CN109919379A - Method, system and the storage medium of boarding gate are distributed for flight - Google Patents
Method, system and the storage medium of boarding gate are distributed for flight Download PDFInfo
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Abstract
The present invention provides a kind of method, system and storage medium that boarding gate is distributed for flight, and method includes: S100, using greedy algorithm generation initial population；S200, crossover operation is carried out to the individual in current initial population, obtains the first population；S300, to current initial population and first population and the individual of concentration carry out mutation operation, obtain the second population；S400, calculate current initial population, first population and second population and concentrate the target function value of each individual；S500, judge whether preset termination condition meets: if so, using the minimum individual of target function value as globally optimal solution and exporting；Otherwise, from current initial population, first population and second population and concentrate the initial populations for selecting multiple individuals to form next iteration processes, and return to S200.The present invention, which proceeds from reality, considers transit passenger's most short route matter of time, improves the reasonability of scheme.
Description
Technical field
The present invention relates to boarding gate distribution technique fields, and in particular to a kind of method, system that boarding gate is distributed for flight
And storage medium.
Background technique
In airport, boarding gate assignment problem (abbreviation AGAP) is one of most important ongoing operations, the mesh of this task
Be will fly each time (i.e. aircraft) be assigned to an available boarding gate, while facilitating passenger and airport to the maximum extent
Operational paradigm.Boarding gate distribution needs to consider the factors such as flight type specification, aircraft gate specification and flight number, wherein processing is made
Industry time conflict and task arrangement are keys, and Major Airlines usually require in dynamic operating environment in the most efficient manner
The different boarding gates on airport are managed, this just needs a kind of to be the scheme of flight reasonable distribution boarding gate.
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, the present invention provides a kind of method, system and storages that boarding gate is distributed for flight
Medium proceeds from reality and considers transit passenger's most short route matter of time, improves the reasonability of scheme.
(2) technical solution
In order to achieve the above object, the present invention is achieved by the following technical programs:
In a first aspect, the present invention provides a kind of method for distributing boarding gate for flight, comprising:
S100, initial population is generated using greedy algorithm；Wherein, the initial population includes multiple individuals, it is each each and every one
Body corresponds to item chromosome, includes n gene on each chromosome, and the position of each gene indicates corresponding flight, often
The value of one gene is expressed as the boarding gate that the corresponding flight in its position is distributed, and n is the quantity of flight to be allocated；
S200, crossover operation is carried out to the individual in current initial population, obtains the first population；
S300, to current initial population and first population and the individual of concentration carry out mutation operation, obtain the
Two populations；
S400, according to preset objective function, calculate current initial population, first population and second population
And concentrate each individual target function value, the objective function with the flow time of transit passenger most it is short for optimization mesh
Mark；
S500, judge whether preset termination condition meets:
If so, the minimum individual of target function value as globally optimal solution and is exported；
Otherwise, according to the target function value, from current initial population, first population and second population
And the initial population for selecting multiple individuals to form next iteration process is concentrated, and return to S200.
Second aspect, the present invention provide a kind of system for distributing boarding gate for flight, comprising:
Population generation module, for executing S100, generating initial population using greedy algorithm；Wherein, the initial population
Including multiple individuals, each individual corresponds to item chromosome, includes n gene on each chromosome, each gene
Position indicates corresponding flight, and the value of each gene is expressed as the boarding gate that the corresponding flight in its position is distributed, and n is wait divide
The quantity for the flight matched；
Individual hybridization module obtains for executing S200, carrying out crossover operation to the individual in current initial population
One population；
Individual variation module, for execute S300, to current initial population and first population and concentration
Body carries out mutation operation, obtains the second population；
Target computing module calculates current initial population, described for executing S400, according to preset objective function
First population and second population and concentrate the target function value of each individual, the objective function is with transit passenger's
It is optimization aim that flow time is most short；
Judgment module is terminated, for executing S500, judging whether preset termination condition meets:
If so, the minimum individual of target function value as globally optimal solution and is exported；
Otherwise, according to the target function value, from current initial population, first population and second population
And the initial population for selecting multiple individuals to form next iteration process is concentrated, and return and execute in the individual hybridization module
S200。
The third aspect, the present invention provide a kind of computer readable storage medium, are stored thereon with computer program, the meter
Calculation machine program can realize above method when being executed by processor.
