CN114943391A - Airport resource scheduling method based on NSGA II - Google Patents
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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
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 taskRequiring allocation of support personnelThat is, n security personnel complete m security tasks; suppose that the support staffIncapacity of completing guarantee taskTasks cannot be assigned in the feasible solution generatedAllocating resources(ii) a Generating time constraint by a scheduling scheme, and generating a Pareto optimal solution within a specified time; setting population genetic algebraAnd 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, ,…,Total workload is a, b, … x, y; general assemblyMean value of work loadScheduling scheme target a score a =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;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 populationGenerating a new individual set through selection, crossover and mutation,And first generation populationAre combined into a population,(ii) a FromN 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,Namely a new population of elite personal sets, namely a second generation population.
The step S4 specifically includes: from the second generationAs the father group, the third generation is generated according to the method of step S3And 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 throughGeneration, generation of population。
The step S5 specifically includes: slave populationAnd 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 4, step 4: calculating the crowdedness degree of each individual from the second individual to the penultimate individual;
And 5: sequencing the individuals from small to large according to the target value of the average moving distance of the guarantee personnel;
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual;
And step 8: sequencing the individuals from small to large according to the target value of the minimum time of the individual task interval;
Step 10: starting from the second individual to the penultimateTwo individuals, calculating the crowdedness of each individual(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、、(ii) a Selecting the optimal individual:
step 1: sorting the advantages and the disadvantages according to the total workload balance degree target A;
and step 3: retainedThe individuals are sorted according to the average moving distance target B of the security personnel;
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:
individualsThe 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 balanceThen, thenDominating(ii) a If it isIs superior to the target A, Is superior to target BThe advantages and disadvantages of each target are mutually good and bad,andare not in dominant relationship; can governThe number of individuals isThe degree of dominance of (1) is that n individuals can be dominated in traversing population individuals,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.
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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 taskRequiring allocation of support personnelThat is, n security personnel complete m security tasks; suppose that the support staffIncapability of completing guarantee taskTasks cannot be assigned in the feasible solution generatedAllocating resources(ii) a Generating time constraint by a scheduling scheme, and generating a Pareto optimal solution within a specified time; setting population genetic algebraAnd 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 ,,…,Total workload is a, b, … x, y; mean of total workloadScheduling scheme target a score a =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;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 randomlyOrGenerating 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 taskDistribution to support personnel without corresponding support capabilityAdopting greedy strategy willSupport personnelChanged to have corresponding security capabilityIf 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(parent population).
The step S3 specifically includes: from the first generation of the populationThrough selection and crossingGenerating new individual set through mutation,And first generation populationAre combined into a population,(ii) a FromN 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,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、、Assigned support staff, individualsIs solved by、、Individual, individualIs solved by、、Individual, individualAnd withThrough crossing and switching、、Provisioning of fragments to form two new solutions、。
As shown in FIG. 3, individuals are selectedPerforming variation and random modification guarantee tasks、A support staff of、Is changed into、Generating a new solutionAnd 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:
individualsThe 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,Then govern(ii) a If it isIs superior to the target A,Is superior to target BThe advantages and disadvantages of each target are mutually good and bad,andare not mutually dominant; can governThe number of individuals isThe degree of dominance of (1) is that n individuals can be dominated in traversing population individuals,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 4, step 4: calculating the crowdedness degree of each individual from the second individual to the penultimate individual;
And 5: sequencing the individuals from small to large according to the target value of the average moving distance of the guarantee personnel;
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual;
And 8: sequencing the individuals from small to large according to the target value of the minimum time of the individual task interval;
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual(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 generationGenerating a third generation as a parent population according to the method of step S3, and iterating in the loopEach generation retains the elite individuals of the parent generation, ensures the gradual convergence of the Pareto optimal solution, and passes throughGeneration, generation of population。
The step S5 specifically includes: slave populationAnd 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、、(ii) a Selecting the optimal individual:
step 1: sorting the advantages and the disadvantages according to the total workload balance degree target A;
and step 3: retainedThe individuals are sorted according to the average moving distance target B of the security personnel;
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 taskRequiring allocation of support personnelI.e. byIs completed by individual security personnelA guarantee task; suppose that the support staffIncapacity of completing guarantee taskTasks cannot be assigned in the feasible solution generatedAllocating resources(ii) a Generating time constraint by a scheduling scheme, and generating a Pareto optimal solution within a specified time; setting population genetic algebraAnd 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,,…,, Total workload is a, b, …, x, y; mean of total workloadScheduling scheme target a score a =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;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 populationGenerating a new individual set through selection, crossover and mutation,And first generation populationAre combined into a population,(ii) a FromN 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,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 generationAs the father group, the third generation is generated according to the method of step S3And 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 throughGeneration, generation of population。
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 4, step 4: calculating the crowdedness degree of each individual from the second individual to the penultimate individual;
And 5: sequencing the individuals from small to large according to the average moving distance target value of the guarantee personnel;
And 7: calculating the crowdedness degree of each individual from the second individual to the penultimate individual;
And 8: sequencing the individuals from small to large according to the target value of the minimum time of the individual task interval;
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、、(ii) a Selecting the optimal individual:
step 1: sorting the advantages and the disadvantages according to the total workload balance degree target A;
and 3, step 3: retainedThe individuals sort the advantages and the disadvantages according to the average moving distance target B of the security personnel;
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:
individualsThe 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 balanceThen, thenDominating(ii) a If it isIs superior to the target A,Is superior to target BThe advantages and disadvantages of each target are mutually good and bad,andare not mutually dominant; can governThe number of individuals isThe degree of dominance of (1) is that n individuals can be dominated in traversing population individuals,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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