CN114943391A - Airport resource scheduling method based on NSGA II - Google Patents

Airport resource scheduling method based on NSGA II Download PDF

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CN114943391A
CN114943391A CN202210887925.7A CN202210887925A CN114943391A CN 114943391 A CN114943391 A CN 114943391A CN 202210887925 A CN202210887925 A CN 202210887925A CN 114943391 A CN114943391 A CN 114943391A
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population
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李富磊
张建翔
徐立中
田秋生
刘晓疆
陈晓
刘青
战嘉馨
丁继存
李坤
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Qingdao Civil Aviation Cares Co ltd
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Abstract

The invention relates to an NSGA II-based airport resource scheduling method, which belongs to the technical field of civil aviation and comprises the following steps: s1, defining constraint conditions of resource scheduling and defining a plurality of targets for quantifying resource scheduling; s2, randomly generating a solution set meeting the resource scheduling constraint condition as an initial solution set; s3, generating a new individual set by the initial population through selection, crossing and variation; selecting a new population of the elite individual set by the new individual set through non-dominated rapid sorting and congestion degree calculation; s4, generating a next generation of population by taking the new population of elite individuals as a father population, and repeating iteration to reach a certain algebra; and S5, selecting an optimal individual from the iterated latest population. The invention has the advantages that: based on the multi-objective optimization idea, a better resource scheduling scheme can be given in a short time under the condition of considering multiple targets of resource scheduling, and the method is applied to daily operation of airports, and each target of scheduling obtains a good optimization effect.

Description

Airport resource scheduling method based on NSGA II
Technical Field
The invention relates to an NSGA II-based airport resource scheduling method, and belongs to the application of NSGA II in the technical field of civil aviation.
Background
In the daily operation of an airport, when a flight stops at the airport, security resources such as a parking space, a baggage picking turntable, a baggage sorting turntable, a boarding gate, security vehicles, security personnel and the like need to be scheduled for the flight so as to meet the normal security requirements. Several quantities of allocated resources are allocated to a certain number of flights, and on the premise of meeting a certain constraint rule, various allocation schemes also exist. The quality of the allocation scheme directly influences the utilization efficiency of airport resources and guarantees the service quality level.
In actual airport operations, scheduling of airport resources requires consideration of a number of factors. For example, when the guarantee personnel distribute the guarantee tasks, the guarantee capability of the guarantee personnel, the balance of the total workload of the guarantee personnel, the dispersion of the personal guarantee work on time and other factors are considered; factors such as the bridge approach rate of flights, the use balance of the flight level, the condition of the applicable model of the flight level and the like which need to be considered in the flight level distribution. At present, two modes of manual or computer automatic allocation based on business rules are mainly adopted in airport resource allocation. In the manual scheduling mode, a dispatcher allocates airport resources for each flight according to experience, so that the workload is large, the efficiency is low, only one feasible scheme is generated according to an operation result, the quality of the allocation scheme cannot be determined, and the randomness of the quality of the allocation scheme influenced by human factors is large. The second method is that a plurality of scheduling schemes can be generated by using a computer according to scheduling rules, the generated schemes have advantages and disadvantages on a plurality of targets, the generated schemes are mostly weighted by each target to calculate total scores for comparison, meanings required by different targets are different from quantification results, and the problem of incorporating the schemes into a unified platform for comparison is difficult. How to ensure to generate a better scheme and design a scheme quality comparison method is the key for realizing an automatic scheduling scheme compared with the best scheme as far as possible.
With the rapid development of the civil aviation industry, the number of flights is increased sharply, and how to efficiently provide a scheduling scheme of airport resources under the condition of considering balance and multiple optimal objectives is an urgent problem to be solved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an airport resource scheduling method based on NSGA II, and the technical scheme of the invention is as follows:
an airport resource scheduling method based on NSGA II comprises the following steps:
s1, defining constraint conditions of resource scheduling and defining a plurality of targets for quantifying resource scheduling;
s2, randomly generating a solution set meeting the resource scheduling constraint condition as an initial solution set;
s3, generating a new individual set by the initial population through selection, crossing and variation; selecting a new population of the elite individual set by the new individual set through non-dominated rapid sorting and congestion degree calculation;
s4, generating a next generation of population by taking the new population of the elite individuals as a parent population, and repeating iteration to reach a certain algebra;
and S5, selecting an optimal individual from the iterated latest population.
