CN108630020A - A kind of spatial domain flow allocating method - Google Patents
A kind of spatial domain flow allocating method Download PDFInfo
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- CN108630020A CN108630020A CN201810437134.8A CN201810437134A CN108630020A CN 108630020 A CN108630020 A CN 108630020A CN 201810437134 A CN201810437134 A CN 201810437134A CN 108630020 A CN108630020 A CN 108630020A
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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
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Abstract
The invention discloses a kind of spatial domain flow allocating methods, are related to aviation scheduling field.The present invention includes the following steps：Step S01 obtains the master data of flight, airport and airborne status；Step S02 establishes the integer programming model of spatial domain flow allocating (Air Traffic Scheduling, ATS) problem, determines object function；Step S03 is using genetic algorithm to the model solution of foundation；Step S04 accelerates genetic algorithm using concurrent technique.The present invention is by obtaining flight and aviation master data, establish the integer programming model of aviation scheduling, determine various parameters in object function, realize that the flight in airport carries out landing management using genetic algorithm mechanism and parallel acceleration technique, under the premise of meeting aviation scheduling Complex Constraints condition, flight management cost is reduced, the efficiency of management is improved, effectively slows down the loss that the factors such as flight delay are brought.
Description
Technical field
The invention belongs to aviation scheduling fields, more particularly to a kind of spatial domain flow allocating method, for realizing the short time
Flight in interior multimachine field carries out landing management.
Background technology
In aviation scheduling field, the scheduling of the aircraft progress landing to multiple airports and spatial domain within a certain period of time is needed.
When carrying out aviation scheduling, need to consider ground and aerial numerous constraintss.Meeting these about
Under conditions of beam, the loss that reasons are brought will be delayed etc. because of flight by, which needing, is minimized.Due to the complex nature of the problem, solve this kind of
Algorithm be difficult that a preferable solution is obtained within the shorter time.
Invention content
The purpose of the present invention is to provide a kind of spatial domain flow allocating methods, by obtaining flight and aviation master data,
The integer programming model for establishing aviation scheduling, determines various parameters in object function, using genetic algorithm mechanism and it is parallel plus
Fast technology realizes that the flight in airport carries out landing management, and it is numerous to solve existing flight management constraints, management cost
High, complex management and the low problem of efficiency.
In order to solve the above technical problems, the present invention is achieved by the following technical solutions：
The present invention is a kind of spatial domain flow allocating method, is included the following steps：
Step S01 obtains the master data of flight, airport and airborne status；
Step S02 establishes the integer programming model of ATS problems, determines object function；
Step S03 is using genetic algorithm to the model solution of foundation；
Step S04 accelerates genetic algorithm using concurrent technique.
Preferably, in the step S02, establishing model needs will wait for that the decision-making time carries out fragment processing, to meet items
Constraints.
Preferably, the constraints includes：
Constraint one：Time interval constraint on runway, for same runway, adjacent two flight takes off or lands
Time difference constrained；
Constraint two：Time interval constraint on way point carries out time interval for the flight to the same way point that flies to
Constraint；
Constraint three：Capacity-constrained between way point, airport, for carrying out capacity-constrained to the flight on course line；
Constraint four：Sector capacity constrains；
Constraint five：The flight landing time constrains.
Preferably, in the step S03, genetic algorithm uses the chromosome coding of nonbinary；The chromosome coding
It is n that each individual, which is set as length,fInteger array, by flight sequential conversions at timetable, then calculate the evaluation of the timetable
Function, the evaluation function calculation formula are：
Wherein, the value range of a is 2 ＜ a ＜ 3, tfFor the flight planning landing time,For flight Actual Time Of Fall,For the fluctuation situation of flight original series.
Preferably, in the step S04, concurrent technique uses greedy algorithm；The greedy algorithm is used for the suitable of flight
Sequence is converted to the specific timetable of flight；The greedy algorithm is as follows：
The flight to take off is recorded as occupying runway and air resource in chromosome coding by T01；
T02 is when new flight is inserted into chromosome coding, according to the pact of existing runway and aerial flight and new flight itself
Beam condition selects the flight earliest departure time；
T03 determines the flight landing time according to the earliest departure time；
The departure time of T04 next flights in handling chromosome coding sequence.
