CN116168566A - Combined scheduling method, device and equipment for aircraft scene and storage medium - Google Patents

Combined scheduling method, device and equipment for aircraft scene and storage medium Download PDF

Info

Publication number
CN116168566A
CN116168566A CN202310119623.XA CN202310119623A CN116168566A CN 116168566 A CN116168566 A CN 116168566A CN 202310119623 A CN202310119623 A CN 202310119623A CN 116168566 A CN116168566 A CN 116168566A
Authority
CN
China
Prior art keywords
aircraft
model
optimization
flight
layer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310119623.XA
Other languages
Chinese (zh)
Other versions
CN116168566B (en
Inventor
万莉莉
叶文婧
孙若飞
田勇
单展鹏
吕洋洋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202310119623.XA priority Critical patent/CN116168566B/en
Publication of CN116168566A publication Critical patent/CN116168566A/en
Application granted granted Critical
Publication of CN116168566B publication Critical patent/CN116168566B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0004—Transmission of traffic-related information to or from an aircraft
    • G08G5/0013—Transmission of traffic-related information to or from an aircraft with a ground station
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Operations Research (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Game Theory and Decision Science (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a method, a device, equipment and a storage medium for jointly scheduling aircraft scenes, wherein the method comprises the following steps: acquiring a flight operation plan and flight operation data, and performing flight pair matching; inputting the flight pairs into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay; and inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain a stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal aircraft scene operation scheduling scheme. The method and the system can solve the problems of lack of multi-resource and multi-stage operation joint scheduling method, lack of consideration of operation elements and single evaluation mode of environmental impact in the conventional scene operation optimization.

