CN111582592B - Regional airport group navigation line network optimization method - Google Patents

Regional airport group navigation line network optimization method Download PDF

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CN111582592B
CN111582592B CN202010397971.XA CN202010397971A CN111582592B CN 111582592 B CN111582592 B CN 111582592B CN 202010397971 A CN202010397971 A CN 202010397971A CN 111582592 B CN111582592 B CN 111582592B
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陈欣
毛亿
盛寅
宣超
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Nanjing University of Finance and Economics
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Abstract

The invention discloses a regional airport group navigation line network optimization method. A regional airport group navigation network is optimized by constructing a nonlinear double-layer planning model, wherein the upper-layer planning model takes the minimum passenger utility loss as an objective function, and the lower-layer planning takes the maximum airline profit as an objective function. The method can be matched with regional aviation requirements, considers constraint conditions such as capacity limit of hub airports, utilization rate of medium and small airports and the like, reduces the airline network isomorphism of airport groups by reasonably setting the organization structure of the regional airline network, is beneficial to realizing maximization of related benefits of passengers and airliners, improves system operation efficiency and reduces system internal consumption.

Description

Regional airport group navigation line network optimization method
Technical Field
The invention relates to a regional airport group navigation line network optimization method, and belongs to the aviation planning technology.
Background
From the view of the airport group, the isomorphism problem of the regional airline network is more prominent, which not only causes the waste of regional airline resources, but also aggravates the disordered competition inside the airport group. Meanwhile, with the continuous improvement of the ground traffic network (particularly the intercity railway) in the airport group, the mutual overlapping degree of the ventral region of each submachine field is increasingly enlarged, which creates conditions for the mutual flow of the aviation requirements among the airports in the region, and makes the airport group as a unit to comprehensively optimize the aviation network structure have a practical foundation.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a regional airport group navigation network optimization method, which solves the problem of airport group navigation network optimization by constructing a nonlinear double-layer planning model and takes an airport group as a unit to comprehensively optimize a navigation network structure.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
the optimization of the airport group navigation network structure aims to ensure that the airport group navigation network structure is matched with regional aviation requirements, constraint conditions such as junction airport capacity limit, medium and small airport utilization rate and the like are considered, and the navigation network isomorphism of the airport group is reduced by reasonably setting the organization structure of the regional navigation network, so that the aim of maximizing related benefits of passengers and airliners is fulfilled, and the optimal navigation network layout form of the system is searched, thereby improving the operation efficiency of the system and reducing the internal consumption of the system.
A regional airport group navigation network optimization method is characterized in that a nonlinear double-layer planning model is constructed to optimize a regional airport group navigation network, the upper-layer planning model takes the minimum passenger utility loss as an objective function, and the lower-layer planning takes the maximum airline profit as an objective function.
Generally, the airport group within 2 hours of ground traffic is called an airport group. Typical airport groups in China include Long triangular airport group, Pearl triangular airport group and Jingjin Ji airport group; of course, we can also refer to some other factors, and use some common features as classification criteria to establish airport groups, but the most important reference factor is also the ground traffic service level.
