Terminal airspace flight scheduling decision method and system based on mixed linear integer programming

Abstract

The embodiment of the application discloses a terminal airspace flight scheduling decision method and system based on mixed linear integer programming, which comprises the following steps: obtaining flight information in a time window participating in scheduling from a flight information base and obtaining environment information from a running environment information base; constructing a flight scheduling set; defining decision variables according to business requirements, constructing a target function of a terminal airspace single-path flight sequencing scheduling model; constructing a constraint condition library of the scheduling model; selecting the model according to business requirements; solving calculation; and outputting decision information of the flights to be scheduled. The complex running logic between the flights in the terminal airspace and the control running rules are accurately abstracted and modeled into a mathematical linear constraint expression, a target function is constructed by minimizing the air waiting time of the approach flights and minimizing the scheduling time deviation of the approach and departure flights, and the flight sequence obtained by the method is more efficient than that obtained by the traditional "first-come-first-served algorithm", and serious flight time slot waste is avoided.

Description

A Terminal Airspace Flight Scheduling Decision-Making Method and System Based on Hybrid Linear Integer Programming Technical Field Technical Field

[0001] This invention relates to the field of flight scheduling technology, specifically to a terminal airspace flight scheduling decision-making method and system based on hybrid linear integer programming. Background Technology Background Technology

[0002] With the continuous growth in air traffic demand, the contradiction between the current high-volume, tightly coupled air traffic flow and airspace resource capacity is becoming increasingly prominent. Traditional air traffic control methods, which rely primarily on radar and procedural control and are coordinated by human experience, result in low operational efficiency and high workload for controllers. Terminal airspace flight scheduling aims to rationally sort aircraft queues in the terminal airspace within the time domain without violating safe operating intervals. It provides each flight with optimal turnaround time, sequence, interval maintenance, speed control, and other scheduling information to assist controllers in formulating terminal airspace scheduling strategies. This technology can effectively improve terminal airspace capacity, alleviate airspace congestion, and reduce high-altitude circling and ground waiting times under existing operational resource conditions.

[0003] Existing technology 1 is a multi-priority mode arrival sorting method and apparatus: This technology involves a multi-priority mode arrival sorting method, which first calculates the priority and sorting point of all flights to be sorted, with the priority from high to low as follows: manually frozen landing time, emergency secondary code, special flights / VIPs, priority routes, and normal priority; then, based on the priorities and the intervals between flights, the flights are sorted to determine the landing order, calculate the arrival order of all flights, and establish an arrival flight sequence table. The disadvantage of this technology is that it is essentially based on a "first-come, first-served" scheduling strategy, that is, sorting according to the priority order of flights, which can only provide one feasible sorting strategy, and this scheduling strategy is highly likely to deviate from the overall optimal strategy.

[0004] Existing technology 2 is a terminal area tailing interval approach management decision support system. This technology comprises two main parts: tailing interval management and time-based management strategies. The time-based management strategy allocates arrival times for each aircraft at pre-specified waypoints; the tailing interval management strategy limits the required distance / time interval between consecutive aircraft. The disadvantages of this technology are: firstly, the constructed model is a nonlinear model, increasing the computational burden and hindering hardware implementation; secondly, this patent neglects several key decision factors in actual operation, such as flight speed and waiting mode decisions.

[0005] Existing technology 3 is a method for rapid sorting and optimized scheduling of approaching flights based on composite allocation rules. This technology constructs a cost-based approaching flight sorting model and designs composite allocation rules to effectively obtain a good landing sequence. Secondly, it constructs an optimization model based on landing order, objective function, and constraints, and uses a solver to obtain the optimized landing time for approaching flights. The drawback of this technology is that it only determines the time of flights at the terminal corridor entrance and runway points, and does not make more granular decisions regarding the time and order of the metering points that flights pass through in the terminal airspace.

[0006] Existing technology 4 is a multi-efficiency optimization ranking method for approaching flights at multi-runway airports. This technology allocates runways and determines approach routes for flights by defining a multi-efficiency runway allocation evaluation function, and optimizes the delay allocation process and determines the landing time of flights based on the comprehensive priority of flights. The drawback of this technology is that the performance of the scheduling model is affected by the accuracy of the evaluation model. When converting the correlation between operational data into evaluation time, flow, and punctuality coefficients, information loss may occur, potentially leading to inaccurate or incomplete evaluation results, thus affecting overall performance. Summary of the Invention Summary of the Invention

[0007] This invention proposes a terminal airspace flight scheduling decision model and system based on hybrid linear integer programming, with the following objectives:

[0008] It assists air traffic controllers in formulating terminal airspace flight scheduling strategies to reduce the workload of air traffic control and realize the transformation from traditional manual experience-based decision-making to data-driven decision-making;

[0009] By rationally planning and scheduling arriving and departing flights, it is possible to improve the terminal airspace capacity and airspace resource utilization rate under the existing operational resource conditions, and effectively alleviate congestion in busy terminal areas.

[0010] By calculating the arrival order and allocated arrival time of flights at each metering point, the minimum time-domain safety interval that flights need to maintain when passing through each metering point is analyzed, thereby improving the ability to perceive future operational situations and shifting the focus of safety incidents or signs from in-process control to pre-event prevention.

