CN113780615B - Tubular airway time-varying network design method based on distribution robust optimization - Google Patents

Tubular airway time-varying network design method based on distribution robust optimization Download PDF

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CN113780615B
CN113780615B CN202110361963.4A CN202110361963A CN113780615B CN 113780615 B CN113780615 B CN 113780615B CN 202110361963 A CN202110361963 A CN 202110361963A CN 113780615 B CN113780615 B CN 113780615B
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叶博嘉
陈刚
蒋兵
田勇
姚虹翔
李海凉
倪超
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EASTERN CHINA AIR TRAFFIC MANAGEMENT BUREAU CAAC
Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a design method of a tubular airway time-varying network based on distributed robust optimization, which comprises the steps of firstly constructing a directed graph system according to historical flight operation data of the tubular airway layout; then, taking the state of each section of the managed navigation path in each period as a decision variable, taking the service benefit and the efficiency of the managed navigation path as an objective function, and setting constraint conditions to construct a multi-objective combined optimization model of the managed navigation path time-varying network; and finally, constructing a distributed uncertainty set by adopting a fitting and chi-square test method according to the change of the air traffic demand, and solving by adopting an NSGA-II algorithm by adopting a distributed robust optimization method to obtain a time-varying network of the tubular route. The invention can fully exert the flexibility characteristics and advantages of the tubular navigation path, effectively improve the use efficiency of the tubular navigation path and save the use cost of the tubular navigation path.