Specifically, the objective function of the upper layer planning model is as follows:
Figure BDA0002488377890000011
wherein: OD pairs represent aviation demand origin-destination pairs, omega represents a set of OD pairs, and K exists between OD pairs ijijA route, ij is belonged to omega, K is belonged to Kij；
Figure BDA0002488377890000021
Represents the passenger flow of the k route between OD and ij (
Figure BDA0002488377890000022
The passenger flow of the k-th route is zero, namely the k-th route is not opened,
Figure BDA0002488377890000023
representing a travel utility function of a k route between OD and ij, representing a preset parameter theta, and belonging to theta (0, 1)]；
The constraint conditions of the upper layer planning model are as follows:
(1) the total passenger flow of all air route flows between the OD and the ij is equal to the total aviation demand
Figure BDA0002488377890000024
Wherein: xijRepresenting the total aviation demand of OD to ij;
(2) firstnThe passenger flow of each air route is less than or equal to the number of seats available of an airline company n
Figure BDA0002488377890000025
Wherein:
Figure BDA0002488377890000026
denotes the lnThe volume of the passenger traffic of the airline,
Figure BDA0002488377890000027
denotes the lnThe number of seats available on a single route line,
Figure BDA0002488377890000028
denotes the lnFlight frequency on each air route is determined by lower-layer planning;
the total number of the airlines is N, and N belongs to N; i isnAll the number of airlines representing the operation of the airline n;
Figure BDA0002488377890000029
indicating that the k-th route between OD and ij is opened by the airline company n, otherwise
Figure BDA00024883778900000210
Indicates that the kth route between OD and ij is not opened by the airline company n, ln∈In。
Specifically, the objective function of the lower layer planning model is:
Figure BDA00024883778900000211
wherein:
Figure BDA00024883778900000212
represents the price of the ticket for the kth route between OD and ij,
Figure BDA00024883778900000213
represents the optimal passenger flow of the k route between the OD and the ij obtained in the upper planning,
Figure BDA00024883778900000214
indicating a route lnThe operating cost of (c);
the constraint conditions of the lower-layer planning model are as follows:
(1) firstnThe passenger flow of each air route is less than or equal to the number of seats available of an airline company n
Figure BDA00024883778900000215
(2) The total number of routes for airline n at airport a ∈ A cannot be greater than the capacity allocated to it by airport a
Figure BDA0002488377890000031
Figure BDA0002488377890000032
Wherein:
Figure BDA0002488377890000033
indicating airport a opennThe course of the flight is,
Figure BDA0002488377890000034
indicating that airport a is not open |nA route;
Figure BDA0002488377890000035
indicating assignment of airport a to airThe capacity of company n; the total number of airports contained in the airport group is a.
(3) FirstnThe frequency of the flights provided on the strip route is not less than the lower limit of the flight frequency provided by the civil aviation management department so as to meet the requirements of airport groups
Figure BDA0002488377890000036
Wherein: f. ofminRepresenting a lower limit of flight frequency;
the problem of optimizing the navigation network belongs to Nonlinear double level Programming (NBLP), and is a strong NP-hard problem. The current main solving algorithm comprises an accurate algorithm, a heuristic algorithm, a group intelligence algorithm and the like. Most of accurate algorithms are based on Kuhn-Tucker (K-T) conditions, objective functions or constraint conditions are required to be microminiature or convexity, the heuristic algorithm needs to convert the original problem, the structure is complex, the solving efficiency depends on specific problems, and the direct popularization and application are difficult. The group intelligence optimization algorithm has low requirements on functions and strong global search capability and is gradually used for solving the NBLP problem. The Particle Swarm Optimization (PSO) algorithm is an intelligent Optimization algorithm based on social group behaviors, is proposed for the first time in 1995 by Kennedy et al, and has attracted much attention in the solution of the NBLP problem in recent years. The invention solves the problem of navigation network optimization by designing a nested PSO algorithm, namely, the upper layer and the lower layer of a double-layer planning model are solved by adopting the PSO algorithm, and then a final solution is obtained by mutual nesting and layered iteration, and the method comprises the following steps:
step 1: initializing algorithm parameters: setting the total number of particles of the particle swarm as m; randomly generating an initial solution of an underlying model that satisfies a constraint
Figure BDA0002488377890000037
Initialize the ith e [1, m]Initial velocity V of individual particleiInitial position of ith particle
Figure BDA0002488377890000038
Initializing optimal solutions for underlying models
Figure BDA0002488377890000039
Optimum position of ith particle
Figure BDA00024883778900000310
Optimum position g of particle groupbest＝{pi_best}；
Step 2: calculating the optimal position p of the ith particlei_bestFitness of time, i.e. to be
Figure BDA00024883778900000311
The fitness pi (p) of the ith particle is calculated by being brought into an objective function of an upper layer planning modeli_best)；
And step 3: for all particles in the population, the following operations are performed:
(a) updating the position of the ith particle by using a PSO algorithm
Figure BDA0002488377890000041
And velocity Vi；
(b) Solving an objective function of the lower-layer planning model: will be provided with
Figure BDA0002488377890000042
The optimal solution of the lower-layer model is updated by utilizing a PSO algorithm in an objective function brought into the lower-layer planning model
Figure BDA0002488377890000043
(c) Will be provided with
Figure BDA0002488377890000044
The fitness of the ith particle is calculated by being brought into an objective function of an upper-layer planning model
Figure BDA0002488377890000045
(d) If it is not
Figure BDA0002488377890000046
Is superior to pi (p)i_best) Then update the optimal position of the ith particle to
Figure BDA0002488377890000047
Updating
Figure BDA0002488377890000048
Updating the optimal position g of the particle swarmbest(ii) a Otherwise, returning to the step (a) until all the particles are updated;
and 4, step 4: determine the optimal position gbestWhether the accuracy requirement is met: if yes, entering step 6; otherwise, entering step 5;
and 5: according to the formula gbest＝gbestX (1+ η × 0.5), η is an arbitrary number between 0 and 1; updating optimal solution f of lower layer model by PSO algorithmlnReturning to the step 3;
step 6: and outputting an optimization result, and finishing the algorithm.
