A method for joint scheduling of airport apron resources based on double-layer programming

Abstract

The application discloses a kind of based on double-layer programming apron resource joint scheduling method.First, apron resource scheduling cost is analyzed, and the cost quantization method including fuel, pollutant, carbon emission and time four factors is proposed;Subsequently, the apron resource joint scheduling model based on double-layer programming is established, the upper target is to minimize the parking space assignment cost, the lower target is to minimize the ground support vehicle scheduling cost, the upper model result is used as the lower model constraint condition, and the overall optimization is achieved through iterative optimization;Finally, apron resource joint scheduling solution is carried out, the upper layer uses genetic algorithm, the lower layer uses brainstorming algorithm, and an improved hybrid genetic brainstorming algorithm is constructed to solve the double-layer programming model.Experimental results show that joint scheduling is superior to independent scheduling in economic cost and resource use, not only effectively reduces the cost of airlines and airports, but also provides decision-making basis for apron scheduling during peak period in large airports.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of apron optimization, gate assignment, ground support vehicle scheduling technology, and specifically designs a gate apron resource joint scheduling method based on double-layer planning. BACKGROUND

[0002] The gate apron resource includes gate resource and ground support vehicle resource, the gate assignment scheme is the prerequisite for ground support vehicle scheduling, and the scheduling efficiency of the vehicle will affect the length of time when the aircraft occupies the gate, and the two are mutually restricted, and jointly affect the ground service quality and punctuality of the flight. In the actual operation of the airport, in order to achieve the overall optimal scheduling, the joint scheduling problem of multiple airport apron resources needs to be considered.

[0003] In terms of research content, most of the current gate apron resource joint scheduling researches are aimed at gate and taxiway resources, and a few researches on ground support vehicles and other resource joint scheduling only consider a single vehicle type; and the overall process of the research is to assign a gate to a flight, then schedule the ground support vehicle, and does not consider the reverse influence of the gate on the ground support vehicle path arrangement. In terms of research methods, set partitioning model, mixed integer linear programming model, etc. are mostly used, and exact algorithms and heuristic algorithms are used for solving, wherein the exact algorithm is inefficient in processing large-scale problems, and it is difficult to solve multi-objective optimization problems; and the heuristic algorithm still has room for improvement in robustness and optimization accuracy. Therefore, it is necessary to conduct overall joint scheduling of gates and multiple ground support vehicles to further improve the efficiency of airport operation. SUMMARY

[0004] The present application discloses a gate apron resource joint scheduling method based on double-layer planning, which aims to conduct overall joint scheduling of gates and ground support vehicles, complete the peak period support task under the condition of meeting the gate and vehicle resource restrictions, and improve the gate apron operation efficiency.

[0005] To achieve the above purpose, the technical scheme provided by the present application is:

[0006] A gate apron resource joint scheduling method based on double-layer planning, characterized in that it comprises the following main steps:

[0007] Step 1: divide the gate apron resource scheduling cost into gate assignment cost and ground support vehicle scheduling cost, and propose a cost quantification method containing four factors of fuel, pollutant, carbon emission and time;

[0008] Step 2: establish a gate apron resource joint scheduling model based on double-layer planning, the upper model is a gate assignment model, and the lower model is a ground support vehicle scheduling model, which minimizes the total gate apron resource scheduling cost while meeting the gate apron resource capacity restriction;

[0009] Step 3: Joint scheduling of apron resources, the upper layer uses an improved genetic algorithm, and the lower layer uses a brainstorming algorithm to construct an improved hybrid genetic brainstorming algorithm (AHGBA) for solving.

[0010] To optimize the above technical solutions, the specific measures / limitations taken also include:

[0011] In step 1, the apron assignment cost is divided into four parts: aircraft fuel cost, aircraft pollutant cost, aircraft carbon emission cost, and aircraft time cost.

[0012] The aircraft fuel cost F generated by flight i i aircraft The calculation formula is as follows. Wherein, is the total arrival and departure taxi time of flight i, r refers to the unit mass of aviation fuel cost, i n refers to the fuel consumption rate of one engine of flight i at 7% thrust, i m refers to the number of engines of flight i.