Description

Combined scheduling method, device and equipment for aircraft scene and storage medium
Technical Field
The invention relates to a method, a device, equipment and a storage medium for jointly scheduling an aircraft scene, belonging to the technical field of operation planning of the aircraft scene.
Background
Along with the development of information technology and computer technology, a plurality of aviation transport large countries and civil aviation organizations all put forward digital, intelligent and integrated future development requirements for civil aviation transportation planning and management, and aiming at the development trend, the scheduling optimization of the operation of the aircraft scene is explored, so that the high efficiency, the safety, the economy and the environmental protection of the aviation transportation can be effectively improved, and the maximization of benefits of passengers, airports, airlines, air traffic control personnel and the like is realized.
At present, for the research of the optimization of the scene of the aircraft, the whole is divided into a plurality of sub-problems, such as runway scheduling problems, stand allocation problems, taxiing scheduling problems and the like, and the relation among the sub-problems is ignored. Meanwhile, researchers set a plurality of assumption conditions for exploring an optimization method, verifying an optimization strategy and simplifying a calculation process, which are far from the actual running situation. In recent years, scholars develop scheduling optimization of scene multi-resource, the mutual influence of a plurality of processes is considered, the simulation experiment is closer to actual operation, and the actual operation situation can be reflected more truly. However, due to the numerous resources of the scene, these studies inevitably ignore some of the elements, such as the taxi speed, taxi thrust, and the effects of other processes on the operation of the scene. In addition, the environmental impact of aviation emissions is measured by merely monitoring or calculating the amount of emissions of various exhaust gases when aviation emissions are of interest.
Thus, the shortcomings of the existing efforts are manifested in: the lack of a joint scheduling optimization model capable of realizing multi-resource and multi-stage operation of the aircraft scene can not sufficiently consider the factors influencing the operation of the aircraft scene, and the environmental impact evaluation mode of aviation emission is single.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an aircraft scene joint scheduling method, device, equipment and storage medium, which can solve the problems of lack of multi-resource and multi-stage operation joint scheduling method, lack of consideration of operation elements and single evaluation mode of environmental impact in the conventional scene operation optimization. In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides an aircraft scene joint scheduling method, including:
acquiring a flight operation plan and flight operation data, and performing flight pair matching;
inputting the flight pairs into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay;
and inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain a stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal aircraft scene operation scheduling scheme.
With reference to the first aspect, further, the flight pair matching includes:
the aircraft registration numbers of the leading flights and the trailing flights are the same, the landing airports of the leading flights are the same as the departure airports of the trailing flights, the airlines of the leading flights and the trailing flights are the same, and the departure time of the trailing flights is increased by at least one standard arrival time after the leading flights land.
With reference to the first aspect, further, the upper layer model is represented by the following formula:
Figure BDA0004079589240000021
Figure BDA0004079589240000022
in the formula (1), match is the matching degree of all flight pairs and the stand, P is a flight pair set, P is a flight pair serial number, j is a stand serial number,
Figure BDA0004079589240000031
indicating whether a flight pair p is assigned to stand j, or not>
Figure BDA0004079589240000032
Indicating that the flight pair p is assigned to the aircraft stand j,
Figure BDA0004079589240000033
indicating that no flight pair p is assigned to stand j, for example>
Figure BDA0004079589240000034
Indicating whether or not the flight pair p has selected a stand matching its own model, +.>
Figure BDA0004079589240000035
Indicating whether or not the flight pair p has selected a stand matching the own airline, +.>
Figure BDA0004079589240000036
Indicating whether the flight pair p selects a near-flight;
in the formula (2), TD is the difference between the actual end taxiing time and the planned end taxiing time of all the aircraft, i is the aircraft number, A is the aircraft set, and T real _ end For the moment when the aircraft i actually ends taxiing,
i T i schedule _ end planning the end sliding time for the aircraft i;
in equations (1) and (2), the decision variables for the optimization of the stand allocation are the stand allocation schemes for each flight pair.
With reference to the first aspect, further, the lower layer model is represented by the following formula:
Figure BDA0004079589240000037
Figure BDA0004079589240000038
in the formula (3), TT is the taxi time of all aircrafts,
Figure BDA0004079589240000039
for the moment of the last node in the taxi time window of aircraft i, T i schedule _ start Planning a starting taxiing moment for the aircraft i; in the formula (3), the decision variable of the taxi scheduling optimization is an aircraft taxi node;
in the formula (4), TC is the number of collision times of all aircrafts, conflict i Number of collisions for aircraft i; in the formula (4), the decision variable for the push-out delay optimization is the moment when the off-site aircraft starts to slide after pushing out and driving.
With reference to the first aspect, further, the number of collisions of the aircraft is obtained by a collision detection and resolution step, which includes:
step a: generating push-out delay time for the off-site aircraft and sequencing according to the starting taxiing time;
step b: generating 3 alternative paths for the aircraft by using a Dijkstra algorithm, calculating the turning times on the 3 alternative paths and the moment of reaching each node, and if the off-site aircraft does not have a preceding aircraft, entering a step f; if yes, entering a step c;
step c: d, sequencing 3 alternative paths according to the sliding time and the turning times from small to large, judging whether the aircraft has the same path nodes as the preceding aircraft, and if the 3 alternative paths have the same nodes, entering the step d; if the alternative path and the lead aircraft have no same node, the step f is carried out;
step d: calculating arrival time of the leading aircraft and the aircraft at the same node, if the aircraft arrives in the unavailable time period of the same node, entering a step e, otherwise, entering a step f;
step e: two strategies are adopted for the aircraft, namely, the aircraft is supposed to stop waiting until the same node is available; secondly, the same node is set as unavailable, and 1 shortest available path is planned again; comparing the sliding time and the turning times of the two strategies;
step f: and determining the path with the shortest total taxi time as the optimal path of the aircraft, and if the taxi time is the same, taking the path with the smallest turning times as the optimal path, and storing the path.
With reference to the first aspect, further, the solving obtains a plurality of sets of double-layer optimized solution sets, including:
inputting each parameter of the network adjacency matrix and the aircraft into the preset double-layer optimization model;
initializing initial parameters of a solving algorithm, generating an initial solution for stand allocation optimization in an upper model, transmitting an upper model result to a lower model, generating an initial solution for pushing out delay optimization, planning a path for an aircraft by using a Dijkstra algorithm, performing conflict detection and release, and calculating a sliding time window of the aircraft;
performing genetic evolution operation on the initial solution of the lower model to generate a new solution, iteratively obtaining a lower-layer optimized pareto solution, and obtaining an optimal solution of the lower model under the current upper-layer model solving result by taking the minimum aircraft sliding time as a target;
and feeding back all optimal aircraft taxitime windows obtained by the lower model to the upper model, performing stand allocation optimization calculation in the upper model, performing genetic evolution operation on an initial solution in the upper model to generate a new solution until the upper iteration reaches the maximum iteration number, and obtaining a plurality of sets of stand allocation, taxiing scheduling and push-out delay control schemes by taking all flight pairs and the maximum stand matching degree as main targets, namely a plurality of sets of double-layer optimization solution sets.
With reference to the first aspect, further, the preset speed control model is expressed by the following formula:
Figure BDA0004079589240000051
Figure BDA0004079589240000052
in the formula (5), TT is the sliding time of all the aircrafts, i is the number of the aircrafts, A is the set of aircrafts, S is the number of straight-line segments on the path of the aircrafts i, S is the set of straight-line segments on the path of the aircrafts i,
Figure BDA0004079589240000053
Figure BDA0004079589240000054
dividing a straight line section s on the path of the aircraft i into sliding time of each stage after three stages of acceleration, uniform speed and deceleration; w is the number of turn segments on the path of aircraft i, W is the set of turn segments on the path of aircraft i, < ->
Figure BDA0004079589240000055
For the taxiing time of the aircraft i on the turn section w;
in the formula (6), TI is the total environmental impact of the aircraft emission, f is the environmental impact index number, k is the aviation tail gas number,
Figure BDA0004079589240000056
k-class exhaust emissions for aircraft i, < >>
Figure BDA0004079589240000057
For whether k types of tail gas have influence on f types of environmental indexes or not, < ->
Figure BDA0004079589240000058
For k kinds of tail gas to influence f kinds of environmental indexes,>
Figure BDA0004079589240000059
for k types of tail gas, no influence is exerted on f types of environmental indexes, and +.>
Figure BDA00040795892400000510
Is the characteristic factor of k-class tail gas to f-class environmental index, N f Is the standardization factor of the f-class environmental index, W f The weight of the f-class environmental index;
in equations (5) and (6), the decision variables of the speed control model are acceleration and maximum coasting speed.
In a second aspect, the present invention provides an aircraft scene joint scheduling device, comprising:
the acquisition module is used for: the method comprises the steps of acquiring a flight operation plan and flight operation data, and performing flight pair matching;
a first optimization module: the flight pair optimization method comprises the steps of inputting a flight pair into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay;
and a second optimization module: the method is used for inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain the airplane stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal airplane scene operation scheduling scheme.
In a third aspect, the present invention provides an electronic device, comprising:
a memory, and one or more processors communicatively coupled to the memory;
the memory has stored therein instructions executable by the one or more processors to cause the one or more processors to implement the method of the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium, wherein a computer program is stored in the readable storage medium, which computer program, when being executed by a processor, implements the method according to the first aspect.