[0011] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a terminal airspace flight scheduling decision-making method based on hybrid linear integer programming, comprising:

[0012] Step 1: Obtain flight information participating in scheduling within the time window from the flight information database, and obtain environmental information from the operating environment information database;

[0013] Step 2: Determine whether rolling time-domain control needs to be enabled. If not enabled, all flights participate in building the flight scheduling set; if enabled, flights within the rolling time-domain step participate in building the flight scheduling set.

[0014] Step 3: Define decision variables according to business requirements, and construct the objective function of the terminal airspace single-path flight sequencing and scheduling model based on minimizing the in-flight waiting time of arriving flights and minimizing the scheduling time deviation of arriving and departing flights;

[0015] Step 4: Construct a constraint library for the terminal airspace single-path flight sequencing and scheduling model;

[0016] Step 5: Select Model 1, Model 2, or Model 3 based on business needs; Model 1, Model 2, and Model 3 include different constraints from the constraint library;

[0017] Step 6: Solve the selected model according to the constraint library and obtain the calculation results;

[0018] Step 7: Output the decision information for the flights that need to be scheduled in the flight scheduling set based on the model and calculation results corresponding to the business requirements;

[0019] Step 8: Send the decision information to the route control position, terminal control position and tower control position respectively.

[0020] Optionally, in step 3, when scroll time-domain control is enabled, additional scroll time-domain constraints are added, including:

[0021] (1) Runway separation constraint for the preceding frozen flight: if it is on the same runway as the preceding frozen flight, then the minimum wake separation constraint is satisfied.

[0022] (2) Minimum flight interval constraint for consecutive metering nodes of the preceding frozen flight: If the preceding frozen flight passes through the same metering point, then the subsequent flight must meet the minimum flight time interval constraint.

[0023] (3) The consecutive metering nodes of the preceding frozen flights are subject to uniqueness constraints to ensure that only one flight passes through the metering node first during the flight assignment process.

[0024] Secondly, this application also provides a terminal airspace flight scheduling decision system based on hybrid linear integer programming, including a flight information database, an operating environment information database, a scheduling model construction module, a solution calculation module, a decision assignment module, and a situation information display terminal;

[0025] The scheduling model construction module is used for:

[0026] Retrieve flight information for scheduling within the time window from the flight information database, and retrieve environmental information from the operating environment information database;

[0027] Based on the acquired flight and environmental information, it is determined whether rolling time-domain control needs to be enabled. If not enabled, all flights participate in the construction of the flight scheduling set; if enabled, flights within the rolling time-domain step participate in the construction of the flight scheduling set.

[0028] Decision variables are defined based on business requirements, and the objective function of the terminal airspace single-path flight sequencing and scheduling model is constructed based on minimizing the in-flight waiting time of arriving flights and minimizing the scheduling time deviation of arriving and departing flights.

[0029] Construct a constraint library for the terminal airspace single-path flight sequencing and scheduling model;

[0030] Choose Model 1, Model 2, or Model 3 based on business needs; Model 1, Model 2, and Model 3 include different constraints from the constraint library;

[0031] The solution calculation module is used to solve the selected model according to the constraint condition library and obtain the calculation results;

[0032] The decision designation module is used to output decision information for flights that need to be scheduled in the flight scheduling set based on the model and calculation results corresponding to business needs.

[0033] The situation information display terminal is used to display the decision information.

[0034] Furthermore, the system also includes an additional constraint building module for adding additional rolling time-domain constraints when rolling time-domain control is enabled.

[0035] The technical solution of this invention is significantly different from the "first-come, first-served" strategy. Specifically, this technology uses a hybrid linear integer programming model to accurately abstract and model the complex operational logic between flight operations and the control operation rules into a series of mathematical linear constraints. Combined with the designed linear objective function, the optimal scheduling strategy can be found in the entire domain through a global optimization algorithm or a mature industrial solver.

[0036] Furthermore, the method proposed in this invention has richer decision-making information compared to existing technologies. It not only includes flight sequence and time in traditional models, but also incorporates decision-making content such as flight speed, interval maintenance, and waiting mode, which can provide more reliable and flexible decision support services in complex operating environments. Attached Figure Description Attached Figure Description

[0037] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0038] Figure 1 is a flowchart of the terminal airspace flight scheduling decision method based on hybrid linear integer programming provided in an embodiment of the present invention;

[0039] Figures 2 and 3 are enlarged views of some modules in Figure 1;

[0040] Figure 4 is another flowchart of the method shown in Figure 1. Detailed Implementation Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0043] Please refer to Figures 1 and 4. An embodiment of the present invention provides a terminal airspace flight scheduling decision-making method based on hybrid linear integer programming, comprising:

[0044] Step 1: Obtain flight information participating in scheduling within the time window from the flight information database, and obtain environmental information from the operating environment information database.

[0045] Specifically, the flight information for the time window is obtained from the flight information database, including flight number, flight arrival / departure type, flight type (light, medium, heavy), flight runway allocation plan, arrival corridor entrance point, and arrival route information.