[0013]

[0014] The total pollutant cost P of flight i in the apron taxi phase i aircraft The calculation formula is as follows. Wherein, P i CO is the carbon monoxide environmental pollution cost of flight i, P i HC is the hydrocarbon environmental pollution cost of flight i, is the nitrogen oxide environmental pollution cost of flight i.

[0015]

[0016] The carbon emission cost of flight i The calculation formula is as follows. Wherein, c e is the unit mass of CO2 emission cost, is the aviation fuel CO2 emission index.

[0017]

[0018] The total time cost T of flight i i aircraft The calculation formula is as follows. Wherein, is the waiting time of flight i at the apron, is the unit time cost of the flight, is the delay time cost of flight i.

[0019]

[0020] The ground support vehicle scheduling cost is divided into vehicle fuel cost, vehicle pollutant cost, vehicle carbon emission cost and vehicle time cost.

[0021] The fuel consumption cost F of vehicle l l vehicle The calculation formula is as follows. Wherein, TD l is the total distance of vehicle l, v vehicle is the vehicle driving speed, is the vehicle fuel cost per unit time, and u is the vehicle fuel consumption.

[0022]

[0023] The total pollutant cost P of vehicle l l vehicle The calculation formula is as follows. Wherein, are respectively the carbon monoxide, hydrocarbon and nitrogen oxide emissions of vehicle l, c CO , c HC , are respectively the carbon monoxide, hydrocarbon and nitrogen oxide emission costs per unit mass.

[0024]

[0025] The carbon emission cost of vehicle l The calculation formula is as follows. Wherein, c e is the CO2 emission cost per unit mass, is the carbon dioxide emission index of vehicle fuel.

[0026]

[0027] The time cost T of vehicle l l vehicle The calculation formula is as follows. Wherein, is the ground support vehicle cost per unit time, RPT l is the vehicle l return to the parking lot time, DPT l is the vehicle l departure time from the parking lot.

[0028]

[0029] In step 2, the aircraft is assigned to a suitable parking stand, and the objective function is to minimize the parking stand assignment cost, including aircraft fuel cost, pollutant cost, carbon emission cost and time cost, and the upper planning model for parking stand assignment is constructed. The formula is as follows. Wherein the decision variable X ikdenotes whether flight i is assigned to stand k.

[0030]

[0031] To arrange reasonable service paths for ground support vehicles to complete the service of four vehicle types, the objective function is to minimize the vehicle scheduling cost, including vehicle fuel cost, pollutant cost, carbon emission cost and time cost. A lower-level planning model for ground support vehicle scheduling is constructed. The formula is as follows. Wherein the decision variable denotes whether vehicle l in vehicle type q services flight i.

[0032]

[0033] The stand assignment scheme obtained by the upper model is passed to the lower model as input, and the ground support vehicle scheduling scheme and scheduling cost obtained by the lower model are returned to the upper model. Through continuous iteration optimization, the overall optimization is finally achieved.

[0034] In step 3, for the upper stand assignment problem, an improved genetic algorithm is designed, using adaptive crossover and mutation probability, increasing memory bank operation, and improving algorithm efficiency.

[0035] In the genetic algorithm, the crossover operator affects the global search ability, and the mutation operator affects the local search ability. In the initial stage of iteration, in order to ensure that the population converges to the global order, a larger crossover probability and a smaller mutation probability need to be set; in the middle and later stages of iteration, the population optimization turns to local search, in order to maintain the diversity of the population, a smaller crossover probability and a larger mutation probability need to be set.

[0036] For the lower ground support vehicle scheduling problem, a brainstorming algorithm is designed to improve the efficiency of the algorithm.

[0037] The local search operation draws on the ideas of "destruction" and "repair" in the neighborhood search algorithm, which first removes several flights from the current scheme to complete the destruction operation, and then inserts the removed flights into the damaged scheme to complete the repair operation

[0038] Based on the idea of evolutionary iteration, an improved hybrid genetic brainstorming algorithm (AHGBA) is constructed to solve the bi-level programming model, and the basic steps are as follows.

