CN-122022283-A - Mixed boarding strategy optimization method for civil aviation airport operation peak period

CN122022283ACN 122022283 ACN122022283 ACN 122022283ACN-122022283-A

Abstract

The invention discloses a mixed boarding strategy optimization method for a civil aviation airport operation peak period. The method comprises the steps of S1 obtaining data of departure flights, remote gate boarding gates, ferrying vehicles and airport ground traffic networks, S2 taking backgate boarding tasks as basic processing units, generating a task set to be selected, constructing a mathematical optimization model for three-dimensional joint decision of boarding scheme selection, boarding gate allocation and ferrying vehicle scheduling, aiming at saving boarding time and operation cost, S3 designing a solving algorithm comprising a composite limit candidate list, continuous flight sheet neighborhood optimization and optimal optimization guiding path reconstruction, S4 solving to obtain an optimal scheme, and S5 importing the result into an airport ground guarantee command system for dynamic monitoring and execution. The boarding efficiency is obviously improved, the time consumption is shortened, the flight delay risk is reduced, and the boarding efficiency is adapted to the operation requirements in the peak period.

Inventors

ZHANG WENYI
CHEN HAO
HUANG AILING
LI HAOLIN
CUI BOJIN
Guo Rongge
WEI ZHENLIN

Assignees

北京交通大学

Dates

Publication Date: 20260512
Application Date: 20260113

Claims (9)