Compared with the prior art, the method, the device, the equipment and the storage medium for jointly scheduling the scene of the aircraft have the following beneficial effects that:
the invention acquires a flight operation plan and flight operation data, and performs flight pair matching; inputting the flight pairs into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay; according to the invention, a double-layer planning idea is introduced into the aircraft scene operation planning, a double-layer optimization model is preset aiming at the problems of stand allocation, sliding scheduling and push-out delay control, the problem that a multi-resource multi-stage operation combined scheduling optimization model is lacking in the conventional scene operation optimization is solved, and stronger practicability is embodied;
the method inputs a plurality of double-layer optimizing solution sets into a preset speed control model, takes minimized sliding time and minimized environmental impact as optimizing targets, and solves to obtain the airplane stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimum, namely the optimal airplane scene operation scheduling scheme; the method comprehensively considers the influence of speed and thrust changes on the scene operation situation in the scene operation process, solves the problem that the evaluation mode of the environmental influence on aviation emission in the prior art is single, and provides a more scientific and effective tool for the joint scheduling optimization of the aircraft scene operation in practical application.
Drawings
Fig. 1 is a flowchart of an aircraft scene joint scheduling method provided in embodiment 1 of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment one:
the embodiment provides an aircraft scene joint scheduling method, which comprises the following steps:
acquiring a flight operation plan and flight operation data, and performing flight pair matching;
inputting the flight pairs into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay;
and inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain a stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal aircraft scene operation scheduling scheme.
The method comprises the following specific steps:
step 1: and acquiring a flight operation plan and flight operation data, and performing flight pair matching.
Step 1.1: a flight operation plan is obtained.
Step 1.2: flight operation data is obtained.
The flight operation data includes important fields such as flight number, airport take-off and landing, airplane stand and runway, actual and predicted landing/take-off/gear-out time, aircraft model, aircraft registration number, and airline.
Step 1.3: and (5) preprocessing data.
And preprocessing the acquired data, cleaning the data with excessive missing fields, and supplementing or modifying the data with obvious abnormality.
Step 1.4: the flight pair matches.
The flight pair matching is performed according to the following principle:
(1) the aircraft registration numbers for the leading and trailing flights should be the same,
(2) the landing airport for a leading flight should be the same as the departure airport for a trailing flight,
(3) the airlines for the leading and trailing flights are the same,
(4) the departure time of the following flights should be increased by at least one standard transit time after the landing of the preceding flights.
Step 2: and inputting the flight pairs into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets.
Step 2.1: and designing a stand allocation optimization strategy.
According to information compiled by airport aviation data, the parking apron and far/near position information of each parking position is set, and the aircraft wing span limit is classified by the information. And obtaining information of airlines of each parking apron according to the statistical analysis of the actual stand use records, and establishing a matching degree calculation method of the aircrafts and the stand by combining the information, wherein the optimization goal of stand allocation optimization is to maximize the matching degree of the flight pairs and the stand and maximize the difference between the actual end sliding time and the planned end sliding time.
The flight pair matching degree with the stand is calculated by the following formula:
Figure BDA0004079589240000081
in the formula (1), match is the matching degree of all flight pairs and the stand, P is a flight pair set, P is a flight pair serial number, j is a stand serial number,
Figure BDA0004079589240000091
indicating whether a flight pair p is assigned to stand j, or not>
Figure BDA0004079589240000092
Indicating that the flight pair p is assigned to the aircraft stand j,
Figure BDA0004079589240000093
indicating that no flight pair p is assigned to stand j, for example>
Figure BDA0004079589240000094
Indicating whether or not the flight pair p has selected a stand matching its own model, +.>
Figure BDA0004079589240000095
Indicating whether or not the flight pair p has selected a stand matching the own airline, +.>
Figure BDA0004079589240000096
Indicating whether the near aircraft position was selected for flight pair p.
The difference between the actual end-of-taxi time and the planned end-of-taxi time is calculated by the following formula:
Figure BDA0004079589240000097
in the formula (2), TD is the difference between the actual end taxiing time and the planned end taxiing time of all the aircraft, i is the aircraft number, A is the aircraft set,
Figure BDA00040795892400000910
for the moment of actual end of taxiing of aircraft i, T i schedule_end And planning the finishing taxi moment for the aircraft i.
The iterative idea based on the genetic algorithm is to optimally design a heuristic optimization algorithm for the stand allocation, and the decision variable is a stand allocation scheme of each flight pair.
Step 2.2: and designing a taxi scheduling optimization strategy.
The airport scene is abstracted into a point-line two-dimensional network according to the plane diagram of the airport flight area, and the distances among important nodes (turning points, departure points, waiting points and parking apron exit/entrance lighting) are measured.
