Aircraft hangar maintenance and warehousing transfer cooperative scheduling method and device

Abstract

The invention discloses an aircraft hangar maintenance and warehousing transfer cooperative scheduling method and device, and relates to the technical field of scheduling optimization technology and aviation engineering management, and the method comprises the steps: building a cooperative scheduling problem model; in a transfer scheduling stage, minimizing transfer time and a maintenance urgency proxy target are taken as optimization targets, and an intelligent optimization algorithm is utilized to solve an aircraft warehousing transfer sequence and actual entrance time; in a maintenance scheduling stage, taking actual entrance time as a constraint, taking maximization of machine group wave availability as an optimization target, and solving a hangar maintenance scheme and maintenance completion time by utilizing an intelligent optimization algorithm; the maintenance completion time is fed back to a transfer scheduling stage, iteration is repeated to convergence, and a converged aircraft in-storage transfer and hangar maintenance scheduling scheme is output. Through repeated execution of a transfer-maintenance-feedback cycle, tight coupling between two sub-problems of transfer and maintenance is effectively solved; and the global optimality of the scheduling scheme is obviously improved.

Description

Technical Field

[0001] This application relates to the fields of scheduling optimization technology and aviation engineering management technology, specifically to the field of computer-aided intelligent scheduling, and particularly to a method and device for collaborative scheduling of aircraft hangar maintenance and inbound transfer. Background Technology

[0002] In large aircraft maintenance centers (Maintenance, Repair, and Overhaul, MRO) scenarios, aircraft scheduling is a crucial support activity. Transferring aircraft from the parking area to dedicated hangars for complex, long-cycle maintenance is a core element in ensuring the health of the fleet. This process naturally involves two closely coupled sub-problems: aircraft arrival and transfer scheduling, and hangar maintenance work scheduling. Transfer scheduling determines when an aircraft arrives at the hangar to begin maintenance, while maintenance scheduling directly affects the aircraft's final completion time, thus impacting its subsequent availability. Related scheduling methods often have significant shortcomings. Traditional manual scheduling heavily relies on the dispatcher's personal experience, making it difficult to make globally optimal decisions in large-scale, multi-constraint, complex scenarios, resulting in low efficiency and a high risk of errors. Some rudimentary computer-aided scheduling systems typically treat transfer and maintenance as two independent, decoupled problems, planning transfer first and maintenance later. This fragmented approach ignores the profound intrinsic connection between the two. For example, a seemingly efficient transfer sequence may cause critical aircraft to be delayed due to waiting for maintenance resources, severely impacting the entire fleet's mission schedule. While other technologies attempt collaborative scheduling, their optimization objectives are often limited to traditional efficiency metrics such as minimizing total completion time or maximizing resource utilization. These metrics cannot directly reflect the scheduling scheme's ability to support future flight mission plans. Especially in large-scale mission applications, the ultimate goal of scheduling is not merely "speed," but ensuring a sufficient number of available aircraft at the predetermined future time. Therefore, related technologies generally lack a collaborative scheduling method that is driven by future mission availability and can effectively handle the dynamic feedback relationship between transport and maintenance. Summary of the Invention

[0003] The purpose of this application is to provide a method and apparatus for coordinated scheduling of aircraft hangar maintenance and inbound transfer, which decomposes the entire complex scheduling problem into a two-stage (transfer scheduling stage and maintenance scheduling stage) iterative optimization process. Through a two-stage iterative optimization framework, deep coordination and global optimization of the transfer and maintenance stages are achieved.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for coordinated scheduling of aircraft hangar maintenance and inbound transfer, including: Based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft transfer tasks, and the preset set of sortie waves, a collaborative scheduling problem model is established, and the collaborative scheduling problem model is decomposed into a process of iterative optimization of the transfer scheduling stage and the maintenance scheduling stage.

[0005] During the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency as the proxy objective, an intelligent optimization algorithm is used to solve the aircraft entry and transfer sequence and determine the actual entry time of each aircraft. The transfer time includes the maximum entry time of all aircraft and the total entry time of the aircraft to be maintained. The maintenance urgency proxy objective is a weighted delay penalty based on the maintenance completion time.