1. The mixed boarding strategy optimization method for the civil aviation airport operation peak period is characterized by comprising the following steps of: The method comprises the steps of S1, obtaining departure flight information, remote gate information, ferry vehicle information and airport ground traffic network data, wherein the departure flight information comprises the number of passengers, a gate, a planned starting gate time and a planned stopping gate time, the remote gate information comprises available time slices, the ferry vehicle information comprises available quantity, maximum capacity and running speed, and the airport ground traffic network data comprises running distances among gate positions and gates; S2, taking a single backgate boarding task formed by splitting each near-range flight as a basic processing unit, calculating the upper limit of the number of viable boarding schemes based on the number of passengers of the flight and the capacity of a ferry vehicle, and generating a backgate boarding task set to be selected; S3, designing a model solving algorithm, wherein the algorithm comprises four core links of initialization, initial solution generation, local search and path reconstruction, and comprises key design of initial solution generation, local search composite limit candidate list generation, local search continuous flight sheet neighborhood optimization and path reconstruction optimal priority strategy; S4, performing checksum algorithm parameter design of a mathematical model, and solving the model by using the algorithm to obtain boarding scheme decisions of each flight, remote boarding gate allocation schemes, and task execution sequences and schedules of each ferry vehicle; And S5, instantly importing the solving result into an airport ground security command system for a dispatcher to execute and dynamically monitor a security process.
2. The method for optimizing mixed boarding strategy for civil airport operation peak period according to claim 1, wherein for near-range flights, partial passengers are shunted from a bridge to a far-range gate according to airplane range distribution, and after being transferred by a ferry vehicle, the passengers are boarding by using a rear gate of the airplane, so that a double-channel boarding mode of 'bridge-front gate' and 'far-range gate-ferry vehicle-rear gate' is formed.
3. The hybrid boarding strategy optimization method for civil airport operation peak period of claim 1, wherein the objective function of the mathematical optimization model is: ; Wherein: ; ; constraint conditions of the mathematical optimization model are as follows: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; Wherein the method comprises the steps of For a set of all of the flights, A set of transportation tasks for all possible ferrys; respectively a ferry vehicle starting point and a ferry vehicle terminal station; For flights All possible ferry transportation task sets, For flights Is the first of (2) A ferry transportation task set included in the boarding scheme, And (2) and To be a collection of available ferry vehicles, To allocate a collection of remotely located gates, Is the union of the departure yard, the flight gate and the remote gate, For the union of terminal yards, airline flights and remote gates, To calculate the set of gate capacity slots, And order Time slice ; Economic value per unit time, unit is yuan/minute; For flights Additional labor costs required for boarding using a hybrid; To enable a new fixed cost (including purchase, maintenance, driver, etc.) of a ferry vehicle; For the task The unit mileage expense of the ferry vehicle; the mileage expense is the empty driving unit of the ferry vehicle; For the task Is time-consuming to get on or off and wait; To be from the place Travel to The mileage required; To be from the place Travel to The time required is time consuming; For flights The upper limit of the number of the viable boarding schemes is obtained by dividing the number of passengers of each flight by the capacity of a ferry vehicle and rounding; For flights The earliest boarding time required; For flights The latest boarding time required; For flights Only the time required for boarding by the gallery bridge is used; For flights Execute the first After the boarding scheme, the boarding bridge takes time; For the task If executed, the time required for passengers to board from the back door is consumed under the condition of no queuing; Study total period for questions; Representing the length of the time slice; Representing time slices Is a start time of (2); Indicating gate Time slices of (2) Whether an unscheduled (i.e., remote) flight has been assigned is 1, otherwise 0; Representing a voyage Whether or not to occupy time slices when using a remotely located gate 1, Otherwise 0; 0-1 variable, if the flight Is arranged to perform the first The boarding scheme is 1, otherwise 0; 0-1 variable, if the flight Is distributed to the boarding gate Taking 1, otherwise taking 0; is 0-1 variable, if ferry vehicle Successive access nodes And Taking 1, otherwise taking 0; Completing tasks for ferry vehicle Is a time of (a) to be used.
4. The method for optimizing mixed boarding strategies for civil aviation airport operation peak period as recited in claim 1, wherein the basic processing units are specifically defined in such a way that all backgate boarding tasks selected by the same near-to-backgate flight are required to be distributed to the same and unique far-to-gate, and a scheduling scheme corresponding to a single backgate boarding task is consistent with an execution process.
5. The method for optimizing the hybrid boarding strategy for the civil aviation airport operation peak period of claim 1, wherein the algorithm applying the design solves the established mathematical optimization model, and specifically comprises the following steps: Step 1, respectively forming a data set from the preprocessed flight information, an optional boarding scheme, a flight subtask, ferry vehicle parameters, available time of a remote boarding gate and a distance matrix; Step 2, initializing a global optimal solution, an elite solution pool and iteration control parameters to enable the iteration times to be equal to each other Continuous non-improvement count of outer layer Sorting the flight sets from small to large according to the boarding time of the flight schedule; Step 3, constructing a feasible scheme list simultaneously comprising three layers of boarding scheme selection, boarding gate allocation and subtask ferry vehicle scheduling decision for the flight, and reserving before reserving according to the sum of the contribution value of each scheme to an objective function and the negative benefit of the decided flight Proportion, forming a Restricted Candidate List (RCL); Step 4, randomly selecting a scheme for the current flight in the limiting candidate list, calling a time repair function to correct the finishing time of the ferry vehicle task, and updating relevant data such as available time slices of a remote boarding gate, ferry vehicle positions and the like; Step 5, repeating the steps 3 to 4 until all the flight decisions are completed, forming an initial solution, calculating an objective function value, and initializing the inner layer continuous non-improvement times ; Step 6, local search is executed by combining Metropolis criteria, the neighborhood optimization structure facing to the continuous flight sheet is adopted, the neighborhood operation comprises changing the boarding scheme of the continuous flight sheet, changing the boarding scheme of the random partial flight, exchanging the remote gate allocation of the flight, randomly rescheduling the partial back gate boarding task and reallocating the ferry vehicle with minimum use intensity, judging whether to update the current optimal solution after each neighborhood operation, if so, enabling the current optimal solution to be updated Otherwise let When the local search number reaches the maximum value or When the local search is exited, if elite solution Chi Weiman is needed, the step 8 is skipped; Step 7, taking the generated local optimal solution as a guide solution, randomly selecting an initial solution from an elite solution pool to carry out an optimal priority path reconstruction process, namely firstly identifying all decision differences of the initial solution and the guide solution on boarding schemes and remote boarding gate allocation and forming a difference set; step 8, judging whether the current optimal solution is better than the global optimal solution, if so, updating the global optimal solution and enabling Otherwise let Updating elite solution pool to make ; Step 9, repeating the steps 3 to 8 until Or (b) And (5) exiting the loop and outputting the global optimal allocation scheme.
6. The method for optimizing mixed boarding strategies for civil aviation airport operation peak period according to claim 1, wherein the candidate boarding schemes of the same flight cover full bridge boarding and mixed boarding, management personnel can select any scheme to execute through an airport ground assurance command system and dynamically monitor the progress, and the full bridge boarding time, the front cabin door boarding time and the rear cabin door boarding time are obtained through passenger boarding data investigation and data fitting.
7. The method for optimizing mixed boarding strategy for civil aviation airport operation peak period of claim 1, wherein the screening rule of the composite constraint candidate list (RCL) is that only the front part with the optimal sum of the contribution value of objective function and the negative benefit of decided flight is reserved In the proportion scheme, the proportion of the total weight of the material is calculated, The value range of (2) is 0.1-1, the recommended value is 0.3-0.5, and the value can be dynamically adjusted according to the actual running scene.
8. An electronic device comprising a memory and a processor, the memory configured to store one or more computer instructions, wherein the one or more computer instructions are executable by the processor to implement a hybrid boarding strategy optimization method of any one of claims 1-8 that is directed to a peak-time of operation of a civil airport.
9. A readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a hybrid boarding strategy optimization method for civil airport operation peak-time according to any one of claims 1-8.