The invention has the advantages that：
The present invention establishes the integer programming model of aviation scheduling, determines target by obtaining flight and aviation master data
Various parameters in function realize that the flight in airport carries out landing management using genetic algorithm mechanism and parallel acceleration technique,
Under the premise of meeting aviation scheduling Complex Constraints condition, flight management cost is reduced, the efficiency of management is improved, effectively slows down
The loss that the factors such as flight delay are brought.
Certainly, it implements any of the products of the present invention and does not necessarily require achieving all the advantages described above at the same time.
Description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, will be described below to embodiment required
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is a kind of spatial domain flow allocating method and step figure of the present invention；
The step of Fig. 2 is greedy algorithm of the present invention is schemed；
Fig. 3 is genetic algorithm evolutionary operator schematic diagram of the present invention；
Fig. 4 is the schematic diagram that inventive algorithm accelerates parallel；
Fig. 5 is the schematic diagram of greedy algorithm of the present invention；
Fig. 6, Fig. 7 are parameters table provided by the invention.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained all other without creative efforts
Embodiment shall fall within the protection scope of the present invention.
Refering to Figure 1, the present invention is a kind of spatial domain flow allocating method, include the following steps：
Step S01 obtains the master data of flight, airport and airborne status；
Step S02 establishes the integer programming model of ATS problems, determines object function；
Step S03 is using genetic algorithm to the model solution of foundation；
Step S04 accelerates genetic algorithm using concurrent technique.
Wherein, in step S02, establishing model needs will wait for that the decision-making time carries out fragment processing, to meet each item constraint item
Part.
It please refers to shown in Fig. 6-7, constraints includes：
Constraint one：Time interval constraint on runway, for same runway, adjacent two flight takes off or lands
Time difference constrained, formula is：
Constraint two：Time interval constraint on way point carries out time interval for the flight to the same way point that flies to
Constraint, formula are：
Ap(f,f′)≤g(f,f)+wp；f,f′·M≤M-Ap(f,f′)；
Constraint three：Capacity-constrained between way point, airport, for carrying out capacity-constrained, formula to the flight on course line
For：
Constraint four：Sector capacity constrains, and formula is：
Constraint five：The flight landing time constrains.
Wherein, in step S03, genetic algorithm uses the chromosome coding of nonbinary；Chromosome coding will each individual
It is n to be set as lengthfInteger array, by flight sequential conversions at timetable, then calculate the evaluation function of the timetable, evaluate letter
Counting calculation formula is：
Wherein, the value range of a is 2 ＜ a ＜ 3, tfFor the flight planning landing time,For flight Actual Time Of Fall,For the fluctuation situation of flight original series；
The evolutionary operator for please referring to the genetic algorithm application shown in Fig. 3, applied in the embodiment of the present invention is as follows；Evolutionary operator
Here mutation operator is only chosen, there are five types of operations：(1) sequence of any two flight in sequence swaps；(2) sequence
One flight is inserted among any position of sequence；(3) a cross-talk sequence is intercepted in former sequence, and this subsequence is heavy
New arrangement；(4) two cross-talk sequences are intercepted in former sequence, this two cross-talks sequence is exchanged into position；(5) by a cross-talk sequence inverted sequence
Arrangement；In these evolutional operations, whole sequence can both be carried out, it can also be to the sequence on runway or certain way points
It is ranked up；Time interval constrains influence bigger of the more stringent way point to final ranking results, therefore has higher preferential
Grade.
In the natural selection stage, due to the negligible amounts of population, therefore use selection evaluation function more small
(PopulationSize-parentSize) a individual；It can obtain compared to simple greedy algorithm with this method more
Good result；The algorithm can be accelerated using parallel method in practical operation.
Please refer to shown in Fig. 4, it can be seen that algorithm above is designed with the concurrency of height, be described below this algorithm and
Row；According to the performance of machine used in operation program, if a shared N+1 process：Process0,Process1,ProcessN；
Here Process0 is set as to be host process, the effect of host process be the evaluation function value for collecting all individuals, natural selection, into
Change and all new chromosome is issued into other several points of processes；In each generation, host process is sent into each point of process
(PopulationSize-parentSize)/(Nprocess- 1) a new individual, then each individual seeks the individual received
Go out evaluation function, and be sent to host process, then host process such as is selected and evolved the operations；As can be seen that this parallel
Design, which greatly reduces program and is used in, asks the time of evaluation function (in the case of non-parallel, evaluation function to be asked to account for each individual
With most times).