[0046] The following information is obtained from the operating environment information database: corridor entrance location information, approach route structure information, metering point location and type information, metering point waiting stack configuration, runway operation mode, and traffic flow management measures.

[0047] Manually output model parameter information, including but not limited to the maximum allowable delay of the approach and departure runways, the maximum allowable waiting time at the approach corridor entrance, the horizontal interval (nautical miles) between consecutive flights in the terminal area, the allowable speed adjustment range, the wake turbulence interval of different types of flights, and the maximum capacity of the holding stack.

[0048] Step 2: Determine whether rolling time-domain control needs to be enabled. If not enabled, all flights participate in building the flight scheduling set; if enabled, flights within the rolling time-domain step participate in building the flight scheduling set.

[0049] Specifically, controllers determine whether to activate rolling time-domain control based on the number of flights that need to be scheduled.

[0050] Step 3: Define decision variables according to business requirements, and construct the objective function of the terminal airspace single-path flight sequencing and scheduling model based on minimizing the in-flight waiting time of arriving flights and minimizing the scheduling time deviation of arriving and departing flights.

[0051] Step 4: Construct a constraint library for the terminal airspace single-path flight sorting and scheduling model.

[0052] This constraint library includes multiple constraints, including but not limited to:

[0053] (1) Terminal boundary point arrival flight time window constraint, that is, to ensure that the arrival time of the arrival flight at the corridor entrance must be within the set time interval;

[0054] (2) Runway metering point arrival and departure flight time window constraint, that is, to ensure that the take-off or landing time of the arrival flight must be within the set time interval;

[0055] (3) Runway metering point arrival and departure flight wake separation constraint, that is, to ensure that the minimum wake separation interval must be met between consecutive flight pairs on the same runway;

[0056] (4) Terminal metering point flight speed range constraint, that is, constrain the maximum / minimum speed range of the flight at the terminal metering point;

[0057] (5) Approach metering point speed range constraint, that is, the maximum / minimum speed range of the flight operating at the approach metering point;

[0058] (6) Time constraint for flights arriving at terminal metering points, i.e., constraining the flight time of a flight passing through adjacent terminal metering nodes at the current speed;

[0059] (7) Based on the waiting stack, the time constraint for the arrival flight passing the point is constrained. If the waiting stack exists at the terminal metering point, the flight time at the current speed is constrained to pass the adjacent terminal metering node.

[0060] (8) Approach meter point transit time constraint, that is, constrain the flight time of a flight at the current speed to pass through the adjacent approach meter point;

[0061] (9) The expression of the flight time interval at the intersection is based on the minimum separation time interval required to be satisfied by quantifying the flight speed of any flight pair at the intersection measurement node ("T-shaped intersection", "Y-shaped intersection").

[0062] (10) Expression of continuous link separation flight time interval, based on the minimum separation time interval required to be satisfied by quantifying the flight speed of any flight pair at the same link metering node;

[0063] (11) Minimum separation flight interval constraint for consecutive flight common nodes, ensuring that the minimum separation time domain interval must be met for any pair of consecutive flights at the common metering node;

[0064] (12) The flight sequence at each metering point is subject to uniqueness constraints. If a flight pair has a common metering node, it is ensured that only one flight passes through the metering node first during the flight assignment process.

[0065] (13) Each metering node prevents overtaking conflicts. Since the flight segment has unidirectional uniqueness, it ensures that no overtaking occurs between flight pairs during the flight.

[0066] (14) Based on the waiting stack, the overtaking constraint allows the following flight to overtake if the preceding flight has a waiting stack; otherwise, overtaking will not occur.

[0067] (15) Waiting stack waiting time range constraint, that is, to ensure that the waiting time of the flight at each terminal metering point is limited to a certain time range;

[0068] (16) Waiting stack waiting capacity constraint, that is, ensuring that the number of flights waiting at each terminal metering point at the same time is less than or equal to the waiting stack capacity.

[0069] Step 5: If you selected to enable rolling time domain control in Step 2, then add additional rolling time domain constraints.

[0070] The additional constraints include:

[0071] (1) Runway separation constraint for the preceding frozen flight: if it is on the same runway as the preceding frozen flight, then the minimum wake separation constraint is satisfied.

[0072] (2) Minimum flight interval constraint for consecutive metering nodes of the preceding frozen flight: If the preceding frozen flight passes through the same metering point, then the subsequent flight must meet the minimum flight time interval constraint.

[0073] (3) The consecutive metering nodes of the preceding frozen flights are subject to uniqueness constraints to ensure that only one flight passes through the metering node first during the flight assignment process.

[0074] Step 6: Select Model 1, Model 2, or Model 3 based on business needs.

[0075] The aforementioned scheduling model includes Model 1, Model 2, or Model 3, and the model can be selected according to business needs:

[0076] For Model 1 (constant speed and no waiting mode considered for metering points), the constraints are (1), (2), (3), (6), (8), (11), (12), and (13) in step 4 above.