Description

Mixed boarding strategy optimization method for civil aviation airport operation peak period Technical Field The invention relates to the field of civil aviation transportation management, in particular to a hybrid boarding strategy optimization method for a civil aviation airport operation peak period. Background In a civil aviation airport operation system, boarding guarantee of a near-level flight is a key link influencing the flight boarding rate and the travel experience of passengers, and the core of the boarding guarantee is dependent on a boarding bridge to finish passenger transportation. According to the model specification and airport infrastructure configuration, the current boarding mode is mainly divided into two types, namely a narrow body machine is generally designed by adopting a single gallery bridge, passengers can only board through a front cabin door, and a wide body machine has double gallery bridge adaptation conditions, can realize double cabin door synchronous boarding, is limited by facility resources and has limited practical application scenes. In the passenger organization flow, two main modes are adopted in the industry, namely, firstly, boarding according to seat partition, leading passengers to board in batches according to the cabin level, the seat row number or the member level, and secondly, randomly boarding, allowing passengers to freely queue and board in sequence after a boarding gate is opened. However, the current mode has obvious limitations that firstly, resources of double-gallery bridges are scarce, the number of double-gallery bridges is insufficient in the airport operation peak period, a part of wide-body aircraft has to use single-gallery bridges for boarding, so that the waiting time of passengers is long, the flight passing time is prolonged, secondly, the regional boarding organization is difficult, the manual batch check boarding pass is low in efficiency, the phenomenon of wrong boarding, fail to register or transregional queuing of passengers is easy to occur, the field organization is difficult, thirdly, the random boarding is easy to cause congestion, passengers intensively rush to boarding gates to easily cause channel blockage, the cabin inner line Li Qu is slow, and passengers at the rear cabin position are always blocked at the channel. These problems not only lead to low boarding efficiency and long boarding time, but also more likely lead to delay of subsequent flights, aggravate anxiety and emotion of passengers and influence travel experience. In recent years, the rapid development of information technologies such as the Internet of things, big data, mobile Internet and the like provides technical support for intelligent upgrading of boarding guarantee of near-air flights. However, the prior art still fails to break through the core bottleneck, and a systematic solution for considering boarding efficiency, cost control and passenger experience is not formed, so that a more flexible hybrid boarding strategy and a more efficient ground resource collaborative scheduling method are needed to be constructed, so as to solve the inherent defects of the current boarding mode. Disclosure of Invention Aiming at the defects in the prior art, the invention provides a mixed boarding strategy optimization method for the operation peak period of a civil aviation airport. In a first aspect, the method for optimizing the hybrid boarding strategy for the civil aviation airport operation peak period provided by the invention comprises the following steps: The method comprises the steps of S1, obtaining departure flight information, remote gate information, ferry vehicle information and airport ground traffic network data, wherein the departure flight information comprises the number of passengers, a gate, a planned starting gate time and a planned stopping gate time, the remote gate information comprises available time slices, the ferry vehicle information comprises available quantity, maximum capacity and running speed, and the airport ground traffic network data comprises running distances among gate positions and gates; S2, taking a single backgate boarding task formed by splitting each near-range flight as a basic processing unit, calculating the upper limit of the number of viable boarding schemes based on the number of passengers of the flight and the capacity of a ferry vehicle, and generating a backgate boarding task set to be selected; S3, designing a model solving algorithm, wherein the algorithm comprises four core links of initialization, initial solution generation, local search and path reconstruction, and comprises key design of initial solution generation, local search composite limit candidate list generation, local search continuous flight sheet neighborhood optimization and path reconstruction optimal priority strategy; S4, performing checksum algorithm parameter design of a mathematical model, and solving the model by using the algorithm to