[0039] Step 1: First, initialize the upper-level planning decision variable, generate a random initial solution, that is, each aircraft stand assignment scheme;

[0040] Step 2: The lower-level planning generates the optimal vehicle and route scheduling arrangement for each individual in the upper-level planning population, and feeds back the optimal scheduling scheme to the upper-level planning;

[0041] Step 3: The upper layer planning calculates the optimal objective function value of each individual by using the fitness function;

[0042] Step 4: Improved genetic operation is performed on the population, and the optimal solution is obtained by using the iterative approximation model.

[0043] Compared with the prior art, the beneficial effects of the present application are:

[0044] The present application establishes a joint scheduling model for the apron resources from a global perspective: At present, most of the airport aprons are scheduled for a single type of resource, and the scheduling process is highly independent. The present application considers the reverse influence of the parking space on the path arrangement of the ground support vehicles, and also considers multiple main vehicle types. The two types of resources are jointly scheduled to break down the barriers between resource scheduling and effectively reduce the costs of airlines and airports, providing decision-making basis for apron scheduling during peak periods in large airports. In addition, an improved hybrid intelligent optimization algorithm is designed to solve the bi-level programming model: The improved genetic algorithm is used to solve the upper layer model, the brainstorming algorithm is used to solve the lower layer model, and methods such as increasing the memory bank, introducing local search and setting adaptive parameters are used to improve the traditional algorithm. An improved hybrid genetic brainstorming algorithm is constructed to improve the solving efficiency of the algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 Flow chart of apron resource joint scheduling method based on bi-level programming.

[0046] Figure 2 Apron resource joint scheduling idea based on bi-level programming.

[0047] Figure 3 Parking space configuration diagram.

[0048] Figure 4 Iteration diagram of upper layer cost and lower layer cost.

[0049] Figure 5 Parking space assignment Gantt chart.

[0050] Figure 6 Support vehicle service Gantt chart. DETAILED DESCRIPTION

[0051] The above content of the present application will be further described in the form of examples, but this should not be understood as limiting the scope of the above subject matter of the present application to the following examples. Any technology realized based on the above content of the present application belongs to the scope of the present application.

[0052] The present application proposes an apron resource joint scheduling method based on bi-level programming, and the flow chart is as shown in Figure 1 The method comprises the following steps:

[0053] (1) The parking apron resource scheduling cost is divided into parking stand assignment cost and ground support vehicle scheduling cost, and a cost quantification method containing four factors of fuel, pollutant, carbon emission and time is proposed.

[0054] The specific steps in step (1) include:

[0055] 1.1. The parking stand assignment cost is divided into four parts of aircraft fuel cost, aircraft pollutant cost, aircraft carbon emission cost and aircraft time cost.

[0056] Based on the ICAO database, the fuel flow rate of 7% rated power is used as the fuel flow rate of the aircraft on the apron. The aircraft fuel cost F produced by flight i is i aircraft The calculation formula is as follows. Wherein, is the total taxiing time of flight i, r is the unit mass of aircraft fuel cost, i n is the fuel consumption rate of one engine of flight i at 7% thrust, i is the number of engines of flight i.

[0057]

[0058] The present application mainly considers the environmental cost of carbon monoxide, hydrocarbons and nitrogen oxides. The total pollutant cost P of flight i in the apron taxiing stage is i aircraft The calculation formula is as follows. Wherein, P i CO , P i HC , are the carbon monoxide, hydrocarbon, nitrogen oxide environmental pollution costs of flight i respectively.

[0059]

[0060] The CO2 emission produced by the aircraft in the taxiing stage is one of the main sources of airport carbon emission, therefore, the carbon emission cost C of flight i is The calculation formula is as follows. Wherein, c e is the unit mass of CO2 emission cost, is the CO2 emission index of aviation fuel.

[0061]

[0062] The aircraft time cost includes the airline cost except fuel and the passenger delay time cost. The total time cost T of flight i is i aircraft The calculation formula is as follows. Wherein, The waiting time of flight i at the side of the parking spot, The unit time cost of flight, The delay time cost of flight i.

[0063]

[0064] 1.2, the ground support vehicle scheduling cost is divided into four parts of vehicle fuel cost, vehicle pollutant cost, vehicle carbon emission cost and vehicle time cost.