Has the advantages that: the regional airport group network-of-flight optimization method provided by the invention can be matched with regional aviation requirements, and considers constraint conditions such as junction airport capacity limitation, utilization rate of medium and small airports, competitiveness of a high-speed rail network and the like, and through reasonably setting the organization structure of the regional network-of-flight, the network-of-flight isomorphism of the airport group is reduced, the maximization of related benefits of passengers and an airline company can be realized, the system operation efficiency is improved, and the internal consumption of the system is reduced.
Detailed Description
The present invention will be further described with reference to examples.
A regional airport group navigation network optimization method is characterized in that a nonlinear double-layer planning model is constructed to optimize a regional airport group navigation network, the upper-layer planning model takes the minimum passenger utility loss as an objective function, and the lower-layer planning takes the maximum airline profit as an objective function.
The objective function of the upper layer planning model is:
Figure BDA0002488377890000049
wherein: OD pairs represent aviation demand origin-destination pairs, omega represents a set of OD pairs, and K exists between OD pairs ijijA route, ij is belonged to omega, K is belonged to Kij；
Figure BDA0002488377890000051
Represents the passenger flow of the k route between OD and ij (
Figure BDA0002488377890000052
The passenger flow of the k-th route is zero, namely the k-th route is not opened,
Figure BDA0002488377890000053
representing a travel utility function of a k route between OD and ij, representing a preset parameter theta, and belonging to theta (0, 1)]；
The constraint conditions of the upper layer planning model are as follows:
(1) the total passenger flow of all air route flows between the OD and the ij is equal to the total aviation demand
Figure BDA0002488377890000054
Wherein: xijRepresenting the total aviation demand of OD to ij;
(2) firstnThe passenger flow of each air route is less than or equal to the number of seats available of an airline company n
Figure BDA0002488377890000055
Wherein:
Figure BDA0002488377890000056
denotes the lnThe volume of the passenger traffic of the airline,
Figure BDA0002488377890000057
denotes the lnThe number of seats available on a single route line,
Figure BDA0002488377890000058
denotes the lnFlight frequency on each air route is determined by lower-layer planning;
the total number of the airlines is N, and N belongs to N; i isnAll the number of airlines representing the operation of the airline n;
Figure BDA0002488377890000059
indicating that the k-th route between OD and ij is opened by the airline company n, otherwise
Figure BDA00024883778900000510
Indicates that the kth route between OD and ij is not opened by the airline company n, ln∈In。
The objective function of the lower layer planning model is:
Figure BDA00024883778900000511
wherein:
Figure BDA00024883778900000512
represents the price of the ticket for the kth route between OD and ij,
Figure BDA00024883778900000513
represents the optimal passenger flow of the k route between the OD and the ij obtained in the upper planning,
Figure BDA00024883778900000514
indicating a route lnThe operating cost of (c);
the constraint conditions of the lower-layer planning model are as follows:
(1) firstnThe passenger flow of each air route is less than or equal to the number of seats available of an airline company n
Figure BDA00024883778900000516
(2) The total number of routes for airline n at airport a ∈ A cannot be greater than the capacity allocated to it by airport a
Figure BDA00024883778900000515
Figure BDA0002488377890000061
Wherein:
Figure BDA0002488377890000062
indicating airport a opennThe course of the flight is,
Figure BDA0002488377890000063
indicating that airport a is not open |nA route;
Figure BDA0002488377890000064
represents the capacity allocated to airline n by airport a; the total number of airports contained in the airport group is a.