In step S1, the constraint condition for defining resource scheduling specifically includes: suppose a guaranteed task
Figure 178433DEST_PATH_IMAGE001
Requiring allocation of support personnel
Figure 564415DEST_PATH_IMAGE002
That is, n security personnel complete m security tasks; suppose that the support staff
Figure 78573DEST_PATH_IMAGE003
Incapacity of completing guarantee task
Figure 896618DEST_PATH_IMAGE004
Tasks cannot be assigned in the feasible solution generated
Figure 914253DEST_PATH_IMAGE005
Allocating resources
Figure 454824DEST_PATH_IMAGE006
(ii) a Generating time constraint by a scheduling scheme, and generating a Pareto optimal solution within a specified time; setting population genetic algebra
Figure 925120DEST_PATH_IMAGE007
And calculating the optimal scheme of resource scheduling in a specified time.
In the step S1, the multiple objectives of the quantized resource scheduling are specifically defined as:
the scheduling scheme balances the total workload of each guarantee staff, the target A represents the total workload balance, and the guarantee staff
Figure 592862DEST_PATH_IMAGE008
，
Figure 996161DEST_PATH_IMAGE009
，…
Figure 495186DEST_PATH_IMAGE010
，
Figure 718357DEST_PATH_IMAGE011
Total workload is a, b, … x, y; general assemblyMean value of work load
Figure 924210DEST_PATH_IMAGE012
Scheduling scheme target a score a =
Figure 916437DEST_PATH_IMAGE014
The smaller A is, the optimal target is represented; according to the current position of the guarantee staff, a nearby dispatching principle is adopted, so that the average moving distance of the guarantee staff is as short as possible, the average moving distance of the guarantee staff is set as a target B, the moving distance is obtained according to the last guarantee working position of the guarantee staff and the position of the distribution guarantee task, and the target B is equal to the average moving distance of the guarantee staff; the scheduling scheme considers the time interval between two guarantee tasks of each guarantee staff, the minimum time of the task interval is set as a target C, and C is equal to the minimum interval time of the two guarantee tasks of each guarantee staff.
The step S2 specifically includes: firstly, randomly generating N scheduling schemes meeting the conditions, namely N solutions form an initial solution set
Figure 64391DEST_PATH_IMAGE015
；
Figure 40437DEST_PATH_IMAGE016
Is a first generation population, which has N individuals, each of which is a scheduling scheme.
The step S3 specifically includes: from the first generation of the population
Figure 784402DEST_PATH_IMAGE017
Generating a new individual set through selection, crossover and mutation
Figure 162294DEST_PATH_IMAGE018
，
Figure 231881DEST_PATH_IMAGE018
And first generation population
Figure 695223DEST_PATH_IMAGE017
Are combined into a population
Figure 134557DEST_PATH_IMAGE019
，
Figure 350644DEST_PATH_IMAGE020
(ii) a From
Figure 325553DEST_PATH_IMAGE019
N excellent individuals are selected through non-dominated quick sequencing and congestion degree calculation, namely N resource scheduling schemes with multiple targets being excellent; the N resource scheduling schemes form a solution set
Figure 745033DEST_PATH_IMAGE021
，
Figure 516286DEST_PATH_IMAGE022
Namely a new population of elite personal sets, namely a second generation population.
The step S4 specifically includes: from the second generation
Figure 337612DEST_PATH_IMAGE023
As the father group, the third generation is generated according to the method of step S3
Figure 263848DEST_PATH_IMAGE024
And through the loop iteration, each generation retains the elite individuals of the parent generation, ensures the gradual convergence of the Pareto optimal solution, and passes through
Figure 170625DEST_PATH_IMAGE025
Generation, generation of population
Figure 60083DEST_PATH_IMAGE026
。
The step S5 specifically includes: slave population
Figure 267074DEST_PATH_IMAGE027
And selecting the optimal solution as a final solution, namely a final resource scheduling scheme.