Description

Tubular airway time-varying network design method based on distribution robust optimization
Technical Field
The invention relates to a management type airway time-varying network design method based on distributed robust optimization, and belongs to the technical field of management type airways.
Background
In recent years, with the continuous high-speed development of the air transportation industry, the situation of shortage of civil aviation space has already presented a trend from point to point. The countries are actively exploring new concepts, new ideas and new methods of upgrading and changing air management systems. Under the background of the increasingly mature new navigation technology, the aeronautical developed countries such as Europe and America first propose a tubular navigation operation concept with the brand-new characteristics of large capacity, independent interval, flexibility and the like.
In terms of the operation efficiency of the tubular course design, especially in terms of the dynamic use of the novel characteristics thereof, there are significant shortcomings and gaps in the related theoretical methods, which are also key to whether the tubular course concept can be ultimately implemented on the ground. Because the flight operation is greatly influenced by external factors and has stronger uncertainty, robust optimization is also required to be added when researching the construction method of the managed navigation time-varying network.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the design method of the tubular airway time-varying network based on the distribution robust optimization can effectively exert the flexibility characteristics and advantages of the tubular airway, promote the utilization rate of the tubular airway, and provide a part of theoretical basis for the final landing implementation of the tubular airway concept, so that the current flight congestion condition is relieved.
The invention adopts the following technical scheme for solving the technical problems:
a design method of a tubular navigation time-varying network based on distributed robust optimization comprises the following steps:
step 1, constructing a managed airway network directed graph G= (N, A) according to the layout of the managed airway network, wherein N represents a set of nodes of the managed airway network, and A represents a set of air segments with interconnected nodes;
step 2, screening out the departure airport and the arrival airport which are both in the tubular airway network according to the flight operation data of the time period to be designedIs used for constructing a flight path directed graph G of each flight f ＝(N f ,A f ) Wherein N is f Representing the set of nodes through which the flight f is flying,A f set of legs representing flight f flying through, < +.>
Step 3, taking the state of each navigation segment in the managed navigation network in each time segment as a decision variable, and constructing an objective function and constraint conditions of a multi-objective combined optimization model of the managed navigation time-varying network;
and 4, optimizing an objective function of the tubular model airway time-varying network multi-objective combination optimization model by a distributed robust optimization method, and solving the optimized objective function by adopting a rapid non-dominant multi-objective optimization algorithm NSGA-II to obtain the tubular model airway time-varying network of the time period to be designed.
As a preferred scheme of the present invention, the specific content of the step 3 is as follows:
3.1, using the state of each navigation segment in each time segment in the managed navigation networkAs decision variable +_>A variable of 0-1, 1 represents activation, 0 represents closing, m, N represents nodes at two ends of the leg, m, N epsilon N f T represents a time slice, < >> Representing a set of time segments;
3.2, constructing an objective function o of mutual restriction 1 (mu) and o 2 (μ)，o 1 (mu) represents the number of flights that the managed airway network can serve, o 2 (mu) represents the average occupancy of the tubular airway network;
the objective function of the managed airway time-varying network multi-objective combination optimization model is: max (max) μ <o 1 (μ),o 2 (μ)>；
Wherein,representing the amount of flights already present in the leg (m, n) at the beginning of the t-th time segment,/for>Representing the amount of flights entering the leg (m, n) at the t-th time segment, ρ representing a penalty factor for flights flying off the tube due to the deactivation of a certain time segment;
3.3, constructing constraint conditions of a multi-objective combined optimization model of the tubular navigation time-varying network as follows:
wherein,indicating the amount of flights flying away from the flight segment (m, n) at the t-th time segment.
As a preferred embodiment of the present invention, the specific content of the step 4 is as follows:
4.1, set upThe random vector of air traffic demand changes for the t-th time segment leg (m, n) is adjusted to:
the objective function after the optimization of the managed airway time-varying network multi-objective combination optimization model is as follows:
wherein,represents the air traffic demand variation of the t-th time segment (m, n), +.>Representing the expected value under the candidate distribution D, Ω (q) representing the distributed uncertainty set, ++>Representing the amount of flights already present in the leg (m, n) at the beginning of the t-th time segment,/for>Representing the number of flights entering the leg (m, n) at the t-th time segment, ρ representing the penalty factor for flight departure from the tubular course due to the deactivation of a certain time segment,/">Representing the status of each leg in each time segment in the managed airway network, +.> Representing a set of time segments;
4.2, setting q to obey discrete distributionAnd the distribution of each time segment +.>Is independent, given->Single variable probability density function P of (2) 0,i Giving the null hypothesis +.>Carrying out chi-square test on the null hypothesis to obtain discrete distribution +.>Comparing all the confidence degrees with the corresponding confidence degrees, and taking the discrete distribution corresponding to the highest confidence degree as a candidate distribution D;
and 4.3, solving the optimized objective function by adopting a rapid non-dominant multi-objective optimization algorithm NSGA-II to obtain the tubular airway time-varying network of the time period to be designed.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
the design method of the tubular airway time-varying network based on the distributed robust optimization can effectively exert the flexibility characteristics and advantages of the tubular airway, promote the utilization rate of the tubular airway, effectively improve the stability and the applicability of the tubular airway time-varying network through the robust optimization, provide a part of theoretical basis for the final landing implementation of the tubular airway concept, and effectively relieve the problem of serious flight delay at present.
Drawings
FIG. 1 is a flow chart of a managed airway time-varying network design method based on distributed robust optimization of the present invention.
FIG. 2 is a diagram of an example of a managed airway network layout in accordance with an embodiment of the present invention.
FIG. 3 is a distributed uncertainty set for different time periods for an embodiment of the invention.