[0065] When calculating the vehicle fuel cost, the driver attribute, road condition and traffic condition are not considered, and the standard fuel consumption value is mainly calibrated through vehicle attribute and natural environment factors. The fuel consumption cost F of vehicle l is l vehicle The calculation formula is as follows. Wherein, TD l is the total distance of vehicle l, v vehicle is the vehicle driving speed, is the unit time vehicle fuel cost, and u is the vehicle fuel consumption.

[0066]

[0067] The vehicle gas pollutant emission index calculation method refers to "Technical Guidelines for Compiling Road Motor Vehicle Air Pollutant Emission Inventory". The total pollutant cost P of vehicle l is l vehicle The calculation formula is as follows. Wherein, are respectively the carbon monoxide, hydrocarbon and nitrogen oxide emission of vehicle l, c CO , c HC , are respectively the unit mass carbon monoxide, hydrocarbon and nitrogen oxide emission cost.

[0068]

[0069] The consumption of fossil fuel by ground support vehicles during operation will increase the carbon emission of the airport. The carbon emission cost C of vehicle l is The calculation formula is as follows. Wherein, c e is the unit mass CO2 emission cost, is the carbon dioxide emission index of vehicle fuel.

[0070]

[0071] The vehicle use time includes vehicle driving time, waiting time and task execution time, that is, the total time from leaving the parking lot to returning to the parking lot. The time cost T of vehicle l is l vehicle The calculation formula is as follows. Wherein, RPT is the ground support vehicle unit time cost l DPT is the vehicle 1 return to the parking lot time l DPT is the vehicle 1 return to the parking lot time

[0072]

[0073] (2) A joint scheduling model of the apron resources based on bi-level programming is established, the upper model is a stand assignment model, and the lower model is a ground support vehicle scheduling model, which minimizes the total cost of apron resource scheduling while meeting the apron resource capacity limit.

[0074] The specific steps in the step (2) include:

[0075] 2.1, for the aircraft assignment of a suitable stand, taking the minimum stand assignment cost as the objective function, an upper programming model for stand assignment is constructed, and the formula is as follows. Wherein the decision variable X ik represents whether the flight i is assigned to the stand k.

[0076]

[0077] 2.2, for the ground support vehicle to arrange a reasonable service path to complete the service of four types of vehicles, taking the minimum vehicle scheduling cost as the objective function, a lower programming model for ground support vehicle scheduling is constructed, and the formula is as follows. Wherein the decision variable represents whether the vehicle l in the vehicle type q serves the flight i.

[0078]

[0079] 2.3, the stand assignment scheme obtained by the upper model is input to the lower model, and the ground support vehicle scheduling scheme and scheduling cost obtained by the lower model are returned to the upper model, and the two are iteratively optimized to finally achieve the overall optimization. The bi-level programming model idea is as shown in Figure 2 .

[0080] (3) The apron resource joint scheduling is solved, the improved genetic algorithm is used in the upper layer, the brainstorming algorithm is used in the lower layer, and an improved hybrid genetic brainstorming algorithm (AHGBA) is constructed to solve.

[0081] The specific steps in the step (3) include:

[0082] 3.1, for the upper stand assignment problem, an improved genetic algorithm is designed, adaptive crossover and mutation probability is used, memory bank operation is increased, and the efficiency of the algorithm is improved.

[0083] The adaptive crossover probability Pc is calculated as follows:

[0084]

[0085] where Pc max represents the maximum value of crossover probability; Pc min represents the minimum value of crossover probability; fit' represents the value of individual fitness function; fit avg represents the average value of population fitness function; fit max represents the maximum value of population fitness function.

[0086] The adaptive mutation probability Pm is calculated as follows:

[0087]

[0088] where Pm max represents the maximum value of mutation probability; Pm min represents the minimum value of mutation probability.

[0089] 3.2, for the lower ground support vehicle scheduling problem, a brainstorming algorithm is designed, and a local search operation is used to improve the efficiency of the algorithm. The local search operation draws on the ideas of "destroy" and "repair" in the neighborhood search algorithm, first removes several flights from the current scheme to complete the destruction operation, and then inserts the removed flights into the damaged scheme to complete the repair operation. The specific steps are as follows:

[0090] Step 1: specify the number of flights to be removed.