[0006] During the maintenance scheduling phase, with the actual arrival time as a constraint and maximizing the availability of aircraft waves as the optimization objective, an intelligent optimization algorithm is used to solve the hangar maintenance plan and determine the maintenance completion time for each aircraft. The availability of aircraft waves is the weighted average of the ratio of the number of aircraft performing each wave task to the total number of aircraft planned to participate in each wave in the set of sortie waves.

[0007] The maintenance completion time in the maintenance scheduling phase is fed back to the transfer scheduling phase to dynamically adjust the maintenance urgency proxy target. This process is repeated iteratively until convergence, and the converged aircraft hangar transfer and hangar maintenance scheduling plan is output.

[0008] In one implementation, with the optimization objective of minimizing transfer time and balancing maintenance urgency, an intelligent optimization algorithm is used to solve for the aircraft storage transfer sequence, specifically including: For the set of inbound transfer tasks, a first objective function is constructed with the optimization objectives of minimizing transfer time, load balancing of transfer groups, and maintenance urgency.

[0009] Using an intelligent optimization algorithm, an iterative search is performed among multiple aircraft entry and transfer schemes based on a first objective function to obtain an aircraft entry and transfer sequence that minimizes the first objective function. The iterative search process involves selecting the current aircraft entry and transfer sequence based on the first objective function value generated in the current iteration and feeding the current aircraft entry and transfer sequence back to the next iteration to gradually approach the optimal solution.

[0010] The formula for calculating the first objective function is: .

[0011] in, Let this be the first objective function; For airplane Entry time; The subset of aircraft awaiting repair is a set. A subset of; To ensure load balance of the dispatching group; For urgent maintenance targets; The preset normalized weight coefficients satisfy... And its value range is [0, 1].

[0012] The formula for calculating the load balance of the dispatching group is: .

[0013] in, Assemble the transport team. For the dispatch team members Total working hours; This represents the average working hours of the dispatch team members.

[0014] The formula for calculating the maintenance urgency proxy target is: .

[0015] in, For the set of dispatch waves, For waves Importance weights To plan to participate in waves A subset of aircraft The function represents the portion of the penalty value that is positive. For airplane Repair completion time, To plan to participate in waves The moment of preparation for deployment begins.

[0016] In one implementation, with actual arrival time as a constraint and maximizing the availability of aircraft fleet waves as the optimization objective, an intelligent optimization algorithm is used to solve the hangar maintenance plan, specifically including: Using the actual arrival time as an input constraint, the earliest time when each aircraft can begin hangar maintenance is limited.

[0017] For the hangar maintenance task set, a second objective function is constructed with the optimization objectives of maximizing the availability of aircraft fleet waves and minimizing the load balancing of maintenance personnel.

[0018] Using intelligent optimization algorithms, a hangar maintenance scheme that minimizes the second objective function is searched in the feasible solution space.

[0019] The formula for calculating the second objective function is as follows: .

[0020] in, The second objective function is... For fleet wave availability; For the load balancing of maintenance personnel; The weighting coefficients for multi-objective optimization have a value range of [0.01, 0.2].

[0021] The formula for calculating the availability of the aircraft fleet wavelets is as follows: .

[0022] in, For the set of dispatch waves; For waves Importance weights, satisfying And for any two waves and ,like Prior to but ; The total number of aircraft; For airplane Repair completion time; For waves The moment preparations for deployment begin; This is a symbolic function, defined as follows: the function value is 1 when its input value is greater than zero, and 0 otherwise.

[0023] The formula for calculating the load balancing degree of maintenance personnel is: .

[0024] in, Assemble the maintenance personnel; For maintenance personnel Total working hours; This represents the average working hours of maintenance personnel.

[0025] In one embodiment, during the maintenance scheduling phase, the constraints also include hangar resource constraints, specifically including: maintenance workstation space constraints and parallel operation constraints in the maintenance workshop.

[0026] The space constraint of the maintenance workstation is: at any time Assigned to any aircraft Used to perform a certain category of maintenance skills The number of maintenance personnel must not exceed the number of aircraft. The maintenance stop position is associated with the aforementioned maintenance skill category. The set available workstation space capacity .

[0027] The parallel operation constraint in the maintenance workshop is: at any given time... In a maintenance workshop where parallel operations are possible The number of maintenance tasks being performed simultaneously in the workshop shall not exceed the number of maintenance tasks being performed simultaneously in the workshop. Maximum number of parallel jobs set The repair workshop Maximum number of parallel jobs The value range is [2, 10].