FIG. 4 is a managed airway time varying network in accordance with an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As shown in FIG. 1, the invention provides a tubular navigation time-varying network design method based on distributed robust optimization, which comprises the following specific steps:
step one: and constructing a managed airway network directed graph G= (N, A) according to the managed airway layout, wherein N represents a set of managed airway nodes and A represents a set of airway segments with interconnected nodes.
Step two: constructing a flight path directed graph G of a flight f according to a flight plan f ＝(N f ,A f ) Wherein, the method comprises the steps of, wherein,node set representing flight f flying through, +.>Representing the set of legs through which the flight f is flying.
Step three: determining decision variables, constructing objective functions and constraint conditions which are mutually restricted, and providing a multi-objective combined optimization model of the tubular navigation time-varying network.
Step four: and constructing a distributed uncertainty set, and solving by adopting an NSGA-II algorithm through a distributed robust optimization method to obtain a time-varying network of the tubular navigation.
An embodiment will be described in detail below.
Step one: taking the tubular route layout as shown in fig. 2 as an example, the route segments in the figure all represent great circle tracks among nodes, a tubular route network directed graph g= (N, a) is constructed, wherein N represents a set of tubular route nodes, a represents a set of route segments with interconnected nodes, and the total number of the route segments comprises 17 nodes and 40 route segments, wherein ZBAA nodes comprise ZBAA and ZBTJ airports, ZSSS nodes comprise ZSSS, ZSPD, ZSHC airports, ZGGG nodes comprise ZGGG and ZGSZ airports, ZJHK airports comprise ZJHK and ZJSY airports, and the rest nodes all represent single airports.
Step two: the flight operation data of the four sides before 3 months in the autumn and summer air season in 2017 are adopted to screen out flights in which a take-off airport and an arrival airport are both in a tubular air route network as flights capable of using tubular air routes, and a flight path directed graph G of the flights f is constructed f ＝(N f ,A f ) Wherein, the method comprises the steps of, wherein,node set representing flight f flying through, +.>Representing the set of legs through which the flight f is flying.
Step three: (3.1) first, the assumption of constructing a tube-type course time-varying network multi-objective combination optimization model is as follows: (1) The capacity of the managed way is sufficient (which can be achieved by extending the number of channels horizontally), all flights can be kept spaced autonomously within the managed way, and flight conflicts caused by intersections in the managed way network are not considered. (2) Considering that the minimum number of flights per day is about 4:00 a.m., in order to divide flights as little as possible into two different operation days, one operation cycle is set to be 4:00 a.m. to 4:00 a.m. the next day. (3) In order to ensure the flexibility of the use of each leg and improve the use benefit, all the legs can be activated and closed for a plurality of times according to the flight requirement in one operation period, and the minimum activation period is 1 hour. (4) There is a correlation in flight demand between the legs, but there is no interaction in operation. (5) When the managed air route is not activated, the flights entering the managed air route need to immediately exit the managed air route, enter the traditional control sector and air route, and the number of flights in the managed air route is cleared; when the managed way continues to be activated, the flight may continue into the managed way.
Decision variable for constructing multi-objective combined optimization model of managed type navigation path time-varying networkThe state of each leg of the tubular route in each period is set to 0-1 variable (1 represents activation and 0 represents closing), wherein m, N epsilon N f Representing the flight node,representing a time slice,/->Representing a collection of time slices.
(3.2) constructing a multi-objective function o of a multi-objective combined optimization model of the tubular airway time-varying network 1 (mu) and o 2 (μ)，o 1 (mu) represents the number of flights (units: frames) that the managed airway network can serve, o 2 And (mu) represents the average occupancy rate (unit: number of frames per hour) of the tubular route, and the two objective functions have a mutually restricted relationship, and the specific expression is as follows:
thereby determining the final objective function of the multi-objective combined optimization model of the managed navigation time-varying network as follows: max (max) μ <o 1 (μ),o 2 (μ)>；
Wherein,representing the amount of flights already present in the tubular route (m, n) at the beginning of the t-th period,/->Representing the amount of flights entering the managed way (m, n) during period t, ρ represents a penalty factor for flights flying off the managed way due to the deactivation of a certain period of time.
(3.3) constructing constraint conditions of a multi-objective combined optimization model of the managed channel time-varying network:
constraint 1:
constraint 2:
constraint 3:
constraint 4:
constraint 5:
wherein,indicating the amount of flights that fly off the managed way (m, n) during period t. Constraint 1 limits the number relationship of existing flights, incoming flights and outgoing flights, constraints 2, 3 and 4 limit the number of existing flights, incoming flights and outgoing flights to 0 or more, and constraint 5 limits the decision variable to 0-1 variable.
Step four: and (4.1) according to the historical data of different operation days, the uncertainty requirement of each navigation segment in different time periods can be calculated. Setting upVector of air traffic demand change for the t-th time period managed way (m, n) to adjust the objective function as:
thereby, the final objective function is adjusted toMaking it robust.
Wherein,represents the air traffic demand variation of the t-th time period tubular route (m, n),/>Representing the expected value under the candidate distribution D, Ω (q) represents a distributed uncertainty set.
(4.2) suppose q obeys an unknown discrete distributionAnd the marginal distribution of each time period +.>Is independent. Given a discrete univariate distribution P 0,i Including single point distribution, bernoulli distribution, binomial distribution, poisson distribution, geometric distribution, hypergeometric distribution, pascal distribution, and negative binomial distribution, giving the null hypothesis +.>And (5) carrying out chi-square test, and marking the discrete distribution with the highest confidence as a candidate distribution D. Taking ZBAA-ZSSS leg as an example, a distributed uncertainty set (with 80% confidence) of air traffic demand changes for different time periods is calculated as shown in fig. 3.
(4.3) solving the above model by using a python language to write an NSGA-II algorithm, wherein the specific parameters are set as follows: the population scale is 500, the termination algebra is 1500, the crossover probability is 0.8, the mutation probability is 0.02, 8 optimal chromosomes are reserved for each generation by elite strategy, and the linear recombination crossover and random factor mutation rule is adopted in genetic operation. And obtaining the tubular navigation time-varying network after solving, as shown in figure 4.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereto, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.

