Hybrid boarding strategy optimization method for civil aviation airport operation peak

Abstract

The invention discloses a hybrid boarding strategy optimization method for a civil aviation airport operation peak period. The method comprises the following steps: S1, acquiring ground traffic network data of departure flights, remote boarding gates, ferry vehicles and airports; s2, taking a rear gate boarding task as a basic processing unit, generating a to-be-selected task set, constructing a mathematical optimization model of boarding scheme selection, boarding gate distribution and ferry vehicle scheduling three-dimensional joint decision, and aiming at saving boarding time and operation cost; s3, designing a solution algorithm containing a composite restriction candidate list, continuous flight fragment neighborhood optimization and optimal optimization guide path reconstruction; s4, solving to obtain an optimal scheme; and S5, importing the result into an airport ground support command system for dynamic monitoring execution. The boarding efficiency is remarkably improved, time consumption is shortened, the flight delay risk is reduced, and the peak period operation requirements are met.

Description

Technical Field

[0001] This invention relates to the field of civil aviation transport management, and specifically to a method for optimizing hybrid boarding strategies during peak operating periods at civil aviation airports. Background Technology

[0002] In the civil aviation airport operation system, boarding for flights near the gate is a crucial link affecting flight punctuality and passenger travel experience, and its core relies on boarding bridges to facilitate passenger transfer. Based on aircraft type and airport infrastructure configuration, current boarding modes are mainly divided into two categories: narrow-body aircraft generally adopt a single boarding bridge design, where passengers can only board through the front cabin door; wide-body aircraft, although capable of dual boarding bridges and simultaneous boarding through both cabin doors, are limited by facility resources, limiting their practical application. Regarding passenger organization processes, the industry mainly adopts two methods: one is boarding by seating area, guiding passengers to board in batches according to cabin class, seat row, or membership level; the other is random boarding, allowing passengers to queue freely and board in turn after the boarding gate opens.

[0003] However, the current model has significant limitations: First, dual jet bridges are scarce. During peak airport operating hours, the number of dual jet bridges is insufficient, forcing some wide-body aircraft to use single jet bridges for boarding, resulting in longer passenger waiting times and extended flight transit times. Second, zoned boarding organization is difficult. Manually verifying boarding passes in batches is inefficient and prone to passengers boarding incorrectly, missing passengers, or cutting in line, making on-site organization challenging. Third, random boarding can easily cause congestion. Passengers rushing to the boarding gate can lead to aisle blockages, and slow baggage handling in the cabin often causes congestion in the aisles for passengers in the rear cabins. These problems not only result in low boarding efficiency and excessively long boarding times but may also cause delays to subsequent flights, exacerbate passenger anxiety, and negatively impact the travel experience.

[0004] In recent years, the rapid development of information technologies such as the Internet of Things, big data, and mobile internet has provided technical support for the intelligent upgrading of boarding services for flights near gates. However, existing technologies have not yet broken through core bottlenecks and have not yet formed a systematic solution that takes into account boarding efficiency, cost control, and passenger experience. There is an urgent need to build more flexible hybrid boarding strategies and more efficient ground resource coordination and scheduling methods to overcome the inherent defects of the current boarding model. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for optimizing hybrid boarding strategies during peak operating periods at civil aviation airports.

[0006] In a first aspect, the hybrid boarding strategy optimization method for peak operating periods of civil aviation airports provided by the present invention includes the following steps: S1: Obtain departure flight information, remote gate boarding information, shuttle bus information, and airport ground transportation network data; the departure flight information includes the number of passengers, gate positions, planned start boarding time, and planned end boarding time; the remote gate boarding information includes available time slices; the shuttle bus information includes available quantity, maximum capacity, and travel speed; the airport ground transportation network data includes the travel distance between each gate and boarding position. S2: Taking each near-gate flight as a single backdoor boarding task as the basic processing unit, calculate the upper limit of the number of feasible boarding schemes based on the number of passengers on the flight and the shuttle bus capacity, and generate a set of candidate backdoor boarding tasks; with the goal of maximizing the saving of boarding time and total operation and scheduling costs, construct a mathematical optimization model to realize the three-dimensional joint decision-making of boarding scheme selection, remote gate boarding allocation and shuttle bus scheduling; the three-dimensional joint decision-making is the synchronous and coordinated optimization of the three, avoiding resource conflicts and efficiency losses caused by the independent execution of a single decision link; S3: Design a model solving algorithm. The algorithm includes four core steps: initialization, initial solution generation, local search, and path reconstruction. It also includes key designs for initial solution generation and local search combined constraint candidate list generation, local search continuous flight slice neighborhood optimization, and path reconstruction best priority strategy. S4: Perform the verification of the mathematical model and design the algorithm parameters, use the algorithm to solve the model, and obtain the boarding scheme decision for each flight, the remote gate boarding gate allocation scheme, and the task execution order and timetable for each shuttle bus. S5: The solution results are imported into the airport ground support command system in real time, so that dispatchers can execute and dynamically monitor the support process.