The speed-up ratio of this Parallel Design of simple analysis below.Assuming that the time for carrying out a greedy algorithm is tgreedy, that
In the case that non-parallel, the operation time per a generation is about tgreedy×(PopulationSize-ParentSize)；Parallel
Speed-up ratio (multiple that i.e. program accelerates) afterwards is about Nprocess, when there is enough calculating check figures, often when the processing of a generation
Between be close to tgreedy。
It please refers to shown in Fig. 5, in step S04, concurrent technique uses greedy algorithm；Greedy algorithm is used for the sequence of flight
It is converted to the specific timetable of flight；
It please refers to shown in Fig. 2, greedy algorithm is as follows：
The flight to take off is recorded as occupying runway and air resource in chromosome coding by T01；
T02 is when new flight is inserted into chromosome coding, according to the pact of existing runway and aerial flight and new flight itself
Beam condition selects the flight earliest departure time；
T03 determines the flight landing time according to the earliest departure time；
The departure time of T04 next flights in handling chromosome coding sequence.
It is worth noting that, in above system embodiment, included each unit is only drawn according to function logic
Point, but it is not limited to above-mentioned division, as long as corresponding function can be realized；In addition, each functional unit is specific
Title is also only to facilitate mutually distinguish, the protection domain being not intended to restrict the invention.
In addition, one of ordinary skill in the art will appreciate that realizing all or part of step in the various embodiments described above method
It is that relevant hardware can be instructed to complete by program, corresponding program can be stored in a computer-readable storage and be situated between
In matter.
Present invention disclosed above preferred embodiment is only intended to help to illustrate the present invention.There is no detailed for preferred embodiment
All details are described, are not limited the invention to the specific embodiments described.Obviously, according to the content of this specification,
It can make many modifications and variations.These embodiments are chosen and specifically described to this specification, is in order to preferably explain the present invention
Principle and practical application, to enable skilled artisan to be best understood by and utilize the present invention.The present invention is only
It is limited by claims and its full scope and equivalent.
Claims (5)
1. a kind of spatial domain flow allocating method, which is characterized in that include the following steps：
Step S01 obtains the master data of flight, airport and airborne status；
Step S02 establishes the integer programming model of ATS problems, determines object function；
Step S03 is using genetic algorithm to the model solution of foundation；
Step S04 accelerates genetic algorithm using concurrent technique.
2. a kind of spatial domain flow allocating method according to claim 1, which is characterized in that in the step S02, establish mould
Type needs will wait for that the decision-making time carries out fragment processing, to meet every constraints.
3. a kind of spatial domain flow allocating method according to claim 2, which is characterized in that the constraints includes：
Constraint one：Time interval constraint on runway, for same runway, adjacent two flight take off or land when
Between difference constrained；
Constraint two：Time interval constraint on way point carries out time interval constraint for the flight to the same way point that flies to；
Constraint three：Capacity-constrained between way point, airport, for carrying out capacity-constrained to the flight on course line；
Constraint four：Sector capacity constrains；
Constraint five：The flight landing time constrains.
4. a kind of spatial domain flow allocating method according to claim 1, which is characterized in that in the step S03, heredity is calculated
Method uses the chromosome coding of nonbinary；It is n that each individual is set as length by the chromosome codingfInteger array, will
Flight sequential conversions are at timetable, then calculate the evaluation function of the timetable, and the evaluation function calculation formula is：
Wherein, the value range of a is 2 ＜ a ＜ 3, tfFor the flight planning landing time,For flight Actual Time Of Fall,
For the fluctuation situation of flight original series.
5. a kind of spatial domain flow allocating method according to claim 1, which is characterized in that in the step S04, parallel skill
Art uses greedy algorithm；The greedy algorithm is used for the sequential conversions of flight into the specific timetable of flight；The greedy calculation
Method is as follows：
The flight to take off is recorded as occupying runway and air resource in chromosome coding by T01；
T02 is when new flight is inserted into chromosome coding, according to the constraint item of existing runway and aerial flight and new flight itself
Part selects the flight earliest departure time；
T03 determines the flight landing time according to the earliest departure time；
The departure time of T04 next flights in handling chromosome coding sequence.
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Cited By (1)
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| CN114330086A (en) * | 2022-03-09 | 2022-04-12 | 北京航空航天大学 | Large-scale flight emergency scheduling method under emergency |
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| CN114330086B (en) * | 2022-03-09 | 2022-05-27 | 北京航空航天大学 | Large-scale flight emergency scheduling method under emergency |
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