In the step S3, in order to maintain the diversity of individuals, when selecting individuals, selecting individuals discrete from multiple target values, and calculating the crowding degree of the individuals by using a crowding operator, wherein the crowding degree is defined as the density of the individuals around the individuals; due to three targets of the total workload balance degree, the average movement distance of the guaranteed personnel and the minimum time of the individual task interval, the congestion degree is calculated three times, and the steps are as follows:
step 1: initializing the individual crowdedness degree in the same level of the dominance degree to be 0;
step 2: firstly, carrying out individual sorting according to a target value of the target total workload balance degree;
and step 3: setting the crowding of the first and last individuals
Figure 849365DEST_PATH_IMAGE028
；
And 4, step 4: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 259749DEST_PATH_IMAGE029
；
And 5: sequencing the individuals from small to large according to the target value of the average moving distance of the guarantee personnel;
step 6: setting the crowding of the first and last individuals
Figure 811953DEST_PATH_IMAGE030
；
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 342291DEST_PATH_IMAGE031
；
And step 8: sequencing the individuals from small to large according to the target value of the minimum time of the individual task interval;
and step 9: setting the crowding of the first and last individuals
Figure 95483DEST_PATH_IMAGE032
；
Step 10: starting from the second individual to the penultimateTwo individuals, calculating the crowdedness of each individual
Figure 193496DEST_PATH_IMAGE033
(ii) a And after the crowding degrees are sorted from large to small, selecting excellent individuals as next generation population individuals in sequence.
In the step S5, based on the consideration of the total workload balance, the average movement distance of the guaranteed persons, and the minimum time of the personal task interval, the optimal individual is selected, the preference weights of the multiple targets are set, and the weights are set to
Figure 159178DEST_PATH_IMAGE034
、
Figure 58870DEST_PATH_IMAGE035
、
Figure 717384DEST_PATH_IMAGE036
(ii) a Selecting the optimal individual:
step 1: sorting the advantages and the disadvantages according to the total workload balance degree target A;
step 2: slave population
Figure 351628DEST_PATH_IMAGE027
Eliminating the worst target value A
Figure 386580DEST_PATH_IMAGE037
Individual, reserve
Figure 907822DEST_PATH_IMAGE038
(ii) individuals;
and step 3: retained
Figure 2817DEST_PATH_IMAGE038
The individuals are sorted according to the average moving distance target B of the security personnel;
and 5: eliminating the worst target value B
Figure 858778DEST_PATH_IMAGE039
Individual, reserve
Figure 556475DEST_PATH_IMAGE040
(ii) individuals;
step 6: and sorting the retained individual sets according to the minimum time target of the personal task interval, selecting the optimal individual, and finally screening out the individual which is the optimal scheduling scheme of the airport resource.
In step S3, the new set of individuals is selected by:
individuals
Figure 398136DEST_PATH_IMAGE041
The total workload balance, the average moving distance of the guaranteed personnel and the minimum time of the individual task interval are all superior to the total workload balance
Figure 664032DEST_PATH_IMAGE042
Then, then
Figure 741710DEST_PATH_IMAGE043
Dominating
Figure 649623DEST_PATH_IMAGE044
(ii) a If it is
Figure 394725DEST_PATH_IMAGE045
Is superior to the target A
Figure 80790DEST_PATH_IMAGE046
，
Figure 911343DEST_PATH_IMAGE044
Is superior to target B
Figure 357368DEST_PATH_IMAGE047
The advantages and disadvantages of each target are mutually good and bad,
Figure 956976DEST_PATH_IMAGE048
and
Figure 830254DEST_PATH_IMAGE049
are not in dominant relationship; can govern
Figure 148103DEST_PATH_IMAGE044
The number of individuals is
Figure 617393DEST_PATH_IMAGE050
The degree of dominance of (1) is that n individuals can be dominated in traversing population individuals
Figure 337087DEST_PATH_IMAGE051
，
Figure 381267DEST_PATH_IMAGE052
The dominance of (c) is n; carrying out layered positive sequence sequencing on the population individuals, wherein the individuals with the same dominance degree are divided into the same layer; selecting good individuals from a level with low dominance, selecting a solution set with 0 dominance, selecting a solution set with 1 dominance, and so on, and selecting N individuals to form a new generation of population, namely a new individual set.
The invention has the advantages that: based on the multi-objective optimization idea, a better resource scheduling scheme can be given in a short time under the condition of considering multiple targets of resource scheduling, and the method is applied to daily operation of airports, and each target of scheduling obtains a good optimization effect.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
FIG. 2 is a schematic cross-section of an individual of the present invention.
FIG. 3 is a schematic diagram of the individual variations of the present invention.
Detailed Description
The invention will be further described with reference to specific embodiments, and the advantages and features of the invention will become apparent as the description proceeds. These examples are illustrative only and do not limit the scope of the present invention in any way. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be made without departing from the spirit and scope of the invention.
