CN112149864A - Aircraft fleet scheduling method and device, electronic equipment and storage medium - Google Patents

Aircraft fleet scheduling method and device, electronic equipment and storage medium Download PDF

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CN112149864A
CN112149864A CN202010796242.1A CN202010796242A CN112149864A CN 112149864 A CN112149864 A CN 112149864A CN 202010796242 A CN202010796242 A CN 202010796242A CN 112149864 A CN112149864 A CN 112149864A
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黄荣
董立伟
康旺
李玉梁
郭瑜
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Beijing Zhiyu Chuangyi Technology Co ltd
Chengdu Guoying Jinjiang Machine Factory
Beijing Aeronautical Engineering Technology Research Center
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Chengdu Guoying Jinjiang Machine Factory
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Abstract

The present disclosure relates to a method, an apparatus, an electronic device and a storage medium for scheduling a fleet of aircraft, the method comprising: acquiring the annual optimal maintenance amount of an airplane maintenance department; determining the flight time of each airplane in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value; and scheduling each airplane based on the flight time of each airplane in each year. The embodiment of the disclosure can solve the problems of insufficient maintenance resource, reduced effective flying time of the cluster, unstable fleet scale and the like caused by unreasonable allocation of annual maintenance tasks of the cluster at present and maintenance peaks in individual years.

Description

Aircraft fleet scheduling method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of aviation operation and maintenance technologies, and in particular, to a method and an apparatus for scheduling an aircraft fleet, an electronic device, and a storage medium.
Background
The measurement and calculation of the maintenance requirements of the airplane fleet is a complex combined optimization problem related to a plurality of constraint conditions, and is also an important link in production scheduling of an aviation maintenance department.
At present, in the process of measuring and calculating the maintenance requirements of an aircraft fleet, the overhaul time of the aircraft is usually calculated according to a calendar life overhaul calculation method and a fatigue life overhaul calculation method, and the number of the aircraft needing overhaul every year is calculated according to a fleet overhaul number calculation method. The traditional calculation method can cause unreasonable distribution of annual maintenance tasks of the cluster. FIG. 1 is a graph of the distribution of the number of aircraft that an airline maintenance department forecasts to be serviced annually in the next decades for that department using prior art techniques. It can be seen from fig. 1 that the number of airplanes that the department needs to maintain each year in the next decades predicted by the prior art will peak in maintenance in individual years (e.g., 2020, 2026, 2032, 2039, 2045, and 2051), which leads to problems of maintenance resource shortage and reduced fleet effective flight time, unstable fleet size, etc. in these years.
Disclosure of Invention
To solve the above technical problem or at least partially solve the above technical problem, the present disclosure provides an aircraft fleet scheduling method, apparatus, electronic device, and storage medium.
In a first aspect, the present disclosure provides a method for scheduling an aircraft fleet, including:
acquiring the annual optimal maintenance amount of an airplane maintenance department;
determining the flight time of each airplane in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value;
and scheduling each airplane based on the flight time of each airplane in each year.
Further, the determining the flight time of each aircraft year based on the optimal maintenance amount includes:
and determining the flight time of each year of each airplane based on the optimal maintenance amount and an enhanced Lagrange particle swarm optimization algorithm.
Further, the determining the flight time of each aircraft year based on the optimal maintenance amount and the enhanced lagrangian particle swarm optimization algorithm comprises:
constructing an objective function based on the optimal maintenance amount, wherein the objective function is a function of flight duration of each year of each airplane;
determining a constraint condition corresponding to the objective function;
converting the objective function into solving an unconstrained extreme value problem by utilizing an enhanced Lagrange multiplier method, the objective function and the constraint condition;
based on the unconstrained extreme value problem, obtaining the optimal value of the flight time of each year of each airplane by utilizing a particle swarm optimization algorithm;
the scheduling of each aircraft based on the flight duration of each aircraft year comprises: and scheduling each airplane based on the optimal value of the flight time of each year of each airplane.
Further, the objective function is
Figure BDA0002625734480000021
Wherein, XiIs a function of the time of flight of each aircraft year, expressed as
Xi＝h(Q)
Figure BDA0002625734480000031
XiPredicted maintenance for year i, b total optimized years, qαβRepresents the flight time of the alpha aircraft in the beta year, wherein alpha is 1,2, …, a; β is 1,2, …, b, t, the best maintenance quantity per year for the aircraft maintenance department.
Further, the objective function is
Figure BDA0002625734480000032
Wherein, XiIs a function of the time of flight of each aircraft year, expressed as
Xi＝h(Q)
Figure BDA0002625734480000033
XiPredicted maintenance for year i, b total optimized years, qαβRepresents the flight time of the alpha aircraft in the beta year, wherein alpha is 1,2, …, a; β is 1,2, …, b, t is the best maintenance amount per year for the aircraft maintenance department, and γ is any positive integer.
Further, XiH (q) is determined based on whether each of the aircraft requires major repairs within each optimization year;
if the alpha aircraft meets at least one of the following conditions in the beta year, judging that the alpha aircraft needs to be overhauled in the beta year:
the year corresponding to the beta year, the year of the last overhaul of the airplane is more than or equal to the specified calendar time;
the flight time of the airplane from the last maintenance to the beta-1 year plus the predicted flight time of the beta year is more than or equal to the specified flight time.
Further, the constraint condition corresponding to the objective function includes:
QαβMIN≤qαβ≤QαβMAX，
wherein Q isαβMINIs the preset shortest flight time length, Q, of the alpha aircraft in the beta yearαβMAXIs the preset maximum flight time of the alpha aircraft in the beta year.
In a second aspect, the present disclosure provides an aircraft fleet scheduling device, including:
the optimal maintenance quantity acquisition module is used for acquiring the annual optimal maintenance quantity of an airplane maintenance department;
the flight time determining module is used for determining the flight time of each year of each airplane based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value;
and the airplane scheduling module is used for scheduling each airplane based on the flight time of each airplane in each year.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, including: a processor and a memory;
the processor is configured to perform the steps of any of the methods described above by calling a program or instructions stored in the memory.
In a fourth aspect, the disclosed embodiments also provide a computer-readable storage medium storing a program or instructions for causing a computer to perform the steps of any of the above methods.
According to the method for scheduling the aircraft fleet provided by the embodiment of the disclosure, the flight time of each aircraft in each year is determined based on the optimal maintenance amount, so that the absolute value of the difference between the number of the aircraft needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value; the method is characterized in that each airplane is dispatched based on the annual flight time of each airplane, the airplanes are reasonably dispatched according to the annual optimal maintenance amount of an airplane maintenance department, so that the number of the airplanes needing to be maintained per year approaches to the optimal maintenance amount, the problems that the current annual maintenance tasks of the fleet are unreasonably distributed, the maintenance peak occurs in individual year, the maintenance resources are insufficient, the effective flight time of the fleet is reduced, the scale of the fleet is unstable and the like are solved, and the purposes of reasonably distributing the annual maintenance tasks of the fleet, avoiding the occurrence of the maintenance peak in individual year, ensuring the stable effective flight time of the fleet and the stable scale of the fleet are achieved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a graph of a distribution of the number of aircraft that an airline maintenance department forecasts to be serviced annually in the coming decades for that department using prior art techniques;
fig. 2 is a flowchart of a method for scheduling an aircraft fleet according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of another method for scheduling an aircraft fleet provided by the embodiments of the present disclosure;
fig. 4 is a schematic flow chart of an enhanced lagrangian particle swarm optimization algorithm provided in the embodiment of the present disclosure;
fig. 5 is a schematic flow chart illustrating a process of obtaining an optimal value of a flight duration of each year of each aircraft by using a particle swarm optimization algorithm according to an embodiment of the present disclosure;
FIG. 6 is an example of a portion of data entered when utilizing the fleet scheduling method provided by the present disclosure;
FIG. 7 is a distribution plot of the number of aircraft that an airline maintenance department needs to maintain each year in the coming decades as predicted by the fleet scheduling method provided by the present disclosure;
fig. 8 is a schematic structural diagram of an aircraft fleet scheduling device according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
As described in the background art, in the prior art, annual maintenance tasks of a fleet are not reasonably distributed, and maintenance peaks occur in individual years, which leads to the problems of maintenance resource shortage, reduction of effective flight time of the fleet, instable fleet scale and the like. The applicant has analysed this problem and has determined that one of the reasons for this problem is the departure of aircraft scheduling from aircraft maintenance. Specifically, the consequences of a surge in aircraft maintenance tasks due to improper scheduling are not fully considered prior to scheduling the aircraft.
The embodiment of the disclosure provides a flying machine group scheduling scheme, which realizes the purpose of reasonably scheduling airplanes according to the annual optimal maintenance amount of an airplane maintenance department, so that the number of airplanes needing to be maintained per year approaches to the optimal maintenance amount, the annual maintenance tasks of the flying machine group are reasonably distributed, maintenance peaks in individual years are avoided, and the effects of stable time duration of effective flying of the flying machine group and stable fleet scale are ensured.
Fig. 2 is a flowchart of a method for scheduling an aircraft fleet according to an embodiment of the present disclosure. Referring to fig. 2, the method for scheduling an aircraft fleet includes:
and S110, acquiring the annual optimal maintenance amount of the aircraft maintenance department.
The optimal maintenance amount is determined according to the resource allocation conditions of personnel, equipment and the like of the aircraft maintenance department.
The implementation method of the step has various methods, and illustratively, the method can be obtained from the annual optimal maintenance quantity reported by an airplane maintenance department; or, the resource allocation condition is calculated according to the resource allocation condition of the airplane maintenance department.
S120, determining the flight time of each airplane in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value.
The implementation method of the step has various types, and exemplarily, the flight time of each year of each airplane is determined based on the optimal maintenance amount and the enhanced Lagrange particle swarm optimization algorithm.
And S130, scheduling each airplane based on the flight time of each airplane in each year.
The essence of this step is to arrange a flight mission for each aircraft based on the time of flight for each aircraft year.
The essence of the technical scheme is that the airplanes are reasonably scheduled according to the annual optimal maintenance amount of airplane maintenance departments, so that the number of the airplanes required to be maintained per year approaches to the optimal maintenance amount, the annual maintenance tasks of the fleet are reasonably distributed, maintenance peaks occurring in individual years are avoided, and the stability of the effective flight time of the fleet and the stability of the scale of a fleet are ensured.
Fig. 3 is a flowchart of another method for scheduling an aircraft fleet according to an embodiment of the present disclosure. Fig. 3 is a specific example of fig. 2. Referring to fig. 3, the method for scheduling an aircraft fleet includes:
and S210, acquiring the annual optimal maintenance amount of the aircraft maintenance department.
S220, constructing an objective function based on the optimal maintenance amount, wherein the objective function is a function of the flight time of each year of each airplane.
In practice, the form of the constructed objective function is various, and the application does not limit the form. Alternatively, the objective function may be constructed as
Figure BDA0002625734480000081
Wherein, XiIs a function of the time of flight of each aircraft year, expressed as
Xi＝h(Q)
Figure BDA0002625734480000082
XiPredicted maintenance for year i, b total optimized years, qαβRepresents the flight time of the alpha aircraft in the beta year, wherein alpha is 1,2, …, a; β is 1,2, …, b, t, the best maintenance quantity per year for the aircraft maintenance department.
Alternatively, the objective function may be constructed as
Figure BDA0002625734480000083
Wherein, XiIs a function of the time of flight of each aircraft year, expressed as
Xi＝h(Q)
Figure BDA0002625734480000084
XiPredicted maintenance for year i, b total optimized years, qαβRepresents the flight time of the alpha aircraft in the beta year, wherein alpha is 1,2, …, a; β is 1,2, …, b, t is the best maintenance amount per year for the aircraft maintenance department, and γ is any positive integer.
Wherein, XiH (Q) means that the estimated maintenance volume for the ith year is a function of Q, which represents the set of estimated flight times for each aircraft year.
It should be noted that, if the second objective function is constructed, the larger the value of γ, the longer the time required for performing S250 later, but the more the number of airplanes to be repaired each year eventually approaches the optimal repair amount, and the more reasonable the repair task allocation is. Optionally, after taking both factors into account, γ is taken to be equal to 4.
Alternatively, XiH (q) is determined based on whether each aircraft requires major repairs within each optimization year; if the alpha-frame airplane meets at least one of the following conditions in the beta year, judging that the alpha-frame airplane is positionedAnd the year beta requires major repair: the year corresponding to the beta year-the year of the last overhaul of the airplane is more than or equal to the specified calendar time; the flight time of the airplane from the last maintenance to the beta-1 year plus the predicted flight time of the beta year is more than or equal to the specified flight time. The specified calendar time and the specified duration of flight may be determined based on at least one of a worker's experience, aircraft performance, and experimental test results, which are not limited in this application.
And S230, determining constraint conditions corresponding to the objective function.
In practice, the constraint condition corresponding to the objective function may be various, and optionally, may be set according to at least one of the overall mission arrangement of the fleet, the service condition of each aircraft, and the performance characteristic of each aircraft. Illustratively, the constraint conditions corresponding to the objective function include:
QαβMIN≤qαβ≤QαβMAX，
wherein Q isαβMINIs the preset shortest flight time length, Q, of the alpha aircraft in the beta yearαβMAXIs the preset maximum flight time of the alpha aircraft in the beta year.
S240, converting the objective function into an unconstrained extreme value problem by utilizing an enhanced Lagrange multiplier method, the objective function and constraint conditions.
In practice, the constraints tend to be inequalities. For inequality constraint, the Lagrange multiplier method is to convert the inequality constraint optimization problem into an equivalent equality constraint optimization problem and then solve the inequality constraint optimization problem by using the Lagrange multiplier method of equality constraint.
Illustratively, the objective function and constraint conditions are summarized as an optimized mathematical model that can be described as:
find X min f(X)
s.t.gj(X)≤0 j＝1，2，…，m
introducing a relaxation variable z to convert the inequality constraint problem into an equality constraint problem, wherein the expanded Lagrangian function is as follows:
Figure BDA0002625734480000101
expanding the function L (X, z, λ, r) according to the requirement that the function has unconstrained extremav) For the relaxation variable z at the optimum pointjThe partial derivative of (j ═ 1,2, …, m) should be zero, so that the enhanced lagrangian function for the inequality constrained optimization problem without the relaxation variable z is derived as follows:
Figure BDA0002625734480000102
the multiplier iteration without the relaxation variable z is as follows:
Figure BDA0002625734480000103
where v is the lagrange multiplier update times. In order to match with the update of the Lagrange multiplier, a dynamic penalty factor update strategy is adopted to improve the convergence of the algorithm:
Figure BDA0002625734480000111
setting the relationship between the penalty factor and the Lagrangian multiplier, which accelerates the convergence process
Figure BDA0002625734480000112
Therefore, solving the inequality constraint optimization problem finally translates to solving the unconstrained extremum problem:
Figure BDA0002625734480000113
the termination conditions for the iteration are as follows:
c＝max{max[0,gj(x)]，j＝1，2，…，m}≤ (5)
when r is a certain value, limc can be always guaranteed to be 0, and the optimal solution of the problem can be found when the enhanced multiplier method of the equality constraint or the inequality constraint has a convergence property.
And S250, obtaining the optimal value of the flight time of each year of each airplane by utilizing a particle swarm optimization algorithm based on the unconstrained extreme value problem.
The particle swarm algorithm is a mode for simulating a bird swarm to find a habitat. Particle swarm optimization is a random heuristic method. The particle swarm algorithm is combined with the enhanced Lagrange multiplier method, so that the respective advantages of the algorithms can be fully exerted, and the key problem is to provide an achievable reasonable operation process and an algorithm implementation technology. For a particle group, the position and velocity of any particle d at the k-th update can be expressed as
Figure BDA0002625734480000121
And
Figure BDA0002625734480000122
the position and velocity of the (k + 1) th iteration of particle d can be expressed as:
Figure BDA0002625734480000123
in the formula, omega is an inertia coefficient; parameter r1And r2Is [0,1 ]]A random number in between. c. C1And c2Is a learning factor.
Figure BDA0002625734480000124
Representing the best position of the particle d in the process of updating the k rounds by itself; while
Figure BDA0002625734480000125
Representing the best particle position in the population for k updates to occur.
Fig. 4 is a schematic flow chart of an enhanced lagrangian particle swarm optimization algorithm provided in the embodiment of the present disclosure. Referring to fig. 4, a specific implementation method of this step may include:
s251, setting an initial value: lagrangeMaximum update times v of daily multiplier and penalty factormaxMaximum number of iterations K of the particle swarm optimizationmaxMaximum coefficient of inertia ωmaxMinimum coefficient of inertia ωminNumber of particles Nd，maxCorrection coefficient c, independent variable number N, program termination precision, constraint condition satisfaction precisiongInitial Lagrangian operator
Figure BDA0002625734480000126
Initial penalty factor
Figure BDA0002625734480000127
S252, generating positions of initial particle groups in the independent variable space
Figure BDA0002625734480000128
And initial velocity
Figure BDA0002625734480000129
S253, calculating the adaptive value L (x) of the enhanced Lagrangian function of each particle according to the formula (4), and finding out the optimal value
Figure BDA00026257344800001210
And
Figure BDA00026257344800001211
s254, k equals k +1, and the particle update iteration is performed according to equation (6).
S255, determining k equals kmaxIf yes, executing S256; otherwise, S253 is performed.
S256, calculating c according to the formula (5)k+1(ii) a Judging whether an iteration termination condition c is metv+1≤、v＝vmaxIf yes, go to S257; otherwise, S258 is executed.
And S257, terminating the calculation and outputting the optimal solution.
And S258, if v is v +1, updating the Lagrange multiplier and the constraint penalty factor according to the formulas (1) to (3), and returning to execute S252.
Fig. 5 is a schematic flow chart illustrating a process of obtaining an optimal value of a flight duration of each year of each aircraft by using a particle swarm optimization algorithm according to an embodiment of the present disclosure. Referring to fig. 5, optionally, based on the enhanced lagrangian particle swarm optimization algorithm, obtaining the optimal value of the flight duration of each aircraft in each year may be that a group of particles is initialized at random, each particle represents a group of solutions, that is, the flight duration of each aircraft in each year, through the foregoing algorithm, the target result value of each particle may be calculated, the particle with the smallest target is found, then the values in all the particles approach the particle with the smallest target in a vector manner for a certain distance, and the target result value of each particle is calculated again.
And S260, scheduling each airplane based on the optimal value of the flight time of each airplane in each year.
FIG. 6 is an example of a portion of data entered when utilizing the fleet scheduling method provided by the present disclosure. FIG. 7 is a distribution plot of the number of aircraft that the department needs to service each year in the coming decades as predicted by the fleet scheduling method provided by the present disclosure. As can be seen from fig. 7, the above solution allows the number of aircraft to be serviced each year to be kept constant.
The technical scheme of the disclosure has the following advantages:
1. according to the method, the constrained problem is converted into the unconstrained problem by using the Lagrange multiplier method, and the unconstrained problem is combined with the better global convergence capability of the particle swarm optimization algorithm to form the enhanced Lagrange particle swarm optimization algorithm, so that the method has the advantages of high solving efficiency, good optimization effect and the like.
2. The method has clear design thought, has the capacity of variable configuration and constraint condition setting, and can quickly obtain the aircraft fleet maintenance demand optimization data through variable setting and model calculation according to different fleet scales and aircraft flight conditions.
3. Based on the optimized measurement and calculation result data of the aircraft fleet maintenance requirements, the planning and regulation of the flight time of the aircraft can be carried out, the centralized maintenance of the aircrafts in the fleet is avoided, and the scale of the fleet is kept relatively stable.
4. The method has strong expansion capability, and the optimization problem and the solving algorithm in the disclosure can be expanded to the problem of the equipment group maintenance strategy.
The embodiment of the invention also provides a flight fleet scheduling device. Fig. 8 is a schematic structural diagram of an aircraft fleet scheduling device according to an embodiment of the present invention. Referring to fig. 8, the flying-fleet scheduling apparatus includes:
an optimal maintenance amount obtaining module 401, configured to obtain an optimal maintenance amount of an aircraft maintenance department every year;
a flight duration determining module 402, configured to determine, based on the optimal maintenance amount, a flight duration of each year of each aircraft, so that an absolute value of a difference between the number of the aircraft needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value;
the airplane scheduling module 403 is configured to schedule each airplane based on the flight duration of each year of each airplane.
The apparatus disclosed in the above embodiments can implement the processes of the methods disclosed in the above method embodiments, and in order to avoid repetition, the details are not described here again.
Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention, as shown in fig. 9, the electronic device may include a mobile phone, a PAD, a computer, and a terminal, and the electronic device includes:
one or more processors 301, one processor 301 being exemplified in fig. 9;
a memory 302;
the electronic device may further include: an input device 303 and an output device 304.
The processor 301, the memory 302, the input device 303 and the output device 304 in the electronic device may be connected by a bus or other means, and fig. 9 illustrates an example of connection by a bus.
The memory 302 is a non-transitory computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for scheduling a fleet of aircraft according to an embodiment of the present invention (for example, the best maintenance amount obtaining module 401, the duration of flight determining module 402, and the aircraft scheduling module 403 shown in fig. 8). The processor 301 executes various functional applications of the server and data processing by running software programs, instructions and modules stored in the memory 302, namely, implements the flying fleet scheduling method of the above-described method embodiment.
The memory 302 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 302 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 302 optionally includes memory located remotely from processor 301, which may be connected to a terminal device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 303 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus. The output means 304 may comprise a display device such as a display screen.
An embodiment of the present invention further provides a computer-readable storage medium, which stores a program or an instruction, where the program or the instruction is used to cause a computer to execute a method for scheduling a fleet of aircraft, where the method includes:
acquiring the annual optimal maintenance amount of an airplane maintenance department;
determining the flight time of each airplane in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value;
and scheduling each airplane based on the flight time of each airplane in each year.
Optionally, the computer executable instruction, when executed by the computer processor, may be further used to implement a technical solution of the fleet scheduling method provided in any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. An aircraft fleet scheduling method, comprising:
acquiring the annual optimal maintenance amount of an airplane maintenance department;
determining the flight time of each airplane in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value;
and scheduling each airplane based on the flight time of each airplane in each year.
2. The flying fleet scheduling method of claim 1,
determining the flight time of each aircraft year based on the optimal maintenance amount comprises the following steps:
and determining the flight time of each year of each airplane based on the optimal maintenance amount and an enhanced Lagrange particle swarm optimization algorithm.
3. The aircraft fleet scheduling method according to claim 2,
the determining the flight time of each aircraft in each year based on the optimal maintenance amount and the enhanced Lagrange particle swarm optimization algorithm comprises the following steps:
constructing an objective function based on the optimal maintenance amount, wherein the objective function is a function of flight duration of each year of each airplane;
determining a constraint condition corresponding to the objective function;
converting the objective function into solving an unconstrained extreme value problem by utilizing an enhanced Lagrange multiplier method, the objective function and the constraint condition;
based on the unconstrained extreme value problem, obtaining the optimal value of the flight time of each year of each airplane by utilizing a particle swarm optimization algorithm;
the scheduling of each aircraft based on the flight duration of each aircraft year comprises: and scheduling each airplane based on the optimal value of the flight time of each year of each airplane.
4. The aircraft fleet scheduling method according to claim 3,
the objective function is
Figure FDA0002625734470000021
Wherein, XiIs a function of the time of flight of each aircraft year, expressed as
Xi＝h(Q)
Figure FDA0002625734470000022
XiPredicted maintenance for year i, b total optimized years, qαβRepresents the flight time of the alpha aircraft in the beta year, wherein alpha is 1,2, …, a; β is 1,2, …, b, t, the best maintenance quantity per year for the aircraft maintenance department.
5. The aircraft fleet scheduling method according to claim 3,
the objective function is
Figure FDA0002625734470000023
Wherein, XiIs a function of the time of flight of each aircraft year, expressed as
Xi＝h(Q)
Figure FDA0002625734470000024
XiPredicted maintenance for year i, b total optimized years, qαβRepresents the flight time of the alpha aircraft in the beta year, wherein alpha is 1,2, …, a; β is 1,2, …, b, t is the best maintenance amount per year for the aircraft maintenance department, and γ is any positive integer.
6. The aircraft fleet scheduling method according to claim 4 or 5,
Xih (q) is determined based on whether each of the aircraft requires major repairs within each optimization year;
if the alpha aircraft meets at least one of the following conditions in the beta year, judging that the alpha aircraft needs to be overhauled in the beta year:
the year corresponding to the beta year, the year of the last overhaul of the airplane is more than or equal to the specified calendar time;
the flight time of the airplane from the last maintenance to the beta-1 year plus the predicted flight time of the beta year is more than or equal to the specified flight time.
7. The aircraft fleet scheduling method according to claim 3,
the constraint condition corresponding to the objective function comprises:
QαβMIN≤qαβ≤QαβMAX，
wherein Q isαβMINIs the preset shortest flight time length, Q, of the alpha aircraft in the beta yearαβMAXIs the preset maximum flight time of the alpha aircraft in the beta year.
8. An aircraft fleet scheduling device, comprising:
the optimal maintenance quantity acquisition module is used for acquiring the annual optimal maintenance quantity of an airplane maintenance department;
the flight time determining module is used for determining the flight time of each year of each airplane based on the optimal maintenance amount, so that the absolute value of the difference between the number of the airplanes needing to be maintained in any one year and the optimal maintenance amount is smaller than a preset value;
and the airplane scheduling module is used for scheduling each airplane based on the flight time of each airplane in each year.
9. An electronic device, comprising: a processor and a memory;
the processor is adapted to perform the steps of the method of any one of claims 1 to 7 by calling a program or instructions stored in the memory.
10. A computer-readable storage medium, characterized in that it stores a program or instructions for causing a computer to carry out the steps of the method according to any one of claims 1 to 7.
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CN108038749A (en) * 2017-12-04 2018-05-15 海南私航会信息科技有限公司 A kind of business airplane intelligent scheduling real-time price quotations method and its system

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CN107408228A (en) * 2015-04-02 2017-11-28 庞巴迪公司 Composite aircraft safeguards route selection and maintenance task scheduling
CN106447107A (en) * 2016-09-27 2017-02-22 中国航空工业集团公司沈阳飞机设计研究所 Maintenance method based on aircraft structure health monitoring
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