The taxi scheduling optimization method is a Dijkstra algorithm, the optimization target is to minimize taxi time and turn times, and the decision variable is an aircraft taxi node.
The coasting time is calculated by the following formula:
Figure BDA0004079589240000098
in the formula (3), TT is the taxi time of all aircrafts,
Figure BDA0004079589240000099
taxiing aircraft iTime of last node in time window, T i schedule_start The taxiing moment is started for the planning of the aircraft i.
The decision variable for taxi schedule optimization is the aircraft taxi node.
Step 2.3: the design pushes out a delay optimization strategy.
The iterative idea based on the genetic algorithm is to push out a delay control optimization design heuristic optimization algorithm, the optimization target is to minimize the sliding time and the minimum expected conflict times, and the decision variable is the sliding starting time after the off-site aircraft pushes out to drive.
The number of collisions for an aircraft is calculated by the following formula:
Figure BDA0004079589240000101
in the formula (4), TC is the number of collision times of all aircrafts, conflict i Is the number of collisions for aircraft i.
The decision variable of the push-out delay optimization is the moment when the off-site aircraft starts to slide after pushing out and driving.
The number of collision times of the aircraft is obtained through a collision detection and release step, and the collision detection and release step comprises the following steps:
step a: generating push-out delay time for the off-site aircraft and sequencing according to the starting taxiing time;
step b: generating 3 alternative paths for the aircraft by using a Dijkstra algorithm, calculating the turning times on the 3 alternative paths and the moment of reaching each node, and if the off-site aircraft does not have a preceding aircraft, entering a step f; if yes, entering a step c;
step c: d, sequencing 3 alternative paths according to the sliding time and the turning times from small to large, judging whether the aircraft has the same path nodes as the preceding aircraft, and if the 3 alternative paths have the same nodes, entering the step d; if the alternative path and the lead aircraft have no same node, the step f is carried out;
step d: calculating arrival time of the leading aircraft and the aircraft at the same node, if the aircraft arrives in the unavailable time period of the same node, entering a step e, otherwise, entering a step f;
step e: two strategies are adopted for the aircraft, namely, the aircraft is supposed to stop waiting until the same node is available; secondly, the same node is set as unavailable, and 1 shortest available path is planned again; comparing the sliding time and the turning times of the two strategies;
step f: and determining the path with the shortest total taxi time as the optimal path of the aircraft, and if the taxi time is the same, taking the path with the smallest turning times as the optimal path, and storing the path.
Step 2.4: and optimizing the preset double-layer optimization model into an upper-layer model by using the stand allocation, and optimizing and pushing out delay by using the sliding schedule to obtain a lower-layer model.
An upper model represented by the following formula:
Figure BDA0004079589240000111
Figure BDA0004079589240000112
the lower model is represented by the following formula:
Figure BDA0004079589240000113
Figure BDA0004079589240000114
step 2.5: and solving a double-layer optimization model.
And inputting the network adjacency matrix and various parameters of the aircraft into the preset double-layer optimization model.
Initializing initial parameters of a solving algorithm, generating an initial solution for stand allocation optimization in an upper model, transmitting an upper model result to a lower model, generating an initial solution for push-out delay optimization, planning a path for an aircraft by using a Dijkstra algorithm, performing conflict detection and release, and calculating a sliding time window of the aircraft.
And carrying out genetic evolution operation on the initial solution of the lower model to generate a new solution, iteratively obtaining a lower-layer optimized pareto solution, and obtaining the optimal solution of the lower model under the current upper-layer model solving result by taking the minimum aircraft sliding time as a target.
And feeding back all optimal aircraft taxitime windows obtained by the lower model to the upper model, performing stand allocation optimization calculation in the upper model, performing genetic evolution operation on an initial solution in the upper model to generate a new solution until the upper iteration reaches the maximum iteration number, and obtaining a plurality of sets of stand allocation, taxiing scheduling and push-out delay control schemes by taking all flight pairs and the maximum stand matching degree as main targets, namely a plurality of sets of double-layer optimization solution sets.
Step 3: and inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain a stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal aircraft scene operation scheduling scheme.
Step 3.1: the method comprises the steps of designing a discrete speed profile, dividing a sliding road section into a straight line section and a turning section, and dispersing all the straight line sections into three processes, namely acceleration, uniform speed and deceleration stages and sliding time of each stage. The calculation method of the sliding time comprises the following steps:
Figure BDA0004079589240000121
in the formula (9), TT is the sliding time of all the aircrafts, i is the number of the aircrafts, A is the set of aircrafts, S is the number of straight-line segments on the path of the aircrafts i, S is the set of straight-line segments on the path of the aircrafts i,
Figure BDA0004079589240000122
Figure BDA0004079589240000123
dividing a straight line section s on the path of the aircraft i into sliding time of each stage after three stages of acceleration, uniform speed and deceleration; w is the number of turn segments on the path of aircraft i, W is the set of turn segments on the path of aircraft i, < ->