Referring to fig. 1 to 3, the present invention relates to an airport resource scheduling method based on NSGA ii, which includes the following steps:
s1, defining constraint conditions of resource scheduling and defining a plurality of targets for quantifying resource scheduling;
s2, randomly generating a solution set meeting the resource scheduling constraint condition as an initial solution set;
s3, generating a new individual set by the initial population through selection, crossing and variation; selecting a new population of the elite individual set by the new individual set through non-dominated rapid sorting and congestion degree calculation;
s4, generating a next generation of population by taking the new population of elite individuals as a father population, and repeating iteration to reach a certain algebra;
and S5, selecting an optimal individual from the iterated latest population.
In step S1, the constraint condition for defining resource scheduling specifically includes: suppose a guaranteed task
Figure 186412DEST_PATH_IMAGE053
Requiring allocation of support personnel
Figure 974239DEST_PATH_IMAGE054
That is, n security personnel complete m security tasks; suppose that the support staff
Figure 282861DEST_PATH_IMAGE055
Incapability of completing guarantee task
Figure 497941DEST_PATH_IMAGE056
Tasks cannot be assigned in the feasible solution generated
Figure 774071DEST_PATH_IMAGE057
Allocating resources
Figure 365589DEST_PATH_IMAGE055
(ii) a Generating time constraint by a scheduling scheme, and generating a Pareto optimal solution within a specified time; setting population genetic algebra
Figure 794296DEST_PATH_IMAGE058
And calculating the optimal scheme of resource scheduling in a specified time.
In the step S1, the multiple objectives of the quantized resource scheduling are specifically defined as:
the scheduling scheme balances the total workload of each support staff, the target A represents the total workload balance, and the support staff
Figure 180278DEST_PATH_IMAGE059
，
Figure 694436DEST_PATH_IMAGE060
，…
Figure 824066DEST_PATH_IMAGE061
，
Figure 107280DEST_PATH_IMAGE062
Total workload is a, b, … x, y; mean of total workload
Figure 677545DEST_PATH_IMAGE063
Scheduling scheme target a score a =
Figure 413420DEST_PATH_IMAGE064
The smaller A is, the optimal target is represented; according to the position of the current guarantee personnel, a nearby dispatching principle is adopted, so that the average moving distance of the guarantee personnel is as short as possible, the average moving distance of the guarantee personnel is set as a target B, the moving distance is obtained according to the last guarantee working position of the guarantee personnel and the position of the distribution guarantee task, and the target B is equal to the average moving distance of the guarantee personnel; the scheduling scheme considers the time interval between two guarantee tasks of each guarantee staff, the minimum time of the task interval is set as a target C, and C is equal to the minimum interval time of the two guarantee tasks of each guarantee staff.
The step S2 specifically includes: firstly, randomly generating N scheduling schemes meeting the conditions, wherein the N scheduling schemes are N solutions to form an initial solution set
Figure 346741DEST_PATH_IMAGE015
；
Figure 484461DEST_PATH_IMAGE065
Is a first generation population, which has N individuals, each of which is a scheduling scheme.
Setting the size of the population as N, and generating a 1 st generation population. n security personnel distribute m security tasks, and each security personnel distributes randomly
Figure 477825DEST_PATH_IMAGE066
Or
Figure 966575DEST_PATH_IMAGE067
Generating a distribution method for each guarantee task, detecting whether the distribution method meets the constraint condition, and if so, generating a distribution method for the guarantee task
Figure 438008DEST_PATH_IMAGE004
Distribution to support personnel without corresponding support capability
Figure 945082DEST_PATH_IMAGE068
Adopting greedy strategy will
Figure 843768DEST_PATH_IMAGE004
Support personnel
Figure 554235DEST_PATH_IMAGE068
Changed to have corresponding security capability
Figure 829358DEST_PATH_IMAGE069
If the allocation method meets the constraint condition, a solution of resource scheduling, namely 1 individual of the population, is obtained. Randomly generating N individuals to form a 1 st generation population
Figure 207250DEST_PATH_IMAGE070
(parent population).