[0007] Furthermore, the mixed boarding refers to the following: for flights with near gates, based on the aircraft cabin layout, some passengers are diverted from the jet bridge to the remote gate, transferred by shuttle bus, and then boarded through the rear door of the aircraft, forming a dual-channel boarding mode of "jet bridge-front door" and "remote gate-shuttle bus-rear door".

[0008] Furthermore, the objective function of the mathematical optimization model is: ; in: ; ; The constraints of the mathematical optimization model are: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; in For the collection of all flights, This is a set of all possible shuttle transport tasks; These are the starting and ending stations for the shuttle bus; For flights A collection of all possible shuttle transportation tasks. For flights The The set of shuttle transportation tasks included in the various boarding schemes. ,and ; For the collection of available shuttle buses, A set of remote gates that can be assigned. It is the union of the starting point parking area, the aircraft stand, and the remote boarding gate. It is the union of the terminal parking lot, the aircraft stand, and the remote boarding gate. The set of time slices used to calculate gate capacity. and order Refers to time slice .

[0009] The economic value per unit of time, expressed in yuan per minute; For flights Additional labor costs required for using mixed boarding; Additional fixed costs (including purchase, maintenance, driver, etc.) to activate a shuttle bus; For the task The cost per unit mileage for shuttle buses; Cost per unit mileage for empty shuttle bus trips; For the task The time spent getting on, off, and waiting; From location Drive to Required mileage; For from location Drive to Time required; For flights The maximum number of feasible boarding options is obtained by dividing half of the number of passengers on each flight by the shuttle bus capacity and then rounding to the nearest integer. For flights The earliest required registration time; For flights The latest boarding time required; For flights Only the time required for boarding using the jet bridge; For flights Execute the After considering the different boarding options, the boarding time via jet bridge is [not specified]. For the task If implemented, the time required for passengers to board through the rear door without queuing; The total timeframe for problem research; Indicates the length of the time slice; Indicates time slice The beginning moment; Indicates the boarding gate Time slice Whether it has been assigned to an unplanned (i.e., remote gate) flight: 1 if yes, 0 otherwise; Indicates flight Does using a remote boarding gate consume time slices? If it is 1, then it is 0; otherwise, it is 0.

[0010] For 0-1 variables, if the flight Arranged to perform the first If there is a valid boarding option, select 1; otherwise, select 0. For 0-1 variables, if the flight Assigned to boarding gate If the value is 1, then the value is 0; otherwise, the value is 0. For 0-1 variables, if the shuttle bus Successive access nodes and If the value is 1, then the value is 0; otherwise, the value is 0. To complete the task for the shuttle bus The time.

[0011] Furthermore, the basic processing unit is specifically defined as follows: all back door boarding tasks selected for the same near gate flight must be assigned to the same and unique remote gate boarding gate, and the scheduling scheme and execution process corresponding to a single back door boarding task must be consistent.