Figure BDA0004079589240000124
For the taxiing time of the aircraft i on the turn section w.
Step 3.2: introducing an LCA evaluation system to evaluate the environmental impact of aviation emission, wherein the environmental impact of aviation emission is calculated by the LCA evaluation system, and the calculation method is as follows:
Figure BDA0004079589240000125
in the formula (10), TI is the total environmental impact of the aircraft emission, f is the environmental impact index number, k is the aviation tail gas number,
Figure BDA0004079589240000126
k-class exhaust emissions for aircraft i, < >>
Figure BDA0004079589240000127
For whether k types of tail gas have influence on f types of environmental indexes or not, < ->
Figure BDA0004079589240000131
For k kinds of tail gas to influence f kinds of environmental indexes,>
Figure BDA0004079589240000132
for k types of tail gas, no influence is exerted on f types of environmental indexes, and +.>
Figure BDA0004079589240000133
Is the characteristic factor of k-class tail gas to f-class environmental index, N f Is the standardization factor of the f-class environmental index, W f Is the weight of the f-class environmental index.
Step 3.3: and (3) taking minimized sliding time and minimized environmental influence as optimization targets, taking acceleration and maximum sliding speed as decision variables, designing a double-target optimization solving algorithm, and constructing a preset speed control model. And solving through an algorithm to obtain an optimal speed control scheme.
The preset speed control model is represented by the following formula:
Figure BDA0004079589240000134
Figure BDA0004079589240000135
and 5 values are taken from the multiple groups of double-layer optimal solution sets at equal distance, and the 5 values are respectively input into a preset speed control model for optimal calculation. And solving 5 values to obtain the stand allocation, the taxi scheduling, the push-out delay control and the taxi speed scheme with the minimum environmental influence as the optimal scene operation scheduling scheme.
According to the method, the double-layer planning concept is introduced into the aircraft scene operation planning, a double-layer optimization model is preset aiming at the problems of stand allocation, sliding scheduling and push-out delay control, the problem that a multi-resource multi-stage operation combined scheduling optimization model is lacking in the existing scene operation optimization is solved, and stronger practicability is achieved.
The method comprehensively considers the influence of speed and thrust changes on the scene operation situation in the scene operation process, solves the problem that the evaluation mode of the environmental influence on aviation emission in the prior art is single, and provides a more scientific and effective tool for the joint scheduling optimization of the aircraft scene operation in practical application.
Embodiment two:
this example was tested in a specific scenario using the method described in example 1.
In this embodiment, the example verification is performed based on historical operation data of the above-sea Pudong airport from 1 st 2019 to 30 th 6 th 2019.
Digitizing airport scene structure, statistically analyzing flight operation data, setting stand attribute and substituting the stand attribute into an optimization model. Simulation runs were performed with a typical flight volume of 76 flights per hour, and an optimized flight pull-out time scheme, taxi path scheme, and stand allocation scheme were generated. And calculating indexes such as the matching degree of the stand, the sliding time, the conflict times, the fuel consumption, the tail gas emission and the like according to the optimized scheme, and comparing the indexes with actual operation indexes calculated according to historical data.
The results show that the estimated number of sliding conflicts after optimization is reduced from 5 times per hour to 2 times, the average approach sliding time is reduced by 2 minutes, the average departure sliding time is reduced by 3 minutes, the bridge rate is increased from 57% to 97%, the fuel consumption is reduced by 20.49%, the carbon dioxide emission is reduced by 20.49%, the carbon monoxide emission is reduced by 21.59%, the hydrocarbon emission is reduced by 21.95%, and the nitrogen oxide emission is reduced by 20.34%.
From the results, the aircraft scene joint scheduling method provided by the embodiment one can reduce the expected conflict while simultaneously realizing the stand, the taxiway and the push-out time scheduling scheme, improves the scene operation efficiency, and assists in realizing green aviation, so that the method has a certain practical significance.
Embodiment III:
the embodiment of the invention provides an aircraft scene joint scheduling device, which comprises the following components:
the acquisition module is used for: the method comprises the steps of acquiring a flight operation plan and flight operation data, and performing flight pair matching;
a first optimization module: the flight pair optimization method comprises the steps of inputting a flight pair into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay;
and a second optimization module: the method is used for inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain the airplane stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal airplane scene operation scheduling scheme.
Embodiment four:
an embodiment of the present invention provides an electronic device, including:
a memory, and one or more processors communicatively coupled to the memory;
the memory stores instructions executable by the one or more processors to cause the one or more processors to implement the method as described in embodiment one.
Fifth embodiment:
the embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored in the readable storage medium, and the computer program realizes the method in the embodiment one when being executed by a processor.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (10)

