CN107230392A - Optimizing distribution method based on the hub aircraft gate for improving ACO algorithms - Google Patents
Optimizing distribution method based on the hub aircraft gate for improving ACO algorithms Download PDFInfo
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Abstract
The invention discloses the optimizing distribution method based on the hub aircraft gate for improving ACO algorithms, it is related to Airport Resources distribution technique field, on the basis of analysis domestic airport airport gate assignment situation and Airport Operation way to manage, for existing Gate Assignment consider target is more single and derivation algorithm precision and it is inefficient the problem of, consider most short with the total distance of passenger's walking, aircraft gate free time is most balanced and optimization aim of airplane parking area minimum number, set up a kind of aircraft gate multiple-objection optimization distribution model, and it is solved using improved ant colony optimization algorithm, flight is set to obtain rationally effective distribution on aircraft gate.
Description
Technical field
The present invention relates to Airport Resources distribution technique field, more particularly to stopped based on the hub for improving ACO algorithms
The optimizing distribution method of seat in the plane.
Background technology
Aircraft gate is the limited valuable source in airport, and it is to realize that flight is quickly and safely stopped, it is ensured that between flight
Effectively linking, improves a most critical factor of whole airport comprehensive operation benefit.Airport break indices refer to considering to stop
In the case that seat in the plane layout, aircraft type, flight enter the factors such as moment of departing from port, in the range of a specified time, by airdrome control
Center is to specify suitable boarding gate to port and outgoing flight, it is ensured that flight is normal, be not delayed.Airport break indices rationally with
It is no, the not only safety of relation airdrome scene operation and smoothness, and to ensureing that the normal realization of flight planning, reduction are transported into
Originally, providing excellent service etc. for passenger has great influence.Therefore, airport break indices are studied, with important theory
Meaning and actual application value.
At present, Gate Assignment is extensively and profoundly studied, and obtains preferable allocation result.Andeatta and
Romanin attempts by founding mathematical models first, the problem of solving the Ground-Holding time and distribute.In the model set up
In, it is assigned with Dynamic Programming in a period of time from multiple field takeoffs, flies to the multi rack airborne vehicle on same airport
Ground-Holding time, optimization aim is that the total loss of delay of all airborne vehicles is minimum.Then, the static multimachine of ground delay decision
Field is conducted in-depth research, and some heuritic approaches, which are suggested, solves these problems.Takashi et al. proposes one kind one
Tie up the solution new method of Gate Assignment.Original minimization problem is converted into a restricted problem, then uses
One heuritic approach is solved.Cheng proposes a kind of Knowledge based engineering airport break indices system, and there is provided one
The solution under static and current intelligence is met in the individual rational calculating time.Haghani et al. proposes a kind of new stop
Gate Position Scheduling problem integer programming model, and solved using an effective heuristic solving strategy method.Luo et al. is proposed
A kind of unit Ground Preserving problems model based on discrete event system.According to the various features of aerodrome capacity, discuss really
The situation and corresponding algorithm of qualitative and randomness.Yan et al. proposes a multi-objective Model, helps airport authorities effective
Efficiently solve Gate Assignment.The model is referred to as multiple target Zero-one integer programming.Yan et al. proposes one and imitated
True framework, can not only analyze influence of the random flight delay to static break indices, and can assess flexible buffering
Time and real-time break indices rule.Yan and Tang propose a kind of heuristic of insertion in the frame, it is intended to help
Airport authorities solve random delayed airport break indices.Drexl and Nikulin propose one kind with minimum unassigned
Flight, total walking distance are most short, improve the airport break indices model that break indices preference is multiple target to greatest extent.Et al. propose a kind of hybrid algorithm based on heuristic and stochastic search methods, for solving airport aircraft gate point
With problem.Yin et al. proposes the DNA computation models of airport break indices.By analysis of aircraft assignment problem, by aircraft gate
Assignment problem is converted into Vertex Coloring Model.Zhao et al. proposes mixed integer model and asked to design airport break indices
Topic, and employ the ant colony optimization for solving model.Prem Kumar et al. propose based on passenger's connection income it is maximum, using into
This minimum and robustness most strong mathematical modeling.These existing break indices models and method for solving can be summarized as following
Several types.Expert system is built upon equipping rules and considers the KBS on the basis of non-quantitation standard.This side
Method have ignored key factor due to the limitation of hunting zone, cause undesirable configuration result.Mathematic programming methods selection optimization
Object function, explores the feasibility configuration of Zero-one integer programming.The subject matter of this method is selection target function.Due to stopping
The influence factor of Gate Position Scheduling is a lot, how to consider that various factors proposes to meet actual optimization object function, designs one soon
Fast effective derivation algorithm.Intelligent algorithm is used to distribute aircraft gate, and solving result is often locally optimal solution.When flight number
When reaching thousands of, it is difficult to meet the requirement distributed in real time.