[0028] In one embodiment, the convergence condition is either a first convergence condition or a second convergence condition.

[0029] The first convergence condition is: in two consecutive iterations, let the... Second and third The availability of the cluster wavelets calculated in the next iteration are as follows: and Its improvement value satisfies ,in The preset convergence threshold has a value range of [0.001, 0.01].

[0030] The second convergence condition is: the number of iterations reaches the preset maximum number of iterations. ,in, The value range is [50, 200].

[0031] In one embodiment, the formula for calculating the repair completion time is: .

[0032] in, For airplane Repair completion time, For airplane A collection of hangar maintenance tasks. For maintenance tasks The start time, For maintenance tasks The actual execution time.

[0033] Secondly, this application provides a collaborative scheduling device for aircraft hangar maintenance and inbound transfer, including: a problem modeling module, a transfer scheduling module, a maintenance scheduling module, and a collaborative optimization module.

[0034] The problem modeling module is used to establish a collaborative scheduling problem model based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft transfer tasks, and the preset set of sortie waves. The collaborative scheduling problem model is decomposed into a transfer scheduling stage and a maintenance scheduling stage for iterative optimization.

[0035] The transfer scheduling module is used to solve the aircraft entry and transfer sequence using intelligent optimization algorithms during the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency. It also determines the actual entry time of each aircraft. The transfer time includes the maximum entry time of all aircraft and the total entry time of the aircraft to be maintained. The maintenance urgency target is a weighted delay penalty based on the maintenance completion time.

[0036] The maintenance scheduling module is used to solve hangar maintenance plans and determine the maintenance completion time for each aircraft during the maintenance scheduling phase, with the actual arrival time as a constraint and the optimization objective of maximizing the availability of aircraft waves. The availability of aircraft waves is the weighted average of the ratio of the number of aircraft performing each wave task to the total number of aircraft planned to participate in each wave in the set of sortie waves.

[0037] The collaborative optimization module is used to feed back the maintenance completion time from the maintenance scheduling phase to the transfer scheduling phase to dynamically correct the maintenance urgency proxy target. This process is repeated iteratively until convergence, and the converged aircraft hangar transfer and hangar maintenance scheduling scheme is output.

[0038] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the aircraft hangar maintenance and inbound transfer coordinated scheduling method described above.

[0039] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aircraft hangar maintenance and inbound transfer coordinated scheduling method described above.

[0040] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aircraft hangar maintenance and inbound transfer coordinated scheduling method described above.

[0041] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and apparatus for coordinated scheduling of aircraft hangar maintenance and inbound transfer. Based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft inbound transfer tasks, and a preset set of sortie waves, a coordinated scheduling problem model is established. The coordinated scheduling problem model is decomposed into an iterative optimization process of transfer scheduling stage and maintenance scheduling stage. In the transfer scheduling stage, the optimization objective is to minimize transfer time and maintenance urgency proxy objectives to solve for an efficient and reasonable aircraft inbound transfer sequence. In the maintenance scheduling stage, the actual arrival time is used as an input constraint, and the optimization objective is to maximize the availability of aircraft waves to solve for the hangar maintenance scheme. The maintenance completion time in the maintenance scheduling stage is fed back to the transfer scheduling stage to dynamically correct the maintenance urgency proxy objective. The "transfer-maintenance-feedback" loop is repeated, and each iteration is optimized based on the previous iteration to obtain a converged aircraft inbound transfer and hangar maintenance scheduling scheme. This effectively solves the tight coupling between the two sub-problems of transfer and maintenance and significantly improves the global optimality of the scheduling scheme. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of a collaborative scheduling method for aircraft hangar maintenance and inbound transfer in one embodiment of this application; Figure 2 A flowchart illustrating a collaborative scheduling method for aircraft hangar maintenance and inbound transfer, provided as an embodiment of this application; Figure 3 A schematic diagram of the functional modules of an aircraft hangar maintenance and storage transfer collaborative scheduling device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0044] Figure label: 1-Problem modeling module, 2-Transfer scheduling module, 3-Maintenance scheduling module, 4-Collaborative optimization module. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] This application provides a method for collaborative optimization of two closely related stages: deep maintenance at the hangar level and transfer to the hangar on the deck or apron. This method can directly link scheduling objectives with future mission requirements and achieve deep collaboration and global optimization of the two stages of transfer and maintenance through an innovative iterative optimization framework.