[0091] Step 2: remove the specified number of flights from the original scheme according to the relevance principle to form a set Move, and the damaged scheme is S destroy .

[0092] Step 3: initialize the repair scheme, S repair =S destroy .

[0093] Step 4: if Move is not empty, go to the next step, otherwise jump to Step 8.

[0094] Step 5: calculate the regret value of all flights in Move inserted into S repair .

[0095] Step 6: find the sequence number of the maximum item in all regret values, find the flight to be inserted back according to the sequence number, and finally insert the flight back to S repair the position with the minimum regret value cost.

[0096] Step 7: update Move, jump to Step 4.

[0097] Step 8: complete the repair, output the repaired scheme S repair.

[0098] 3.3, based on the idea of evolutionary iteration, an improved hybrid genetic brainstorming algorithm (AHGBA) is constructed to solve the bi-level programming model, the specific steps are as follows:

[0099] Step1: First, initialize the upper-level programming decision variables, generate a random initial solution, that is, each aircraft parking assignment scheme;

[0100] Step2: The lower-level programming generates the optimal vehicle and route scheduling arrangement for each individual in the upper-level programming population, and feeds back the optimal scheduling scheme to the upper-level programming;

[0101] Step3: The upper-level programming calculates the optimal objective function value of each individual using the fitness function;

[0102] Step4: Improved genetic operation is performed on the population, and the optimal solution is obtained using the iterative approximation model.

[0103] The technical scheme of the present application will be further described below with a specific embodiment.

[0104] A large airport in China is selected as the research object (hereinafter referred to as A airport), A airport has two terminals, three runways, and 224 aircraft parking spaces, including 142 near parking spaces and 82 far parking spaces. The domestic part of the parking space of T2 terminal of A airport is taken as the research object for simulation verification, the research scope includes the west five-finger corridor, the west six-finger corridor, the west four overnight, and the far parking space of T2 terminal. The parking space system has a total of 36 parking spaces, including 29 near parking spaces and 7 far parking spaces, and the configuration is as shown in Figure 3 .

[0105] (1) Cost calculation related data

[0106] The fuel consumption rate of the aircraft during the taxiing process uses the fuel flow rate of each aircraft model at 7% thrust, and the data is from the engine emission database compiled by ICAO; the taxiing speed of the aircraft is 45km / h; the fuel cost of the aircraft is 8RMB / L.

[0107] The ground support vehicle data mainly includes vehicle driving speed, vehicle unit fuel cost, tank truck capacity and the number of different vehicle types that can be used. The vehicle driving speed is 30km / h; the unit fuel cost of the vehicle is 8.43RMB / L; the maximum capacity of the tank truck is 45000L. According to the "Guidelines for the Configuration of Special Equipment for Flight Support in Civil Transport Airports" formulated by the Civil Aviation Bureau, the number of four vehicle types that can be used is calculated.

[0108] (2) Solution of parking apron resource scheduling based on bi-level programming model

[0109] The calculation result shows that the final target function value is 92936 yuan in consideration of the operation cost, the upper layer parking space assignment cost is 59212 yuan, and the lower layer ground support vehicle scheduling cost is 33724 yuan. The simulation result is shown in Table 1. Figure 4

[0110] Table 1 is a parking space assignment scheme.

[0111] Table 1 is a parking space assignment scheme

[0112]

[0113] The ground support vehicles are 19 in total, including 8 towing vehicles, 4 shuttle vehicles, 2 tank-type refueling vehicles and 5 pipeline refueling vehicles. The flight service plan of each vehicle type is shown in Table 2.

[0114] Table 2 is a ground support vehicle scheduling scheme

[0115]

[0116] Figure 5 a parking space Gantt chart is assigned, Figure 6 a support vehicle service Gantt chart.