(3) FirstnThe frequency of the flights provided by the flight line is not less than the lower limit of the frequency of the flights so as to meet the requirements of airport groups
Figure BDA0002488377890000065
Wherein: f. ofminRepresenting a lower limit of flight frequency;
the scheme adopts a particle swarm optimization algorithm (PSO algorithm for short) to solve the double-layer planning model, and comprises the following steps:
step 1: initializing algorithm parameters: assuming that the total number of particles in the particle group is m, i belongs to [1, m ∈ ]](ii) a Randomly generating an initial solution of an underlying model that satisfies a constraint
Figure BDA0002488377890000066
Initializing the initial velocity V of the ith particleiInitial position of ith particle
Figure BDA0002488377890000067
Initializing optimal solutions for underlying models
Figure BDA0002488377890000068
Optimum position of ith particle
Figure BDA0002488377890000069
Optimum position g of particle groupbest＝{pi_best}；
Step 2: calculating the optimal position p of the ith particlei_bestFitness of time, i.e. to be
Figure BDA00024883778900000610
The fitness pi (p) of the ith particle is calculated by being brought into an objective function of an upper layer planning modeli_best)；
And step 3: for all particles in the population, the following operations are performed:
(a) updating the position of the ith particle by using a PSO algorithm
Figure BDA00024883778900000611
And velocity Vi；
(b) Solving an objective function of the lower-layer planning model: will be provided with
Figure BDA00024883778900000612
The optimal solution of the lower-layer model is updated by utilizing a PSO algorithm in an objective function brought into the lower-layer planning model
Figure BDA00024883778900000613
(c) Will be provided with
Figure BDA00024883778900000614
The fitness of the ith particle is calculated by being brought into an objective function of an upper-layer planning model
Figure BDA00024883778900000615
(d) If it is not
Figure BDA00024883778900000616
Is superior to pi (p)i_best) Then update the optimal position of the ith particle to
Figure BDA00024883778900000617
Updating
Figure BDA00024883778900000618
Updating the optimal position g of the particle swarmbest(ii) a Otherwise, returning to the step (a) until all the particles are updated;
and 4, step 4: determine the optimal position gbestWhether the accuracy requirement is met: if yes, entering step 6; otherwise, entering step 5;
and 5: according to the formula gbest＝gbestX (1+ η × 0.5), η is an arbitrary number between 0 and 1; updating the optimal solution of the underlying model using the PSO algorithm
Figure BDA0002488377890000071
Returning to the step 3;
step 6: and outputting an optimization result, and finishing the algorithm.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (3)

1. A regional airport group navigation network optimization method is characterized in that a regional airport group navigation network is optimized by constructing a nonlinear double-layer planning model, wherein an upper layer planning model takes passenger utility loss minimum as an objective function, and a lower layer planning takes airline profit maximization as an objective function, and comprises the following steps: solving the double-layer planning model by adopting a particle swarm optimization algorithm, comprising the following steps of:
step 1: initializing algorithm parameters: setting the total number of particles of the particle swarm as m; randomly generating lower layer mode satisfying constraint conditionInitial solution of type
Figure FDA0003470054340000011
Initialize the ith e [1, m]Initial velocity V of individual particleiInitial position of ith particle
Figure FDA0003470054340000012
Initializing optimal solutions for underlying models
Figure FDA0003470054340000013
Optimum position of ith particle
Figure FDA0003470054340000014
Optimum position g of particle groupbest＝{pi_best}；
Step 2: calculating the optimal position p of the ith particlei_bestFitness of time, i.e. to be
Figure FDA0003470054340000015
The fitness pi (p) of the ith particle is calculated by being brought into an objective function of an upper layer planning modeli_best)；
And step 3: for all particles in the population, the following operations are performed:
(a) updating the position of the ith particle by utilizing a particle swarm optimization algorithm
Figure FDA0003470054340000016
And velocity Vi；
(b) Solving an objective function of the lower-layer planning model: will be provided with
Figure FDA0003470054340000017
The optimal solution of the lower layer model is updated by utilizing a particle swarm optimization algorithm in an objective function brought into the lower layer planning model
Figure FDA0003470054340000018
(c) Will be provided with
Figure FDA0003470054340000019
The fitness of the ith particle is calculated by being brought into an objective function of an upper-layer planning model
Figure FDA00034700543400000110
(d) If it is not
Figure FDA00034700543400000111
Is superior to pi (p)i_best) Then update the optimal position of the ith particle to
Figure FDA00034700543400000112
Updating
Figure FDA00034700543400000113
Updating the optimal position g of the particle swarmbest(ii) a Otherwise, returning to the step (a) until all the particles are updated;
and 4, step 4: determine the optimal position gbestWhether the accuracy requirement is met: if yes, entering step 6; otherwise, entering step 5;
and 5: according to the formula gbest＝gbestX (1+ η × 0.5), η is an arbitrary number between 0 and 1; updating the optimal solution of the lower model by utilizing a particle swarm optimization algorithm
Figure FDA00034700543400000114
Returning to the step 3;
step 6: and outputting an optimization result, and finishing the algorithm.