The step S3 specifically includes: from the first generation of the population
Figure 276837DEST_PATH_IMAGE071
Through selection and crossingGenerating new individual set through mutation
Figure 740179DEST_PATH_IMAGE018
，
Figure 38568DEST_PATH_IMAGE018
And first generation population
Figure 270966DEST_PATH_IMAGE017
Are combined into a population
Figure 511454DEST_PATH_IMAGE019
，
Figure 462093DEST_PATH_IMAGE072
(ii) a From
Figure 79019DEST_PATH_IMAGE019
N excellent individuals are selected through non-dominated quick sequencing and congestion degree calculation, namely N resource scheduling schemes with multiple targets being excellent; the N resource scheduling schemes form a solution set
Figure 900345DEST_PATH_IMAGE073
，
Figure 826581DEST_PATH_IMAGE023
Namely a new population of elite individual sets, namely a second generation population.
As shown in FIG. 2, the new filial individuals are generated by the parent population through selection, crossover and mutation.
Guarantee mission
Figure 998936DEST_PATH_IMAGE074
、
Figure 153974DEST_PATH_IMAGE075
、
Figure 360965DEST_PATH_IMAGE076
Assigned support staff, individuals
Figure 943256DEST_PATH_IMAGE077
Is solved by
Figure 337328DEST_PATH_IMAGE078
、
Figure 296057DEST_PATH_IMAGE079
、
Figure 105356DEST_PATH_IMAGE080
Individual, individual
Figure 858549DEST_PATH_IMAGE081
Is solved by
Figure 5496DEST_PATH_IMAGE082
、
Figure 236758DEST_PATH_IMAGE083
、
Figure 152761DEST_PATH_IMAGE084
Individual, individual
Figure 342434DEST_PATH_IMAGE085
And with
Figure 225945DEST_PATH_IMAGE086
Through crossing and switching
Figure 526476DEST_PATH_IMAGE087
、
Figure 31407DEST_PATH_IMAGE088
、
Figure 391981DEST_PATH_IMAGE089
Provisioning of fragments to form two new solutions
Figure 513521DEST_PATH_IMAGE090
、
Figure 352164DEST_PATH_IMAGE091
。
As shown in FIG. 3, individuals are selected
Figure 242760DEST_PATH_IMAGE092
Performing variation and random modification guarantee tasks
Figure 524968DEST_PATH_IMAGE093
、
Figure 868224DEST_PATH_IMAGE094
A support staff of
Figure 510558DEST_PATH_IMAGE095
、
Figure 255660DEST_PATH_IMAGE096
Is changed into
Figure 958037DEST_PATH_IMAGE097
、
Figure 788590DEST_PATH_IMAGE098
Generating a new solution
Figure 969035DEST_PATH_IMAGE099
And the new solution meets the constraint condition, namely the new individual is obtained.
And combining the generated individual group and the parent group into a group. And selecting N excellent individuals from the combined population as a new parent population.
In step S3, the new set of individuals is selected by:
individuals
Figure 83491DEST_PATH_IMAGE100
The total workload balance, the average moving distance of the guaranteed personnel and the minimum time of the individual task interval are all superior to the total workload balance
Figure 956769DEST_PATH_IMAGE051
，
Figure 9039DEST_PATH_IMAGE101
Then govern
Figure 993175DEST_PATH_IMAGE102
(ii) a If it is
Figure 712869DEST_PATH_IMAGE100
Is superior to the target A
Figure 757049DEST_PATH_IMAGE103
，
Figure 562194DEST_PATH_IMAGE104
Is superior to target B
Figure 832245DEST_PATH_IMAGE105
The advantages and disadvantages of each target are mutually good and bad,
Figure 406445DEST_PATH_IMAGE106
and
Figure 621526DEST_PATH_IMAGE107
are not mutually dominant; can govern
Figure 648388DEST_PATH_IMAGE103
The number of individuals is
Figure 239906DEST_PATH_IMAGE108
The degree of dominance of (1) is that n individuals can be dominated in traversing population individuals
Figure 668614DEST_PATH_IMAGE109
，
Figure 54596DEST_PATH_IMAGE103
The dominance of (c) is n; carrying out layered positive sequence sequencing on population individuals, wherein individuals with the same dominance degree are divided into the same layer; selecting good individuals from a level with low dominance, selecting a solution set with 0 dominance, selecting a solution set with 1 dominance, and so on, and selecting N individuals to form a new generation of population, namely a new individual set.