[0117] The waiting time of the towing vehicle and the shuttle vehicle is long, and the two types of vehicles are strictly time-constrained, so the cost is mainly reduced from the driving distance. The tank-type refueling vehicle is strictly limited by the vehicle resource capacity, so the cost is mainly reduced from the number of vehicles used. The pipeline refueling vehicle has a relatively loose service start time constraint, so the vehicle type has almost no waiting time. Since there is also no resource limit, it can complete all tasks and then return to the yard. The vehicle type mainly reduces the cost from the vehicle use time.

[0118] (3) Result comparison

[0119] Using the same data and parameters, the results of the joint scheduling model of the parking apron resource proposed in the application are compared with the results of the independent scheduling model. The total cost and the cost of each layer of the two scheduling models are shown in Table 3. The joint scheduling reduces the vehicle scheduling cost by 18.74%, the number of vehicles by 17.39%, and the vehicle driving distance by 9.22% while ensuring that the upper layer parking space assignment cost remains basically unchanged. This shows that the joint scheduling considers the degree of convenience provided by the parking space assignment scheme for vehicle scheduling from the perspective of global optimization, ultimately reduces the total scheduling cost and the number of vehicle resources used, and achieves the purpose of improving the service efficiency of the parking apron.

[0120] Table 3 is a comparison of the cost values of each layer of independent scheduling and joint scheduling

[0121]

[0122] ​The above merely describes preferred embodiments of the present application, and is not intended to limit the present application in any form. Any modification, equivalent replacement, and improvement of the above embodiments made by any person skilled in the art, without departing from the technical solution of the present application, according to the technical essence of the present application, are still within the protection scope of the present application.

Claims

1. A joint scheduling method for apron resources based on two-level planning, characterized in that, Includes the following steps: Step 1: Divide the cost of apron resource scheduling into parking space allocation cost and ground support vehicle scheduling cost, and propose a cost quantification method that includes four factors: fuel, pollutants, carbon emissions and time. Step 2: Establish a joint scheduling model for apron resources based on two-layer planning. The upper-layer model is the parking space assignment model, and the lower-layer model is the ground support vehicle scheduling model. The goal is to minimize the total scheduling cost of apron resources while meeting the capacity constraints of apron resources. Step 3: For the joint scheduling phase of apron resources, an improved genetic algorithm is used at the upper level and a brainstorming algorithm is used at the lower level to construct an improved hybrid genetic brainstorming algorithm for solving the problem; The specific process of step 3 is as follows: Step 3.1: To address the upper-level parking space assignment problem, an improved genetic algorithm is designed, using adaptive crossover and mutation probabilities, increasing memory bank operations, and improving algorithm efficiency; Step 3.2: To address the problem of scheduling vehicles for lower-level ground support, a brainstorming algorithm was designed, using local search operations to improve algorithm efficiency; Step 3.3: Based on the idea of ​​evolutionary iteration, an improved hybrid genetic brainstorming algorithm is constructed to solve the bilevel programming model; In step 3.1, The adaptive crossover probability Pc is calculated as follows: In the formula, Pc max Pc represents the maximum crossover probability; min The value represents the minimum crossover probability; 'fit' represents the individual fitness function value; avg The fitness function represents the average value of the population; max This represents the maximum value of the fitness function in the population; The adaptive mutation probability Pm is calculated as follows: In the formula, Pm max Pm represents the maximum probability of mutation. min This represents the minimum probability of mutation; The specific process of step 3.2 is as follows: Step 1: Specify the number of flights that need to be removed; Step 2: Remove a specified number of flights from the original scheme according to the relevance principle, forming a set Move. The scheme after the disruption is S. destroy ; Step 3: Initialize the repair plan, S repair =S destroy ; Step 4: If Move is not empty, proceed to the next step; otherwise, go to Step 8. Step 5: Calculate all flights inserted into S during Move. repair The regret value; Step 6: Find the index of the item with the largest regret value among all regret values, locate the flight that is about to be inserted back based on the index, and finally insert that flight back into S. repair The position with the lowest regret value cost; Step 7: Update Move, skip to Step 4; Step 8: Complete the repair and output the repaired solution S. repair .

