CN108630020B - Airspace flow allocation method - Google Patents
Airspace flow allocation method Download PDFInfo
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- CN108630020B CN108630020B CN201810437134.8A CN201810437134A CN108630020B CN 108630020 B CN108630020 B CN 108630020B CN 201810437134 A CN201810437134 A CN 201810437134A CN 108630020 B CN108630020 B CN 108630020B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/003—Flight plan management
Abstract
The invention discloses an airspace flow allocation method, and relates to the field of aviation scheduling. The invention comprises the following steps: step S01, obtaining basic data of flight, airport and air state; step S02, establishing an integer programming model of an Air Traffic Scheduling (ATS) problem, and determining a target function; step S03, solving the established model by using a genetic algorithm; step S04 accelerates the genetic algorithm using a parallel technique. According to the invention, flight and aviation basic data are obtained, an integer programming model of aviation scheduling is established, various parameters in an objective function are determined, and the flight in an airport is managed by using a genetic algorithm mechanism and a parallel acceleration technology, so that flight management cost is reduced, management efficiency is improved, and loss caused by factors such as flight delay is effectively reduced on the premise of meeting complicated constraint conditions of aviation scheduling.
Description
Technical Field
The invention belongs to the field of aviation scheduling, and particularly relates to an airspace flow allocation method which is used for realizing take-off and landing management of flights in multiple airports in a short time.
Background
In the field of aviation scheduling, it is necessary to schedule the taking-off and landing of airplanes in a plurality of airports and airspaces within a certain time.
When carrying out aviation scheduling, a plurality of constraints on the ground and in the air need to be considered. Under the condition that these constraints are satisfied, it is necessary to minimize the loss due to flight delay and the like. Due to the complexity of the problem, it is difficult to solve such algorithms in a short time to obtain a good solution.
Disclosure of Invention
The invention aims to provide an airspace flow allocation method, which comprises the steps of obtaining flight and aviation basic data, establishing an integer programming model of aviation scheduling, determining various parameters in a target function, utilizing a genetic algorithm mechanism and a parallel acceleration technology to realize the take-off and landing management of flights in an airport, and solving the problems of numerous constraint conditions, high management cost, complex management and low efficiency of the conventional flight management.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a method for allocating airspace flow, which comprises the following steps:
step S01, obtaining basic data of flight, airport and air state;
step S02, establishing an integer programming model of the ATS problem, and determining a target function;
step S03, solving the established model by using a genetic algorithm;
step S04 accelerates the genetic algorithm using a parallel technique.
Preferably, in step S02, building the model requires that the time to be decided is sliced to satisfy each constraint condition.
Preferably, the constraint condition includes:
restraining one: the time interval constraint on the runway is used for constraining the time difference of take-off or landing of two adjacent flights on the same runway;
and (2) constraining: the time interval constraint on the waypoints is used for carrying out time interval constraint on flights flying to the same waypoint;
and (3) constraining: capacity constraint between the waypoints and the airport, which is used for carrying out capacity constraint on flights on the airline;
and (4) constraining: sector capacity constraints;
and (5) constraining: flight landing time constraints.
Preferably, in step S03, the genetic algorithm uses non-binary chromosome coding; the chromosome coding sets each individual to be of length nfConverting the flight sequence into a schedule, and calculating an evaluation function of the schedule, wherein the evaluation function calculation formula is as follows:
wherein, a is more than 2 and less than 3, tfFor counting flightsThe time of the landing is drawn,
for the actual time of flight to land,
the fluctuation situation of the original sequence of the flight.
Preferably, in the step S04, the parallelization technique adopts a greedy algorithm; the greedy algorithm is used for converting the sequence of the flights into a specific schedule of the flights; the greedy algorithm comprises the following specific steps:
t01 records the flight taking off as occupying runway and air resources in the chromosome coding;
t02 when the new flight inserts the chromosome code, according to the existing runway and the air flight and the new flight's own constraint conditions, choose the flight earliest time of takeoff;
t03 determining flight landing time according to the earliest takeoff time;
t04 is processing the departure time of the next flight in the chromosome coding sequence.