[0077] Model 2 (flight speed and intervals are involved in the decision but waiting mode is not considered), then the constraints are (1), (2), (3), (4), (5), (6), (8), (9), (10), (11), (12), (13) in step 4 above;

[0078] Model 3 (flight speed and interval participate in decision-making and waiting mode is considered), then the constraints are selected from step 4 above (1), (2), (3), (4), (5), (7), (8), (9), (10), (11), (12), (14), (15), (16).

[0079] Step 7: Solve the selected model according to the constraint library and obtain the calculation results.

[0080] In practice, the solution calculation method can be a commercial solver such as CPLEX / GURBOI, or a population simulation algorithm such as genetic algorithm or bird flock algorithm, or a traditional mathematical programming method such as branch and bound or simplex method.

[0081] Step 8: Output the decision information for the flights that need to be scheduled in the flight scheduling set based on the model and calculation results corresponding to the business requirements.

[0082] Among them, Model 1 outputs decision information including the time of flight passing each meter point and the order in which flights pass through the meter points;

[0083] Model 2 outputs decision information including flight time at each meter point, flight sequence at each meter point, flight speed at each meter point, and time-domain flight interval maintenance recommendations at each meter point.

[0084] Model 3 outputs decision information including flight time at each metering point, flight sequence at each metering point, flight speed at each metering point, time-domain flight interval maintenance recommendations at each metering point, whether a flight executes a waiting procedure at each metering point, and the waiting time at each metering point.

[0085] Step 9: Send the decision information to the route control position, terminal control position and tower control position respectively.

[0086] Furthermore, the mathematical expression of the above model is explained below:

[0087] The mathematical expressions of the objective function and constraints of each model are explained in detail for the above steps.

[0088] Based on step 1, the following data can be obtained. Let:

[0089] For the assembly of incoming flights;

[0090] For departing flights to assemble;

[0091] This refers to the scheduled arrival time of the flight;

[0092] This refers to the scheduled departure time of the flight;

[0093] This refers to the arrival time of inbound flights at the terminal corridor entrance.

[0094] For the terminal spatial topology edge set; For flights The edge set according to the standard entry procedure; For flights The set of terminal edges according to the standard entry procedure; For flights Approach edge set according to standard approach procedure;

[0095] This refers to the set of metering nodes in the terminal's airspace topology. For flights Set of runway measurement points For flights Set of terminal metering points For flights Final approach measurement point set, For flights The set of all measurement points in the standard entry procedure. For all runway points, Set up all corridor entrances.

[0096] Let be the Euclidean distance length of the edge (g, v);

[0097] / Maximum / minimum waiting time for a flight in the waiting stack;

[0098] This represents the maximum waiting capacity of the waiting stack.

[0099] Manually input model parameters:

[0100] To meet the default horizontal separation requirements of flights, it is generally set to 3NM;

[0101] This refers to the maximum allowed waiting time at the entrance corridor.

[0102] The maximum permissible delay time for approach and departure runways;

[0103] For preceding flights With subsequent flights The wake interval;

[0104] The historical nominal speed value of the flight;

[0105] For flights Through measurement points Maximum speed value;

[0106] For flights Through measurement points The minimum speed value;

[0107] It is a very large constant, typically taking the value of ;

[0108] (a) Model 1 Mathematical Expression

[0109] For the mathematical expression of Model 1 in step 6 above, the following decision variables are defined:

[0110] 1) Decision variables for arrival flight arrival time: Let f be the arrival time of the inbound flight at node v. This refers to the arrival time of approaching flight f at the runway node;

[0111] 2) Decision variables for departing flights' departure time: This refers to the departure time of departing flight f at the runway point;

[0112] 3) Decision variables for flight order at common nodes: A binary 0-1 decision variable, representing if the flight exist Previously reached public node The value is 1 if the value is 1, otherwise it is 0.

[0113] According to step 3, the objective function (1) of model 1 is set to minimize the arrival and departure flight scheduling time deviation, which can be expressed in the following form:

[0114]

[0115] The objective function (2) is set to minimize the in-flight waiting time of arriving flights, which can be expressed in the following form:

[0116]

[0117] Therefore, the overall objective function of Model 1 is expressed as:

[0118]

[0119] in, and As a weighting factor, satisfying ,and .

[0120] The constraints for Model 1 are the constraints (1), (2), (3), (6), (8), (11), (12), and (13) from step 4 above. For constraint (1) of Model 1, which is the time window constraint for the arrival flight at the terminal boundary point, it can be expressed in the following form:

[0121]

[0122]

[0123] For the constraint (2) of Model 1, which is the time window constraint for arrival and departure flights at the runway metering point, it can be expressed in the following form:

[0124]

[0125]

[0126]

[0127]

[0128] For the constraint (3) of Model 1, which is the runway metering point approach and departure flight wake turbulence interval constraint, it can be expressed in the following form:

[0129]

[0130]

[0131]

[0132]

[0133] For the constraint (6) of Model 1, which is the time constraint for the arrival flight at the terminal metering point, it can be expressed in the following form:

[0134]

[0135] For the constraint (8) of Model 1, which is the time constraint for approaching flights to pass through the approach metering point, it can be expressed in the following form:

[0136]

[0137] For the constraint (11) of Model 1, which is the minimum separation flight interval constraint for consecutive flights with common nodes (including terminal nodes and approach nodes), it can be expressed in the following form:

[0138]

[0139] Regarding the constraint (12) of Model 1, which is a unique constraint on the flight sequence at each measurement point, it can be expressed in the following form:

[0140]

[0141] The constraint (13) of Model 1, which is a constraint to prevent overtaking conflicts at each metering node, can be expressed in the following form:

[0142]

[0143] After Model 1 is solved in step 7 above, step 8 outputs decision information including the flight's transit time at each measurement point. and the order in which flights pass through metering points .