(3) beneficial effect
The embodiment of the invention provides a kind of method, system and storage mediums that boarding gate is distributed for flight, first to first
Beginning population carries out crossover operation, then carries out mutation operation again, calculates the target function value of individual, objective function is with transit passenger
Flow time most it is short be optimization aim, and then according to target function value select it is multiple individual as next iteration process
Initial population, and then recycle again, until meeting termination condition, final globally optimal solution is exported.Target in the present invention
The optimization aim of function is the flow time for minimizing transit passenger, it is seen that the present invention is in boarding gate assignment problem, from reality
Situation, which is set out, considers transit passenger's most short route matter of time, improves the convenience of transit passenger's transfer.The present invention couple
Individual carries out hybridization processing, then carries out variation processing from the individual in the individual and initial population that hybridization obtains, and then from initial
The individual obtained after the individual that is obtained after population, hybridization, variation and concentrate the multiple individuals of selection formed next iterations just
Beginning population, not only can improve quality individual in population, and can rapid solving, converge to globally optimal solution, drop
It is sunken enter local optimum probability.In addition, the present invention generates initial population by greedy algorithm, this method is more first than what is generated at random
Beginning population, individual quality is higher, accelerates the convergence of algorithm, reduces a large amount of unnecessary search.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the flow diagram for distributing the method for boarding gate in one embodiment of the invention for flight.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
In a first aspect, the present invention provides a kind of method for distributing boarding gate for flight, as shown in Figure 1, this method comprises:
S100, initial population is generated using greedy algorithm；
Wherein, the initial population includes multiple individuals, the corresponding item chromosome of each individual, on each chromosome
Including n gene, the position of each gene indicates corresponding flight, and the value of each gene is expressed as the corresponding boat in its position
The boarding gate that class is distributed, n are the quantity of flight to be allocated；
It will be appreciated that an individual corresponds to a chromosome, a chromosome i.e. one solution.
For example, there is the flight of 10 boarding gates to be allocated within some day, altogether there are four boarding gate is available, generate
Initial population in the corresponding chromosome of an individual or solution be T=(1223341233), wherein first position indicates first
A flight, the corresponding value 1 in first position indicate that boarding gate used in first flight is first boarding gate, third position
Setting indicates third flight, and the value 2 of third position indicates that the boarding gate that third flight uses is second boarding gate.It can
See, the position of gene indicates flight, and the value of gene indicates boarding gate.
In practical applications, may include: using the detailed process that greedy algorithm generates initial population
S101, one parameter g is set for each landing gate, and each parameter g is initialized as -1, parameter gkTable
Show the time that the last one flight of boarding gate k leaves；
That is, the time that its last one flight leaves is indicated for each boarding gate one parameter of setting,
The initial value of the parameter is -1.
S102, each flight is ranked up according to time departure, it is then successively initial to the distribution of each flight to step on
Machine mouth, specific assigning process may include: to search whether in corresponding boarding gate set for each flight to be allocated
There are its parameter g to be less than the boarding gate that the flight begins to use the time of boarding gate；If it exists, then its parameter g is less than the boat
Class begins to use any one boarding gate in the boarding gate of the time of boarding gate to distribute to the flight, and will it is described any one
The parameter g of boarding gate is updated to the time that the flight leaves boarding gate.
For example, i-th of flight is directed to, in the boarding gate set K used for the flightiIn search whether that there are its parameters
Less than i-th flight of g begins to use the time of boarding gate to reach the time of boarding gate, if it exists then distributes to the boarding gate
I-th of flight, and the g parameter of the boarding gate is updated, i.e., the time that i-th of flight leaves boarding gate is assigned to this and stepped on
The g parameter of machine mouth.And so on, initial boarding gate is distributed to all flights to be allocated, and to the g parameter of each boarding gate
It is updated.
It will be appreciated that the method based on greedy algorithm generates initial population, it is ensured that initial population is feasible solution.
