B62.
Step B7: generating a neighborhood solution of the flight runway sequential recombination solution;
in a specific implementation, a neighborhood solution of the flight runway sequential reorganization solution can be generated by performing local search.
As a specific example, the local search may be divided into four local search processes, which are: NS (server) 1 、NS 2 、NS 3 、NS 4 The four processes are as follows:
NS 1 and B, performing two-item exchange on flight queues of the same runway, randomly selecting a runway, selecting two flights in the flight queues on the premise of meeting the constraint condition in the step A, exchanging the sequence of the flights between the two flights in the flight queues under the condition of meeting the constraint condition, checking all possible exchanges between the flights in the pair, generating a new flight runway queue, calculating a new objective function value, and keeping the current operation if the objective function value is reduced.
NS 2 The method comprises the steps of carrying out binary exchange on flight queues of different runways, randomly selecting one runway, selecting two flights in the flight queue on the premise of meeting constraint conditions, exchanging the flights between the two flights in the flight queue with the flight queue which is on the other runway and is closest to the using time of the two flight runways on the other runway, generating a new flight runway queue, calculating a new objective function value, and keeping current operation if the objective function value is reduced.
NS 3 The method comprises the steps of transforming flight sequences of the same runway, randomly selecting a runway, checking all flights on the runway on the premise of meeting constraint conditions, respectively inserting the flights into the front or the back of the flight with the closest using time to the runway, generating a new flight runway queue, calculating a new objective function value, and keeping the current operation if the objective function value is reduced.
NS 4 The method comprises the steps of transforming flight sequences of different runways, randomly selecting one runway, checking all flights on the runway on the premise of meeting constraint conditions, respectively inserting the flights into the front or the back of the flight which is closest to the runway service time on the different runway, generating a new flight runway queue, calculating a new objective function value, and keeping the current operation if the objective function value is reduced.
WhereinNS 1 、NS 3 Attempting to modify flight runway utilization time, NS 2 、NS 4 And (3) trying to change the using runway of the flight, sequentially executing four local searching processes after the flight runway sequencing solution is subjected to the destroying and recombining processes, and repeating the current local searching method when a more optimal solution appears.
And step B8: obtaining 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, step C may also be returned after step B9.
Step B10: confirming the current temperature of the algorithm;
specifically, T = λ T. If the current temperature-optimum solution value is the same as the last temperature-optimum 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 (4) terminating the algorithm, outputting the optimal solution B and the 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:
and C: constructing a rolling time domain control strategy algorithm framework;
specifically, the flight runway sequencing optimization problem can be divided into a plurality of sub-problems in a Rolling Horizon Control (RHC) strategy framework, so that the problem solving rate is increased.
In a specific implementation, the RHC policy can be used to make a relevant decision to forward look at C time domains, that is, when optimizing the current k-th rolling time domain, the optimal scheduling forwards rolls off-flight information in the C time domains, but only implements a flight scheduling result in the k-th rolling time domain, and repeats the same process in the next time domain. When the flight information on each time domain is optimized by the rolling time domain control strategy adopted in the embodiment of the present specification, the SALNS algorithm in step B in the foregoing embodiment is adopted to perform solution to form an RHC-SALNS algorithm, which can further improve the efficiency of solving a ranking model of the multi-runway approach and departure flight runway, and is not described herein again, and the working process of the RHC-SALNS algorithm refers 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 on the current time domain;
only executing the decision on the first time window of the current time domain, and abandoning the decisions on other time windows on the rolling time domain;
determining whether the current time domain reaches an end time;
if the ending time is reached, outputting the flight runway sequencing information, otherwise, rolling backwards for a time sequence, and continuously reading the flight information on the current rolling time domain and subsequent steps until the ending time is reached.
In order to make the embodiment of the present invention better understood and implemented by those skilled in the art, the following describes the flight course sequencing process in a specific application scenario.
As a specific example, the embodiments of the present specification use actual operation data of the warhan sky river airport in 2020 as a benchmark example of flight runway sequencing.
In a specific implementation, examples of different scales may be divided by planning period and number of flights.
As a specific example, the embodiment of the present specification divides 13 benchmark test cases, specifically referring to the flight ordering example shown in table 3, as shown in table 3, the first column indicates the name of the example, the second column indicates the number (n) of airplanes in each example, and the third column indicates the time length (T) of the planning period of the example. In these examples, the number of aircraft is from 10 to 500. Each instance has a different number of flights to be scheduled and instances 1 to 8 are considered small scale instances. Examples 9 to 13 are considered large scale examples.