2. The regional airport group network optimization method of claim 1, wherein: the objective function of the upper layer planning model is:
Figure FDA0003470054340000021
wherein: OD pairs represent aviation demand origin-destination pairs, omega represents a set of OD pairs, and K exists between OD pairs ijijA route, ij is belonged to omega, K is belonged to Kij；
Figure FDA0003470054340000022
Representing the volume of traffic for the kth lane between OD pair ij,
Figure FDA0003470054340000023
representing a travel utility function of a k route between OD and ij, representing a preset parameter theta, and belonging to theta (0, 1)]；
The constraint conditions of the upper layer planning model are as follows:
(1) the total passenger flow of all air route flows between the OD and the ij is equal to the total aviation demand
Figure FDA0003470054340000024
Wherein: xijRepresenting the total aviation demand of OD to ij;
(2) firstnThe passenger flow of each air route is less than or equal to the number of seats available of an airline company n
Figure FDA0003470054340000025
Wherein:
Figure FDA0003470054340000026
denotes the lnThe volume of the passenger traffic of the airline,
Figure FDA0003470054340000027
denotes the lnThe number of seats available on a single route line,
Figure FDA0003470054340000028
denotes the lnFlight frequency on a courseRate, determined by the underlying plan;
the total number of the airlines is N, and N belongs to N; i isnAll the number of airlines representing the operation of the airline n;
Figure FDA0003470054340000029
indicating that the k-th route between OD and ij is opened by the airline company n, otherwise
Figure FDA00034700543400000210
Indicates that the kth route between OD and ij is not opened by the airline company n, ln∈In。
3. The regional airport group network optimization method of claim 1, wherein: the objective function of the lower layer planning model is:
Figure FDA00034700543400000211
wherein:
Figure FDA00034700543400000212
represents the price of the ticket for the kth route between OD and ij,
Figure FDA00034700543400000213
represents the optimal passenger flow of the k route between the OD and the ij obtained in the upper planning,
Figure FDA00034700543400000214
indicating a route lnThe operating cost of (c);
the constraint conditions of the lower-layer planning model are as follows:
(1) firstnThe passenger flow of each air route is less than or equal to the number of seats available of an airline company n
Figure FDA0003470054340000031
(2) The total number of routes for airline n at airport a ∈ A cannot be greater than the capacity allocated to it by airport a
Figure FDA0003470054340000032
Figure FDA0003470054340000033
Wherein:
Figure FDA0003470054340000034
indicating airport a opennThe course of the flight is,
Figure FDA0003470054340000035
indicating that airport a is not open |nA route;
Figure FDA0003470054340000036
represents the capacity allocated to airline n by airport a; the total number of airports contained in the airport group is A;
(3) firstnThe frequency of the flights provided by the flight line is not less than the lower limit of the frequency of the flights so as to meet the requirements of airport groups
Figure FDA0003470054340000037
Wherein: f. ofminRepresenting a lower limit for flight frequency.
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Publication number Priority date Publication date Assignee Title
CN101477649A (en) * 2008-12-31 2009-07-08 中山大学 Air flight planning method based on particle swarm algorithm
CN105700549A (en) * 2016-01-21 2016-06-22 北京理工大学 Unmanned plane multi-track planning method based on sequence ecological niche PSO (particle swarm optimization) algorithm
CN109598984A (en) * 2019-01-14 2019-04-09 南京航空航天大学 Air route resources configuration optimization system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101477649A (en) * 2008-12-31 2009-07-08 中山大学 Air flight planning method based on particle swarm algorithm
CN105700549A (en) * 2016-01-21 2016-06-22 北京理工大学 Unmanned plane multi-track planning method based on sequence ecological niche PSO (particle swarm optimization) algorithm
CN109598984A (en) * 2019-01-14 2019-04-09 南京航空航天大学 Air route resources configuration optimization system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于双层规划的航空公司航线航班优化研究;杨新湦等;《航空计算技术》;20180525;第1-7页 *

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