[0012] Furthermore, the method of using the designed algorithm to solve the established mathematical optimization model specifically includes: Step 1: Generate datasets from the preprocessed flight information, optional boarding options, flight sub-tasks, shuttle bus parameters, available time of remote gates, and distance matrix. Step 2: Initialize the global optimal solution, the elite solution pool, and the iteration control parameters, setting the number of iterations... Outer layer continuous no-improvement count The flight groups are sorted from smallest to largest according to the scheduled boarding time. Step 3: Construct a list of feasible solutions for each flight, encompassing three levels of decision-making: boarding scheme selection, gate allocation, and shuttle bus scheduling. Then, retain the previous solution based on its contribution to the objective function and the sum of its negative impacts on the already decided flights. The proportion is used to form a restricted candidate list (RCL). Step 4: Randomly select a plan for the current flight from the restricted candidate list, call the time repair function to correct the shuttle bus task completion time, and update relevant data such as the available time slice of the remote gate and the shuttle bus location; Step 5: Repeat steps 3 to 4 until all flight decisions are completed, form an initial solution and calculate the objective function value, and initialize the number of consecutive no-improvement iterations in the inner layer. ; Step 6: Perform a local search using the Metropolis criterion, employing the neighborhood optimization structure for consecutive flight segments. Neighborhood operations include changing the boarding scheme for consecutive flight segments, changing the boarding scheme for random segments of flights, exchanging remote gate assignments, randomly rescheduling some backdoor boarding tasks, and reassigning the shuttle bus with the least usage intensity. After each neighborhood operation, determine whether to update the current optimal solution. If updated, let... Otherwise When the number of local searches reaches the maximum or If the local search is not complete, proceed to step 8. Step 7: Using the generated local optimum as the guiding solution, and randomly selecting an initial solution from the elite solution pool, a best-priority path reconstruction process is performed: First, identify all decision differences between the initial solution and the guiding solution in boarding scheme and remote gate allocation, and form a difference set; then, traverse all remaining difference items in the difference set, evaluate them one by one, select the difference item with the largest contribution gain to adjust the decision of the initial solution, and delete the difference item from the difference set; update the current optimum solution; repeat the above reconstruction process until the difference set is empty.

[0013] Step 8: Determine if the current optimal solution is better than the global optimal solution: If it is, update the global optimal solution and set... Otherwise Update the elite pool to make... ; Step 9: Repeat steps 3 through 8 until... or Exit the loop and output the globally optimal allocation scheme.

[0014] Furthermore, the candidate boarding schemes for the same flight include boarding via all jet bridges and mixed boarding. Management personnel can select and execute any scheme and dynamically monitor the process through the airport ground support command system. The boarding time via all jet bridges, the boarding time at the front door, and the boarding time at the rear door are obtained through passenger boarding data surveys and data fitting.

[0015] Furthermore, the selection rule for the composite restricted candidate list (RCL) is as follows: only the top candidates whose contribution to the objective function is optimal plus the sum of the negative effects of the decided flights are retained. Proportional scheme, The value range is 0.1-1, with a recommended value of 0.3-0.5. It can also be dynamically adjusted according to the actual operating scenario.

[0016] In a second aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a hybrid boarding strategy optimization method for peak operating periods of civil aviation airports as described in the first aspect.

[0017] Thirdly, the present invention provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a hybrid boarding strategy optimization method for peak operating periods at civil aviation airports as described in the first aspect.

[0018] The hybrid boarding strategy optimization method for peak operating periods at civil aviation airports described in this invention has the following advantages: 1. This invention proposes a flexible "airbridge + shuttle bus" hybrid boarding method by reorganizing and optimizing the traditional boarding process for near-gate flights. This method overcomes the shortcomings of the traditional near-gate flight mode, which only uses airbridges, such as long passenger waiting time and congestion in the cabin. With only a small investment, the total boarding time can be significantly shortened, thereby reducing flight delays caused by low boarding efficiency and improving flight punctuality.

[0019] 2. This invention provides airlines and airports with more flexible boarding strategy options. While ensuring flight punctuality, the time when passengers officially begin boarding can be delayed as needed, reducing the time passengers spend waiting in the boarding gate area. Passengers can also correspondingly postpone their arrival time at the airport. This flexible arrangement makes the boarding process more humane, effectively alleviating passenger anxiety and improving overall travel satisfaction.

[0020] 3. The mathematical model of this invention uses the back door boarding task as the smallest processing optimization unit, which significantly reduces the problem decision scale and computational complexity compared to the method of making diversion decisions based on the number of individual passengers.

[0021] 4. The solution algorithm of this invention can quickly and stably obtain high-quality joint scheduling solutions, which can meet various timeliness requirements in actual operation scenarios.

[0022] 5. This invention can be implemented directly on the basis of existing airport facilities without the need to renovate infrastructure such as boarding bridges and waiting areas. It has low implementation costs, is highly compatible with existing processes, and is applicable to airports of different sizes and configurations, thus having strong practical and promotional value. Attached Figure Description

[0023] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 This is a flowchart of the algorithm of the present invention; Figure 3 This is a comparison of the optimal solution results and solution time between the algorithm of this invention and the Gurobi solver; Figure 4 This is a comparison of the average solution results and average coefficient of variation between the algorithm of this invention and the traditional GRASP algorithm under the same iterative conditions. Detailed Implementation

[0024] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the following embodiments are given for illustrative purposes only and are not intended to limit the scope of the present invention. Those skilled in the art can make various modifications and substitutions to the present invention without departing from its spirit and essence.

[0025] A method for optimizing hybrid boarding strategies during peak operating periods at civil aviation airports includes the following steps: Step 1: Obtain departure flight information, remote gate information, shuttle bus information, and airport ground transportation network data; departure flight information includes the number of passengers, gate positions, planned start boarding time, and planned end boarding time; remote gate information includes available time slots; shuttle bus information includes available number, maximum capacity, and travel speed; airport ground transportation network data includes the travel distance between each gate and gate. Step 2: Taking each near-gate flight as a basic processing unit, calculate the upper limit of the number of feasible boarding schemes based on the number of passengers on the flight and the shuttle bus capacity, and generate a set of back door boarding tasks to be selected; with the goal of saving boarding time and total operation and scheduling costs to the greatest extent, construct a mathematical optimization model to realize three-dimensional joint decision-making of boarding scheme selection, remote gate boarding allocation and shuttle bus scheduling. Step 3: Design the model solution algorithm; First, the preprocessed flight information, optional boarding options, flight sub-tasks, shuttle bus parameters, remote gate availability time, and distance matrix are respectively used to form datasets; then, the global optimal solution, elite solution pool, and iteration control parameters are initialized, and the number of iterations is set. Outer layer continuous unimproved count (Outer layer no improvement threshold set to) The flight set is sorted in ascending order based on the scheduled boarding start time. A list of feasible solutions is constructed for each flight, encompassing three levels of decision-making: boarding scheme selection, gate allocation, and shuttle bus scheduling. The remaining flights are selected based on the sum of their contribution to the objective function and their negative impact on the already decided flights. A restricted candidate list (RCL) is formed based on proportions. Within the RCL, a specific solution is randomly selected for the current flight. A time-correction function is called to adjust the shuttle bus task completion time, and relevant data such as the available time slice for remote gates and the shuttle bus location are updated. This solution construction and selection process is repeated until all flight decisions are completed, forming an initial solution and calculating the objective function value. Simultaneously, the number of consecutive improvements without improvement in the inner layer is initialized. (Inner layer no improvement threshold set to) ); Utilizing neighborhood operations including changing the boarding scheme for consecutive flights, changing the boarding scheme for random flights, exchanging remote gate assignments, randomly rescheduling some backdoor boarding tasks, and redistributing shuttle buses with minimum usage intensity, a local search is performed using the Metropolis criterion. After each neighborhood operation, it is determined whether to update the current solution; if updated, then... Reset to 0, otherwise Add 1 when the number of local searches reaches the maximum value or If the local search is not complete, the elite solution pool is updated. Using the generated local optimum as the guiding solution, an initial solution is randomly selected from the elite solution pool to perform a best-priority path reconstruction process: First, all decision differences between the initial solution and the guiding solution regarding boarding arrangements and remote gate allocation are identified and a difference set is formed. Then, the remaining difference items are iterated to evaluate the contribution gain of each difference item adjustment to the objective function value. The difference item with the largest gain is selected to adjust the initial solution and then deleted. The current solution is updated synchronously. This process is repeated until the difference set is empty. Then, it is determined whether the current solution is better than the global optimum. If it is, the global optimum is updated and the current solution is updated. Reset to 0, otherwise Add 1), after completion, update the elite pool and make Repeat the above process of scheme construction, random selection, local search, and path reconstruction until... Reaching the maximum number of iterations or Exit the loop and output the globally optimal allocation scheme.