The invention has the following beneficial effects:
according to the invention, flight and aviation basic data are obtained, an integer programming model of aviation scheduling is established, various parameters in an objective function are determined, and the flight in an airport is managed by using a genetic algorithm mechanism and a parallel acceleration technology, so that flight management cost is reduced, management efficiency is improved, and loss caused by factors such as flight delay is effectively reduced on the premise of meeting complicated constraint conditions of aviation scheduling.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a step diagram of a spatial domain flow allocation method according to the present invention;
FIG. 2 is a diagram of the steps of the greedy algorithm of the present invention;
FIG. 3 is a schematic diagram of the genetic algorithm evolution operator of the present invention;
FIG. 4 is a schematic diagram of parallel acceleration of the algorithm of the present invention;
FIG. 5 is a schematic diagram of a greedy algorithm of the present invention;
fig. 6 and fig. 7 are parameter tables provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a method for allocating airspace traffic, including the following steps:
step S01, obtaining basic data of flight, airport and air state;
step S02, establishing an integer programming model of the ATS problem, and determining a target function;
step S03, solving the established model by using a genetic algorithm;
step S04 accelerates the genetic algorithm using a parallel technique.
In step S02, the building of the model requires that the time to be decided is sliced to satisfy each constraint condition.
Referring to fig. 6-7, the constraints include:
restraining one: the time interval constraint on the runway is used for constraining the time difference of take-off or landing of two adjacent flights on the same runway, and the formula is as follows:
and (2) constraining: the time interval constraint on the waypoints is used for carrying out time interval constraint on flights flying to the same waypoint, and the formula is as follows:
Ap(f,f′)≤g(f,f)+wp；f,f′·M≤M-Ap(f,f′)；
and (3) constraining: capacity constraint between waypoints and airports is used for carrying out capacity constraint on flights on an airline, and the formula is as follows:
and (4) constraining: sector capacity constraint, the formula is:
and (5) constraining: flight landing time constraints.
In step S03, the genetic algorithm uses non-binary chromosome coding; chromosome coding sets each individual to a length of nfThe integral array converts the flight sequence into a schedule, and then calculates the evaluation function of the schedule, wherein the calculation formula of the evaluation function is as follows:
wherein, a is more than 2 and less than 3, tfThe time to land is scheduled for the flight,
for the actual time of flight to land,
the fluctuation condition of the original sequence of the flight is taken as the fluctuation condition;
referring to fig. 3, the genetic algorithm applied in the embodiment of the present invention uses the following evolution operator; the evolution operator only selects the mutation operator, and has five operations: (1) the order of any two flights in the sequence is exchanged; (2) one flight of the sequence is inserted in the middle of any position of the sequence; (3) intercepting a sub-sequence from the original sequence and rearranging the sub-sequence; (4) intercepting two subsequences from the original sequence, and exchanging the positions of the two subsequences; (5) arranging a section of subsequence in reverse order; in these evolutionary operations, sequences may be performed both on the whole sequence and on the runway or on certain waypoints; the waypoints with more strict time interval constraints have greater influence on the final sequencing result, so the priority is higher.
In the natural selection stage, because the number of the population is small, the selection evaluation function is adopted to be smaller (PopulationSize-parentSize) individuals; by using the method, better results compared with a simple greedy algorithm can be obtained; in practice, the algorithm can be accelerated by a parallel method.