2. The method for joint scheduling of apron resources based on two-layer planning according to claim 1, characterized in that, The specific process of step 1 is as follows: Step 1.1: Divide the parking space allocation cost into four parts: aircraft fuel cost, aircraft pollutant cost, aircraft carbon emission cost, and aircraft time cost; Step 1.2: Divide the cost of dispatching ground support vehicles into four parts: vehicle fuel cost, vehicle pollutant cost, vehicle carbon emission cost, and vehicle time cost.

3. The method for joint scheduling of apron resources based on two-level planning according to claim 2, characterized in that, The cost of assigning parking positions includes... Aircraft fuel costs F generated by flight i i aircraft The calculation formula is as follows: in, This represents the total taxiing time for flight i upon arrival and departure. The cost of aviation fuel per unit mass, r i This refers to the fuel consumption rate of one of the engines of flight i at 7% thrust, n i The number of engines in flight i; The total pollutant cost P of flight i during the surface taxiing phase i aircraft The calculation formula is as follows: Among them, P i CO For the carbon monoxide environmental pollution cost of flight i, P i HC The environmental cost of hydrocarbon pollution from flight i, The environmental pollution cost of nitrogen oxides from flight i; Carbon emission costs of flight i The calculation formula is as follows: Among them, c e Cost per unit mass of CO2 emissions CO2 emission index for aviation fuel; Total time cost T of flight i i aircraft The calculation formula is as follows: in, This refers to the waiting time of flight i next to the parking position. The unit time cost of a flight. Let i be the time cost of flight i's delay. In the aforementioned ground support vehicle dispatching costs Fuel consumption cost F of vehicle l l vehicle The calculation formula is as follows: Among them, TD l Let v be the total distance traveled by vehicle l. vehicle For vehicle speed, Let u be the vehicle fuel cost per unit time, and u be the vehicle fuel consumption. Total pollutant cost P of vehicle l l vehicle The calculation formula is as follows: in, These represent the emissions of carbon monoxide, hydrocarbons, and nitrogen oxides from vehicle l, respectively. CO c HC , These are the emission costs per unit mass of carbon monoxide, hydrocarbons, and nitrogen oxides, respectively. Carbon emission costs of vehicle l The calculation formula is as follows: Among them, c e Cost per unit mass of CO2 emissions The carbon dioxide emission index for vehicle fuel; The time cost T of vehicle l l vehicle The calculation formula is as follows: in, To improve the unit time cost of ground support vehicles, RPT l DPT is the time it takes for vehicle l to return to the depot. l The time when vehicle l leaves the parking lot.

4. The joint scheduling method for apron resources based on two-layer planning according to claim 3, characterized in that, The specific process of step 2 is as follows: Step 2.1: Assign suitable parking positions to aircraft, and construct a high-level planning model for parking position assignment with the objective function of minimizing the total cost of parking position assignment; Step 2.2: Arrange reasonable service routes for ground support vehicles to complete the service for the four vehicle types. With the goal of minimizing the total vehicle scheduling cost, construct a lower-level planning model for ground support vehicle scheduling. Step 2.3: Pass the parking space allocation scheme obtained by the upper-level model as input to the lower-level model, and return the ground support vehicle scheduling scheme and scheduling cost obtained by the lower-level model to the upper-level model. The two are continuously iterated and optimized to eventually reach the overall optimum.

5. The joint scheduling method for apron resources based on bi-level planning according to claim 4, characterized in that, The formula for the upper-level planning model is as follows: Among them, decision variable X ik This indicates whether flight i has been assigned to parking position k; The formula for the lower-level planning model is as follows: Among them, decision variables This indicates whether vehicle l in model q serves flight i.

6. The joint scheduling method for apron resources based on two-layer planning according to claim 1, characterized in that, The specific process of step 3.3 is as follows: Step 1: First, initialize the upper-level planning and decision variables and generate a random initial solution, i.e., the parking position assignment scheme for each aircraft; Step 2: The lower-level planning generates the optimal vehicle and route scheduling arrangement for each individual in the upper-level planning population, and feeds back the optimal scheduling scheme to the upper-level planning. Step 3: Upper-level planning uses the fitness function to calculate the optimal objective function value for each individual; Step 4: Perform improved genetic operations on the population and use iterative approximation to find the optimal solution of the model.

Images