[0144] (II) Mathematical Expression of Model Two

[0145] For the mathematical expression of Model 2 in step 6 above, the following decision variables are defined:

[0146] 1) Decision variables for arrival flight arrival time: Let f be the arrival time of the inbound flight at node v. This refers to the arrival time of approaching flight f at the runway node;

[0147] 2) Decision variables for departing flights: This refers to the departure time of departing flight f at the runway point;

[0148] 3) Common node flight order decision variables: A binary 0-1 decision variable, representing if the flight exist Previously reached public node The value is 1 if the value is 1, otherwise it is 0.

[0149] 4) Decision variables for the speed of approaching flights passing through various measurement points: For flights Through measurement points The reciprocal of the speed.

[0150] 5) Minimum flight time interval constraints for flights passing through common metering points: For preceding flights With subsequent flights Through public metering points The minimum flight time interval.

[0151] 6) Linearization of auxiliary decision variables: , , These are binary 0-1 auxiliary decision variables, used to help transform nonlinear equations into linear equations, and have no special physical meaning.

[0152] The objective function of Model 2 has the same mathematical expression as that of Model 1, please refer to the above description.

[0153] The constraints for Model 2 are the constraints (1), (2), (3), (4), (5), (6), (8), (9), (10), (11), (12), and (13) from step 4 above; among them, constraints (1), (2), (3), (12), and (13) are expressed the same as those in Model 1, and will not be repeated here. The following will explain the constraint expressions that are different from those in Model 1:

[0154] For Model 2 constraint (4), which is a flight speed range constraint at the terminal measurement point, and the flight maintains a constant speed or decelerates during the approach process in the terminal airspace, it can be expressed in the following form:

[0155]

[0156]

[0157] For the constraint (5) of Model 2, which is the flight speed range constraint at the approach measurement point, it can be expressed in the following form:

[0158]

[0159]

[0160] For the constraint (6) of Model 2, which is the time constraint for the arrival flight at the terminal metering point, it can be expressed in the following form:

[0161]

[0162]

[0163]

[0164]

[0165] For the constraint (8) of Model 2, which is the time constraint for approaching flights to pass through the approach metering point, it can be expressed in the following form:

[0166]

[0167]

[0168]

[0169]

[0170] For the constraint (9) of Model 2, which is expressed as the time interval between the divergence points, it can be represented in the following form:

[0171] when Then the interval satisfies

[0172] when Then the interval satisfies

[0173]

[0174] The above constraint (9) is expressed in a linear form:

[0175]

[0176]

[0177]

[0178]

[0179] For the constraint (10) of Model 2, which is expressed as the continuous link separation flight time interval, it can be represented in the following form:

[0180]

[0181] The above constraint (10) is linearized into the following form:

[0182]

[0183] For Model 2 constraint (11), which is the minimum separation flight interval constraint for the common node of consecutive flights, the results calculated based on the above constraints (9)-(10) are as follows: The minimum separation interval between consecutive flights at the common node can be expressed in the following form:

[0184]

[0185]

[0186] After Model 2 is solved in step 7 above, step 8 outputs decision information including the flight's transit time at each measurement point. Flight passing through metering points sequence Flight speed values ​​at each metering point 1 / Recommendations for maintaining time-domain flight intervals at each metering point. .

[0187] (III) Mathematical Expression of Model 3

[0188] For the mathematical expression of Model 3 in step 6 above, the following decision variables are defined:

[0189] 1) Decision variables for arrival flight arrival time: Let f be the arrival time of the inbound flight at node v. This refers to the arrival time of approaching flight f at the runway node;

[0190] 2) Decision variables for departing flights: This refers to the departure time of departing flight f at the runway point;

[0191] 3) Common node flight order decision variables: A binary 0-1 decision variable, representing if the flight exist Previously reached public node The value is 1 if the value is 1, otherwise it is 0.

[0192] 4) Decision variables for the speed of approaching flights passing through various measurement points: For flights Through measurement points The reciprocal of the speed.

[0193] 5) Minimum flight time interval variable for flights passing through a common meter point: For preceding flights With subsequent flights Through public metering points The minimum flight time interval.

[0194] 6) Flight waiting time variable at the terminal metering point: For flights Select from the previous node to When nodes are present, The waiting time required for the node's wait stack;

[0195] 7) Decision variables for flights waiting at the terminal metering point: For binary 0-1 decision variables, flight Select from the previous node to the current node. When nodes are present, The node's wait stack is set to 1 if a wait occurs, and 0 otherwise.