Referring to FIG. 4, it can be seen that the above algorithm is designed with a high degree of parallelism, and the parallelism of the present algorithm is described below; according to the performance of a machine used by an operating program, setting a total of N +1 processes: process0, Process1, Process n; the Process0 is set as a main Process, and the main Process is used for collecting evaluation function values of all individuals, naturally selecting, evolving and sending all new chromosomes to other subprocesses; in each generation, the master process sends (PopulationSize-parentSize)/(N) to each child processprocess-1) new individuals, then each individual calculates an evaluation function for the received individuals and sends the evaluation function to the main process, and then the main process performs operations such as selection and evolution; it can be seen that this parallel design greatly reduces the time that the program spends evaluating functions for each individual (in the non-parallel case, evaluating functions take a significant portion of the time).
The acceleration ratio of this parallel design is briefly analyzed below. Assume that the greedy algorithm is performed once at time tgreedyThen, in the non-parallel case, each generationIs about tgreedyX (PopulationSize-ParentSize); the acceleration ratio after parallel (i.e. a multiple of program acceleration) is about NprocessWhen there are enough calculation cores, the processing time of each generation can approach tgreedy。
Referring to FIG. 5, in step S04, the parallelization technique employs a greedy algorithm; the greedy algorithm is used for converting the sequence of the flights into a specific schedule of the flights;
referring to FIG. 2, the greedy algorithm includes the following steps:
t01 records the flight taking off as occupying runway and air resources in the chromosome coding;
t02 when the new flight inserts the chromosome code, according to the existing runway and the air flight and the new flight's own constraint conditions, choose the flight earliest time of takeoff;
t03 determining flight landing time according to the earliest takeoff time;
t04 is processing the departure time of the next flight in the chromosome coding sequence.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (3)
1. A method for allocating airspace flow is characterized by comprising the following steps:
step S01, obtaining basic data of flight, airport and air state;
step S02, establishing an integer programming model of the ATS problem, and determining a target function;
step S03, solving the established model by using a genetic algorithm;
step S04, accelerating the genetic algorithm by using a parallel technology;
in step S03, the genetic algorithm uses non-binary chromosome coding; the chromosome coding sets each individual to be of length nfConverting the flight sequence into a schedule, and calculating an evaluation function of the schedule, wherein the evaluation function calculation formula is as follows:
wherein, a is more than 2 and less than 3, tfThe time to land is scheduled for the flight,
for the actual time of flight to land,
the fluctuation condition of the original sequence of the flight is taken as the fluctuation condition;
in step S04, the parallel technique uses a greedy algorithm; the greedy algorithm is used for converting the sequence of the flights into a specific schedule of the flights; the greedy algorithm comprises the following specific steps:
t01 records the flight taking off as occupying runway and air resources in the chromosome coding;
t02 when the new flight inserts the chromosome code, according to the existing runway and the air flight and the new flight's own constraint conditions, choose the flight earliest time of takeoff;
t03 determining flight landing time according to the earliest takeoff time;
t04 is processing the departure time of the next flight in the chromosome coding sequence.
2. The method of allocating airspace flow according to claim 1, wherein in step S02, building a model requires slicing the time to be decided so as to satisfy each constraint condition.
3. The spatial domain flow dispatching method according to claim 2, wherein the constraint condition comprises:
restraining one: the time interval constraint on the runway is used for constraining the time difference of take-off or landing of two adjacent flights on the same runway;
and (2) constraining: the time interval constraint on the waypoints is used for carrying out time interval constraint on flights flying to the same waypoint;
and (3) constraining: capacity constraint between the waypoints and the airport, which is used for carrying out capacity constraint on flights on the airline;
and (4) constraining: sector capacity constraints;
and (5) constraining: flight landing time constraints.
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| US8504281B2 (en) * | 2010-04-09 | 2013-08-06 | Metron Aviation, Inc. | Method and system for flight substitution and reroute |
| CN105489066A (en) * | 2014-09-16 | 2016-04-13 | 北京航空航天大学 | Air traffic flow regulation and control method |
| CN106297414A (en) * | 2015-06-05 | 2017-01-04 | 北京航空航天大学 | The regulation and control method and apparatus of flight flow |
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