1. An aircraft scene joint scheduling method, comprising:
acquiring a flight operation plan and flight operation data, and performing flight pair matching;
inputting the flight pairs into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay;
and inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain a stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal aircraft scene operation scheduling scheme.
2. The aircraft scene joint scheduling method according to claim 1, wherein the flight pair matching comprises:
the aircraft registration numbers of the leading flights and the trailing flights are the same, the landing airports of the leading flights are the same as the departure airports of the trailing flights, the airlines of the leading flights and the trailing flights are the same, and the departure time of the trailing flights is increased by at least one standard arrival time after the leading flights land.
3. The aircraft scene joint scheduling method according to claim 1, wherein the upper layer model is represented by:
Figure FDA0004079589230000011
Figure FDA0004079589230000012
in the formula (1), match is the matching degree of all flight pairs and the stand, P is a flight pair set, P is a flight pair serial number, j is a stand serial number,
Figure FDA0004079589230000013
indicating whether a flight pair p is assigned to stand j, or not>
Figure FDA0004079589230000014
Indicating that the flight pair p is assigned to the stand j, for example>
Figure FDA0004079589230000015
Indicating that no flight pair p is assigned to stand j, for example>
Figure FDA0004079589230000016
Indicating whether or not the flight is selected for pSelecting a stand matched with the own model, < > for>
Figure FDA0004079589230000017
Indicating whether or not the flight pair p has selected a stand matching the own airline, +.>
Figure FDA0004079589230000021
Indicating whether the flight pair p selects a near-flight;
in the formula (2), TD is the difference between the actual end taxiing time and the planned end taxiing time of all the aircraft, i is the aircraft number, A is the aircraft set, and T i real_end For the moment of actual end of taxiing of aircraft i, T i schedule_end Planning the end sliding time for the aircraft i;
in equations (1) and (2), the decision variables for the optimization of the stand allocation are the stand allocation schemes for each flight pair.
4. A method of joint scheduling of aircraft scenes according to claim 3, wherein the underlying model is represented by:
Figure FDA0004079589230000022
Figure FDA0004079589230000023
in the formula (3), TT is the taxi time of all aircrafts,
Figure FDA0004079589230000024
for the moment of the last node in the taxi time window of aircraft i, T i schedule_start Planning a starting taxiing moment for the aircraft i; in the formula (3), the decision variable of the taxi scheduling optimization is an aircraft taxi node;
in the formula (4), TC is all aviationNumber of collisions of the device, conflict i Number of collisions for aircraft i; in the formula (4), the decision variable for the push-out delay optimization is the moment when the off-site aircraft starts to slide after pushing out and driving.
5. The aircraft scene joint scheduling method according to claim 4, wherein the number of collisions of the aircraft is obtained by a collision detection and resolution step, the collision detection and resolution step comprising:
step a: generating push-out delay time for the off-site aircraft and sequencing according to the starting taxiing time;
step b: generating 3 alternative paths for the aircraft by using a Dijkstra algorithm, calculating the turning times on the 3 alternative paths and the moment of reaching each node, and if the off-site aircraft does not have a preceding aircraft, entering a step f; if yes, entering a step c;
step c: d, sequencing 3 alternative paths according to the sliding time and the turning times from small to large, judging whether the aircraft has the same path nodes as the preceding aircraft, and if the 3 alternative paths have the same nodes, entering the step d; if the alternative path and the lead aircraft have no same node, the step f is carried out;
step d: calculating arrival time of the leading aircraft and the aircraft at the same node, if the aircraft arrives in the unavailable time period of the same node, entering a step e, otherwise, entering a step f;
step e: two strategies are adopted for the aircraft, namely, the aircraft is supposed to stop waiting until the same node is available; secondly, the same node is set as unavailable, and 1 shortest available path is planned again; comparing the sliding time and the turning times of the two strategies;
step f: and determining the path with the shortest total taxi time as the optimal path of the aircraft, and if the taxi time is the same, taking the path with the smallest turning times as the optimal path, and storing the path.
6. The aircraft scene joint scheduling method according to claim 4, wherein the solving obtains a plurality of sets of double-layer optimal solution sets, including:
inputting each parameter of the network adjacency matrix and the aircraft into the preset double-layer optimization model;
initializing initial parameters of a solving algorithm, generating an initial solution for stand allocation optimization in an upper model, transmitting an upper model result to a lower model, generating an initial solution for pushing out delay optimization, planning a path for an aircraft by using a Dijkstra algorithm, performing conflict detection and release, and calculating a sliding time window of the aircraft;
performing genetic evolution operation on the initial solution of the lower model to generate a new solution, iteratively obtaining a lower-layer optimized pareto solution, and obtaining an optimal solution of the lower model under the current upper-layer model solving result by taking the minimum aircraft sliding time as a target;
and feeding back all optimal aircraft taxitime windows obtained by the lower model to the upper model, performing stand allocation optimization calculation in the upper model, performing genetic evolution operation on an initial solution in the upper model to generate a new solution until the upper iteration reaches the maximum iteration number, and obtaining a plurality of sets of stand allocation, taxiing scheduling and push-out delay control schemes by taking all flight pairs and the maximum stand matching degree as main targets, namely a plurality of sets of double-layer optimization solution sets.
7. The aircraft scene joint scheduling method according to claim 1, wherein the preset speed control model is represented by the following formula:
Figure FDA0004079589230000041
Figure FDA0004079589230000042
in the formula (5), TT is the sliding time of all the aircrafts, i is the number of the aircrafts, A is the set of aircrafts, S is the number of straight-line segments on the path of the aircrafts i, S is the set of straight-line segments on the path of the aircrafts i,
Figure FDA0004079589230000043
Figure FDA0004079589230000044
dividing a straight line section s on the path of the aircraft i into sliding time of each stage after three stages of acceleration, uniform speed and deceleration; w is the number of turn segments on the path of aircraft i, W is the set of turn segments on the path of aircraft i, < ->
Figure FDA0004079589230000045
For the taxiing time of the aircraft i on the turn section w;
in the formula (6), TI is the total environmental impact of the aircraft emission, f is the environmental impact index number, k is the aviation tail gas number,
Figure FDA0004079589230000046
k-class exhaust emissions for aircraft i, < >>
Figure FDA0004079589230000047
For whether k types of tail gas have influence on f types of environmental indexes or not, < ->
Figure FDA0004079589230000048
For k kinds of tail gas to influence f kinds of environmental indexes,>
Figure FDA0004079589230000049
for k types of tail gas, no influence is exerted on f types of environmental indexes, and +.>
Figure FDA00040795892300000410
Is the characteristic factor of k-class tail gas to f-class environmental index, N f Is the standardization factor of the f-class environmental index, W f The weight of the f-class environmental index;
in equations (5) and (6), the decision variables of the speed control model are acceleration and maximum coasting speed.
8. An aircraft scene joint scheduling device, comprising:
the acquisition module is used for: the method comprises the steps of acquiring a flight operation plan and flight operation data, and performing flight pair matching;
a first optimization module: the flight pair optimization method comprises the steps of inputting a flight pair into a preset double-layer optimization model, and solving to obtain a plurality of groups of double-layer optimization solution sets; the preset double-layer optimization model is optimized to an upper-layer model through stand allocation, and is optimized to a lower-layer model through sliding scheduling optimization and push-out delay;
and a second optimization module: the method is used for inputting a plurality of double-layer optimizing solution sets into a preset speed control model, taking minimized sliding time and minimized environmental impact as optimizing targets, and solving to obtain the airplane stand allocation, sliding scheduling, push-out delay control and sliding speed scheme when the environmental impact is minimized, namely the optimal airplane scene operation scheduling scheme.
9. An electronic device, comprising:
a memory, and one or more processors communicatively coupled to the memory;
stored in the memory are instructions executable by the one or more processors to cause the one or more processors to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer program is stored in the readable storage medium, which computer program, when being executed by a processor, implements the method of any of claims 1 to 7.
CN202310119623.XA 2023-02-15 2023-02-15 Combined scheduling method, device and equipment for aircraft scene and storage medium Active CN116168566B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310119623.XA CN116168566B (en) 2023-02-15 2023-02-15 Combined scheduling method, device and equipment for aircraft scene and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310119623.XA CN116168566B (en) 2023-02-15 2023-02-15 Combined scheduling method, device and equipment for aircraft scene and storage medium

