CN112149864B - Method and device for scheduling flight crew, electronic equipment and storage medium - Google Patents

Method and device for scheduling flight crew, electronic equipment and storage medium Download PDF

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CN112149864B
CN112149864B CN202010796242.1A CN202010796242A CN112149864B CN 112149864 B CN112149864 B CN 112149864B CN 202010796242 A CN202010796242 A CN 202010796242A CN 112149864 B CN112149864 B CN 112149864B
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CN112149864A (en
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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
Beijing Aeronautical Engineering Technology Research Center
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Abstract

The disclosure relates to a method, a device, an electronic device and a storage medium for scheduling a flight crew, wherein the method comprises the following steps: obtaining the annual optimal maintenance amount of an aircraft maintenance department; determining the flight time of each aircraft in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the aircraft needing maintenance in any year and the optimal maintenance amount is smaller than a preset value; each aircraft is scheduled based on the time of flight of each aircraft for each year. The embodiment of the disclosure can solve the problems of shortage of maintenance resources, reduction of the effective flight time of the fleet, unstable fleet scale and the like caused by unreasonable allocation of the annual maintenance tasks of the current fleet and maintenance peaks appearing in individual years.

Description

Method and device for scheduling flight crew, electronic equipment and storage medium
Technical Field
The disclosure relates to the technical field of aviation operation and maintenance, in particular to a method and a device for scheduling an aircraft group, electronic equipment and a storage medium.
Background
The aircraft fleet maintenance requirement calculation is a complex combined optimization problem involving a plurality of constraint conditions and is also an important link in the production scheduling of aviation maintenance departments.
Currently, in the process of measuring and calculating the maintenance requirements of an aircraft cluster, the overhaul time of the aircraft is generally calculated according to a calendar service life overhaul calculation method and a fatigue service life overhaul calculation method, and the number of aircraft needing overhaul each year is calculated according to a cluster overhaul number calculation method. Traditional computing methods can result in unreasonable allocation of annual maintenance tasks for clusters. FIG. 1 is a distribution diagram of the number of aircraft that a certain aviation repair sector predicts to need repair each year in the next decades using prior art. As can be seen from fig. 1, the number of aircraft that need to be serviced by the department each year in the next decades predicted by the prior art is peak in service in individual years (e.g., 2020, 2026, 2032, 2039, 2045, and 2051), which results in problems of shortage of service resources and reduction of the effective flight duration of the fleet, unstable fleet size, etc. in these years.
Disclosure of Invention
In order to solve the technical problems described above or at least partially solve the technical problems described above, the present disclosure provides a method, an apparatus, an electronic device, and a storage medium for scheduling a fleet of aircraft.
In a first aspect, the present disclosure provides a method for fleet scheduling, comprising:
obtaining the annual optimal maintenance amount of an aircraft maintenance department;
determining the flight time of each aircraft in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the aircraft needing maintenance in any year and the optimal maintenance amount is smaller than a preset value;
each aircraft is scheduled based on the time of flight of each aircraft for each year.
Further, the determining the time of flight of each aircraft in each year based on the optimal maintenance amount includes:
and determining the flight time of each aircraft in each year based on the optimal maintenance amount and an enhanced Lagrangian particle swarm optimization algorithm.
Further, the determining the time of flight of each aircraft in each year based on the optimal maintenance amount and the enhanced lagrangian particle swarm optimization algorithm includes:
constructing an objective function based on the optimal maintenance amount, wherein the objective function is a function of the flying time length of each aircraft in each year;
determining constraint conditions corresponding to the objective function;
converting the objective function into an unconstrained extremum problem by using an enhanced Lagrangian multiplier method, the objective function and the constraint condition;
based on the unconstrained extremum problem, obtaining an optimal value of the flight duration of each aircraft in each year by using a particle swarm optimization algorithm;
the scheduling of each aircraft based on the time of flight of each aircraft for each year includes: and scheduling each aircraft based on the optimal value of the flight time of each aircraft in each year.
Further, the objective function is
Wherein X is i Is a function of the length of flight for each aircraft year, expressed as
X i ＝h(Q)
X i For the i-th year maintenance amount, b is the total optimized years, q αβ Representing the length of flight of the alpha aircraft in the beta year, alpha = 1,2, …, a; β=1, 2, …, b, t is the best maintenance amount per year for the aircraft maintenance sector.
Further, the objective function is
Wherein X is i Is a function of the length of flight for each aircraft year, expressed as
X i ＝h(Q)
X i For the i-th year maintenance amount, b is the total optimized years, q αβ Representing the length of flight of the alpha aircraft in the beta year, alpha = 1,2, …, a; β=1, 2, …, b, t is the best maintenance amount per year for the aircraft maintenance sector, and γ is any positive integer.
Further, X i =h (Q) based on whether each of the aircraft requires major repair within each of the optimal years;
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 last overhaul year of the airplane is more than or equal to the specified calendar time;
the flight time of the aircraft from last maintenance to the beta-1 th year+the estimated flight time of the beta-th year is more than or equal to the specified flight time.
Further, the constraint conditions corresponding to the objective function include:
Q αβMIN ≤q αβ ≤Q αβMAX ，
wherein Q is αβMIN For the preset shortest flight time of the alpha-frame aircraft in the beta year, Q αβMAX The maximum flight time of the preset alpha-frame aircraft in the beta year is set.
In a second aspect, the present disclosure provides a fleet scheduling device comprising:
the optimal maintenance amount acquisition module is used for acquiring the annual optimal maintenance amount of an aircraft maintenance department;
the flight duration determining module is used for determining the flight duration of each aircraft in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the aircraft needing maintenance in any year and the optimal maintenance amount is smaller than a preset value;
and the aircraft scheduling module is used for scheduling each aircraft based on the flight time of each aircraft in each year.
In a third aspect, an embodiment of the present disclosure further proposes an electronic device, including: a processor and a memory;
the processor is operable to perform the steps of any of the methods described above by invoking a program or instruction stored in the memory.
In a fourth aspect, the disclosed embodiments also provide a computer-readable storage medium storing a program or instructions that cause a computer to perform the steps of any of the methods described above.
According to the aircraft group scheduling method provided by the embodiment of the disclosure, the flight duration 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 maintenance in any year and the optimal maintenance amount is smaller than a preset value; based on the flight time of each aircraft in each year, each aircraft is scheduled, essentially, the aircraft is reasonably scheduled according to the annual optimal maintenance amount of an aircraft maintenance department, so that the number of the aircraft needing to be maintained each year approaches to the optimal maintenance amount, the problems of unreasonable allocation of the annual maintenance tasks of the current aircraft group, maintenance peaks, shortage of maintenance resources, reduction of the effective flight time of the aircraft group, unstable aircraft team scale and the like caused by individual years are solved, and the aims of reasonable allocation of the annual maintenance tasks of the aircraft group, avoidance of the maintenance peaks in the individual years, and stable effective flight time of the aircraft group and stable aircraft team scale are achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a distribution diagram of the number of aircraft that a certain aviation repair department needs to repair each year in the next decades predicted by the prior art;
fig. 2 is a flowchart of a method for scheduling a fleet of aircraft according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of another method for fleet scheduling provided by an embodiment of the present disclosure;
fig. 4 is a schematic flow chart of an enhanced lagrangian particle swarm optimization algorithm according to an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart of obtaining an optimal value of a flight duration of each aircraft year by using a particle swarm optimization algorithm according to an embodiment of the present disclosure;
FIG. 6 is an example of partial data entered when utilizing the fleet scheduling method provided by the present disclosure;
FIG. 7 is a graph of the number of aircraft that a certain aviation repair department needs to repair each year in the next decades predicted using the fleet scheduling method provided by the present disclosure;
fig. 8 is a schematic structural diagram of an aircraft group scheduling device according to an embodiment of the present application;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
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 otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
As described in the background art, in the prior art, annual maintenance tasks of the fleet are not reasonably distributed, and maintenance peaks appear in individual years, so that problems of shortage of maintenance resources, reduction of effective flight time of the fleet, unstable fleet scale and the like are caused. The applicant has analysed this problem and has thought that one of the reasons for this problem is that aircraft scheduling is decoupled from aircraft maintenance. Specifically, the consequences of the proliferation of aircraft maintenance tasks due to improper scheduling are not fully accounted for prior to aircraft scheduling.
The embodiment of the disclosure provides a scheduling scheme of a flight fleet, which is used for reasonably scheduling the aircrafts according to the annual optimal maintenance amount of an aircraft maintenance department, so that the number of aircrafts needing to be maintained each year is close to the optimal maintenance amount, the annual maintenance tasks of the fleet are reasonably distributed, the occurrence of maintenance peaks in individual years is avoided, and the effects of stable effective flight duration of the fleet and stable fleet scale are ensured.
Fig. 2 is a flowchart of a method for scheduling a fleet of aircraft according to an embodiment of the present disclosure. Referring to fig. 2, the aircraft group scheduling method includes:
s110, obtaining the annual best maintenance amount of an aircraft maintenance department.
The optimal maintenance amount is determined according to the resource allocation conditions of personnel, equipment and the like of an aircraft maintenance department.
The implementation of this step is varied and can be obtained, for example, from the annual best maintenance reported by the aircraft maintenance sector; or the resource allocation situation of the aircraft maintenance department is calculated.
And S120, determining the flight time of each aircraft in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the aircraft needing maintenance in any year and the optimal maintenance amount is smaller than a preset value.
The method of implementing this step is various, and illustratively, based on the optimal maintenance amount and the enhanced Lagrangian particle swarm optimization algorithm, the time of flight of each aircraft for each year is determined.
S130, scheduling each aircraft based on the flight time of each aircraft in each year.
The essence of this step is to schedule flight tasks for each aircraft based on the time of flight of each aircraft for each year.
The essence of the technical scheme is that the aircraft is reasonably scheduled according to the annual optimal maintenance amount of the aircraft maintenance department, so that the number of aircraft needing maintenance each year approaches to the optimal maintenance amount, the annual maintenance tasks of the aircraft group are reasonably distributed, the occurrence of maintenance peaks in individual years is avoided, and the effective flight duration stability of the aircraft group and the stability of the fleet scale are ensured.
Fig. 3 is a flowchart of another aircraft group scheduling method provided in an embodiment of the present disclosure. Fig. 3 is a specific example of fig. 2. Referring to fig. 3, the aircraft group scheduling method includes:
s210, obtaining the annual best maintenance amount of an aircraft maintenance department.
S220, constructing an objective function based on the optimal maintenance amount, wherein the objective function is a function of the flying time length of each aircraft in each year.
In practice, there are a variety of forms of objective functions constructed, and the application is not limited in this regard. Alternatively, the objective function may be constructed as
Wherein X is i Is a function of the length of flight for each aircraft year, expressed as
X i ＝h(Q)
X i For the i-th year maintenance amount, b is the total optimized years, q αβ Representing the length of flight of the alpha aircraft in the beta year, alpha = 1,2, …, a; β=1, 2, …, b, t is the best maintenance amount per year for the aircraft maintenance sector.
Alternatively, the objective function may also be constructed as
Wherein X is i Is a function of the length of flight for each aircraft year, expressed as
X i ＝h(Q)
X i For the i-th year maintenance amount, b is the total optimized years, q αβ Representing the length of flight of the alpha aircraft in the beta year, alpha = 1,2, …, a; β=1, 2, …, b, t is the best maintenance amount per year for the aircraft maintenance sector, and γ is any positive integer.
Wherein X is i =h (Q) means that the estimated maintenance amount for the i-th year is a function of Q, which represents the set of estimated time of flight for each aircraft year.
It should be noted that, if the second objective function is constructed, the larger the value of γ is, the longer the time required for executing S250 is, but the closer the number of aircraft to be serviced each year finally is to the optimal maintenance amount, the more reasonable the maintenance task allocation is. Alternatively, γ is taken to be 4 after both factors are taken into account.
Alternatively, X i =h (Q) is determined based on whether each aircraft requires major repair within each optimal 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 last overhaul year of the airplane is more than or equal to the specified calendar time; the flight time of the aircraft from last maintenance to the beta-1 th year+the estimated flight time of the beta-th year is more than or equal to the specified flight time. The prescribed calendar time and the prescribed flight time may be determined based on at least one of a worker's experience, aircraft performance, and experimental test results, as the application is not limited in this regard.
S230, determining constraint conditions corresponding to the objective function.
In practice, the constraint conditions corresponding to the objective function may be various, and optionally may be set according to at least one of an overall mission arrangement of the fleet, a service condition of each aircraft, and a performance characteristic of each aircraft. Illustratively, the constraints corresponding to the objective function include:
Q αβMIN ≤q αβ ≤Q αβMAX ，
wherein Q is αβMIN For the preset shortest flight time of the alpha-frame aircraft in the beta year, Q αβMAX The maximum flight time of the preset alpha-frame aircraft in the beta year is set.
S240, converting the objective function into an unconstrained extremum problem by using the enhanced Lagrangian multiplier method, the objective function and the constraint condition.
In practice, the constraint is often an inequality. For inequality constraint, the Lagrange multiplier method converts the inequality constraint optimization problem into an equivalent equality constraint optimization problem, and then solves the inequality constraint Lagrange multiplier method.
Illustratively, the objective function and constraints are summarized as an optimized mathematical model, which can be described as:
find X min f(X)
s.t.g j (X)≤0 j＝1，2，…，m
introducing the relaxation variable z converts the inequality constraint problem into an equality constraint problem, whose extended Lagrangian function is as follows:
expanding the function L (X, z, lambda, r) according to the requirement that the function has unconstrained extremum v ) At the optimum point for relaxation variable z j The partial derivatives of (j=1, 2, …, m) should be zero, so that the enhanced lagrangian function without the relaxation z-free inequality constraint optimization problem is derived as follows:
the multiplier iteration without the relaxation variable z is as follows:
where v is the number of lagrange multiplier updates. In order to match the updating of Lagrange multiplier, a dynamic penalty factor updating strategy is adopted to improve the convergence of the algorithm:
the relationship between the penalty factor and the Lagrangian multiplier is set to accelerate the convergence process
Therefore, solving the inequality constraint optimization problem is ultimately translated into solving the unconstrained extremum problem:
the termination conditions for its iteration are as follows:
c＝max{max[0,g j (x)]，j＝1，2，…，m}≤ε (5)
when r is a certain value, limc=0 can be always ensured, and whether the enhancement multiplier method of the equality constraint or the inequality constraint has convergence property, the optimal solution of the problem can be found.
S250, obtaining an optimal value of the flight duration of each aircraft in each year by using a particle swarm optimization algorithm based on the unconstrained extremum problem.
The particle swarm algorithm is a model that simulates a bird swarm looking for habitats. The particle swarm algorithm is a stochastic heuristic. By combining the particle swarm algorithm with the enhanced Lagrangian multiplier method, the respective advantages of the algorithm can be fully exerted, and the key problem is to provide an achievable reasonable operation process and an implementation technology of the algorithm. For a particle group, the position and velocity of any particle d at the kth update can be expressed asAnd->The position and velocity of the kth+1st iteration of particle d can be expressed as:
wherein ω is an inertia coefficient; parameter r 1 And r 2 Is [0,1]A random number in between. c 1 And c 2 Is a learning factor.Representing the best position of the particle d in the process of updating the k round by itself; but->Representing the best particle positions in the particle swarm where k updates occur.
Fig. 4 is a schematic flow chart of an enhanced lagrangian particle swarm optimization algorithm according to an embodiment of the present disclosure. Referring to fig. 4, the specific implementation method of this step may include:
s251, giving an initial value: maximum update times v of Lagrangian multiplier and penalty factor max Maximum iteration number K of particle swarm algorithm max Maximum coefficient of inertia omega max Minimum coefficient of inertia omega min Group particle number N d，max Correction coefficient c, number of independent variables N, program termination precision epsilon, constraint conditions meeting precision epsilon g Initial Lagrangian operatorInitial penalty factor->
S252, generating initial particle group positions in the independent variable spaceInitial speed->
S253, calculating the adaptation value L (x) of the enhanced Lagrange function of each particle according to the formula (4), and finding the optimal valueAnd->
S254, k=k+1, and the particle update iteration is performed according to equation (6).
S255, determining k=k max If yes, executing S256; otherwise, S253 is performed.
S256, calculating c according to (5) k+1 The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the iteration termination condition c is satisfied v+1 ≤ε、v＝v max If yes, executing S257; otherwise, S258 is performed.
S257, terminating the calculation and outputting the optimal solution.
And S258, v=v+1, the lagrangian multiplier and constraint penalty factor are updated according to equations (1) to (3), and the routine returns to S252.
Fig. 5 is a schematic flow chart of obtaining an optimal value of a flight duration of each aircraft in each year by using a particle swarm optimization algorithm according to an embodiment of the present disclosure. Referring to fig. 5, alternatively, based on the enhanced lagrangian particle swarm optimization algorithm, the optimal value of the time of flight of each aircraft in each year may be obtained by randomly initializing a group of particles, each particle represents a group of solutions, that is, the time of flight of each aircraft each year, calculating the target result value of each particle according to the algorithm, finding out the particle of the minimum target, then making the values in all the particles approach to the particle of the minimum target in a vector manner for a certain distance, and calculating the target result value of each particle again.
And S260, scheduling each aircraft based on the optimal value of the flight time of each aircraft in each year.
Fig. 6 is an example of partial data entered when utilizing the fleet scheduling method provided by the present disclosure. Fig. 7 is a graph of the number of aircraft that the department needs to repair each year in the next few decades predicted using the fleet scheduling method provided by the present disclosure. As can be seen from fig. 7, the above approach brings the number of aircraft that need to be serviced each year closer to steady.
The technical scheme disclosed by the application has the following advantages:
1. the method converts the constraint problem into the unconstrained problem by using the Lagrange multiplier method, and combines the unconstrained problem with the better global convergence capacity of the particle swarm optimization algorithm to form the enhanced Lagrange particle swarm optimization algorithm, and has the advantages of high solving efficiency, good optimizing effect and the like.
2. The method has clear design thought and variable configuration and constraint condition setting capability, and can quickly obtain the maintenance requirement optimization data of the aircraft clusters through variable setting and model calculation according to different cluster scales and aircraft flight conditions.
3. Based on the optimized aircraft fleet maintenance demand measurement result data, aircraft flight time planning and regulation can be performed, and the occurrence of the conditions of centralized maintenance and the like of the aircraft of the fleet is avoided, so that 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 method can be expanded to the equipment group maintenance strategy problem.
The embodiment of the application also provides a device for dispatching the flight crew. Fig. 8 is a schematic structural diagram of an aircraft group scheduling device according to an embodiment of the present application. Referring to fig. 8, the fleet scheduling device includes:
an optimal maintenance amount obtaining module 401, configured to obtain an optimal annual maintenance amount of an aircraft maintenance department;
a flight duration determining module 402, configured to determine a flight duration of each aircraft in each year based on the optimal maintenance amount, so that an absolute value of a difference between the number of aircraft to be maintained in any one year and the optimal maintenance amount is less than a preset value;
the aircraft scheduling module 403 is configured to schedule each aircraft based on a time of flight of each aircraft in each year.
The device disclosed in the above embodiments can implement the flow of the method disclosed in the above method embodiments, and in order to avoid repetition, a description is omitted here.
Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application, where, as shown in fig. 9, the electronic device may include a terminal such as a mobile phone, a PAD, a computer, and the electronic device includes:
one or more processors 301, one processor 301 being illustrated 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 means 303 and the output means 304 in the electronic device may be connected by a bus or by other means, in fig. 9 by way of example.
The memory 302 serves as a non-transitory computer readable storage medium, and may be used to store software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the fleet scheduling method in the embodiments of the present application (e.g., the optimal maintenance amount acquisition module 401, the duration of flight determination 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, namely, implements the aircraft group scheduling method of the above-described method embodiment by running software programs, instructions and modules stored in the memory 302.
Memory 302 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device, etc. In addition, 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 may optionally include memory located remotely from processor 301, which may be connected to the 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 means 303 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the electronic device. The output device 304 may include a display device such as a display screen.
Embodiments of the present application also provide a computer-readable storage medium storing a program or instructions that when executed by a computer cause the computer to perform a method for fleet scheduling, the method comprising:
obtaining the annual optimal maintenance amount of an aircraft maintenance department;
determining the flight time of each aircraft in each year based on the optimal maintenance amount, so that the absolute value of the difference between the number of the aircraft needing maintenance in any year and the optimal maintenance amount is smaller than a preset value;
each aircraft is scheduled based on the time of flight of each aircraft for each year.
Optionally, the computer executable instructions, when executed by the computer processor, may also be used to perform the technical solution of the fleet scheduling method provided by any of the embodiments of the present application.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present application.
It should be noted that in this document, relational terms such as "first" and "second" and the like are 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the 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 and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. A method of fleet scheduling comprising:
obtaining the annual optimal maintenance amount of an aircraft maintenance department;
constructing an objective function based on the optimal maintenance amount, wherein the objective function is a function of the flying time length of each aircraft in each year;
determining constraint conditions corresponding to the objective function;
converting the objective function into an unconstrained extremum problem by using an enhanced Lagrangian multiplier method, the objective function and the constraint condition;
based on the unconstrained extremum problem, obtaining an optimal value of the flight time of each aircraft in each year by using a particle swarm optimization algorithm, so that the absolute value of the difference between the number of the aircraft needing to be maintained in any year and the optimal maintenance amount is smaller than a preset value;
and scheduling each aircraft based on the optimal value of the flight time of each aircraft in each year.
2. The method of fleet scheduling according to claim 1, wherein,
the objective function is
Wherein X is i Is a function of the length of flight for each aircraft year, expressed as
X i ＝h(Q)
X i For the i-th year maintenance amount, b is the total optimized years, q αβ Representing the length of flight of the alpha aircraft in the beta year, alpha = 1,2, …, a; β=1, 2, …, b, t is the best maintenance amount per year for the aircraft maintenance sector.
3. The method of fleet scheduling according to claim 1, wherein,
the objective function is
Wherein X is i Is a function of the length of flight for each aircraft year, expressed as
X i ＝h(Q)
X i For the i-th year maintenance amount, b is the total optimized years, q αβ Representing the length of flight of the alpha aircraft in the beta year, alpha = 1,2, …, a; β=1, 2, …, b, t is the best maintenance amount per year for the aircraft maintenance sector, and γ is any positive integer.
4. A method for fleet scheduling according to claim 2 or 3,
X i =h (Q) based on whether each of the aircraft requires major repair within each of the optimal years;
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 last overhaul year of the airplane is more than or equal to the specified calendar time;
the flight time of the aircraft from last maintenance to the beta-1 th year+the estimated flight time of the beta-th year is more than or equal to the specified flight time.
5. The method of fleet scheduling according to claim 1, wherein,
the constraint conditions corresponding to the objective function comprise:
Q αβMIN ≤q αβ ≤Q αβMAX ，
wherein Q is αβMIN For the preset shortest flight time of the alpha-frame aircraft in the beta year, Q αβMAX The maximum flight time of the preset alpha-frame aircraft in the beta year is set.
6. A fleet scheduling device, comprising:
the optimal maintenance amount acquisition module is used for acquiring the annual optimal maintenance amount of an aircraft maintenance department;
the flight duration determining module is used for constructing an objective function based on the optimal maintenance quantity, wherein the objective function is a function of the flight duration of each aircraft in each year; determining constraint conditions corresponding to the objective function; converting the objective function into an unconstrained extremum problem by using an enhanced Lagrangian multiplier method, the objective function and the constraint condition; based on the unconstrained extremum problem, obtaining an optimal value of the flight time of each aircraft in each year by using a particle swarm optimization algorithm, so that the absolute value of the difference between the number of the aircraft needing to be maintained in any year and the optimal maintenance amount is smaller than a preset value;
and the aircraft scheduling module is used for scheduling each aircraft based on the optimal value of the flight duration of each aircraft in each year.
7. An electronic device, comprising: a processor and a memory;
the processor is adapted to perform the steps of the method according to any one of claims 1 to 5 by invoking a program or instruction stored in the memory.
8. A computer readable storage medium storing a program or instructions for causing a computer to perform the steps of the method according to any one of claims 1 to 5.
CN202010796242.1A 2020-08-10 2020-08-10 Method and device for scheduling flight crew, electronic equipment and storage medium Active CN112149864B (en)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN107408228A (en) * 2015-04-02 2017-11-28 庞巴迪公司 Composite aircraft safeguards route selection and maintenance task scheduling
CN107730014A (en) * 2017-10-23 2018-02-23 哈尔滨工业大学 A kind of fleet repair determining method based on CBM
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

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN107730014A (en) * 2017-10-23 2018-02-23 哈尔滨工业大学 A kind of fleet repair determining method based on CBM
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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