TABLE 3 flight ordering example
In a specific implementation, the programming may be performed using a MATLAB R2016a environment, with the code running environment being 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 specification may be specifically designed according to the example parameters shown in table 4, which are specifically as follows:
TABLE 4 EXAMPLES parameters
As a specific example, referring to table 5, the embodiment of the present disclosure shows the comparison of the traditional simulated annealing algorithm (SA), the Genetic Algorithm (GA) solution result, and the algorithm solution result proposed by the present invention, and from this lateral comparison, it can be concluded that the RHC-SALNS algorithm has good performance in the in-flight runway sequencing.
TABLE 5 comparison of results of the algorithm
As another specific example, in order to evaluate the performance of the RHC-SALNS algorithm proposed in the present invention, this embodiment selects real operation data of a certain day of 2020 at the wuhan tianhe airport for analysis, selects an operation peak time of the airport, the number of flight in an hour is 30, and compares the RHC-SALNS algorithm solution process with the conventional SA algorithm through algorithm sorting, so as to obtain an iterative variation curve of a target function value with the algorithm, as shown in fig. 5. As can be seen from fig. 5, the RHC-SALNS algorithm is far better than SA in convergence speed, the iterative optimization effect of SALNS is significantly better than SA, and SALNS completes convergence in generation 43, while SA completes convergence in generation 148. In addition, the convergence effect of the SALNS is better than that of the SA, the SALNS can finally converge to the example internationally known optimal solution, and the SA cannot.
It should be understood that the data and parameters shown in the embodiments of the present specification are only exemplary data, and the algorithm shown in the embodiments of the present specification does not specifically limit the use data and parameters.
Embodiments of the present specification further provide a storage medium storing a computer program or instructions, which when executed, implement the method in any one of the above flight runway sequencing method embodiments.
Although the embodiments of the present invention are disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A flight runway sequencing method, comprising:
constructing a multi-runway approach 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;
wherein, 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 of 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:
and dividing the multi-runway incoming and outgoing flight runway sequencing problem into a plurality of sub-problems.
3. The method of claim 1, wherein constructing a multi-runway approach and departure flight runway sequencing model comprises:
setting an objective function of a multi-runway approach and departure flight runway sequencing model;
setting a flight safety interval constraint condition;
setting a constraint condition of a time window of a flight using runway;
and setting a constraint condition of the occupied time of the flight runway.
4. The method of claim 3, wherein the objective function comprises: flight weighted total delay time is minimal; and
the flight safety interval constraints include:
the second flight should use the runway at least later than the first flight by the minimum 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 using the runway by any two flights is one and only one;
only one runway can be used by any flight; and
the flight usage runway time window constraints include:
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 departing flight must be within a time window defined by the expected 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.
5. The method of claim 4, wherein the calculating of the flight weighted total delay time comprises a delay cost coefficient for a flight, wherein the delay cost coefficient for a flight is reflected by a priority.
6. The method of claim 1, wherein the constructing a large-scale neighborhood search simulated annealing algorithm solves the flight runway sequencing model, comprising:
initializing parameters of a simulated annealing algorithm;
generating a flight runway sequence initial solution of the multi-runway approach and departure flight runway sequencing model;
calculating an optimal objective function value of the flight runway sequence initial solution;
when the iteration times of the algorithm reach preset cycle times, destroying the flight runway sequence initial solution to obtain a flight runway sequence destruction solution;
performing flight recombination based on the flight runway sequence destruction solution to obtain a flight runway sequence recombination solution;
generating a neighborhood solution of the flight runway sequence based on the recombination solution;
obtaining an objective function value of a domain solution of the flight track sequence;
confirming that the objective function value of the domain solution of the flight track sequence meets the preset requirement;
confirming that the current temperature of the algorithm meets the algorithm termination requirement;
and acquiring a flight runway sequencing scheme.
7. The method of claim 6, wherein the destroying the flight runway order initial solution to obtain a flight runway order destruction solution comprises at least one of:
adjacent flight removal;
flight removal is saved to the maximum extent;
random flight removal;
and removing a single point.
8. The method of claim 6, wherein the obtaining a flight runway sequential reorganization solution by performing flight reorganization based on the flight runway sequential destruction solution comprises:
and reinserting the removed flights in the damage solution into the damage solution in a greedy random insertion mode to carry out flight recombination and obtain a flight runway sequential recombination solution.
9. The method of claim 6, wherein generating a neighborhood solution for a flight runway order based on the reformulation solution comprises:
generating a neighborhood solution of the flight runway sequential reorganization solution by adopting a local search mode, wherein the local search mode comprises the following steps:
carrying out two exchanges on flight queues of the same runway;
carrying out two exchanges on flight queues of different runways;
changing the flight sequence of the same runway;
and changing the flight sequence of different runways.
10. A storage medium, storing a computer program or instructions which, when executed, implement the method of any one of claims 1 to 9.
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