In practical applications, after generating initial population, a collison matrix, each of collison matrix can be constructed
Element value indicates whether have time conflict between the corresponding flight of its abscissa and the corresponding flight of its ordinate.For example, needle
W to i-th of flight and j-th of flight, in collison matrixijOr wjiIndicate whether have between i-th of flight and j-th of flight
Time conflict, if Ai≥Bj, i.e. the time that j-th of flight leaves will be earlier than the arrival time of i-th of flight, it is seen that the two is not deposited
In time conflict, w can be enabled at this timeij=1, otherwise enable wij=0.
Further, it is based on above-mentioned collison matrix, left function and right function can be constructed:
It is T for a feasible solution, the i-th bit left side genic value of landing gate l, T are that the collection of the position of the gene of l is combined into L,
Genic value is that the collection of the position of the gene of l is combined into R on the right of the i-th bit of T.For example, T=(1223341233), l=2, i=4, then L
={ 2,3 }, R={ 8 }.If set L is empty set, left (T, i, l) is 1, and otherwise left (T, i, l) is wij；If set R is sky
Collection, then right (T, i, l) is 1, and otherwise right (T, i, l) is wij。
Wherein, when left (T, i, l) is assigned a value of wijWhen, wijSubscript in j be that i-th bit left side genic value is on individual T
Near the gene location of near position i in the set of the position of the gene of l.When right (T, i, l) is assigned a value of wijWhen, wijSubscript
In j be the gene that genic value is l on the right of i-th bit on individual T position set near near position i gene location.
Here left function and right function is for use in subsequent hybridization and mutation process.
S200, crossover operation is carried out to the individual in current initial population, obtains the first population；
In practical applications, the operation hybridized to initial population may include following process:
Following steps are executed for the every two individual in current initial population:
S201, a hybridization position is generated at random, and generate a random number in (0,1) range；
For example, two individuals T1 and T2, T1=(i1,i2......ik......in), T2=(j1,
j2......jk......jn), the hybridization position generated at random is k, generates the random number r in (0,1) range.
S202, judge two individuals after the hybridization of the hybridization position with the presence or absence of air flight times conflict and the random number
Whether be less than preset hybrid rate: air flight times conflict and the random number are less than the hybrid rate if it does not exist, then to this two
Individual carries out crossover operation in the hybridization position；
It will be appreciated that air flight times conflict or random number are more than or equal to preset hybrid rate if it exists, then without
Hybridization processing.
In practical applications, it can use above-mentioned left function and right function judge whether there is flight conflict, example
Such as, hybridization position is k, and random number r, two individuals are above-mentioned T1 and T2, if left (T1, k, jk)×right(T1,k,jk)=
1, then flight conflict is not present, otherwise, there are air flight times conflicts.
Based on the definition above to left function and right function it is found that if set L be empty set, left (T1, k,
jk) it is 1, otherwise, left (T1, k, jk) it is wkj；Set L is that the genic value on individual T1 on the left of gene location k is jkGene
The set of position, the gene location k are hybridization position；If set R is empty set, right (T1, k, jk) it is 1, otherwise right
(T1,k,jk) it is wkj；Set R is that the genic value on individual T1 on the right side of gene location k is jkGene position set；wkjFor
Element in the collison matrix W of individual T1, if the arrival time of upper k-th of the flight of individual TI is greater than or equal to j-th of flight
Time departure, then wkjIt is 1, otherwise wkjIt is 0.
Wherein, as left (T1, k, jk) it is assigned a value of wkjWhen, wkjSubscript in j be on individual T1 on the left of gene location k
Genic value is jkGene position set near the position of gene location k.As right (T1, k, jk) it is assigned a value of wkj
When, wkjSubscript in j be genic value on individual T1 on the right side of gene location k be jkGene position set near
The position of gene location k.
It in practical applications, may include: to enable individual T1 in gene position to the individual T1 and T2 process for carrying out hybridization processing
It sets the value on k and is updated to value of the individual T2 on gene location k.It will be appreciated that this is to be carried out using individual T2 to individual T1
The process of hybridization.If two individuals selected are T2 and T1, the value on the gene location k of T2 can be replaced with T1 in base
Because of the value on the k of position.
Wherein, above-mentioned preset hybrid rate can be calculated using following formula:
pc=0.9gen
In formula, pcFor hybrid rate, gen indicates evolutionary generation.