In step S3, the population size of the new generation population is satisfied, and some individuals are selected from the same-level individuals with the same dominance degree to enter the new population, and the remaining individuals are discarded to be eliminated. In order to keep the diversity of individuals, selecting the individuals which are mutually discrete on a plurality of target values when selecting the individuals, and calculating the crowding degree of the individuals by using a crowding operator, wherein the crowding degree is defined as the density of the individuals around the individuals; due to the three targets of the total workload balance degree, the average movement distance of the guarantee staff and the minimum time of the individual task interval, the congestion degree is calculated for three times, and the steps are as follows:
step 1: initializing the individual crowdedness degree in the same level of the dominance degree to be 0;
step 2: firstly, carrying out individual sequencing according to a target value of the target total workload balance;
and step 3: setting the crowding of the first and last individuals
Figure 83600DEST_PATH_IMAGE110
；
And 4, step 4: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 947651DEST_PATH_IMAGE111
；
And 5: sequencing the individuals from small to large according to the target value of the average moving distance of the guarantee personnel;
step 6: setting the crowding of the first and last individuals
Figure 230865DEST_PATH_IMAGE112
；
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 53327DEST_PATH_IMAGE113
；
And 8: sequencing the individuals from small to large according to the target value of the minimum time of the individual task interval;
step 6: setting the crowding of the first and last individuals
Figure 54781DEST_PATH_IMAGE114
；
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 722523DEST_PATH_IMAGE115
(ii) a And after the crowding degrees are sorted from large to small, selecting excellent individuals as next generation population individuals in sequence.
The step S4 specifically includes: from the second generation
Figure 610976DEST_PATH_IMAGE116
Generating a third generation as a parent population according to the method of step S3, and iterating in the loop
Figure 604340DEST_PATH_IMAGE024
Each generation retains the elite individuals of the parent generation, ensures the gradual convergence of the Pareto optimal solution, and passes through
Figure 561931DEST_PATH_IMAGE117
Generation, generation of population
Figure 502206DEST_PATH_IMAGE118
。
The step S5 specifically includes: slave population
Figure 9279DEST_PATH_IMAGE119
And selecting the optimal solution as a final solution, namely a final resource scheduling scheme.
In the step S5, based on the consideration of the total workload balance, the average movement distance of the guaranteed persons, and the minimum time of the personal task interval, the optimal individual is selected, the preference weights of the multiple targets are set, and the weights are set to
Figure 907965DEST_PATH_IMAGE120
、
Figure 884011DEST_PATH_IMAGE121
、
Figure 159135DEST_PATH_IMAGE122
(ii) a Selecting the optimal individual:
step 1: sorting the advantages and the disadvantages according to the total workload balance degree target A;
step 2: slave population
Figure 271447DEST_PATH_IMAGE123
Eliminating the worst target value A
Figure 341035DEST_PATH_IMAGE124
Individual, reserve
Figure 538798DEST_PATH_IMAGE125
(ii) individuals;
and step 3: retained
Figure 365415DEST_PATH_IMAGE126
The individuals are sorted according to the average moving distance target B of the security personnel;
and 5: deselecting the target B that is worst
Figure 597813DEST_PATH_IMAGE127
Individual, reserve
Figure DEST_PATH_IMAGE128
(ii) individuals;
and 6: and sorting the retained individual sets according to the minimum time target of the personal task interval, selecting the optimal individual, and finally screening out the individual which is the optimal scheduling scheme of the airport resource.
The method can give consideration to a plurality of targets and can generate a Pareto optimal solution which is in line with the practical use of airport operation in a short time.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (10)

1. An airport resource scheduling method based on NSGA II is characterized by comprising the following steps:
s1, defining constraint conditions of resource scheduling and defining a plurality of targets for quantifying resource scheduling;
s2, randomly generating a solution set meeting the resource scheduling constraint condition as an initial solution set;
s3, generating a new individual set by the initial population through selection, crossing and variation; selecting a new population of the elite individual set by the new individual set through non-dominated rapid sorting and congestion degree calculation;
s4, generating a next generation of population by taking the new population of elite individuals as a father population, and repeating iteration to reach a certain algebra;
and S5, selecting an optimal individual from the iterated latest population.