[0026] Step 4: Perform the verification of the mathematical model and design the algorithm parameters, and use the designed algorithm to solve the established mathematical optimization model to obtain the boarding plan decision for each flight, the remote gate boarding gate allocation plan, and the task execution order and timetable for each shuttle bus. Step 5: Immediately import the obtained mixed boarding scheme, boarding gate allocation results, and shuttle bus scheduling plan into the airport ground support command system so that dispatchers can execute and dynamically monitor the support process.

[0027] In this invention, the mathematical model described in step 2 is defined as follows: Objective function: ; ; ; Constraints: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; Wherein: Formula 1 indicates that the hybrid boarding decision aims to maximize savings in boarding time and total operational scheduling costs; Formula 2 indicates the sum of boarding time savings for all flights using the hybrid boarding mode; Formula 3 indicates the total operational scheduling costs required for the hybrid boarding mode, including labor costs, shuttle bus fixed costs, and shuttle bus operating costs; Formula 4 indicates that each flight must and can only select one boarding mode, including full jet bridge boarding and hybrid boarding; Formula 5 indicates that remote gates must be assigned to flights selecting the hybrid boarding mode; Formula 6 indicates that shuttle buses must be assigned to the sub-tasks included in flights selecting the hybrid boarding mode; Formula 7 indicates that shuttle bus services can only be provided for flights using the hybrid boarding mode; Formula 8 indicates that the number of available vehicles is limited, and no vehicle is required to be used; Formula 9 indicates that each vehicle from departure... Shuttle buses departing from the terminal station must eventually return to the destination terminal station; Formula 10 indicates that the inflow and outflow of shuttle buses at each task point must be balanced; Formula 11 indicates that the time when a shuttle bus arrives at the flight gate must be later than the scheduled start time of the flight's boarding; Formula 12 indicates that the time when a shuttle bus arrives at the flight gate plus the boarding time required for it and its subsequent tasks must be earlier than the scheduled end time of the flight's boarding; Formula 13 indicates that the same shuttle bus service visiting two points (including task points and terminals) in succession must meet the corresponding time relationship, taking into account travel time and fixed consumption time; Formula 14 indicates that shuttle services at different task points of the same flight have a priority order, and tasks with smaller sequence numbers should be served first; Formula 15 indicates that the same time slice at a remote gate can only be allocated to one flight or reserved for unplanned flights; Formulas 16 to 19 are the basic definitions of decision variables.

[0028] For the collection of all flights, This is a set of all possible shuttle transport tasks; These are the starting and ending stations for the shuttle bus; For flights A collection of all possible shuttle transportation tasks. For flights The The set of shuttle transportation tasks included in the various boarding schemes. ,and ; For the collection of available shuttle buses, A set of remote gates that can be assigned. It is the union of the starting point parking area, the aircraft stand, and the remote boarding gate. It is the union of the terminal parking lot, the aircraft stand, and the remote boarding gate. The set of time slices used to calculate gate capacity. and order Refers to time slice . The economic value per unit of time, expressed in yuan per minute; For flights Additional labor costs required for using mixed boarding; Additional fixed costs (including purchase, maintenance, driver, etc.) to activate a shuttle bus; For the task The cost per unit mileage for shuttle buses; Cost per unit mileage for empty shuttle bus trips; For the task The time spent getting on, off, and waiting; For from location Drive to Required mileage; For from location Drive to Time required; For flights The maximum number of feasible boarding options is obtained by dividing half of the number of passengers on each flight by the shuttle bus capacity and then rounding to the nearest integer. For flights The earliest required boarding time; For flights The latest boarding time required; For flights Only the time required for boarding using the jet bridge; For flights Execute the After considering the different boarding options, the boarding time via jet bridge is [not specified]. For the task If implemented, the time required for passengers to board through the rear door without queuing; The total timeframe for problem research; Indicates the length of the time slice; Indicates time slice The beginning moment; Indicates the boarding gate Time slice Whether it has been assigned to an unplanned (i.e., remote gate) flight: 1 if yes, 0 otherwise; Indicates flight Does using a remote boarding gate consume time slices? If it is 1, then it is 0; otherwise, it is 0. For 0-1 variables, if the flight Arranged to perform the first If there is a valid boarding option, select 1; otherwise, select 0. For 0-1 variables, if the flight Assigned to boarding gate If the value is 1, then the value is 0; otherwise, the value is 0. For 0-1 variables, if the shuttle bus Successive access nodes and If the value is 1, then the value is 0; otherwise, the value is 0. To complete the task for the shuttle bus The time.

[0029] Example 1 This case study uses domestic near-gate flights at Beijing Capital International Airport as an example. Flight data for this case study is taken from 06:00 to 10:00 on September 27, 2025, with a total of 60 near-gate flights. The distance between the parking position and the boarding gate is set according to the terminal layout of Capital Airport. The fixed operating cost of the shuttle bus is set at 1500 yuan, with a unit mileage cost of 3 yuan when fully loaded and 2 yuan when empty. The maximum passenger capacity of the shuttle bus is 80.

[0030] Table 1 shows the optimization effect of the present invention on flights at different time periods compared with the traditional model.

[0031] Table 2 shows the cost-benefit results of this invention under different values ​​of λ.

[0032] Table 1 ; Table 2 ; All flights near the gate are based on actual airport operation records. The back door boarding task is used as the smallest optimization unit. The designed algorithm is used to calculate and allocate boarding schemes by combining relevant data such as flight passenger capacity, shuttle bus capacity, and remote gate availability. The results are compared with the results of the current traditional single jet bridge boarding mode.

[0033] The comparative results show that under the scheme of the present invention, the average boarding time of all flights is reduced by 7.02 minutes, which is 31.44% lower than the traditional mode. The highest reduction in a single period can reach 37.21%, indicating that the new method has great potential for efficiency improvement.

[0034] The comparative results show that the investment effect of this invention is very significant. An investment of 0.31 million yuan can shorten the boarding time by 5.66 hours (the unit investment return is 18.26 hours / 10,000 yuan). Further investment can save even more boarding time, which has high potential practical value.

[0035] The comparison results show that in small-scale cases, the calculation results of the algorithm of the present invention are consistent with those of Gurobi; in larger-scale cases, the solution results and time of the algorithm of the present invention are better than those of Gurobi (the latter cannot find the optimal solution), and the superiority increases significantly with the increase of problem size, indicating that the algorithm of the present invention has both quality and efficiency advantages and can fully meet the actual needs. The comparison results show that the average solution results of the algorithm of the present invention are better than those of the traditional GRASP algorithm in all cases, and the average coefficient of variation is smaller than that of the traditional GRASP algorithm. This indicates that the solution quality of the algorithm of the present invention is better and more stable, and further demonstrates that the algorithm improvement is effective.

[0036] This invention provides civil aviation airports with a comprehensive, integrated, and intelligent near-gate hybrid boarding system, which can effectively reduce flight boarding time, improve passenger boarding convenience, and reduce the probability of flight delays. It can also provide technical support for improving the operational efficiency of civil aviation airports during peak periods and enhancing service quality.

[0037] Example 2: An electronic device includes a memory and a processor, the memory being used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the above-described method for optimizing a hybrid boarding strategy for peak operating periods at civil aviation airports.

[0038] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the electronic device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0039] Example 3: A computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the method in Embodiment 1.

[0040] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0041] This invention is described with reference to flowchart illustrations and / or block diagrams of the method, terminal device (system), and computer program product according to the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0042] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0043] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0044] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0045] Finally, it should be noted that those skilled in the art can implement this invention in a wide range of ways with equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without requiring unnecessary experiments. Although specific embodiments are given in this invention, it should be understood that further modifications can be made to the invention. In summary, according to the principles of this invention, this application is intended to include any changes, uses, or improvements to the invention, including changes made using conventional techniques known in the art that depart from the scope disclosed herein. Some basic features can be applied within the scope of the following appended claims.

Claims

1. A method for optimizing hybrid boarding strategies during peak operating periods at civil aviation airports, characterized in that, Includes the following steps: S1: Obtain departure flight information, remote gate boarding information, shuttle bus information, and airport ground transportation network data; the departure flight information includes the number of passengers, gate positions, planned start boarding time, and planned end boarding time; the remote gate boarding information includes available time slices; the shuttle bus information includes available quantity, maximum capacity, and travel speed; the airport ground transportation network data includes the travel distance between each gate and boarding position. S2: Taking each near-gate flight as a single backdoor boarding task formed by splitting it into individual backdoor boarding tasks as the basic processing unit, calculate the upper limit of the number of feasible boarding schemes based on the number of passengers on the flight and the capacity of the shuttle bus, and generate a set of candidate backdoor boarding tasks. With the goal of maximizing savings in boarding time and total operational scheduling costs, a mathematical optimization model is constructed to realize three-dimensional joint decision-making for boarding scheme selection, remote gate allocation, and shuttle bus scheduling. The three-dimensional joint decision-making involves synchronous and coordinated optimization of the three components, avoiding resource conflicts and efficiency losses caused by the independent execution of a single decision-making link. S3: Design a model solving algorithm. The algorithm includes four core steps: initialization, initial solution generation, local search, and path reconstruction. It also includes key designs for initial solution generation and local search combined constraint candidate list generation, local search continuous flight slice neighborhood optimization, and path reconstruction best priority strategy. S4: Perform the verification of the mathematical model and design the algorithm parameters, use the algorithm to solve the model, and obtain the boarding scheme decision for each flight, the remote gate boarding gate allocation scheme, and the task execution order and timetable for each shuttle bus. S5: The solution results are imported into the airport ground support command system in real time, so that dispatchers can execute and dynamically monitor the support process.

2. The method for optimizing a hybrid boarding strategy during peak operating hours at civil aviation airports as described in claim 1, characterized in that, The mixed boarding process involves diverting some passengers from the jet bridge to the remote boarding gate based on the aircraft's cabin layout. After being transferred by shuttle bus, passengers board the aircraft through the rear cabin door, forming a dual-channel boarding mode of "jet bridge-front cabin door" and "remote boarding gate-shuttle bus-rear cabin door".

3. The method for optimizing a hybrid boarding strategy during peak operating hours at civil aviation airports as described in claim 1, characterized in that, The objective function of the mathematical optimization model is: ； in: ； ； The constraints of the mathematical optimization model are: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； in For the collection of all flights, This is a set of all possible shuttle transport tasks; These are the starting and ending stations for the shuttle bus; For flights A collection of all possible shuttle transportation tasks. For flights The The set of shuttle transportation tasks included in the various boarding schemes. ,and For the collection of available shuttle buses, A set of remote gates that can be assigned. It is the union of the starting point parking area, the aircraft stand, and the remote boarding gate. It is the union of the terminal parking lot, the aircraft stand, and the remote boarding gate. The set of time slices used to calculate gate capacity. and order Refers to time slice ; The economic value per unit of time, expressed in yuan per minute; For flights Additional labor costs required for using mixed boarding; Additional fixed costs (including purchase, maintenance, driver, etc.) to activate a shuttle bus; For the task The cost per unit mileage for shuttle buses; Cost per unit mileage for empty shuttle bus trips; For the task The time spent getting on, off, and waiting; For from location Drive to Required mileage; For from location Drive to Time required; For flights The maximum number of feasible boarding options is obtained by dividing half of the number of passengers on each flight by the shuttle bus capacity and then rounding to the nearest integer. For flights The earliest required boarding time; For flights The latest boarding time required; For flights Only the time required for boarding using the jet bridge; For flights Execute the After considering the different boarding options, the boarding time via jet bridge is [not specified]. For the task If implemented, the time required for passengers to board through the rear door without queuing; The total timeframe for problem research; Indicates the length of the time slice; Indicates time slice The beginning moment; Indicates the boarding gate Time slice Whether it has been assigned to an unplanned (i.e., remote gate) flight: 1 if yes, 0 otherwise; Indicates navigation Does using a remote boarding gate consume time slices? If yes, it is 1; otherwise, it is 0. For 0-1 variables, if the flight Arranged to perform the first If there is a valid boarding option, select 1; otherwise, select 0. For 0-1 variables, if the flight Assigned to boarding gate If the value is 1, then the value is 0; otherwise, the value is 0. For 0-1 variables, if the shuttle bus Successive access nodes and If the value is 1, then the value is 0; otherwise, the value is 0. To complete the task for the shuttle bus The time.

4. The method for optimizing a hybrid boarding strategy during peak operating hours at civil aviation airports as described in claim 1, characterized in that, The specific limitation of the basic processing unit is as follows: all back door boarding tasks selected for the same near gate flight must be assigned to the same and unique remote gate boarding gate, and the scheduling scheme and execution process corresponding to a single back door boarding task must be consistent.

5. The method for optimizing a hybrid boarding strategy during peak operating periods at civil aviation airports as described in claim 1, characterized in that, The algorithm designed is used to solve the established mathematical optimization model, specifically including: Step 1: Generate datasets from the preprocessed flight information, optional boarding options, flight sub-tasks, shuttle bus parameters, available time of remote gates, and distance matrix. Step 2: Initialize the global optimal solution, the elite solution pool, and the iteration control parameters, setting the number of iterations... Outer layer continuous no-improvement count The flight groups are sorted from smallest to largest according to the scheduled boarding time. Step 3: Construct a list of feasible solutions for each flight, encompassing three levels of decision-making: boarding scheme selection, gate allocation, and shuttle bus scheduling. Then, retain the previous solution based on its contribution to the objective function and the sum of its negative impacts on the already decided flights. The proportion is used to form a restricted candidate list (RCL). Step 4: Randomly select a plan for the current flight from the restricted candidate list, call the time repair function to correct the shuttle bus task completion time, and update relevant data such as the available time slice of the remote gate and the shuttle bus location; Step 5: Repeat steps 3 to 4 until all flight decisions are completed, form an initial solution and calculate the objective function value, and initialize the number of consecutive no-improvement iterations in the inner layer. ; Step 6: Perform a local search using the Metropolis criterion, employing the neighborhood optimization structure for consecutive flight segments. Neighborhood operations include changing the boarding scheme for consecutive flight segments, changing the boarding scheme for random segments of flights, exchanging remote gate assignments, randomly rescheduling some backdoor boarding tasks, and reassigning the shuttle bus with the least usage intensity. After each neighborhood operation, determine whether to update the current optimal solution. If updated, let... Otherwise When the number of local searches reaches the maximum or If the local search is not complete, proceed to step 8. Step 7: Using the generated local optimum as the guiding solution, and randomly selecting an initial solution from the elite solution pool, perform the best priority path reconstruction process: First, identify all decision differences between the initial solution and the guiding solution in boarding scheme and remote gate allocation, and form a difference set; then, iterate through all remaining difference items in the difference set, evaluate them one by one, select the difference item with the largest contribution gain to adjust the decision of the initial solution, and delete the difference item from the difference set; update the current optimum solution; repeat the above reconstruction process until the difference set is empty; Step 8: Determine if the current optimal solution is better than the global optimal solution: If it is, update the global optimal solution and set... Otherwise Update the elite pool to make... ; Step 9: Repeat steps 3 through 8 until... or Exit the loop and output the globally optimal allocation scheme.

6. The method for optimizing a hybrid boarding strategy during peak operating periods at civil aviation airports as described in claim 1, characterized in that, The candidate boarding options for the same flight include boarding via all jet bridges and mixed boarding. Management personnel can select to implement any option and monitor the process dynamically through the airport ground support command system. The boarding times for the entire boarding bridge, the front door, and the rear door were obtained through passenger boarding data surveys and data fitting.

7. The method for optimizing a hybrid boarding strategy during peak operating hours at civil aviation airports as described in claim 1, characterized in that, The selection rule for the Restricted Candidate List (RCL) is as follows: only the candidates with the best sum of contribution to the objective function and the negative impact of the decided flights are retained. Proportional scheme, The value range is 0.1-1, with a recommended value of 0.3-0.

5. It can also be dynamically adjusted according to the actual operating scenario.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a hybrid boarding strategy optimization method for peak operating periods of civil aviation airports as described in any one of claims 1-8.

9. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the hybrid boarding strategy optimization method for peak operating periods of civil aviation airports as described in any one of claims 1-8.

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