Ant group optimization (ant colony optimization, ACO) algorithm is a kind of typical colony intelligence optimized algorithm,
Pheromones play an important role in cooperating between ant, and ant can leave information in its paths traversed
Element, and walking path is selected according to the concentration of pheromones.It preferably controls the diversity of colony by pheromones, it is to avoid calculate
Method is absorbed in precocious stagnation, and ability is explored with stronger space.However, because traditional ACO algorithms are using fixed pheromones
Increase and decrease carries out Pheromone update so that easily occur that convergence rate is slow, be absorbed in the phenomenons such as local optimum, operation time length.
The content of the invention
, can be with the embodiments of the invention provide the optimizing distribution method based on the hub aircraft gate for improving ACO algorithms
Solve problem present in existing break indices technology.
Based on the optimizing distribution method for the hub aircraft gate for improving ACO algorithms, including
Set up object function：Multiple specific item scalar functions are set up, the specific item scalar functions are standardized, standardized
Object function afterwards, wherein the sub-goal function includes：
Aircraft gate free time most balanced specific item scalar functions：
The most short specific item scalar functions of passenger's walking distance are：
The minimum specific item scalar functions of flight number being assigned on airplane parking area are：
Wherein, n is flight sum, and m is the number of aircraft gate, SikWhen reaching aircraft gate k for flight i, the sky of this aircraft gate
Between idle, SSkRepresent to complete the aircraft gate free time after all services, i.e., last flight being assigned on each seat in the plane
Departure airport and flight schedule in last flight Departure airport between difference, qikRefer to be assigned to shutdown
Passenger's shift number in flight i on the k of position, fkRefer to that passenger reaches the distance passed by needed for the k of aircraft gate, yik=1 is to navigate
Class i is assigned to aircraft gate k, is otherwise 0, giRepresent whether flight is rested on airplane parking area, only when flight i is assigned to shutdown
Level ground duration is 1, is otherwise 0；
Above three specific item scalar functions are carried out with no quantization setting and is carried out after standardization processing, the mesh after being standardized
Scalar functions：
Wherein,Weight W1=0.4, W2=0.4, W3=0.2,Q=1,2,3, and
Set up constraints；
The object function after the standardization is solved according to the constraints, specific method is：
Flight and aircraft gate information are inputted, the conflict relationship between flight is recorded with a matrix type；
Initiation parameter：Including population quantity r, maximum iteration NC_max and current iteration times N C=1, according to
Shut down bit quantity and ant quantity r × s is set, initialization information element is c, configuration information element volatility coefficient ρ, configuration information prime factor
α and heuristic greedy method β；
The real time data on airport is read, the time is begun to use using arrival time of flight for reaching at first as aircraft gate,
End use time using the Departure airport of last flight as aircraft gate；
To the ant v, u=1,2 ... of u groups, r, v=1,2 ..., s select next boat being put into according to following formula (5)
Class, if ant v does not have flight to be put into, judges whether next ant v++ has flight to be put into：
Wherein, d is ant position, and l is the position that ant can reach, and Λ is the collection for the position that ant can reach
Close, τdlFor by the pheromones intensity in position d to l path, ηdlFor position d to l path visibility,It is ant v from position
Put d to l transition probability；
Judge whether all ants have flight to be put into, if the no flight of all ants in previous step can be put
Enter, then into next step, otherwise re-start previous step；
The target function value of each group ant, the functional value of the current best ant group of record and path are calculated, according to the following formula
(6), (7) and (8) update the pheromones on the path of best ant group：
τdl∈[τmin,τmax] (8)
Wherein, NC is iterations, and s is ant number, and Q is the pheromones that ant v leaves on position d to l path
Quantity, LvThe path length that ant v passes through is represented,For pheromones increment, BestSolution represents optimal path, τmin
And τmaxRespectively τdlValue lower and upper limit；
Iterations NC increases by 1, if NC<NC_max, then return and judge whether all ants have flight to be put into, otherwise defeated
Go out optimal result, method terminates.
Preferably, the constraints of foundation includes：
Exclusivity is constrained：A certain flight only needs and must distribute an aircraft gate or airplane parking area, in its holding time,
The seat in the plane can not be the service of other flights：
Flight-seat in the plane type matching constraint：Flight can be only assigned in the aircraft gate matched with its type, and flight can be with
It is assigned in the aircraft gate more than or equal to its correspondence model, and cannot be assigned in the aircraft gate less than its correspondence model：
Gk≥Fi,yik=1 (10)
GkRepresent the model of aircraft gate, FiRepresent the model of flight；
Personal distance is constrained：The interval docked between adjacent two flights of order of same aircraft gate should be more than etc.
In safe time interval 5 minutes：
LikRepresent that flight i reaches aircraft gate k time, EjkRepresent that flight j leaves aircraft gate k time, ZijkRepresent boat
Class i and flight j is connecting flight, and flight i first reaches aircraft gate k than flight j；
Nearly seat in the plane is preferentially using constraint：When flight i is reached, the nearly seat in the plane of prioritizing selection is answered to stop, next to that remote seat in the plane, finally
Consideration rests in airplane parking area, and airplane parking area is used unrestrictedly：
Fnear>Ffar>gi (12)
FnearRepresent nearly seat in the plane, FfarRemote seat in the plane is represented, the aircraft gate the use priority of other constraints is met：Nearly seat in the plane>Far
Seat in the plane>Airplane parking area.
Preferably, after pheromones complete to update, the pheromone concentration on each side is limited in interval [τmin,τmax] in,
If τdl<τminThen make τdl=τmin；If τdl>τmaxThen make τdl=τmax, the upper bound that initial information element is its span is set, i.e.,
τdl(0)=τmax。
The optimizing distribution method based on the hub aircraft gate for improving ACO algorithms in the embodiment of the present invention, in analysis
On the basis of domestic airport airport gate assignment situation and Airport Operation way to manage, consider for existing Gate Assignment
Target is more single and derivation algorithm precision and it is inefficient the problem of, consider with the most short, aircraft gate of the total distance of passenger's walking
Free time is most balanced and optimization aim of airplane parking area minimum number, sets up a kind of aircraft gate multiple-objection optimization distribution model,
And it is solved using improved ant colony optimization algorithm, flight is obtained rationally effective distribution on aircraft gate.
Multiple-objection optimization distribution model and its validity of derivation algorithm are verified by instantiation.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the solution flow chart of object function of the present invention；
Fig. 2 is distribution flight number schematic diagram on each aircraft gate；
Fig. 3 is break indices result Ganluo figure；
Fig. 4 is that 10 suboptimization are worth comparative result schematic diagram；
Fig. 5 is the process signal of basic ACO algorithms and improved ACO Algorithm for Solving break indices Model for Multi-Objective Optimization
Figure；
Fig. 6 is walking distance comparison schematic diagram.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
The optimizing distribution method based on the hub aircraft gate for improving ACO algorithms provided in the embodiment of the present invention, should
Method includes：
Set up object function, the object function that the present invention is set up includes that aircraft gate free time is most balanced, passenger's walking away from
From the most short and flight number minimum of three specific item scalar functions that are assigned on airplane parking area, specifically：
For each aircraft gate, if free time section is harmonious poor, then this allocation plan can not often be answered
Entering to flight the change at moment of departing from port, (under actual conditions, flight enters to depart from port the moment, often due to the reason such as stream control, weather is sent out
Changing), so choosing aircraft gate free time variance minimum as object function, be specially
Can abbreviation beTherefore aircraft gate free time most balanced specific item scalar functions are：
In formula：N is flight sum, and m is the number of aircraft gate；SikWhen reaching aircraft gate k for flight i, the sky of this aircraft gate
Between idle, S represents the average value of aircraft gate free time；SSkRepresent to complete the aircraft gate free time after all services, i.e., it is each
The Departure airport of last flight in the Departure airport and flight schedule of last flight being assigned on individual seat in the plane
Between difference.
By setting up passenger's walking matrix, it can quantify and provide the distance between different aircraft gates and check-in sales counter, thus
The travel distance of passenger is determined, so as to distinguish the quality of aircraft gate.The most short specific item scalar functions of passenger's walking distance are：
In formula：qikRefer to passenger's shift number in the flight i that is assigned on the k of aircraft gate；fkRefer to that passenger reaches to stop
The distance passed by needed for the k of seat in the plane；yik=1 refers to that flight i is assigned to aircraft gate k, is otherwise 0.
The utilization rate of aircraft gate is improved, so as to reduce the resource consumption on airport, airport benefit is improved, so to ensure to the greatest extent may be used
Flight is assigned on aircraft gate more than energy, carrys out the quality of measure algorithm with the number for the flight being assigned on aircraft gate.Distribution
The minimum specific item scalar functions of flight number on to airplane parking area are：
In formula：giRepresent whether flight is rested on airplane parking area, be 1 only when flight i is assigned to airplane parking area duration, otherwise
For 0.
For F1、F2And F3Three different object functions, the actual numerical value of three is not easy to determine and be likely to difference
It is huge, so it is difficult to solving the optimal feasible solution for making us very satisfied simply by Reasonable adjustment weight, therefore
No quantization setting must be carried out to this class function.
Set weight W1=0.4, W2=0.4, W3=0.2, set the function asIf(q=1,2,3) andThen standardizing goals function isIn real processOften it is difficult to which simple determine, it is therefore desirable to F1、F2And F3Value be modified, so needing selection one
Empirical value is organized to determineValue.IfMesh after standardization
Scalar functions are：
Constraints is set up, the constraints that the present invention is set up includes exclusivity constraint, flight-aircraft gate type matching about
Beam, personal distance constraint and nearly seat in the plane are preferentially using constraint.Specifically：
Exclusivity is constrained：A certain flight only needs and must distribute an aircraft gate or airplane parking area, in its holding time,
The seat in the plane can not be the service of other flights：
Flight-seat in the plane type matching constraint：Flight can be only assigned in the aircraft gate matched with its type, general flight
It can be assigned in the aircraft gate more than or equal to its correspondence model, and the aircraft gate less than its correspondence model cannot be assigned to
In：
Gk≥Fi,yik=1 (6)
GkRepresent the model of aircraft gate, FiRepresent the model of flight.
Personal distance is constrained：The interval docked between adjacent two flights of order of same aircraft gate should be more than etc.
In safe time interval 5 minutes：
LikRepresent that flight i reaches aircraft gate k time, EjkRepresent that flight j leaves aircraft gate k time, ZijkRepresent boat
Class i and flight j is connecting flight, and flight i first reaches aircraft gate k than flight j.
Nearly seat in the plane is preferentially using constraint：When flight i is reached, the nearly seat in the plane of prioritizing selection is answered to stop, next to that remote seat in the plane, finally
Consideration rests in airplane parking area, and airplane parking area is used unrestrictedly：
Fnear>Ffar>gi (8)
FnearRepresent nearly seat in the plane, FfarRemote seat in the plane is represented, the aircraft gate the use priority of other constraints is met：Nearly seat in the plane>Far
Seat in the plane>Airplane parking area.
Object function is solved, for the scale of real airport aircraft gate and flight, break indices mathematical modeling category
In NP-Hard problems.Similar to the integer programming that branch defines or other are traditional, derivation algorithm is very within effective time
Hardly possible is applied to the solution of the problem, therefore, and the present invention proposes to solve the problem using improved Ant Colony Optimization Algorithm, such as Fig. 1 institutes
Show, be specially：
Step 100, input flight and aircraft gate information, record the conflict relationship between flight with a matrix type；
Step 200, initiation parameter：Including population quantity r, maximum iteration NC_max and current iteration times N C
=1, ant quantity r × s is set according to bit quantity is shut down, initialization information element is c, configuration information element volatility coefficient ρ, sets letter
Cease prime factor α and heuristic greedy method β；
Step 300, the real time data on airport is read, starting by aircraft gate of arrival time of flight for reaching at first makes
With the time, the end use time using the Departure airport of last flight as aircraft gate；
Step 400, to the ant v, u=1,2 ... of u groups, r, v=1,2 ..., s select next according to following formula (9)
The flight being put into, if ant v does not have flight to be put into, judges whether next ant v++ has flight to be put into：
Wherein, d is ant position, and l is the position that ant can reach, and Λ is the collection for the position that ant can reach
Close, τdlFor by the pheromones intensity in position d to l path, ηdlFor enlightening information, shown herein as position d to l path energy
Degree of opinion,For transition probabilities of the ant v from position d to l；
Step 500, judge whether all ants have flight to be put into, if all ants in step 400 are without flight
It can be put into, then into step 600, otherwise re-start step 400；
Step 600, the target function value of each group ant, the functional value of the current best ant group of record and path are calculated,
(10), (11) and (12) update the pheromones on the path of best ant group according to the following formula：
τdl∈[τmin,τmax] (12)
Wherein, NC is iterations, and s is ant number, and Q is the pheromones that ant v leaves on position d to l path
Quantity, LvThe path length that ant v passes through is represented,For pheromones increment, BestSolution represents optimal path, τmin
And τmaxRespectively τdlValue lower and upper limit；
After pheromones complete to update, the pheromone concentration on each side is limited in interval [τmin,τmax] in, if τdl<τmin
Then make τdl=τmin；If τdl>τmaxThen make τdl=τmax, the upper bound that initial information element is its span, i.e. τ are setdl(0)=
τmax；
Step 700, iterations NC increases by 1, if NC<NC_max, then return to step 500, otherwise export optimal result,
Flow terminates.
Data simulation and analysis
Experimental situation：Test the relevant parameter chosen：R=20, NC_max=200, s=30, c=1, ρ=0.2, α=2,
β=3, Q=0.1, algorithm is compiled by matlab language, in 8G running memory i7 processors windows10 operation system
Run under system environment.
Experimental data：212 interior for a period of time flights of 30 aircraft gates and the airport of certain hub are chosen, wherein
Provide passenger's walking step number be less than 950 for nearly seat in the plane, the model of aircraft gate is divided into large, medium and small three kinds, shuts down bit attribute such as table
1, operative flight information such as table 2.
Certain the hub aircraft gate data information of table 1
Table 2 certain hub interior part flight data data for a period of time
Experimental result and comparative analysis：In order to prove the optimization performance of improved ant colony optimization algorithm, basic ACO algorithms quilt
For solving aircraft gate multiple-objection optimization distribution model.Minimum time interval between two adjacent flights t=5 minutes, to avoid
Conflict.Carry out 10 test experiments.Experimental result of selection is further analyzed and studied.Break indices result such as table
Shown in 3, as indicated with 2, corresponding Ganluo figure is as shown in Figure 3 for corresponding quantity allotted figure.
Certain the hub break indices result of table 3
It is can be seen that from table 3, Fig. 2 and Fig. 3 for a certain hub 30 aircraft gates for a period of time and 212 boats
Class, wherein 205 flights have been assigned to nearly seat in the plane, 7 flights have been assigned to remote seat in the plane, and nearly Gate Position Scheduling rate reaches
96.7%.Therefore nearly Gate Position Scheduling result is preferable.Utilize constructed aircraft gate multiple-objection optimization distribution model and improvement
Ant colony optimization algorithm distribution aircraft gate there is not the aircraft gate of free time, and the flight distributed on each aircraft gate is relative is
Compare in a balanced way.It is well known that rigid constraint condition of the Airport Operation safety as break indices method, idle with aircraft gate
The machine hinge that the flight number that time is most balanced, passenger's walking distance is most short, be assigned on airplane parking area is at least set up for object function
The Model for Multi-Objective Optimization of Gate Assignment can improve the utilization rate and balanced ratio of aircraft gate, and passenger satisfaction
Degree.Improved ant colony optimization algorithm can effectively solve Model for Multi-Objective Optimization, have in solving complexity optimization problem
Preferably optimize performance.
In order to further analyze the optimization that improved ant colony optimization algorithm solves break indices Model for Multi-Objective Optimization
Can, basic ant colony optimization algorithm is used for comparative analysis.10 experiments are continuously performed, comparative result such as table 4 and Fig. 4 institutes is tested
Show.
Table 4 tests comparative result
Table 4 and Fig. 4 are to be utilized respectively basic ACO algorithms and improve ACO Algorithm for Solving multiple-objection optimizations distribution model to obtain
Experimental result.Where it can be seen that in 10 times are tested, basic ACO algorithms obtain optimal value in the 96th iteration, and its value is
0.4, and 10 average optimal values of the algorithm are 0.4105, the Average Iteration number for finding optimal value is that the improved ACO of 103.3. are calculated
Method obtains optimal value in the 24th iteration, and its value is 0.3821, and 10 average optimal values of improved ACO algorithms are 0.3953,
The Average Iteration number for finding optimal value is 92.6.Therefore, for solving break indices Model for Multi-Objective Optimization, improved ACO
The average optimal value that algorithm is obtained and the number of iterations for finding optimal value are better than the acquisition of basic ACO algorithms.Substantially ACO algorithms is flat
Equal run time is 34.87 seconds, and the average operating time of improved ACO algorithms is 662.54 seconds.Therefore improved ACO algorithms
More time cost is make use of to solve the break indices Model for Multi-Objective Optimization of structure, but improved ACO algorithms ratio
Basic ACO algorithms obtain the quality of higher solution.Therefore improved ACO algorithms are solving break indices Model for Multi-Objective Optimization
In, it can obtain more preferable convergence efficiency and optimization ability by sacrificing the regular hour.It, which has, escapes local minimum and carries
The ability of high ability of searching optimum.
The process of basic ACO algorithms and improved ACO Algorithm for Solving break indices Model for Multi-Objective Optimization, such as Fig. 5 institutes
Show.From fig. 5, it can be seen that the initial fitness value of basic ACO algorithms is 0.47, convergence fitness value is 0.4181；And improve
The initial fitness values of ACO algorithms be 0.455, convergence fitness value is 0.3994.It is improved by comparative analysis
ACO algorithms can obtain preferable convergence efficiency and preferable optimization ability, with escape local minimum and raising global search
The ability of ability.
Single-goal function (formula 2) and multiple objective function (formula 4) are further analyzed, to illustrate that multiple objective function is solving shutdown
Meaning in bit allocation problem.Present invention selection passenger walking distance most short target is furtherd investigate.Improved ACO algorithms
For solving break indices Optimized model.Shown in experimental result table 5 and Fig. 6.
Fig. 5 experimental results
It is can be seen that from table 5 and Fig. 6 for the walking distance of passenger and 10 experimental results, break indices multiple target
It is 24040465 that model obtains optimal value in 122 iteration, and average optimal value is 26017444, and mean iterative number of time is 120.1,
Average operating time is 511.65 seconds.It is 23549610 that break indices single goal model obtains optimal value in 145 iteration, is put down
Equal optimal value is 23988838, and mean iterative number of time is 80.4, and average operating time is 513.55 seconds.In the base analyzed and compared
As can be seen that break indices Model for Multi-Objective Optimization allocation result distributes knot than break indices single object optimization model on plinth
Fruit is poor.But airport break indices need to consider various factors, thus build with aircraft gate free time it is most equal
Weighing apparatus, the hub break indices that passenger's walking distance is most short, the flight number that is assigned on airplane parking area is at least object function
The Model for Multi-Objective Optimization of problem, can effectively improve the operation ability on whole airport.Therefore, research airport break indices is more
Objective optimisation problems tool is of great significance.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, system or computer program
Product.Therefore, the present invention can be using the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.Moreover, the present invention can be used in one or more computers for wherein including computer usable program code
The computer program production that usable storage medium is implemented on (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The present invention is the flow with reference to method according to embodiments of the present invention, equipment (system) and computer program product
Figure and/or block diagram are described.It should be understood that can be by every first-class in computer program instructions implementation process figure and/or block diagram
Journey and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided
The processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced by the instruction of computer or the computing device of other programmable data processing devices for real
The device for the function of being specified in present one flow of flow chart or one square frame of multiple flows and/or block diagram or multiple square frames.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which is produced, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that in meter
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, thus in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
, but those skilled in the art once know basic creation although preferred embodiments of the present invention have been described
Property concept, then can make other change and modification to these embodiments.So, appended claims are intended to be construed to include excellent
Select embodiment and fall into having altered and changing for the scope of the invention.
Obviously, those skilled in the art can carry out the essence of various changes and modification without departing from the present invention to the present invention
God and scope.So, if these modifications and variations of the present invention belong to the scope of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to comprising including these changes and modification.
Claims (3)
1. the optimizing distribution method based on the hub aircraft gate for improving ACO algorithms, it is characterised in that including
Set up object function：Multiple specific item scalar functions are set up, the specific item scalar functions are standardized, after being standardized
Object function, wherein the sub-goal function includes：
Aircraft gate free time most balanced specific item scalar functions：
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<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msubsup>
<mi>SS</mi>
<mi>k</mi>
<mn>2</mn>
</msubsup>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
The most short specific item scalar functions of passenger's walking distance：
<mrow>
<mi>min</mi>
<mi> </mi>
<msub>
<mi>F</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>q</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<msub>
<mi>f</mi>
<mi>k</mi>
</msub>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
The minimum specific item scalar functions of flight number being assigned on airplane parking area：
<mrow>
<mi>min</mi>
<mi> </mi>
<msub>
<mi>F</mi>
<mn>3</mn>
</msub>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>g</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, n is flight sum, and m is the number of aircraft gate, SikWhen reaching aircraft gate k for flight i, during the free time of this aircraft gate
Between, SSkRepresent to complete the aircraft gate free time after all services, i.e., last flight being assigned on each seat in the plane from
Difference between the Departure airport of last flight in ETA estimated time of arrival and flight schedule, qikRefer to be assigned to aircraft gate k
On flight i in passenger's shift number, fkRefer to that passenger reaches the distance passed by needed for the k of aircraft gate, yik=1 refers to flight i
Aircraft gate k is assigned to, is otherwise 0, giRepresent whether flight is rested on airplane parking area, only when flight i is assigned to airplane parking area
Duration is 1, is otherwise 0；
Above three specific item scalar functions are carried out with no quantization setting and is carried out after standardization processing, the target letter after being standardized
Number：
<mrow>
<mi>F</mi>
<mo>=</mo>
<msub>
<mi>&mu;</mi>
<mn>1</mn>
</msub>
<mo>&lsqb;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msubsup>
<mi>S</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msubsup>
<mi>SS</mi>
<mi>k</mi>
<mn>2</mn>
</msubsup>
<mo>&rsqb;</mo>
<mo>+</mo>
<msub>
<mi>&mu;</mi>
<mn>2</mn>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>q</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<msub>
<mi>f</mi>
<mi>k</mi>
</msub>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&mu;</mi>
<mn>3</mn>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<msub>
<mi>g</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,Weight W1=0.4, W2=0.4, W3=0.2,Q=1,2,3, and
Set up constraints；
The object function after the standardization is solved according to the constraints, specific method is：
Flight and aircraft gate information are inputted, the conflict relationship between flight is recorded with a matrix type；
Initiation parameter：Including population quantity r, maximum iteration NC_max and current iteration times N C=1, according to shutdown
Bit quantity sets ant quantity r × s, and initialization information element is c, configuration information element volatility coefficient ρ, configuration information prime factor α with
Heuristic greedy method β；
The real time data on airport is read, begins to use the time using arrival time of flight for reaching at first as aircraft gate, with most
The Departure airport of latter frame flight as aircraft gate end use time；
To the ant v, u=1,2 ... of u groups, r, v=1,2 ..., s select next flight being put into, such as according to following formula (5)
Fruit ant v does not have flight to be put into, then judges whether next ant v++ has flight to be put into：
<mrow>
<msubsup>
<mi>p</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>v</mi>
</msubsup>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mi>&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>&alpha;</mi>
</msubsup>
<msubsup>
<mi>&eta;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>&beta;</mi>
</msubsup>
</mrow>
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>l</mi>
<mo>&Element;</mo>
<mi>&Lambda;</mi>
</mrow>
</munder>
<msubsup>
<mi>&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>&alpha;</mi>
</msubsup>
<msubsup>
<mi>&eta;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>&beta;</mi>
</msubsup>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, d is ant position, and l is the position that ant can reach, and Λ is the set for the position that ant can reach,
τdlFor by the pheromones intensity in position d to l path, ηdlFor position d to l path visibility,It is ant v from position d
To l transition probability；
Judge whether all ants have flight to be put into, if the no flight of all ants in previous step can be put into,
Into next step, previous step is otherwise re-started；
Calculate the target function value of each group ant, the record current preferably functional value of ant group and path, according to the following formula (6),
And (8) update the pheromones on the path of best ant group (7)：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mi>C</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>&rho;</mi>
<mo>&times;</mo>
<msub>
<mi>&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mi>C</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>v</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>s</mi>
</munderover>
<msubsup>
<mi>&Delta;&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>v</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>&Delta;&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>v</mi>
</msubsup>
<mo>=</mo>
<mfrac>
<mi>Q</mi>
<mrow>
<msub>
<mi>&Sigma;L</mi>
<mi>v</mi>
</msub>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>&Delta;&tau;</mi>
<mrow>
<mi>d</mi>
<mi>l</mi>
</mrow>
<mi>v</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mi>Q</mi>
<msup>
<mn>2</mn>
<mrow>
<msub>
<mi>L</mi>
<mi>v</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>B</mi>
<mi>e</mi>
<mi>s</mi>
<mi>t</mi>
<mi>S</mi>
<mi>o</mi>
<mi>l</mi>
<mi>u</mi>
<mi>t</mi>
<mi>i</mi>
<mi>o</mi>
<mi>n</mi>
</mrow>
</msup>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
τdl∈[τmin,τmax] (8) wherein, NC is iterations, and s is ant number, and Q is ant v on position d to l path
The pheromones quantity left, LvThe path length that ant v passes through is represented,For pheromones increment, BestSolution is represented
Optimal path, τminAnd τmaxRespectively τdlValue lower and upper limit；
Iterations NC increases by 1, if NC<NC_max, then return and judge whether all ants have flight to be put into, otherwise export most
Excellent result, method terminates.
2. the method as described in claim 1, it is characterised in that the constraints of foundation includes：
Exclusivity is constrained：A certain flight only needs and must distribute an aircraft gate or airplane parking area, in its holding time, the machine
Position can not be the service of other flights：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>g</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
Flight-seat in the plane type matching constraint：Flight be can be only assigned in the aircraft gate matched with its type, and flight can be distributed
Into the aircraft gate more than or equal to its correspondence model, and it cannot be assigned in the aircraft gate less than its correspondence model：
Gk≥Fi,yik=1 (10)
GkRepresent the model of aircraft gate, FiRepresent the model of flight；
Personal distance is constrained：The interval docked between adjacent two flights of order of same aircraft gate should be more than or equal to peace
Full time interval 5 minutes：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>L</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>j</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<mn>5</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Z</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
LikRepresent that flight i reaches aircraft gate k time, EjkRepresent that flight j leaves aircraft gate k time, ZijkRepresent flight i and
Flight j is connecting flight, and flight i first reaches aircraft gate k than flight j；
Nearly seat in the plane is preferentially using constraint：When flight i is reached, the nearly seat in the plane of prioritizing selection is answered to stop, next to that remote seat in the plane, finally considers
Airplane parking area is rested in, and airplane parking area is used unrestrictedly：
Fnear>Ffar>gi (12)
FnearRepresent nearly seat in the plane, FfarRemote seat in the plane is represented, the aircraft gate the use priority of other constraints is met：Nearly seat in the plane>Remote seat in the plane>
Airplane parking area.
3. the method as described in claim 1, it is characterised in that after pheromones complete to update, the pheromones on each side are dense
Degree is limited in interval [τmin,τmax] in, if τdl<τminThen make τdl=τmin；If τdl>τmaxThen make τdl=τmax, initial information is set
Element is the upper bound of its span, i.e. τdl(0)=τmax。
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Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107886260A (en) * | 2017-12-28 | 2018-04-06 | 南京航空航天大学 | A kind of break indices method based on robustness distribution model |
| CN108460531A (en) * | 2018-03-07 | 2018-08-28 | 中国人民大学 | A kind of airport aircraft gate fast distribution method |
| CN108510227A (en) * | 2018-03-23 | 2018-09-07 | 东华大学 | A kind of real-time planning system of vehicle-mounted logistics distribution based on machine learning |
| CN109919379A (en) * | 2019-03-07 | 2019-06-21 | 合肥工业大学 | Method, system and the storage medium of boarding gate are distributed for flight |
| CN110288857A (en) * | 2019-06-26 | 2019-09-27 | 中国民航大学 | It is a kind of based on when Spatial Dimension decompose airport aircraft gate fast dispatch method |
| CN111133492A (en) * | 2017-12-19 | 2020-05-08 | 日本音响工程株式会社 | Device for acquiring actual performance information of aircraft in shipping |
| CN111243342A (en) * | 2018-11-29 | 2020-06-05 | 顺丰科技有限公司 | Aircraft stand allocation method and device |
| CN111243341A (en) * | 2018-11-29 | 2020-06-05 | 顺丰科技有限公司 | Method and device for allocating stand of multiple aircrafts |
| CN111369090A (en) * | 2018-12-25 | 2020-07-03 | 顺丰科技有限公司 | Freight flight stop allocation method, device, equipment and storage medium |
| CN111475769A (en) * | 2020-04-03 | 2020-07-31 | 北京百度网讯科技有限公司 | Machine position scheduling method and device, electronic equipment and storage medium |
| CN111554127A (en) * | 2019-02-12 | 2020-08-18 | 通用电气公司 | Aircraft stand recovery optimization |
| CN111627255A (en) * | 2019-02-27 | 2020-09-04 | 阿里巴巴集团控股有限公司 | Information processing method, device and system |
| CN111707267A (en) * | 2020-06-18 | 2020-09-25 | 哈尔滨工程大学 | Multi-unmanned aerial vehicle collaborative track planning method |
| CN111784157A (en) * | 2020-06-30 | 2020-10-16 | 中国民航信息网络股份有限公司 | Allocation method and device for boarding gate resources |
| CN111951145A (en) * | 2020-08-12 | 2020-11-17 | 青岛民航凯亚***集成有限公司 | GA-DQN-based shutdown position allocation method |
| CN112836999A (en) * | 2021-03-29 | 2021-05-25 | 中铁第一勘察设计院集团有限公司 | Layout planning method for multi-level rail transit transfer hub |
| CN113095543A (en) * | 2021-03-01 | 2021-07-09 | 北京工业大学 | Distribution method and system for airport parking space and taxiways |
| CN113570247A (en) * | 2021-07-28 | 2021-10-29 | 南京航空航天大学 | Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition |
| CN114648895A (en) * | 2022-05-12 | 2022-06-21 | 苏州数智源信息技术有限公司 | Airport apron safety early warning method and system capable of reducing false alarm rate |
| CN114936804A (en) * | 2022-06-23 | 2022-08-23 | 北京航空航天大学 | Airport multidimensional resource cooperative scheduling method |
| CN115115097A (en) * | 2022-06-10 | 2022-09-27 | 中国民航大学 | Combined optimization method for airport parking space and aircraft sliding path |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106779210A (en) * | 2016-12-13 | 2017-05-31 | 中国人民解放军国防科学技术大学 | Algorithm of Firepower Allocation based on ant group algorithm |
-
2017
- 2017-06-08 CN CN201710427323.2A patent/CN107230392B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106779210A (en) * | 2016-12-13 | 2017-05-31 | 中国人民解放军国防科学技术大学 | Algorithm of Firepower Allocation based on ant group algorithm |
Non-Patent Citations (8)
| Title |
|---|
| CHEN DINGJUN: "Study on the Optimization Model of Utilization Scheme of Railway Passenger Station Tracks Based on an Improved ACO Algorithm", 《2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION》 * |
| LU WANG: "Ant Colony Optimization for task allocation in Multi-Agent Systems", 《CHINA COMMUNICATIONS》 * |
| NA-NA LI: "Ant colony optimization algorithm for expert assignment problem", 《2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS》 * |
| QIAN XIANG: "Improved ant colony optimization for multi-objective route planning of dangerous goods", 《2012 8TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION》 * |
| 刘志虎: "基于改进蚁群算法的柔性车间调度研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 刘长有: "基于运行安全的停机位再分配问题研究", 《中国民航大学学报》 * |
| 解英全: "基于遗传蚁群混合算法的机场停机位分配研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 陈华群: "基于图论和蚁群算法的机场停机位分配优化研究", 《科技通报》 * |
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| CN111243342B (en) * | 2018-11-29 | 2021-09-28 | 顺丰科技有限公司 | Aircraft stand allocation method and device |
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| CN109919379B (en) * | 2019-03-07 | 2020-09-08 | 合肥工业大学 | Method, system and storage medium for allocating gate for flight |
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| CN111475769A (en) * | 2020-04-03 | 2020-07-31 | 北京百度网讯科技有限公司 | Machine position scheduling method and device, electronic equipment and storage medium |
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| CN111707267A (en) * | 2020-06-18 | 2020-09-25 | 哈尔滨工程大学 | Multi-unmanned aerial vehicle collaborative track planning method |
| CN111784157A (en) * | 2020-06-30 | 2020-10-16 | 中国民航信息网络股份有限公司 | Allocation method and device for boarding gate resources |
| CN111951145A (en) * | 2020-08-12 | 2020-11-17 | 青岛民航凯亚***集成有限公司 | GA-DQN-based shutdown position allocation method |
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| CN112836999A (en) * | 2021-03-29 | 2021-05-25 | 中铁第一勘察设计院集团有限公司 | Layout planning method for multi-level rail transit transfer hub |
| CN112836999B (en) * | 2021-03-29 | 2024-04-05 | 中铁第一勘察设计院集团有限公司 | Multi-level rail transit transfer hub layout planning method |
| CN113570247A (en) * | 2021-07-28 | 2021-10-29 | 南京航空航天大学 | Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition |
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| CN114648895A (en) * | 2022-05-12 | 2022-06-21 | 苏州数智源信息技术有限公司 | Airport apron safety early warning method and system capable of reducing false alarm rate |
| CN115115097A (en) * | 2022-06-10 | 2022-09-27 | 中国民航大学 | Combined optimization method for airport parking space and aircraft sliding path |
| CN114936804A (en) * | 2022-06-23 | 2022-08-23 | 北京航空航天大学 | Airport multidimensional resource cooperative scheduling method |
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