2. The NSGA II-based airport resource scheduling method of claim 1, wherein in said step S1, the constraint condition for defining resource scheduling is specifically: suppose a guaranteed task
Figure 948684DEST_PATH_IMAGE001
Requiring allocation of support personnel
Figure 610609DEST_PATH_IMAGE002
I.e. by
Figure 280625DEST_PATH_IMAGE003
Is completed by individual security personnel
Figure 864053DEST_PATH_IMAGE004
A guarantee task; suppose that the support staff
Figure 910507DEST_PATH_IMAGE005
Incapacity of completing guarantee task
Figure 161359DEST_PATH_IMAGE006
Tasks cannot be assigned in the feasible solution generated
Figure 500812DEST_PATH_IMAGE007
Allocating resources
Figure 571536DEST_PATH_IMAGE005
(ii) a Generating time constraint by a scheduling scheme, and generating a Pareto optimal solution within a specified time; setting population genetic algebra
Figure 890522DEST_PATH_IMAGE008
And calculating the optimal scheme of resource scheduling in a specified time.
3. The NSGA II-based airport resource scheduling method of claim 1, wherein in said step S1, a plurality of objectives for defining quantized resource scheduling are specifically:
the scheduling scheme balances the total workload of each guarantee staff, the target A represents the total workload balance, and the guarantee staff
Figure 261460DEST_PATH_IMAGE009
，
Figure 7699DEST_PATH_IMAGE010
，…，
Figure 831299DEST_PATH_IMAGE011
，
Figure 953976DEST_PATH_IMAGE012
Total workload is a, b, …, x, y; mean of total workload
Figure 179421DEST_PATH_IMAGE013
Scheduling scheme target a score a =
Figure 362140DEST_PATH_IMAGE014
The smaller A is, the optimal target is represented; according to the position of the current guarantee personnel, a nearby dispatching principle is adopted, so that the average moving distance of the guarantee personnel is as short as possible, the average moving distance of the guarantee personnel is set as a target B, the moving distance is obtained according to the last guarantee working position of the guarantee personnel and the position of the distribution guarantee task, and the target B is equal to the average moving distance of the guarantee personnel; the scheduling scheme considers the time interval between two guarantee tasks of each guarantee staff, the minimum time of the task interval is set as a target C, and C is equal to the minimum interval time of the two guarantee tasks of each guarantee staff.
4. The NSGA II-based airport resource scheduling method of claim 2 or 3, wherein said step S2 is specifically: firstly, randomly generating N scheduling schemes meeting the conditions, namely N solutions form an initial solution set
Figure 407457DEST_PATH_IMAGE015
；
Figure 333824DEST_PATH_IMAGE016
Is a first generation population, which has N individuals, each of which is a scheduling scheme.
5. The NSGA II-based airport resource scheduling method of claim 4, wherein said step S3 specifically comprises: from the first generation of the population
Figure 915241DEST_PATH_IMAGE017
Generating a new individual set through selection, crossover and mutation
Figure 3282DEST_PATH_IMAGE018
，
Figure 801474DEST_PATH_IMAGE018
And first generation population
Figure 265954DEST_PATH_IMAGE019
Are combined into a population
Figure 465991DEST_PATH_IMAGE020
，
Figure 724934DEST_PATH_IMAGE021
(ii) a From
Figure 10422DEST_PATH_IMAGE020
N excellent individuals are selected through non-dominated quick sequencing and congestion degree calculation, namely N resource scheduling schemes with multiple targets being excellent; the N resource scheduling schemes form a solution set
Figure 278592DEST_PATH_IMAGE022
，
Figure 67556DEST_PATH_IMAGE023
Namely a new population of elite personal sets, namely a second generation population.
6. The NSGA II-based airport resource scheduling method of claim 5, wherein said step S4 specifically comprises: from the second generation
Figure 762980DEST_PATH_IMAGE024
As the father group, the third generation is generated according to the method of step S3
Figure 270185DEST_PATH_IMAGE025
And through the loop iteration, each generation retains the elite individuals of the parent generation, ensures the gradual convergence of the Pareto optimal solution, and passes through
Figure 76467DEST_PATH_IMAGE026
Generation, generation of population
Figure 484052DEST_PATH_IMAGE027
。
7. The NSGA II-based airport resource scheduling method of claim 5, wherein said step S5 specifically comprises: slave population
Figure 350377DEST_PATH_IMAGE028
And selecting the optimal solution as a final solution, namely a final resource scheduling scheme.
8. The NSGA II-based airport resource scheduling method of claim 5, wherein in said step S3, in order to maintain the diversity of individuals, when selecting individuals, selecting individuals discrete from each other in terms of multi-objective values, and calculating the crowding degree of the individuals using a crowding operator, the crowding degree being defined as the density of the individuals around the individuals; due to three targets of the total workload balance degree, the average movement distance of the guaranteed personnel and the minimum time of the individual task interval, the congestion degree is calculated three times, and the steps are as follows:
step 1: initializing the individual crowdedness degree in the same level of the dominance degree to be 0;
step 2: firstly, carrying out individual sequencing according to a target value of the target total workload balance;
and step 3: setting the crowdedness of the first and the last individuals
Figure 344878DEST_PATH_IMAGE029
；
And 4, step 4: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 954850DEST_PATH_IMAGE030
；
And 5: sequencing the individuals from small to large according to the average moving distance target value of the guarantee personnel;
step 6: setting the crowding of the first and last individuals
Figure 452828DEST_PATH_IMAGE031
；
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 755633DEST_PATH_IMAGE032
；
And 8: sequencing the individuals from small to large according to the target value of the minimum time of the individual task interval;
and step 9: setting the crowding of the first and last individuals
Figure 971851DEST_PATH_IMAGE033
；
Step 10: calculating the crowdedness degree of each individual from the second individual to the penultimate individual
Figure 119936DEST_PATH_IMAGE034
(ii) a And after the crowding degrees are sorted from large to small, selecting excellent individuals as next generation population individuals in sequence.
9. The NSGA II-based airport resource scheduling method of claim 7, wherein in said step S5, based on the consideration of three objectives of total workload balance, guaranteed average moving distance of personnel and minimum time of individual task interval, the optimal individual is selected, the preference weights of multiple objectives are set, and the weights are set to be
Figure 3578DEST_PATH_IMAGE035
、
Figure 477285DEST_PATH_IMAGE036
、
Figure 947842DEST_PATH_IMAGE037
(ii) a Selecting the optimal individual:
step 1: sorting the advantages and the disadvantages according to the total workload balance degree target A;
step 2: slave population
Figure 899618DEST_PATH_IMAGE038
Eliminating the worst target value A
Figure 637767DEST_PATH_IMAGE039
Individual, reserve
Figure 16796DEST_PATH_IMAGE040
(ii) individuals;
and 3, step 3: retained
Figure 473185DEST_PATH_IMAGE041
The individuals sort the advantages and the disadvantages according to the average moving distance target B of the security personnel;
and 5: deselecting the target B that is worst
Figure 963072DEST_PATH_IMAGE042
Individual, reserve
Figure 821306DEST_PATH_IMAGE043
(ii) individuals;
and 6: and sorting the retained individual sets according to the minimum time target of the personal task interval, selecting the optimal individual, and finally screening out the individual which is the optimal scheduling scheme of the airport resource.
10. The NSGA ii based airport resource scheduling method of claim 1, wherein in said step S3, a new set of individuals is selected by:
individuals
Figure 105657DEST_PATH_IMAGE044
The total workload balance, the average moving distance of the guaranteed personnel and the minimum time of the individual task interval are all superior to the total workload balance
Figure 49342DEST_PATH_IMAGE045
Then, then
Figure 342921DEST_PATH_IMAGE046
Dominating
Figure 790082DEST_PATH_IMAGE047
(ii) a If it is
Figure 275028DEST_PATH_IMAGE048
Is superior to the target A
Figure 174851DEST_PATH_IMAGE047
，
Figure 272120DEST_PATH_IMAGE049
Is superior to target B
Figure 839368DEST_PATH_IMAGE050
The advantages and disadvantages of each target are mutually good and bad,
Figure 731100DEST_PATH_IMAGE051
and
Figure 383798DEST_PATH_IMAGE052
are not mutually dominant; can govern
Figure 19179DEST_PATH_IMAGE053
The number of individuals is
Figure 440933DEST_PATH_IMAGE054
The degree of dominance of (1) is that n individuals can be dominated in traversing population individuals
Figure 769146DEST_PATH_IMAGE055
，
Figure 909141DEST_PATH_IMAGE052
The dominance of (c) is n; for seed of another speciesCarrying out hierarchical positive sequence sorting on the group individuals, and dividing the individuals with the same dominance degree into the same level; selecting excellent individuals from a level with low dominance, selecting a solution set with 0 dominance, selecting a solution set with 1 dominance, and so on, and selecting N individuals to form a new generation of population, namely a new individual set.
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