Publications (2)

Publication Number Publication Date
CN116168566A true CN116168566A (en) 2023-05-26
CN116168566B CN116168566B (en) 2023-11-17

Family

ID=86411021

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310119623.XA Active CN116168566B (en) 2023-02-15 2023-02-15 Combined scheduling method, device and equipment for aircraft scene and storage medium

Country Status (1)

Country Link
CN (1) CN116168566B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116933662A (en) * 2023-09-14 2023-10-24 中国民用航空飞行学院 Airport stand allocation method and device, storage medium and electronic equipment
CN117933490A (en) * 2024-03-14 2024-04-26 中国民航大学 Airport scene dragging scheduling optimization method, electronic equipment and storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014079637A1 (en) * 2012-11-21 2014-05-30 Siemens Aktiengesellschaft Method and device for minimizing the energy consumption of vehicles
US20150220865A1 (en) * 2014-02-06 2015-08-06 Amadeus S.A.S. Vehicle stand allocation
CN112800382A (en) * 2021-01-14 2021-05-14 南京航空航天大学 Method and system for optimizing deducing time slot allocation based on flight departure cost
CN113570247A (en) * 2021-07-28 2021-10-29 南京航空航天大学 Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014079637A1 (en) * 2012-11-21 2014-05-30 Siemens Aktiengesellschaft Method and device for minimizing the energy consumption of vehicles
US20150220865A1 (en) * 2014-02-06 2015-08-06 Amadeus S.A.S. Vehicle stand allocation
CN112800382A (en) * 2021-01-14 2021-05-14 南京航空航天大学 Method and system for optimizing deducing time slot allocation based on flight departure cost
CN113570247A (en) * 2021-07-28 2021-10-29 南京航空航天大学 Multi-objective optimization method for distribution of stand-to-halt positions based on resource limited condition

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
赵志奇: "面向大气环境的机场停机位分配优化研究", pages: 18 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116933662A (en) * 2023-09-14 2023-10-24 中国民用航空飞行学院 Airport stand allocation method and device, storage medium and electronic equipment
CN116933662B (en) * 2023-09-14 2023-12-15 中国民用航空飞行学院 Airport stand allocation method and device, storage medium and electronic equipment
CN117933490A (en) * 2024-03-14 2024-04-26 中国民航大学 Airport scene dragging scheduling optimization method, electronic equipment and storage medium

Also Published As

Publication number Publication date
CN116168566B (en) 2023-11-17

Similar Documents

Publication Publication Date Title
CN116168566B (en) Combined scheduling method, device and equipment for aircraft scene and storage medium
Ikli et al. The aircraft runway scheduling problem: A survey
CN113570247B (en) Multi-objective stand allocation optimization method based on resource limitation condition
Chen et al. Toward a more realistic, cost-effective, and greener ground movement through active routing: a multiobjective shortest path approach
CN110689764B (en) Aircraft departure and passing ordering method based on dynamic simulation
CN101694752B (en) System and method for automatically detecting and reconciling conflicts in airspace operation simulation
Soltani et al. An eco-friendly aircraft taxiing approach with collision and conflict avoidance
CN104751681A (en) Statistical learning model based gate position allocation method
CN110363361A (en) A kind of method and system for predicting variable sliding time based on big data
Çeçen et al. Aircraft sequencing and scheduling in TMAs under wind direction uncertainties
CN107341620B (en) BADA fuel consumption rate-based method for calculating delay cost of incoming flights in short-term weather
Veresnikov et al. Methods for solving of the aircraft landing problem. I. Exact solution methods
CN111581780A (en) Airport group airspace simulation modeling and verification method and device under complex airspace scene
CN115292928A (en) Unmanned aerial vehicle vertical take-off and landing field capacity assessment method and device
Wei et al. A multiple objective optimization model for aircraft arrival and departure scheduling on multiple runways
Wollenheit et al. Operational and environmental assessment of electric taxi based on fast-time simulation
Zhao et al. Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm
Zaninotto et al. A testbed for performance analysis of algorithms for engineless taxiing with autonomous tow trucks
CN115049100B (en) Airport resource allocation optimization method, device and equipment and readable storage medium
CN113095543B (en) Distribution method and system for airport stand and taxiway
CN114783212A (en) Method for constructing model feature set for prediction of departure taxi time of aircraft in busy airport
Hejji et al. AI-based models for resource allocation and resource demand forecasting systems in aviation: A survey and analytical study
Cecen A stochastic programming model for the aircraft sequencing and scheduling problem considering flight duration uncertainties
Jansen Robust coupled optimization of aircraft family design and fleet allocation for multiple markets
CN113011640A (en) Method and system for rapidly accounting aviation carbon dioxide emission

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant