System and method for constructing intelligent flight allocation model

Abstract

This application discloses a system and method for constructing an intelligent flight allocation model. The system for constructing an intelligent flight allocation model for an airport cluster includes: an acquisition unit for acquiring flight information, airport information, and corridor information of airport cluster N; a construction unit configured to construct an intelligent flight allocation model for the airport cluster based on the flight information, airport information, and corridor information of airport cluster N, wherein the intelligent flight allocation model includes an objective function, decision variables, and constraints; an optimization unit configured to optimize the intelligent flight allocation model for the airport cluster based on the constraints; and a solution unit configured to solve the optimized intelligent flight allocation model for the airport cluster to obtain the flight timetable for airport cluster N. This invention solves the flight timetable management problem from the perspective of mutual coordination, avoiding potential risks to on-time performance from the initial generation of flight timetables, and providing a scientific management solution for strategic flight timetable optimization for airport clusters.

Description

Technical Field

[0001] This invention relates to civil aviation technology, and in particular to an intelligent flight allocation system and method for airport clusters. Background Technology

[0002] With the development of air transport and the continuous construction of airports, competition and cooperation among airports in close proximity are becoming increasingly apparent, and multiple airport clusters are forming in China. Flight slots refer to the right of an aircraft to use relevant infrastructure and services at a designated date and time for arrival or departure from a particular airport. Flight slot allocation involves various factors, including airline profits and development, air traffic control safety, and airport capacity, affecting the entire system. Airlines face fierce competition for available slots at busy domestic airports, with strong demand for popular airports and slots. The Civil Aviation Administration of China (CAAC) hopes that flight slot resources can be allocated fairly and efficiently, and that flight operations can be normal and orderly, while ensuring safety. Air traffic control authorities hope for smooth and orderly air operations, avoiding large traffic peaks and troughs. Airports hope to handle as many takeoffs and landings as possible within their capacity. Passengers hope for a wide selection of flights, many slots, on-time departures, and direct arrivals. However, the allocation of flight slots in an airport cluster is not only related to the airport itself, but also to the status of the air route network used and the slot arrangements of other airports to which it needs to be reached. Therefore, the flight slot allocation problem in an airport cluster is a multi-objective, multi-constraint, and complex large-scale system scheduling optimization problem. Thus, it is necessary to solve this complex multi-dimensional optimization problem and directly formulate a reasonable flight schedule that conforms to actual operation from a strategic perspective, so as to optimize the allocation of flight slot resources more efficiently.

[0003] Current research on flight schedule optimization in China suffers from the following problems: Existing flight schedule allocation studies primarily focus on the capabilities of individual airports, rarely considering multiple airports. Even those studies that do focus on multiple airports often fail to comprehensively address congestion at key nodes in the airspace above airport clusters, and rarely address the coordination between flights within airport clusters. Most optimization efforts are tactical, focusing on real-time flight schedule management for arrivals and departures, rather than strategically developing reasonable timetables in advance. There is a lack of effective management recommendations based on Civil Aviation Administration regulations, policies, and related operational restrictions, and proposed optimization schemes may not be applicable to actual operations, remaining merely theoretical. Most studies only optimize short-term schedules, neglecting longer-term optimization schemes, thus failing to optimize flight schedules for the entire flight season. Furthermore, there is a lack of optimization management for different directions within airport clusters; some studies only provide brief explanations without offering effective models to address the differences in corridor crossings and actual transit times for different directions. Summary of the Invention

[0004] To address the aforementioned issues, the present invention aims to provide an intelligent flight allocation model for airport clusters that meets future operational needs, and to propose optimization schemes for flight schedules throughout the entire flight season from a strategic perspective.

[0005] According to one aspect of the present invention, a system for constructing an intelligent flight allocation model for an airport group is provided, characterized in that it comprises: an acquisition unit configured to acquire flight information, airport information, and corridor information of an airport group N; a construction unit configured to construct an intelligent flight allocation model for the airport group based on the flight information, airport information, and corridor information of the airport group N, wherein the intelligent flight allocation model for the airport group includes an objective function, decision variables, and constraints; an optimization unit configured to optimize the intelligent flight allocation model for the airport group based on the constraints; and a solution unit configured to solve the optimized intelligent flight allocation model for the airport group to obtain the flight timetable for the airport group N.

[0006] Preferably, the constraints include at least one of the following: setting uniqueness, time adjustment range, airport capacity limit, transit time of consecutive flights, and corridor capacity limit.

[0007] Preferably, the system for constructing a smart flight allocation model for an airport cluster involves parameters including at least one of the following: the estimated time slice in the original pre-flight schedule, the optimized allocated time slice, the original estimated time slice for arrival flights from each direction to airport n in the airport cluster N passing through the corresponding corridor, the optimized allocated time slice for arrival flights from each direction to airport n passing through the corresponding corridor, the original estimated time slice for departure flights from airport n to each direction passing through the corresponding corridor, and the optimized allocated time slice for departure flights from airport n to each direction passing through the corresponding corridor. The time slice at the corridor entrance, the set of all airports in the airport group N, the set of corridor entrances, the arrival flights of airport n, the departure flights of airport n, the sets of flights in different directions, consecutive flight pairs, the time slice corresponding to the taxiing time, the time slice corresponding to the time taken for a departure flight from airport n to the direction j through corridor entrance w from the runway to corridor entrance w, the time slice corresponding to the time taken for an arrival flight from the direction j through corridor entrance w to airport n from the direction j through corridor entrance w to the runway, and the maximum arrival and departure capacity within the allowed time interval i of airport n.

[0008] Preferably, the objective function is the sum of the delay minimization functions for arriving and departing flights of the airport group N and the delay minimization functions for connecting flights of the airport group N, wherein the objective function is expressed as:

[0009] In the diagram, F represents the set of all flights from each airport n in the airport group N, where n ∈ N, f represents a flight in the set of all flights, t represents the time slice of each flight in the set of all flights, and T represents the set of all time slices of the set of all flights. s This indicates the estimated time slice in the original flight schedule, t p S represents the optimized time slice allocation. nft P represents the decision variable representing the estimated arrival and departure times of flight f from airport n before optimization. nft F represents the decision variable indicating the estimated arrival and departure times of flight f from airport n after optimization. z Let F represent the set of flights from each airport n in the airport group N in the direction j, where F zj Let f ∈ F be a flight in the set of flights in the direction j. zj This indicates that flight f is one of the flights in the direction j. This indicates the estimated time slice for an incoming flight originally heading from direction j to airport n to pass through the corresponding corridor entrance. This represents the optimized time slice for an arriving flight from direction j to airport n to pass through the corresponding corridor entrance. This indicates the estimated time for a departing flight originally traveling from airport n to the direction j to pass through the corresponding corridor entrance. This represents the optimized estimated time slice for departing flights from airport n to the direction j, passing through the corresponding corridor entrance. Let $\mathbfl$ represent the decision variables for incoming flights from direction $j to airport $n, before optimization, regarding the allocation of flights through the corresponding corridor entrances. Let $\mathbfl$ represent the decision variables for the optimized arrival flights from direction $j to airport $n, and how they are assigned to the corresponding corridor entrances. Let $\mathbfl$ represent the decision variables for departing flights from airport $n to direction $j, before optimization, and how they are assigned to the corresponding corridor entrances. Let $\mathbfl$ represent the decision variables for the optimized departure flights from airport $n$ to direction $j$, which are assigned to the corresponding corridor entrances.

[0010] Preferably, the delay minimization function for arrival and departure flights of airport group N is the sum of the difference between the optimized allocated arrival and departure times and the arrival and departure times predicted in the original pre-flight schedule, and the number of arrival and departure flight delays. The delay minimization function for arrival and departure flights of airport group N is expressed as follows:

[0011]

[0012] in, Indicates the number of arrival and departure flight delays;

[0013] This indicates the difference between the optimized arrival and departure times and the arrival and departure times predicted in the original pre-flight schedule.

[0014] Preferably, S nft Represented as:

[0015]

[0016] The P nft Represented as:

[0017]

[0018] Preferably, the delay minimization function for flights passing through the corridor of airport group N is expressed as:

[0019] in,

[0020] This represents the number of delays and the amount of delay for arriving flights from direction j to each airport n in the airport group N, passing through the corresponding corridor entrance;

[0021]

[0022] This represents the number of delays and the amount of delay for departing flights from airport n in the airport group N to direction j passing through the corresponding corridor entrance.

[0023] Preferably, Represented as:

[0024]

[0025] The Represented as:

[0026]

[0027] The Represented as:

[0028]

[0029] The Represented as:

[0030]

[0031] According to another aspect of the present invention, a method for intelligent flight allocation for an airport cluster is provided, comprising the following steps: an acquisition step, acquiring flight information, airport information, and corridor information of airport cluster N; a construction step, constructing an intelligent flight allocation model for the airport cluster based on the flight information, airport information, and corridor information of airport cluster N, wherein the intelligent flight allocation model for the airport cluster includes an objective function, decision variables, and constraints; an optimization step, optimizing the intelligent flight allocation model for the airport cluster based on the constraints; and a solution step, solving the optimized intelligent flight allocation model for the airport cluster to obtain the flight timetable for airport cluster N.

[0032] The intelligent flight allocation model for airport clusters provided by this invention has the following beneficial effects: It studies flight schedule management from the perspective of mutual coordination among flights within an airport cluster, resulting in optimized flight schedules at a strategic level. Based on the mutual constraints between adjacent airports in the air, it avoids potential issues affecting on-time performance from the outset. Since airport clusters involve multiple airports and numerous factors need to be considered, it greatly helps in practically solving congestion or resource waste caused by flights flying in different directions within the airport cluster, facilitating collaborative allocation. At the strategic level, it provides auxiliary support to the authorities, offering reasonable early-stage timetables in flight schedule management, and providing a scientific management solution for strategic flight schedule optimization within airport clusters. Attached Figure Description

[0033] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0034] Figure 1 A schematic diagram of a system for constructing an intelligent flight allocation model for airport clusters according to an embodiment of the present invention is shown; and

[0035] Figure 2 A flowchart of a flight intelligent allocation method according to an embodiment of the present invention is shown. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the following embodiments are provided to further illustrate the present invention in detail.

[0037] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0039] In this invention, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used in relation to the direction shown in the accompanying drawings, or in relation to the vertical, perpendicular, or gravitational direction of the component itself; similarly, for ease of understanding and description, "inner" and "outer" refer to the inner and outer contours of each component itself, but the above directional terms are not intended to limit this invention.

[0040] Figure 1 A schematic diagram of a system for constructing an intelligent flight allocation model for an airport cluster, according to an embodiment of the present invention, is shown. Figure 1 As shown, the system 10 for constructing an intelligent flight allocation model for an airport cluster includes: an acquisition unit 102 configured to acquire flight information, airport information, and corridor information of airport cluster N; a construction unit 104 configured to construct an intelligent flight allocation model for the airport cluster based on the flight information, airport information, and corridor information of airport cluster N, wherein the intelligent flight allocation model for the airport cluster includes an objective function, decision variables, and constraints; an optimization unit 106 configured to optimize the intelligent flight allocation model for the airport cluster based on the constraints; and a solution unit 108 configured to solve the optimized intelligent flight allocation model for the airport cluster to obtain the flight timetable for airport cluster N.

[0041] The definitions of the parameters involved in System 10, which is used to construct the intelligent flight allocation model for airport clusters, are shown in Table 1:

[0042] Table 1

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] The definitions of the decision variables involved in System 10, which is used to construct the intelligent flight allocation model for airport clusters, are shown in Table 2:

[0049] Table 2

[0050]

[0051]

[0052] In system 10 used to construct an intelligent flight allocation model for airport clusters, construction unit 104 constructs an intelligent flight allocation model for airport clusters based on flight information, airport information, and corridor information of airport cluster N obtained by acquisition unit 102. The intelligent flight allocation model for airport clusters includes an objective function, decision variables, and constraints. The objective function includes determining the optimization objective of minimizing the delays of both arriving / departing flights and flights passing through corridors, as expressed in equation (1):

[0053]

[0054] Equation (1) consists of two parts. One part of equation (1) is:

[0055]

[0056] This indicates the optimization goal of minimizing arrival and departure flight delays;

[0057] The optimization objective of minimizing arrival and departure flight delays includes the number of delayed arrival and departure flights and the difference between the optimized allocated arrival and departure times and the arrival and departure times predicted in the original pre-flight schedule, i.e., the delay amount; among which, Indicates the number of arrival and departure flight delays; This represents the difference between the optimized arrival and departure times and the arrival and departure times predicted in the original pre-flight schedule, i.e., the delay.

[0058] Another part of equation (1):

[0059] This represents the optimization objective of minimizing delays for flights crossing the corridor, specifically minimizing the delay between the optimized corridor crossing time and the estimated corridor crossing time obtained from the original pre-flight schedule based on actual operational data; the first part of this objective is:

[0060] This indicates the number of delays and the amount of delay for arriving flights from each direction to each airport as they pass through the corresponding corridor entrance. The amount of delay is the difference between the optimized allocation of the arrival times of arriving flights from each direction to each airport as they pass through the corresponding corridor entrance and the estimated times of passing through the corridor entrance.

[0061] The latter part:

[0062]

[0063] This indicates the number of delays and the amount of delay for departing flights from each airport to different directions passing through the corresponding corridor entrance. The amount of delay is the difference between the optimized allocation time of departing flights from each airport to different directions passing through the corresponding corridor entrance and the estimated time of passing through the corridor entrance.

[0064] Next, the optimization unit 106 optimizes the intelligent flight allocation model for the airport cluster constructed by the construction unit 104 according to the constraints. The constraints include setting uniqueness, time adjustment range, airport capacity limit, transit time of consecutive flights and corridor capacity limit; wherein, the uniqueness constraint is expressed by the following formula (2):

[0065]

[0066] Equation (2) indicates that all flights within the airport group can only be allocated one time slot and cannot occupy multiple time slots simultaneously;

[0067] The time adjustment range constraints include time adjustment range constraints for delayed flights and time adjustment range constraints for early flights;

[0068] The time adjustment range constraint for the delayed flights is expressed by the following formula (3):

[0069]

[0070] Equation (3) indicates that the optimized time of each delayed flight meets the maximum allowable adjustment amount for flight delay compared to the original time;

[0071] The time adjustment range constraint for the advance flight is expressed by the following formula (4):

[0072]

[0073] Equation (4) indicates that the optimized time of each advanced flight meets the maximum allowable adjustment amount for the flight to be advanced compared to the original time;

[0074] The airport capacity constraint conditions are as follows: the arrival capacity, departure capacity, and total arrival and departure capacity of each airport in the airport group are restricted for different time intervals; wherein, the arrival capacity restriction is expressed by the following formula (5):

[0075]

[0076] Equation (5) indicates that the total number of arriving flights at each airport in each time interval within the airport group cannot exceed the arrival capacity of each airport in each time interval.

[0077] The flight departure capacity limit is expressed by the following formula (6):

[0078]

[0079] Equation (6) indicates that the total number of departing flights at each airport in each time interval within the airport group cannot exceed the departure capacity of each airport in each time interval;

[0080] The total capacity limit for arriving and departing flights is expressed by the following formula (7):

[0081]

[0082] Equation (7) indicates that the total number of arrival and departure flights at each airport in the airport group during each time interval cannot exceed the total arrival and departure capacity of each airport during each time interval;

[0083] The transit time constraints for consecutive flights are as follows: Each airport sets its own transit time constraints for consecutive flights based on its actual situation, as expressed by the following formula (8):

[0084]

[0085] Equation (8) indicates the minimum turnaround time that consecutive flights must meet;

[0086] The corridor capacity limit constraints are as follows: For flights from different directions, separate capacity limits are imposed on each corridor entrance, including the time required for departing flights to reach the corridor entrance from the runway, the time required for arriving flights to reach the runway from the corridor entrance, and taxiing time. The corridor capacity limit satisfied by arriving flights from different directions passing through the corridor entrance to various airports is expressed by the following formula (9):

[0087]

[0088] Equation (9) indicates that the total number of arriving flights from different directions passing through the corridor entrance to each airport cannot exceed the maximum capacity allowed by the corridor entrance. The time of passing through the corridor entrance is the arrival time minus the time it takes for the arriving flight to fly from the corridor entrance to the runway.

[0089] The corridor capacity limit satisfied by departing flights from various airports to different directions passing through the corridor is expressed by the following formula (10):

[0090]

[0091] Equation (10) indicates that the total number of departing flights from each airport to different directions passing through the corridor entrance cannot exceed the maximum capacity allowed by the corridor entrance. The time of passing through the corridor entrance is the departure time plus the time taken for the departing flight to fly from the runway to the corridor entrance and the taxiing time. Based on the objective function and constraints, the construction unit 104 and the optimization unit 106 construct the optimized intelligent flight allocation model for the airport cluster.

[0092] Next, the solution unit 108 uses, for example, the CPLEX tool to solve the optimized airport group flight intelligent allocation model to obtain the flight timetable of airport group N.

[0093] Figure 2 A flowchart of a flight intelligent allocation method according to an embodiment of the present invention is shown. Figure 2As shown, the intelligent flight allocation method includes the following steps: Step 202, obtaining flight information, airport information, and corridor information of airport group N; Step 204, constructing an intelligent flight allocation model for airport group N based on the flight information, airport information, and corridor information of airport group N, wherein the intelligent flight allocation model for airport group N includes an objective function, decision variables, and constraints; Step 206, optimizing the intelligent flight allocation model for airport group N based on the constraints; Step 208, solving the optimized intelligent flight allocation model for airport group N to obtain the flight timetable for airport group N.

[0094] Next, taking the Beijing-Tianjin-Hebei airport cluster as an example, the intelligent flight allocation method for airport clusters in this application will be described in detail. The Beijing-Tianjin-Hebei airport cluster is a typical, developing, and ultra-large-scale airport cluster. The intelligent flight allocation model for airport clusters provided by this invention will be explained in detail below with reference to the appendix and a specific example of the Beijing-Tianjin-Hebei airport cluster.

[0095] The intelligent flight allocation model for airport clusters provided by this invention includes an objective function and constraints. The implementation of the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster proceeds in the following order:

[0096] 1) Define the parameters of the objective function and constraints for the intelligent flight allocation model of the Beijing-Tianjin-Hebei airport cluster:

[0097] To realize the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster, Table 3 was added to the parameter definitions based on Table 1.

[0098] 2) Define the objective function and decision variables of the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster:

[0099] To achieve the objective function and constraints of the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster, Table 4 was added to the definition of decision variables based on Table 2.

[0100] 3) Determine the objective function with the goal of minimizing the delays of both arriving and departing flights and flights passing through the corridor. The implementation of the objective function is shown in 4.1.

[0101] 4) Set constraints for uniqueness, time adjustment range, airport capacity limit, transit time constraint for consecutive flights, and corridor capacity limit. See 4.2 for how to implement the constraints.

[0102] 5) Based on the objective function in step 3) and the constraints in step 4), construct an intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster.

[0103] 6) Analyze the relevant airport coordination parameters, corridor capacity parameters, and corridor-related time parameters involved in the intelligent flight allocation model of the Beijing-Tianjin-Hebei airport cluster, in order to implement and verify the model.

[0104] The Beijing-Tianjin-Hebei airport cluster comprises three main coordinating airports: Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin Binhai International Airport. The following examples illustrate six scenarios involving corridor entrances: Assume the airspace above the terminal area contains six corridor entrances, primarily studying flights in the south-central, southwest-northwest, and east-central directions. An intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster is established with the optimization objective of minimizing delays for both arriving and departing flights and flights passing through corridor entrances. This model considers uniqueness constraints, timetable adjustment ranges, airport capacity limitations, transit time constraints for consecutive flights, and corridor entrance capacity limitations. The optimized model yields a flight schedule that aligns with actual operational needs. The intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster is established as follows:

[0105] 4.1 Definition of Model Parameters

[0106] The parameter definitions for the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster are shown in Tables 1 and 3 (Table 1 will not be repeated here):

[0107] Table 3 Parameter Definitions

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] 4.2 Definition of Model Decision Variables

[0115] The definitions of decision variables for the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster are shown in Tables 2 and 4 (Table 2 will not be repeated here):

[0116] Table 4 Definitions of Decision Variables

[0117]

[0118]

[0119] 4.3 Establishing the Optimization Objective of the Model

[0120] Based on equation (1), the objective function of the intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster includes minimizing the delays of both arriving and departing flights and flights crossing corridors within the cluster. The model further refines the objective function, determining the corridor delays for flights from the three main coordinating airports (Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport) in the three directions of Central and Southern China, Southwest and Northwest China, and East China respectively. The objective function of the model is expressed as equation (11):

[0121]

[0122] Equation (11) consists of two parts, one of which is:

[0123]

[0124] This indicates the optimization goal of minimizing arrival and departure flight delays;

[0125] The optimization objective of minimizing arrival and departure flight delays includes the number of arrival and departure flight delays and the difference between the optimized allocated arrival and departure times and the arrival and departure times predicted in the original pre-flight schedule, i.e., the delay amount; where, in equation (11) Indicates the number of arrival and departure flight delays; This represents the difference between the optimized arrival and departure times and the arrival and departure times predicted in the original pre-flight schedule, i.e., the delay.

[0126] Another part of equation (11):

[0127] This represents the optimization objective of minimizing delays for flights crossing the corridor, specifically minimizing the delay between the optimized corridor crossing time and the estimated corridor crossing time obtained from the original pre-flight schedule based on actual operational data. This objective is achieved for three directions:

[0128] This indicates the number of delays and the amount of delay for inbound flights from the central and southern directions to various airports as they pass through the corresponding corridor entrances. The amount of delay is the difference between the optimized allocation of the time when inbound flights from the central and southern directions to various airports pass through the corresponding corridor entrances and the estimated time when they pass through the corridor entrances.

[0129] This indicates the number of delays and the amount of delay for departing flights from each airport to the central and southern directions passing through the corresponding corridor. The amount of delay is the difference between the optimized allocation of the departure time of departing flights from each airport to the central and southern directions passing through the corresponding corridor and the estimated time of passing through the corridor.

[0130] This indicates the number of delays and the amount of delay for inbound flights from the southwest and northwest directions to each airport as they pass through the corresponding corridor entrance. The amount of delay is the difference between the optimized allocation time of inbound flights from the southwest and northwest directions passing through the corresponding corridor entrance and the estimated time of passing through the corridor entrance.

[0131] This indicates the number of delays and the amount of delay for departing flights from each airport to the southwest and northwest directions passing through the corresponding corridor entrance. The amount of delay is the difference between the optimized allocation time of departing flights from each airport to the southwest and northwest directions passing through the corresponding corridor entrance and the estimated time of passing through the corridor entrance.

[0132] This indicates the number of delays and the amount of delay for inbound flights from East China to various airports passing through the corresponding corridor entrance. The amount of delay is the difference between the optimized allocation of the time when inbound flights from East China to various airports pass through the corresponding corridor entrance and the estimated time when they pass through the corridor entrance.

[0133] This indicates the number of delays and the amount of delay for departing flights from each airport to East China passing through the corresponding corridor. The amount of delay is the difference between the optimized allocation time of departing flights from each airport to East China passing through the corresponding corridor and the estimated time of passing through the corridor.

[0134] In equation (11), the parameter f is defined in Table 1-22, F is defined in Table 1-16, t is defined in Table 1-3, T is defined in Table 1-2, and F... zgpj Defined in Table 3-13, F zul Defined in Table 3-14, F zs Defined in Table 3-15, t p Defined in Table 1-5, t s Defined in Table 1-4, Defined in Table 3-2, Defined in Table 3-1, Defined in Table 3-8, Defined in Table 3-7, Defined in Table 3-4, Defined in Table 3-3, Defined in Table 3-10, Defined in Table 3-9, Defined in Table 3-6, Defined in Table 3-5, Defined in Table 3-12, Defined in Table 3-11.

[0135] The decision variable P in equation (11) nft Defined in Table 2-2, S nft Defined in Table 2-1, Defined in Table 4-7, Defined in Table 4-1, Defined in Table 4-8, Defined in Table 4-2, Defined in Table 4-9, Defined in Table 4-3, Defined in Table 4-10, Defined in Table 4-4, Defined in Table 4-11, Defined in Table 4-5, Defined in Table 4-12, Defined in Table 4-6.

[0136] 4.4 Setting Constraints for the Model

[0137] The constraints include setting uniqueness, time adjustment range, airport capacity limit, transit time of consecutive flights, and corridor capacity limit; wherein, the uniqueness constraint is expressed by the following formula (2):

[0138]

[0139] Equation (2) indicates that all flights within the Beijing-Tianjin-Hebei airport cluster can only be allocated one time slot and cannot occupy multiple time slots at the same time.

[0140] In equation (2), parameter f is defined in Table 1-22, F in Table 1-16, n in Table 1-11, N in Table 1-10, t in Table 1-3, and T in Table 1-2; decision variable P nft Defined in Table 2-2.

[0141] The time slot adjustment range constraints include the time slot adjustment range constraints for delayed flights and the time slot adjustment range constraints for early flights; among them, the time slot adjustment range constraints for delayed flights are expressed by the following formula (3):

[0142]

[0143] Equation (3) represents the maximum allowable adjustment amount for each delayed flight time compared to the original time after optimization.

[0144] In equation (3), parameter f is defined in Table 1-22, F in Table 1-16, n in Table 1-11, N in Table 1-10, t in Table 1-3, and T in Table 1-2. p Defined in Table 1-5, t s Defined in Table 1-4, δ dmax Defined in Table 1-35.

[0145] The constraint on the time adjustment range of advance flights is expressed by the following formula (4):

[0146]

[0147] Equation (4) indicates that the optimized time of each advanced flight meets the maximum allowable adjustment amount for the flight to be advanced compared to the original time.

[0148] In equation (4), parameter f is defined in Table 1-22, F in Table 1-16, n in Table 1-11, N in Table 1-10, t in Table 1-3, and T in Table 1-2. s Defined in Table 1-4, t p Defined in Table 1-5, δ emax Defined in Table 1-34.

[0149] Airport capacity limit constraints: Limits are imposed on the arrival capacity, departure capacity, and total arrival and departure capacity of each airport in the Beijing-Tianjin-Hebei airport cluster for different time intervals; for example, the different time intervals are 15-minute capacity limits and 1-hour capacity limits.

[0150] Based on equation (5), the hourly flight arrival capacity limit for each airport in the Beijing-Tianjin-Hebei airport cluster is expressed by the following equation (12):

[0151]

[0152] Equation (12) indicates that the total number of inbound flights at each airport within the Beijing-Tianjin-Hebei airport cluster within one hour cannot exceed the inbound capacity of each airport within one hour.

[0153] In equation (12), parameter t is defined in Table 1-3, T is defined in Table 1-2, and n is defined in Table 1-11. s1 Defined in Table 3-16, N is defined in Table 1-10, f is defined in Table 1-22, F na Defined in Table 1-17, Defined in Table 3-30; Decision variable P naft Defined in Table 2-3.

[0154] Based on equation (6), the one-hour flight departure capacity limit for each airport in the Beijing-Tianjin-Hebei airport cluster is expressed by the following equation (13):

[0155]

[0156] Equation (13) indicates that the total number of departing flights at each airport in the Beijing-Tianjin-Hebei airport cluster within one hour cannot exceed the departure capacity of each airport within one hour;

[0157] In equation (13), parameter t is defined in Table 1-3, T is defined in Table 1-2, and n is defined in Table 1-11. s1Defined in Table 3-16, N is defined in Table 1-10, f is defined in Table 1-22, F nd Defined in Table 1-18, Defined in Table 3-31; Decision variable P ndft Defined in Table 2-4.

[0158] Based on equation (5), the capacity limit for flights arriving within 15 minutes at each airport in the Beijing-Tianjin-Hebei airport cluster is expressed by the following equation (14):

[0159]

[0160] Equation (14) indicates that the total number of arriving flights within 15 minutes at each airport in the Beijing-Tianjin-Hebei airport cluster cannot exceed the arrival capacity of each airport within 15 minutes.

[0161] In equation (14), parameter t is defined in Table 1-3, T is defined in Table 1-2, and n is defined in Table 1-11. s2 Defined in Table 3-17, N is defined in Table 1-10, f is defined in Table 1-22, F na Defined in Table 1-17, Defined in Table 3-32; Decision variable P naft Defined in Table 2-3.

[0162] Based on equation (6), the departure capacity limit for flights within 15 minutes at each airport in the Beijing-Tianjin-Hebei airport cluster is expressed by the following equation (15):

[0163]

[0164] Equation (15) indicates that the total number of departing flights within 15 minutes at each airport in the Beijing-Tianjin-Hebei airport cluster cannot exceed the departure capacity of each airport within 15 minutes.

[0165] In equation (15), parameter t is defined in Table 1-3, T is defined in Table 1-2, and n is defined in Table 1-11. s2 Defined in Table 3-17, N is defined in Table 1-10, f is defined in Table 1-22, F nd Defined in Table 1-18, Defined in Table 3-33; Decision variable P ndft Defined in Table 2-4.

[0166] Based on equation (7), the total capacity limit for arrival and departure flights at each airport within the Beijing-Tianjin-Hebei airport cluster within one hour is expressed by the following equation (16):

[0167]

[0168] Equation (16) indicates that the total number of arriving and departing flights at each airport in the Beijing-Tianjin-Hebei airport cluster within one hour cannot exceed the total arrival and departure capacity of each airport within one hour.

[0169] In equation (16), parameter t is defined in Table 1-3, T is defined in Table 1-2, and n is defined in Table 1-11. s1 Defined in Table 3-16, N is defined in Table 1-10, f is defined in Table 1-22, F n Defined in Table 1-19, Defined in Table 3-34; Decision variable P nft Defined in Table 2-2.

[0170] Based on equation (7), the total capacity limit for arrival and departure flights within 15 minutes at each airport in the Beijing-Tianjin-Hebei airport cluster is expressed by the following equation (17):

[0171]

[0172] Equation (17) indicates that the total number of arrival and departure flights within 15 minutes at each airport in the Beijing-Tianjin-Hebei airport cluster cannot exceed the total arrival and departure capacity of each airport within 15 minutes.

[0173] In equation (17), parameter t is defined in Table 1-3, T is defined in Table 1-2, and n is defined in Table 1-11. s2 Defined in Table 3-17, N is defined in Table 1-10, f is defined in Table 1-22, F n Defined in Table 1-19, Defined in Table 3-35; Decision variable P nft Defined in Table 2-2.

[0174] The turnaround time constraint for consecutive flights: Each airport in the Beijing-Tianjin-Hebei airport cluster sets its own turnaround time constraint for consecutive flights according to the actual situation, which is expressed by the following formula (8):

[0175]

[0176] Equation (8) indicates the minimum turnaround time that consecutive flights must meet;

[0177] In equation (8), parameter n is defined in Table 1-11, N is defined in Table 1-10, {a,d} and S are defined in Table 1-25, and T ad Defined in Table 1-26, m defined in Table 1-1, m = 288 (dividing the day into 288 time slices with 5-minute intervals), t defined in Table 1-3; decision variable P ndft Defined in Table 2-4, P naft Defined in Table 2-3.

[0178] Corridor entrance capacity restriction constraints: Based on the current congestion problem caused by shared corridor entrances in the Beijing-Tianjin-Hebei airport cluster, it is proposed to impose capacity restrictions on each corridor entrance separately for flights in different directions, and to introduce the time required for departing flights to fly from the runway to the corridor entrance, the time required for arriving flights to fly from the corridor entrance to the runway, and the taxiing time.

[0179] The following examples illustrate six scenarios of arrival and departure flights from different directions passing through corridors at the three main coordinating airports in the Beijing-Tianjin-Hebei airport cluster: Assume that arrival flights from the southwest and northwest directions to Beijing Capital International Airport pass through corridor 1; arrival flights from the central and southern directions to Beijing Daxing International Airport and Tianjin International Airport share corridor 2; arrival flights from the east China direction to Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport share corridor 3; departure flights from Beijing Capital International Airport to the southwest and northwest directions pass through corridor 4; departure flights from Beijing Daxing International Airport and Tianjin International Airport to the central and southern directions share corridor 5; and departure flights from Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport to the east China direction share corridor 6.

[0180] Based on equation (9), the corridor capacity limit for inbound flights from the southwest and northwest directions through corridor 1 to the Capital Airport is expressed by the following equation (18):

[0181]

[0182] Equation (18) indicates that the total number of flights passing through corridor 1 from the southwest and northwest directions to Beijing Capital International Airport within one hour cannot exceed the maximum capacity allowed by corridor 1. The time of passing through the corridor is calculated by subtracting the time taken for the arriving flight to travel from corridor 1 to the runway of Beijing Capital International Airport from the time of arrival at Beijing Capital International Airport.

[0183] In equation (18), parameter t is defined in Table 1-3, and T is defined in Table 1-2. s1 Defined in Table 3-16, f is defined in Table 1-22, F zul Defined in Table 3-14, T 1a1 Defined in Table 3-18, C w1 Defined in Table 3-36; Decision variable P 1aft Defined in Table 2-3.

[0184] Based on equation (9), the corridor capacity limit for inbound flights from the south-central direction passing through corridor entrance 2 to Daxing Airport and Tianjin Airport is expressed by the following equation (19):

[0185]

[0186] Equation (19) indicates that the total number of flights passing through corridor 2 from the south-central direction to Daxing Airport and Tianjin Airport within one hour cannot exceed the maximum capacity allowed by corridor 2. The time of passing through the corridor is calculated by subtracting the time taken for the arriving flight to reach the runway of Daxing Airport or Tianjin Airport from the time of arrival at Daxing Airport or Tianjin Airport.

[0187] In equation (19), parameter t is defined in Table 1-3, and T is defined in Table 1-2. s1 Defined in Table 3-16, f is defined in Table 1-22, F zgpj Defined in Table 3-13, T 2a2 Defined in Table 3-19, T 3a2 Defined in Table 3-20, C w2 Defined in Table 3-37; Decision variable P 2aft and P 3aft Defined in Table 2-3.

[0188] Based on equation (9), the corridor capacity limit for inbound flights from East China to Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport via corridor entrance 3 is expressed by the following equation (20):

[0189]

[0190] Equation (20) indicates that the total number of flights passing through corridor 3 from the East China direction to Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport within one hour cannot exceed the maximum capacity allowed by corridor 3. The time of passing through the corridor is calculated by subtracting the time taken for the arriving flight to fly from corridor 3 to the runway of the three airports from the time of arrival at Beijing Capital International Airport, Beijing Daxing International Airport, or Tianjin International Airport.

[0191] In equation (20), parameter t is defined in Table 1-3, and T is defined in Table 1-2. s1 Defined in Table 3-16, f is defined in Table 1-22, F zs Defined in Table 3-15, T 1a3 Defined in Table 3-21, T 2a3 Defined in Table 3-22, T 3a3 Defined in Table 3-23, C w3 Defined in Table 3-38; Decision variable P 1aft P 2aft and P 3aft Defined in Table 2-3.

[0192] Based on equation (10), the corridor capacity limit satisfied by departing flights from Beijing Capital International Airport to the southwest and northwest directions via corridor 4 is expressed by the following equation (21):

[0193]

[0194] Equation (21) indicates that the total number of departing flights from Beijing Capital International Airport to the southwest and northwest directions passing through Corridor 4 within one hour cannot exceed the maximum capacity allowed by Corridor 4. The time of passing through the corridor is the time of departure from Beijing Capital International Airport plus the time of departure from the runway of Beijing Capital International Airport to the time of taxiing to Corridor 4.

[0195] In equation (21), parameter t is defined in Table 1-3, and T is defined in Table 1-2. s1 Defined in Table 3-16, f is defined in Table 1-22, F zul Defined in Table 3-14, T t Defined in Table 1-27, T 1d4 Defined in Table 3-24, C w4 Defined in Table 3-39; Decision variable P 1dft Defined in Table 2-4.

[0196] Based on equation (10), the corridor capacity limit satisfied by departing flights from Daxing Airport and Tianjin Airport to the Central and Southern directions via corridor 5 is expressed by the following equation (22):

[0197]

[0198] Equation (22) indicates that the total number of departing flights from Daxing Airport and Tianjin Airport to the Central and Southern directions passing through Corridor 5 within one hour cannot exceed the maximum capacity allowed by Corridor 5. The time of passing through the corridor is calculated by adding the departure time from Daxing Airport or Tianjin Airport to the time of the departing flight from the runway of Daxing Airport or Tianjin Airport to Corridor 5 and the taxiing time.

[0199] In equation (22), parameter t is defined in Table 1-3, and T is defined in Table 1-2. s1 Defined in Table 3-16, f is defined in Table 1-22, F zgpj Defined in Table 3-13, T t Defined in Table 1-27, T 2d5 Defined in Table 3-25, T 3d5 Defined in Table 3-26, C w5 Defined in Table 3-40; Decision variable P 2dft and P 3dft Defined in Table 2-4.

[0200] Based on equation (10), the corridor capacity limit for departing flights from Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport to the Central and Southern directions via corridor 6 is expressed by the following equation (23):

[0201]

[0202] Equation (23) indicates that the total number of departing flights from Beijing Capital International Airport, Beijing Daxing International Airport, and Tianjin International Airport to the Central and Southern directions passing through Corridor 6 within one hour cannot exceed the maximum capacity allowed by Corridor 6. The time of passing through the corridor is the departure time of Beijing Capital International Airport, Beijing Daxing International Airport, or Tianjin International Airport, plus the time taken for the departing flight to fly from the runway of the three airports to Corridor 6 and the taxiing time.

[0203] In equation (23), parameter t is defined in Table 1-3, and T is defined in Table 1-2. s1 Defined in Table 3-16, f is defined in Table 1-22, F zs Defined in Table 3-15, T t Defined in Table 1-27, T 1d6 Defined in Table 3-27, T 2d6 Defined in Table 3-28, T 3d6 Defined in Table 3-29, C w6 Defined in Table 3-41; Decision variable P 1dft P 2dft and P 3dft Defined in Table 2-4.

[0204] 4.5 Results of Model Solution

[0205] The intelligent flight allocation model for the Beijing-Tianjin-Hebei airport cluster has been solved using the CPLEX tool. The objective function and constraints of the model were constructed through programming. Airport coordination parameters, corridor capacity parameters, and corridor-related time parameters of the Beijing-Tianjin-Hebei airport cluster were processed and input. The corridor-related time parameters were obtained from Thales radar data, including the time required for departing flights from the three main coordinating airports to reach their respective corridor entrances and the time required for arriving flights to reach their runways from their corridor entrances. The final optimized flight schedule was output. The model solution yielded an optimized solution of 1170, with 39 flights experiencing time changes and a delay of 585 minutes. The optimization results for some of the changed flights are shown in Table 5.

[0206] Table 5 shows the optimization results of the model solution.

[0207]

[0208]

[0209] The model helped to obtain reasonable flight schedule optimization solutions, and the analysis of the optimization results can provide suggestions for flight schedule management. It is of great help in solving the problem of congestion at the airspace corridor entrances of the Beijing-Tianjin-Hebei airport cluster, and provides auxiliary support for the authorities to manage flight schedules at the strategic level.

[0210] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A system for building an intelligent flight allocation model for an airport cluster, characterized in that, The system comprises: an acquisition unit configured to acquire flight information, airport information, and corridor information of an airport group N; a construction unit configured to construct an airport group flight intelligent allocation model according to the flight information, the airport information, and the corridor information of the airport group N, wherein the airport group flight intelligent allocation model comprises a target function, decision variables, and constraint conditions; an optimization unit configured to optimize the airport group flight intelligent allocation model according to the constraint conditions; and a solution unit configured to solve the optimized airport group flight intelligent allocation model to obtain a flight schedule of the airport group N, the constraint conditions comprise at least one of the following: a set uniqueness, a time adjustment range, an airport capacity limit, a transit time of consecutive flights, and a corridor capacity limit, the target function is a sum of a delay minimization function of arrival and departure flights of the airport group N and a delay minimization function of corridor transit flights of the airport group N, wherein the target function is expressed as: F = {f}, f e F, where F denotes the set of all flights of each airport n of the airport group N, where n e N, f denotes one flight of the set of all flights, t denotes a time slice of each flight of the set of all flights, T denotes the set of all time slices of the set of all flights, t s denotes the estimated time slice in the original estimated flight schedule, t p denotes the allocated time slice after optimization, S nft denotes the decision variable of the estimated time of departure of the flight f from the airport n before optimization, P nft denotes the decision variable of the estimated time of departure of the flight f from the airport n after optimization, F z denotes the set of flights of the direction j of each airport n of the airport group N, where F zj denotes a flight of the set of flights of the direction j, f e F zj denotes that the flight f is one of the flights of the direction j, denotes the estimated time slice of the original arrival flight from the direction j to the airport n passing through the corresponding corridor, t denotes the time slice of the optimized arrival flight from the direction j to the airport n passing through the corresponding corridor, t denotes the estimated time slice of the original departure flight from the airport n to the direction j passing through the corresponding corridor, t denotes the estimated time slice of the optimized departure flight from the airport n to the direction j passing through the corresponding corridor, t denotes the decision variable of the allocated passing through the corresponding corridor of the arrival flight from the direction j to the airport n before optimization, S denotes the decision variable of the allocated passing through the corresponding corridor of the arrival flight from the direction j to the airport n after optimization, S denotes the decision variable of the allocated passing through the corresponding corridor of the departure flight from the airport n to the direction j before optimization, S denotes the decision variable of the allocated passing through the corresponding corridor of the departure flight from the airport n to the direction j after optimization, the S nft denotes: The P nft is represented as: The is represented as: The is represented as: The is represented as: The is represented as:

2. The system for constructing an airport group flight intelligent allocation model according to claim 1, wherein parameters involved in the system for constructing an airport group flight intelligent allocation model comprise at least one of the following: a predicted time slice in a previously planned flight schedule, an allocated time slice after optimization, a predicted time slice of an arrival flight from each direction to an airport n in the airport group N passing through a corresponding corridor, an allocated time slice of the arrival flight from each direction to the airport n passing through the corresponding corridor after optimization, a predicted time slice of a departure flight from the airport n to each direction passing through a corresponding corridor, an allocated time slice of the departure flight from the airport n to each direction passing through the corresponding corridor after optimization, a set of all airports in the airport group N, a set of corridors, an arrival flight of the airport n, a departure flight of the airport n, a set of flights in different directions, a pair of consecutive flights, a time slice corresponding to a taxiing time, a time slice corresponding to a time taken by a departure flight from the airport n to a j direction passing through a corridor w from a runway to the corridor w, a time slice corresponding to a time taken by an arrival flight from the j direction passing through the corridor w to the airport n from the corridor w to a runway, and a maximum arrival and departure capacity within a time interval i range allowed by the airport n.

3. The system for constructing an airport group flight intelligent allocation model according to claim 1, wherein the delay minimization function of the arrival and departure flights of the airport group N is a sum of a difference between an allocated arrival and departure time after optimization and a predicted arrival and departure time in a previously planned flight schedule and a number of flight delays, and the delay minimization function of the arrival and departure flights of the airport group N is expressed as: wherein, represents the number of arrival and departure flight delays; represents the difference between the optimized arrival and departure times and the originally scheduled arrival and departure times in the original flight plan.

4. The system for constructing an airport group flight intelligent allocation model according to claim 1, wherein the delay minimization function of the corridor transit flights of the airport group N is expressed as: wherein, which represents a sum of a number of delays and an amount of delays of arrival flights from a direction j to each airport n in the airport group N passing through a corresponding corridor. representing the number of delays and the amount of delays of the outbound flights from each airport n of the airport cluster N to the direction j passing through the corresponding corridor gate.

5. An intelligent flight distribution method for an airport cluster, characterized in that, The method comprises the following steps: an acquisition step of acquiring flight information, airport information, and corridor gate information of the airport cluster N; a construction step of constructing an airport cluster flight intelligent allocation model according to the flight information, the airport information, and the corridor gate information of the airport cluster N, wherein the airport cluster flight intelligent allocation model comprises an objective function, decision variables, and constraint conditions; an optimization step of optimizing the airport cluster flight intelligent allocation model according to the constraint conditions; a solution step of solving the optimized airport cluster flight intelligent allocation model to obtain a flight timetable of the airport cluster N; the constraint conditions comprise at least one of the following: a set uniqueness, a time adjustment range, an airport capacity limit, a transit time of consecutive flights, a corridor gate capacity limit, the objective function is a sum of a delay minimization function of the inbound and outbound flights of the airport cluster N and a delay minimization function of the flights passing through the corridor of the airport cluster N, wherein the objective function is represented as: F = {f|n∈N, f∈F}, wherein F denotes a set of all flights of each airport n of the airport group N, wherein n∈N, f denotes one flight in the set of all flights, t denotes a time slice of each flight in the set of all flights, T denotes a set of all time slices of the set of all flights, t s denotes a scheduled time slice in the original scheduled flight plan, t p denotes a time slice allocated after optimization, S nft denotes a decision variable of the scheduled time for the flight f to depart from the airport n before optimization, P nft denotes a decision variable of the scheduled time for the flight f to depart from the airport n after optimization, F z denotes a set of flights of the direction j of each airport n of the airport group N, wherein F zj denotes a flight in the set of flights of the direction j, f∈F zj denotes that the flight f is one of the flights of the direction j, denotes a scheduled time slice for the original approach flight from the direction j to the airport n to pass through the corresponding corridor, t denotes a time slice for the optimized approach flight from the direction j to the airport n to pass through the corresponding corridor, t denotes a scheduled time slice for the original departure flight from the airport n to the direction j to pass through the corresponding corridor, t denotes a scheduled time slice for the optimized departure flight from the airport n to the direction j to pass through the corresponding corridor, t denotes a decision variable of the allocated time slice for the approach flight from the direction j to the airport n to pass through the corresponding corridor before optimization, S denotes a decision variable of the allocated time slice for the approach flight from the direction j to the airport n to pass through the corresponding corridor after optimization, S denotes a decision variable of the allocated time slice for the departure flight from the airport n to the direction j to pass through the corresponding corridor before optimization, S denotes a decision variable of the allocated time slice for the departure flight from the airport n to the direction j to pass through the corresponding corridor after optimization, S nft denotes: The P nft is represented as: The denoted as: The is represented as: The is represented as: The is represented as:

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