Claims (2)

1. A design method of a tubular airway time-varying network based on distribution robust optimization is characterized by comprising the following steps:
step 1, constructing a managed airway network directed graph G= (N, A) according to the layout of the managed airway network, wherein N represents a set of nodes of the managed airway network, and A represents a set of air segments with interconnected nodes;
step 2, screening out flight operation data of a time period to be designed according to the flight operation dataFlights of take-off airport and arrival airport in managed airway network, and constructing a flight path directed graph G of each flight f ＝(N f ,A f ) Wherein N is f Representing the set of nodes through which the flight f is flying,A f set of legs representing flight f flying through, < +.>
Step 3, taking the state of each navigation segment in the managed navigation network in each time segment as a decision variable, and constructing an objective function and constraint conditions of a multi-objective combined optimization model of the managed navigation time-varying network; the specific contents are as follows:
3.1, using the state of each navigation segment in each time segment in the managed navigation networkAs decision variable +_>A variable of 0-1, 1 represents activation, 0 represents closing, m, N represents nodes at two ends of the leg, m, N epsilon N f T represents a time slice, < >> Representing a set of time segments;
3.2, constructing an objective function o of mutual restriction 1 (mu) and o 2 (μ)，o 1 (mu) represents the number of flights that the managed airway network can serve, o 2 (mu) represents the average occupancy of the tubular airway network;
the objective function of the managed airway time-varying network multi-objective combination optimization model is: max (max) μ <o 1 (μ),o 2 (μ)>；
Wherein,representing the amount of flights already present in the leg (m, n) at the beginning of the t-th time segment,/for>Representing the amount of flights entering the leg (m, n) at the t-th time segment, ρ representing a penalty factor for flights flying off the tube due to the deactivation of a certain time segment;
3.3, constructing constraint conditions of a multi-objective combined optimization model of the tubular navigation time-varying network as follows:
wherein,representing the amount of flights flying away from the flight segment (m, n) at the t-th time segment;
and 4, optimizing an objective function of the tubular model airway time-varying network multi-objective combination optimization model by a distributed robust optimization method, and solving the optimized objective function by adopting a rapid non-dominant multi-objective optimization algorithm NSGA-II to obtain the tubular model airway time-varying network of the time period to be designed.
2. The method for designing a tubular airway time-varying network based on distributed robust optimization according to claim 1, wherein the specific contents of the step 4 are as follows:
4.1, set upThe random vector of air traffic demand changes for the t-th time segment leg (m, n) is adjusted to:
the objective function after the optimization of the managed airway time-varying network multi-objective combination optimization model is as follows:
wherein,represents the air traffic demand variation of the t-th time segment (m, n), +.>Representing the expected value under the candidate distribution D, Ω (q) representing a distributed uncertainty set;
4.2, setting q to obey discrete distributionAnd the distribution of each time segment +.>Is independent, given->Single variable probability density function P of (2) 0,i Giving the null hypothesis +.>Carrying out chi-square test on the null hypothesis to obtain discrete distribution +.>Comparing all the confidence degrees with the corresponding confidence degrees, and taking the discrete distribution corresponding to the highest confidence degree as a candidate distribution D;
and 4.3, solving the optimized objective function by adopting a rapid non-dominant multi-objective optimization algorithm NSGA-II to obtain the tubular airway time-varying network of the time period to be designed.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107025806A (en) * 2017-05-11 2017-08-08 中国电子科技集团公司第二十八研究所 A kind of single phase interim flight path robust Optimal methods
CN112257898A (en) * 2020-09-18 2021-01-22 南京航空航天大学 Pipe-type route network dynamic design and dynamic use method based on delay
JP2021034059A (en) * 2019-08-27 2021-03-01 南京航空航天大学 Method for measuring airport flight waveform similarity based on trend distance and spectral clustering
CN112489498A (en) * 2020-11-25 2021-03-12 中国电子科技集团公司第二十八研究所 Fine route change planning method for route traffic

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107025806A (en) * 2017-05-11 2017-08-08 中国电子科技集团公司第二十八研究所 A kind of single phase interim flight path robust Optimal methods
JP2021034059A (en) * 2019-08-27 2021-03-01 南京航空航天大学 Method for measuring airport flight waveform similarity based on trend distance and spectral clustering
CN112257898A (en) * 2020-09-18 2021-01-22 南京航空航天大学 Pipe-type route network dynamic design and dynamic use method based on delay
CN112489498A (en) * 2020-11-25 2021-03-12 中国电子科技集团公司第二十八研究所 Fine route change planning method for route traffic

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