[0196] 8) Linearization of flight intervals as auxiliary decision variables: , , These are binary 0-1 auxiliary decision variables, used to help transform nonlinear equations into linear equations, and have no special physical meaning.

[0197] 9) Linearizing the waiting stack capacity as an auxiliary decision variable: For binary 0-1 auxiliary decision variables, when subsequent flights meet the requirements... When the value is 1, it is 1; otherwise, it is 0.

[0198] The objective function of Model 3 differs from that of Models 1 and 2. The objective function of Model 1 (1) is set to minimize the arrival and departure flight scheduling time deviation, which can be expressed in the following form:

[0199]

[0200] The objective function (2) is set to minimize the in-flight waiting time of arriving flights, which can be expressed in the following form:

[0201]

[0202] in, The following constraints must be met:

[0203]

[0204] Therefore, the overall objective function of Model 3 is expressed as:

[0205]

[0206] in, and As a weighting factor, satisfying ,and .

[0207] The constraints for Model 3 are the constraints (1), (2), (3), (4), (5), (7), (8), (9), (10), (11), (12), (14), (15), (16) in step 4 above; where the constraints (1), (2), (3), (4), (5), (8), (9), (10), (11), (12) are the same as those for Model 2, and will not be repeated here.

[0208] The following section will explain the constraint expressions that differ from Model 2:

[0209] For the constraint (7) of Model 3, which is the arrival time constraint of the waiting stack for the arriving flight, it can be expressed in the following form:

[0210]

[0211] For the constraint (14) of Model 3, which is a metering point overtaking constraint based on the waiting stack, it can be expressed in the following form:

[0212]

[0213] For the constraint (15) of Model 3, which is a waiting time range constraint for the waiting stack, it can be expressed in the following form:

[0214]

[0215] For the constraint (16) of Model 3, which is a waiting stack waiting capacity constraint, it can be expressed in the following form:

[0216]

[0217] in, For the indicator function, considering the control load, the maximum capacity of the wait stack is... The value is 1 when the departure time of the subsequent flight meets the interval, and 0 otherwise. The constraint (16) is linearized as follows:

[0218]

[0219] After Model 3 is solved in step 7 above, step 8 outputs decision information including the flight's transit time at each measurement point. Flight passing through metering points sequence Flight speed values ​​at each metering point 1 / Recommendations for maintaining time-domain flight intervals at each metering point. The waiting time of flights at each metering point .

[0220] Based on the mathematical expression of the aforementioned models, the mixed linear integer programming form of each model is described below:

[0221] (1) Model 1 Mixed Linear Integer Programming Form:

[0222] objective function

[0223]

[0224] Constraints:

[0225]

[0226] (2) Model 2 mixed linear integer programming form:

[0227] objective function

[0228]

[0229] Constraints:

[0230]

[0231] (3) Model 3 Hybrid Linear Integer Programming Form:

[0232] Objective function:

[0233]

[0234] Constraints:

[0235]

[0236]

[0237] As can be seen from the above description, the terminal airspace flight scheduling decision-making method based on hybrid linear integer programming provided in this embodiment of the invention has the following advantages:

[0238] (1) This invention precisely abstracts and models the complex operational logic and control rules between flight pairs operating in the terminal airspace into a mathematical linear constraint expression. By minimizing the in-flight waiting time of arriving flights and minimizing the scheduling time deviation of arriving and departing flights, the objective function is constructed. Compared with the traditional "first-come, first-served algorithm", it can obtain a more efficient flight sequence without causing serious waste of flight time slots.

[0239] (2) The objective function and constraints constructed in this invention contain both integer and continuous variables, and are all linearized objectives and constraints. Therefore, the terminal airspace flight scheduling decision model can be transformed into a mixed integer linear programming model. Compared with other patented nonlinear models, its greatest advantage is that it can search for the optimal solution in the entire domain and can use commercially available industrial solvers to increase the solution quality.

[0240] (3) This invention takes into account the complexity of actual operating scenarios and fully considers various key decision-making factors in actual operation that are neglected by traditional scheduling schemes. This makes scheduling decisions closer to the needs of actual operating scheduling scenarios, thereby enabling more accurate optimization of time slot resources and avoiding potential conflicts, time slot waste, or scheduling bottlenecks in traditional scheduling methods. At the same time, multiple decision suggestions enable controllers to make scheduling decisions quickly and accurately, improving the scientific nature and practicality of the scheduling scheme.

[0241] (4) This invention relates to a mathematical construction method for various scheduling models, which can extract the objective function, constraint conditions and solution algorithm corresponding to the business scenario, realize the rapid response and support for the scheduling needs of the business scenario, improve the model reusability, and improve the integration and selection convenience of the scheduling scheme.

[0242] (5) This invention also introduces rolling time-domain control to establish a planning model with a forward-looking time window. By dividing the large set of flight data into several subsets of scheduled flights, rolling local optimization is repeatedly performed to decompose the global optimization problem into multiple local optimization problems. This step not only simplifies the complexity of the problem but also approximates the global optimal solution to a certain extent, thus ensuring both computational efficiency and optimization effect. At the same time, the use of rolling time-domain control can dynamically adjust the plan according to real-time status and demand changes, and adjust the plan in a timely manner to cope with unexpected events and changes. This dynamic adjustment capability enables the model to better cope with uncertainties and changes in the system, and improves the flexibility and adaptability of the scheduling scheme.

[0243] Based on the same inventive concept, this embodiment of the invention also provides a terminal airspace flight scheduling decision system based on hybrid linear integer programming, including a flight information database, an operating environment information database, a scheduling model construction module, a solution calculation module, a decision specification module, and a situation information display terminal;

[0244] The scheduling model construction module is used for:

[0245] Retrieve flight information for scheduling within the time window from the flight information database, and retrieve environmental information from the operating environment information database;

[0246] Based on the acquired flight and environmental information, it is determined whether rolling time-domain control needs to be enabled. If not enabled, all flights participate in the construction of the flight scheduling set; if enabled, flights within the rolling time-domain step participate in the construction of the flight scheduling set.

[0247] Decision variables are defined based on business requirements, and the objective function of the terminal airspace single-path flight sequencing and scheduling model is constructed based on minimizing the in-flight waiting time of arriving flights and minimizing the scheduling time deviation of arriving and departing flights.

[0248] Construct a constraint library for the terminal airspace single-path flight sequencing and scheduling model;

[0249] Choose Model 1, Model 2, or Model 3 based on business needs; Model 1, Model 2, and Model 3 include different constraints from the constraint library;

[0250] The solution calculation module is used to solve the selected model according to the constraint condition library and obtain the calculation results;

[0251] The decision designation module is used to output decision information for flights that need to be scheduled in the flight scheduling set based on the model and calculation results corresponding to business needs.

[0252] The situation information display terminal is used to display the decision information.

[0253] Furthermore, the system also includes an additional constraint building module for adding additional rolling time-domain constraints when rolling time-domain control is enabled.

[0254] It should be noted that for a more detailed description of the system's workflow, please refer to the aforementioned method implementation section, which will not be repeated here.

[0255] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A terminal airspace flight scheduling decision-making method based on hybrid linear integer programming, characterized in that, The process includes: Step 1: Obtain flight information participating in scheduling within the time window from the flight information database, and obtain environmental information from the operating environment information database; Step 2: Determine whether rolling time-domain control needs to be enabled. If not enabled, all flights participate in constructing the flight scheduling set; if enabled, flights within the rolling time-domain step participate in constructing the flight scheduling set. Step 3: Define decision variables according to business requirements, and construct the objective function of the terminal airspace single-path flight sequencing and scheduling model based on minimizing the in-flight waiting time of arriving flights and minimizing the scheduling time deviation of arriving and departing flights; Step 4: Construct a constraint condition library for the terminal airspace single-path flight sequencing and scheduling model; Step 5: Select Model 1, Model 2, or Model 3 according to business requirements; Model 1, Model 2, and Model 3 include different constraints in the constraint condition library; Model 1 is applicable to scenarios with constant speed and no waiting mode considered at the metering point; Model 2 is applicable to scenarios where flight speed and interval are involved in the decision but waiting mode is not considered; Model 3 is applicable to scenarios where flight speed and interval are involved in the decision and waiting mode is considered; Step 6: Solve the selected model according to the constraint condition library to obtain the calculation results; Step 7: Output the decision information of the flights to be scheduled in the flight scheduling set according to the model corresponding to the business requirements and the calculation results; The decision information of Model 1 includes the arrival time of arriving flight f at node v. Departure time of departing flight f at the runway point and the order in which flights pass through the metering points The decision information for Model 2 includes the arrival time of the incoming flight f at node v. Departure time of departing flight f at the runway point Flight passing through metering points sequence Flight speed values ​​at each metering point 1 / Recommendations for maintaining time-domain flight intervals at each metering point. The decision information for Model 3 includes the arrival time of the incoming flight f at node v. Departure time of departing flight f at the runway point Flight passing through metering points sequence Flight speed values ​​at each metering point 1 / Recommendations for maintaining time-domain flight intervals at each metering point. The waiting time of flights at each metering point ； Step 8: Send the decision information to the en route control position, terminal control position, and tower control position respectively; Objective functions of Model 1 and Model 2 Objective function of Model 3 For the assembly of incoming flights, For departing flights to gather, For the scheduled arrival time of the flight, For the scheduled departure time of the flight, The arrival time of the incoming flight at the terminal corridor entrance. This refers to the arrival time of approaching flight f at the runway node. The departure time of departing flight f at the runway point. This refers to the waiting time performed by the flight at each metering point. and As a weighting factor, satisfying ,and 。 2. The method as described in claim 1, characterized in that, In step 3, when rolling time domain control is enabled, additional rolling time domain constraints are added, including: (1) Runway separation constraint for preceding frozen flights, if the preceding frozen flight is on the same runway, the minimum wake separation constraint is satisfied; (2) Minimum flight interval constraint for consecutive metering nodes of preceding frozen flights, if the preceding frozen flight passes through the same metering point, the subsequent flight must satisfy the minimum flight time domain interval constraint; (3) Uniqueness constraint for consecutive metering nodes of preceding frozen flights, ensuring that only one flight passes through the metering node first during the flight assignment process.

3. The method as described in claim 1, characterized in that, The constraints in the constraint library include: (1) time window constraints for approaching flights at terminal boundary points; (2) time window constraints for approaching and departing flights at runway metering points; (3) wake turbulence interval constraints for approaching and departing flights at runway metering points; (4) flight speed range constraints at terminal metering points; (5) flight speed range constraints at approach metering points; (6) time constraints for approaching flights passing through terminal metering points; (7) time constraints for approaching flights passing through terminal metering points based on waiting stacks, where if waiting stacks exist at terminal metering points, the flight time at which a flight passes through adjacent terminal metering nodes at the current speed is constrained; (8) time constraints for approaching flights passing through terminal metering points; and (9) separation of intersection points. (10) Flight time interval expression, based on the minimum separation time interval required to be satisfied by quantifying the flight speed of any flight pair at the cross metering node; (11) Continuous link separation flight time interval expression, based on the minimum separation time interval required to be satisfied by quantifying the flight speed of any flight pair at the same link metering node; (12) Minimum separation flight interval constraint for continuous flight common nodes; (13) Uniqueness constraint for flight sequence passing through each metering point; (14) Overtaking conflict prevention constraint for each metering node; (15) Overtaking allowance constraint for metering points based on waiting stack; (16) Waiting time range constraint for waiting stack; (17) Limited waiting capacity constraint for waiting stack.

4. The method as described in claim 1, characterized in that, Model 1 includes constraints (1), (2), (3), (6), (8), (11), (12), (13); Model 2 includes constraints (1), (2), (3), (4), (5), (6), (8), (9), (10), (11), (12), (13); Model 3 includes constraints (1), (2), (3), (4), (5), (7), (8), (9), (10), (11), (12), (14), (15), (16).

5. A terminal airspace flight scheduling decision system based on hybrid linear integer programming, characterized in that, It includes a flight information database, an operating environment information database, a scheduling model construction module, a solution calculation module, a decision assignment module, and a situation information display terminal. The scheduling model construction module is used for: obtaining flight information participating in scheduling within a time window from the flight information database and obtaining environmental information from the operating environment information database; determining whether rolling time-domain control needs to be enabled; if not enabled, all flights participate in constructing the flight scheduling set; if enabled, flights within the rolling time-domain step participate in constructing the flight scheduling set; defining decision variables according to business requirements; constructing the objective function of the terminal airspace single-path flight sequencing and scheduling model based on minimizing the in-flight waiting time of arriving flights and minimizing the arrival / departure flight scheduling time deviation; and constructing the terminal airspace single-path flight sequencing and scheduling model. A constraint library is used; Model 1, Model 2, or Model 3 is selected based on business requirements; Model 1, Model 2, and Model 3 include different constraints from the constraint library; Model 1 is applicable to scenarios with constant speed and no waiting mode considered at the measurement point; Model 2 is applicable to scenarios where flight speed and interval are involved in the decision-making process but waiting mode is not considered; Model 3 is applicable to scenarios where flight speed and interval are involved in the decision-making process and waiting mode is considered; the solution calculation module is used to solve the selected model according to the constraint library and obtain the calculation results; the decision specification module is used to output the decision information of the flights to be scheduled in the flight scheduling set according to the model corresponding to the business requirements and the calculation results; the decision information of Model 1 includes the arrival time of the arriving flight f at node v. Departure time of departing flight f at the runway point and the order in which flights pass through the metering points The decision information for Model 2 includes the arrival time of the incoming flight f at node v. Departure time of departing flight f at the runway point Flight passing through metering points sequence Flight speed values ​​at each metering point 1 / Recommendations for maintaining time-domain flight intervals at each metering point. The decision information for Model 3 includes the arrival time of the incoming flight f at node v. Departure time of departing flight f at the runway point Flight passing through metering points sequence Flight speed values ​​at each metering point 1 / Recommendations for maintaining time-domain flight intervals at each metering point. The waiting time of flights at each metering point The situation information display terminal is used to display the decision information; the objective functions of Model 1 and Model 2. Objective function of Model 3 For the assembly of incoming flights, For departing flights to gather, For the scheduled arrival time of the flight, For the scheduled departure time of the flight, The arrival time of the incoming flight at the terminal corridor entrance. This refers to the arrival time of approaching flight f at the runway node. The departure time of departing flight f at the runway point. This refers to the waiting time performed by the flight at each metering point. and As a weighting factor, satisfying ,and 。 6. The system as described in claim 5, characterized in that, The system also includes an additional constraint building module, which adds additional rolling time-domain constraints when rolling time-domain control is enabled, including: (1) runway separation constraint for preceding frozen flights, if the preceding frozen flight is on the same runway, the minimum wake separation constraint is satisfied; (2) minimum flight interval constraint for consecutive metering nodes of preceding frozen flights, if the preceding frozen flight passes through the same metering point, the subsequent flight must satisfy the minimum flight time-domain interval constraint; (3) uniqueness constraint for consecutive metering nodes of preceding frozen flights, ensuring that only one flight passes through the metering node first during the flight assignment process.

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