It is found that randomly choosing a gene location in the step as hybridization position, after judging two chromosomal hybridation processing
Whether air flight times can clash, and in case of conflicting, then without hybridization, otherwise be hybridized.
S300, to current initial population and first population and the individual of concentration carry out mutation operation, obtain the
Two populations；
It will be appreciated that not only variation processing is carried out to the individual in current initial population here, after also handling hybridization
Individual carry out variation processing.
In practical applications, to current initial population and first population and the individual of concentration carry out mutation operation
May include: to execute following steps for each of current initial population and the union of first population individual:
S301, it is directed to the individual, randomly chooses the position i an of mutant gene, generates initial first set H0=Ki-
{ki, kiFor boarding gate of the individual on the i of position, KiSet for the boarding gate used for flight i, Ki={ k0,k1,
...kl}；First the number of iterations p=0；
It will be appreciated that position i, that is, flight i of mutant gene.
Here, initial first set is from KiIn remove ki, that is, the set from the boarding gate used for flight i
In to remove boarding gate in the individual on gene location i because of so-called variation be to become the value of the position i of mutant gene
Different from original value, i.e. the corresponding boarding gate position i is varied from original boarding gate.
Here it is provided with the first the number of iterations, initial value 0, maximum the number of iterations is l, as KiIn the last one step on
The subscript of machine mouth is designated as K under thisiThe quantity of middle boarding gate and the sum of 1.
If S302, right (T, i, kx)×left(T,i,ky)=0 is then deleted first in current first set and is stepped on
Machine mouth, to realize the update to first set, kxIt is stepped on to execute first before deletion acts in first set in this step
Machine mouth, kyTo execute the last one boarding gate before deletion acts in first set in this step；Otherwise, first set is kept
It is constant；
Due to kxTo execute first boarding gate before deletion acts in first set, k in this stepyFor in this step
Execute the last one boarding gate before deletion acts in first set, when first time executing step S302, kxFor k0, kyFor
kl。
It will be appreciated that if right (T, i, kx)×left(T,i,ky)=0 then illustrates that there is no boats after the individual variation
Class's time conflict, otherwise, there are air flight times conflicts after the individual variation.The premise of time conflict is not present only after variation
Under, it just will do it update first set, and then make a variation.
S303, judge whether the first the number of iterations reaches KiThe quantity of middle boarding gate and the sum of 1 l:
If so, selecting an integer instead of the position of mutant gene in current first set with preset aberration rate
Boarding gate on i；
Otherwise, the first the number of iterations is added 1, and returns to S302.
It will be appreciated that variation processing is carried out after the first the number of iterations reaches l, specially from current first set
It is middle to select an integer to substitute the value of mutant gene i, that is, substitute the boarding gate on mutant gene i.
Wherein, the calculation formula of preset aberration rate is as follows:
pm=(gen/maxgen)2
In formula, gen indicates that evolutionary generation, maxgen indicate maximum evolutionary generation, pmFor aberration rate.
S400, according to preset objective function, calculate current initial population, first population and second population
And concentrate each individual target function value, the objective function with the flow time of transit passenger most it is short for optimization mesh
Mark；
In practical applications, the optimization aim of objective function is the flow time for minimizing transit passenger in the application.Under
First item in text in objective function is the flow time for minimizing transit passenger, and the current prior art seldom considers transfer trip
The flow time of visitor, it is possible that transit passenger be arranged probability, arrange wrong probability, most short route time etc. that cannot reach
To the case where optimum control, and the application proceeds from reality and considers the convenience of transit passenger.Section 2 is penalty,
The purpose of penalty is to force flights arrangement to one's own boarding gate.The objective function may include:
The wherein parameters meaning in first item: S is target function value；M is the quantity of boarding gate；N is to be allocated
The quantity of flight, HijFor the interline passenger's number for reaching from flight i and being left from flight j；CklIt is gone to for passenger from boarding gate k
The most short route time required for boarding gate l；yikValue is 0 or 1, and value is when flight i is assigned to landing gate k
1, otherwise value is 0；Value is 0 or 1, if flight i can be assigned to landing gate k, value 1, otherwise value is 0.
It will be appreciated that yjlMeaning and yikIt is similar,Meaning withIt is similar.
Wherein in Section 2 parameters meaning: δkValue is 0 or 1, if visitor's transfer failure or flight are not pacified
It is discharged to fixed boarding gate, then δkValue is 0, and otherwise value is 1；M is penalty coefficient, if passenger changes to failure, M value is
360, if the flight of passenger is not allocated to fixed boarding gate, M value is 60.
It will be appreciated that Section 2 increases by 360 minutes, when the flight of a passenger does not have when a passenger transference fails
Have when being assigned to fixed boarding gate, Section 2 increases by 60 minutes, is arranged the advantage of doing so is that increasing passenger as far as possible
Probability and as far as possible reduce by mistake arrange probability.
In practical applications, the constraint condition of above-mentioned target may include as follows:
yikyjk(Bj-Ai)(Bi-Aj)≤0(4)
In formula, KiSet for the boarding gate used for flight i；AiAt the time of beginning to use boarding gate for flight i；Bi
At the time of leaving boarding gate for flight i.
It will be appreciated that above-mentioned formula (1), which ensures that each flight (i.e. each airplane) cannot be distributed to, is not belonging to it
Boarding gate；Above-mentioned formula (2) ensures that each flight is assigned to the boarding gate for belonging to it；Above-mentioned formula (3) ensures each
The arrival time of flight will be earlier than its departure time, that is, time departure；Above-mentioned formula (4), which ensures, is assigned to same boarding gate
Two flights cannot be overlapped in time.Pass through constraint condition, it is ensured that there is no conflicts between flight.
Based on constraint condition, the target function value of each individual can be calculated.It will be appreciated that target function value is got over
It is low, illustrate that the corresponding solution quality of individual is better, and then initial population, the first population and the can be selected according to target function value
Two populations and concentration optimum individual.
S500, judge whether preset termination condition meets:
If so, the minimum individual of target function value as globally optimal solution and is exported；
Otherwise, according to the target function value, from current initial population, first population and second population
And the initial population for selecting multiple individuals to form next iteration process is concentrated, and return to S200.
In practical application, above-mentioned termination condition can be set to the number of iterations, can also be worth the minimum target function value of setting
Value range.For example, when the minimum target function value in S400 is lower than some value, then it is assumed that termination condition meets.Otherwise,
Can from and the individual of concentrating a certain number of target function values of selection minimum form the initial population during next iteration.
Method provided by the invention carries out crossover operation to initial population first, then carries out mutation operation again, calculates a
The target function value of body, objective function is most short for optimization aim with the flow time of transit passenger, and then according to target function value
Initial population of multiple individuals as next iteration process is selected, and then is recycled again, it, will most until meeting termination condition
Whole globally optimal solution output.The optimization aim of objective function in the present invention is the flow time for minimizing transit passenger, can
See that the present invention in boarding gate assignment problem, proceeds from reality and considers transit passenger's most short route matter of time, mention
The high convenience of transit passenger's transfer.The present invention carries out hybridization processing, then the individual obtained from hybridization and initial kind to individual
Individual in group carries out variation processing, and then the union of the individual obtained after initial population, hybridization, the individual obtained after variation
The middle multiple individuals of selection form the initial population of next iteration, not only can improve quality individual in population, and
Can rapid solving, converge to globally optimal solution, reduce the probability for falling into local optimum.In addition, the present invention passes through greedy algorithm
Initial population is generated, for this method than the initial population generated at random, individual quality is higher, accelerates the convergence of algorithm, reduces
A large amount of unnecessary search.
Second aspect, the present invention provide a kind of system for distributing boarding gate for flight, which includes:
Population generation module, for executing S100, generating initial population using greedy algorithm；Wherein, the initial population
Including multiple individuals, each individual corresponds to item chromosome, includes n gene on each chromosome, each gene
Position indicates corresponding flight, and the value of each gene is expressed as the boarding gate that the corresponding flight in its position is distributed, and n is wait divide
The quantity for the flight matched；
Individual hybridization module obtains for executing S200, carrying out crossover operation to the individual in current initial population
One population；
Individual variation module, for execute S300, to current initial population and first population and concentration
Body carries out mutation operation, obtains the second population；
Target computing module calculates current initial population, described for executing S400, according to preset objective function
First population and second population and concentrate the target function value of each individual, the objective function is with transit passenger's
It is optimization aim that flow time is most short；
Judgment module is terminated, for executing S500, judging whether preset termination condition meets:
If so, the minimum individual of target function value as globally optimal solution and is exported；
Otherwise, according to the target function value, from current initial population, first population and second population
And the initial population for selecting multiple individuals to form next iteration process is concentrated, and return and execute in the individual hybridization module
S200。
The third aspect, the application provide a kind of computer readable storage medium, are stored thereon with computer program, the meter
The method that above-mentioned first aspect provides may be implemented in calculation machine program when it is executed by processor.
It will be appreciated that storage medium and first aspect that system and the third aspect that second aspect provides provide provided
Method is corresponding, and the contents such as explanation, citing, beneficial effect in relation to content can refer to the corresponding portion in first aspect,
Details are not described herein again.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
Invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each implementation
Technical solution documented by example is modified or equivalent replacement of some of the technical features；And these modification or
Replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of method for distributing boarding gate for flight characterized by comprising
S100, initial population is generated using greedy algorithm；Wherein, the initial population includes multiple individuals, each individual is right
Item chromosome to be answered, includes n gene on each chromosome, the position of each gene indicates corresponding flight, each
The value of gene is expressed as the boarding gate that the corresponding flight in its position is distributed, and n is the quantity of flight to be allocated；
S200, crossover operation is carried out to the individual in current initial population, obtains the first population；
S300, to current initial population and first population and the individual of concentration carry out mutation operation, obtain second
Group；
S400, according to preset objective function, calculate current initial population, first population and second population and
The target function value of each individual is concentrated, the objective function is most short for optimization aim with the flow time of transit passenger；
S500, judge whether preset termination condition meets:
If so, the minimum individual of target function value as globally optimal solution and is exported；
Otherwise, according to the target function value, from current initial population, the union of first population and second population
The middle multiple individuals of selection form the initial population of next iteration process, and return to S200.
2. the method according to claim 1, wherein the objective function includes:
In formula, S is target function value；M is the quantity of boarding gate；HijFor the interline passenger for reaching from flight i and being left from flight j
Number；CklThe most short route time required for boarding gate l is gone to from boarding gate k for passenger；yikValue is 0 or 1, and if only if
Value is 1 when flight i is assigned to landing gate k, and otherwise value is 0；Value is 0 or 1, if flight i can be assigned to boarding
Door k, then value is 1, and otherwise value is 0；δkValue be 0 or 1, if visitor transfer failure or flight be not arranged to it is fixed
Boarding gate, then δkValue is 0, and otherwise value is 1；M is penalty coefficient, if passenger changes to failure, M value is 360, if passenger
Flight be not allocated to fixed boarding gate, then M value be 60；KiSet for the boarding gate used for flight i.
3. according to the method described in claim 2, it is characterized in that, the bound for objective function includes:
yikyjk(Bj-Ai)(Bi-Aj)≤0
In formula, AiAt the time of beginning to use boarding gate for flight i；BiAt the time of leaving boarding gate for flight i.
4. the method according to claim 1, wherein described generate initial population using greedy algorithm, comprising:
One parameter g is set for each landing gate, and each parameter g is initialized as -1, parameter gkIndicate boarding gate k
Time for leaving of the last one flight；
Each flight to be allocated is from morning to night ranked up according to time departure, then successively each flight is distributed initial
Boarding gate, assigning process includes: to search whether exist in corresponding boarding gate set for each flight to be allocated
Its parameter g is less than the boarding gate that the flight begins to use the time of boarding gate；If it exists, then its parameter g is less than the flight to open
Begin to distribute to the flight using any one boarding gate in the boarding gate of the time of boarding gate, and will any one described boarding
The parameter g of mouth is updated to the time that the flight leaves boarding gate.
5. the method according to claim 1, wherein the individual in current initial population hybridizes
Operation:
Following steps are executed for the every two individual in current initial population:
A hybridization position is generated at random, and generates a random number in (0,1) range；
Judge whether two individuals are less than after the hybridization of the hybridization position with the presence or absence of air flight times conflict and the random number
Preset hybrid rate；Air flight times conflict and the random number are less than the hybrid rate if it does not exist, then exist to two individuals
The hybridization position carries out crossover operation.
6. according to the method described in claim 5, it is characterized in that, two individuals of the judgement are after the hybridization position hybridizes
It is no that there are air flight times conflicts, comprising:
If left (T1, k, jk)×right(T1,k,jkFlight conflict is then not present in)=1, and otherwise, there are air flight times conflicts；
Wherein, if set L is empty set, left (T1, k, jk) it is 1, otherwise, left (T1, k, jk) it is wkj；Set L is individual T1
Genic value on the left of upper gene location k is jkGene position set, the gene location k be hybridization position；T1 and T2 are
Described two individuals, T1=(i1,i2......ik......in), T2=(j1,j2......jk......jn)；If set R is sky
Collect, then right (T1, k, jk) it is 1, otherwise right (T1, k, jk) it is wkj；Set R is on individual T1 on the right side of gene location k
Genic value is jkGene position set；wkjFor the element in the collison matrix W of individual T1, if upper k-th of the boat of individual TI
The arrival time of class is greater than or equal to the time departure of j-th of flight, then wkjIt is 1, otherwise wkjIt is 0；
Corresponding, described pair of two individuals carry out crossover operation in the hybridization position, comprising: enable individual T1 in gene location k
On value be updated to value of the individual T2 on gene location k.
7. according to the method described in claim 6, it is characterized in that, described to current initial population and first population
And the individual concentrated carries out mutation operation:
Following steps are executed for each of current initial population and the union of first population individual:
S301, it is directed to the individual, randomly chooses the position i an of mutant gene, generates initial first set H0=Ki-{ki,
kiFor boarding gate of the individual on the i of position, KiSet for the boarding gate used for flight i, Ki={ k0,k1,...kl}；
First the number of iterations p=0；
If S302, right (T, i, kx)×left(T,i,kyFirst boarding in current first set is then deleted in)=0
Mouthful, to realize the update to first set, kxTo execute first boarding before deletion acts in first set in this step
Mouthful, kyTo execute the last one boarding gate before deletion acts in first set in this step；Otherwise, first set is kept not
Become；
S303, judge whether the first the number of iterations reaches KiThe quantity of middle boarding gate and the sum of 1 l:
If so, an integer is selected to replace on the position i of mutant gene in current first set with preset aberration rate
Boarding gate；
Otherwise, the first the number of iterations is added 1, and returns to S302.
8. the method according to the description of claim 7 is characterized in that the preset hybrid rate are as follows: pc=0.9gen；And/or institute
State preset aberration rate are as follows: pm=(gen/maxgen)2；In formula, gen indicates that evolutionary generation, maxgen indicate maximum and evolve generation
Number, pcFor hybrid rate, pmFor aberration rate.
9. a kind of system for distributing boarding gate for flight characterized by comprising
Population generation module, for executing S100, generating initial population using greedy algorithm；Wherein, the initial population includes
Multiple individuals, each individual correspond to item chromosome, include n gene, the position of each gene on each chromosome
Indicate corresponding flight, the value of each gene is expressed as the boarding gate that the corresponding flight in its position is distributed, and n is to be allocated
The quantity of flight；
Individual hybridization module obtains the first for executing S200, carrying out crossover operation to the individual in current initial population
Group；
Individual variation module, for execute S300, to current initial population and first population and concentration individual into
Row variation operation, obtains the second population；
Target computing module calculates current initial population, described first for executing S400, according to preset objective function
Population and second population and concentrate the target function value of each individual, the objective function is with the process of transit passenger
It is optimization aim that time is most short；
Judgment module is terminated, for executing S500, judging whether preset termination condition meets:
If so, the minimum individual of target function value as globally optimal solution and is exported；
Otherwise, according to the target function value, from current initial population, the union of first population and second population
The middle multiple individuals of selection form the initial population of next iteration process, and return in the individual hybridization module and execute S200.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
Method as described in any one of claims 1 to 8 can be realized when being executed by processor.
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