[0048] The aircraft hangar maintenance and inbound transfer collaborative scheduling method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on other servers. Terminal 101 can send the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft entry and transfer tasks, and the preset set of sortie waves to server 102. Server 102 receives these data and establishes a collaborative scheduling problem model. During the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency, an intelligent optimization algorithm is used to solve for the aircraft entry and transfer sequence, and the actual entry time of each aircraft is determined. The transfer time includes the maximum entry time among all aircraft and the total entry time of the aircraft to be maintained. The maintenance urgency proxy objective is a weighted delay penalty based on maintenance completion time. During the maintenance scheduling phase, with actual arrival time as a constraint and maximizing fleet wave availability as the optimization objective, an intelligent optimization algorithm is used to solve the hangar maintenance plan and determine the maintenance completion time for each aircraft. The fleet wave availability is the weighted average of the ratio of the number of aircraft performing tasks in each wave to the total number of aircraft planned to participate in each wave within the sortie wave set. The maintenance completion time from the maintenance scheduling phase is fed back to the transfer scheduling phase to dynamically correct the maintenance urgency proxy objective. This process is iterated until convergence, and the converged aircraft arrival / transfer and hangar maintenance scheduling plan is output. Server 102 can feed back the obtained aircraft arrival / transfer and hangar maintenance scheduling plan to terminal 101. In addition, in some embodiments, the aircraft hangar maintenance and inbound transfer collaborative scheduling method can also be implemented by the server 102 or the terminal 101 separately. For example, the terminal 101 can directly perform the aircraft hangar maintenance and inbound transfer collaborative scheduling processing, or the server 102 can obtain the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft inbound transfer tasks, and the preset set of sortie waves from the data storage system to perform the aircraft hangar maintenance and inbound transfer collaborative scheduling processing.

[0049] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0050] In one exemplary embodiment, such as Figure 2As shown, a collaborative scheduling method for aircraft hangar maintenance and inbound transfer is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 201 to 204. Wherein: Step 201: Based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft transfer tasks, and the preset set of sortie waves, establish a collaborative scheduling problem model, and decompose the collaborative scheduling problem model into a transfer scheduling stage and a maintenance scheduling stage for iterative optimization.

[0051] Step 202: In the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency proxy objective, an intelligent optimization algorithm is used to solve the aircraft entry and transfer sequence, and the actual entry time of each aircraft is determined; the transfer time includes the maximum entry time of all aircraft and the total entry time of the aircraft to be maintained; the maintenance urgency proxy objective is a weighted delay penalty based on the maintenance completion time.

[0052] Step 203: In the maintenance scheduling phase, with the actual arrival time as a constraint and maximizing the availability of aircraft waves as the optimization objective, the hangar maintenance plan is solved using an intelligent optimization algorithm, and the maintenance completion time of each aircraft is determined; the availability of aircraft waves is the weighted average of the ratio of the number of aircraft performing each wave task to the total number of aircraft planned to participate in each wave in the set of sortie waves.

[0053] Step 204: Feed back the maintenance completion time of the maintenance scheduling phase to the transfer scheduling phase to dynamically adjust the maintenance urgency proxy target, repeat the iteration until convergence, and output the converged aircraft hangar transfer and hangar maintenance scheduling plan.

[0054] By implementing steps 201 to 204 above, this application establishes a collaborative scheduling problem model based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft entry and transfer tasks, and the preset set of sortie waves. The collaborative scheduling problem model is then decomposed into an iterative optimization process involving transfer scheduling and maintenance scheduling stages. In the transfer scheduling stage, the optimization objective is to minimize transfer time and the maintenance urgency proxy objective, resulting in an efficient and reasonable aircraft entry and transfer sequence. In the maintenance scheduling stage, the actual entry time is used as an input constraint, and the optimization objective is to maximize the availability of aircraft waves, resulting in a hangar maintenance plan. The maintenance completion time from the maintenance scheduling stage is fed back to the transfer scheduling stage to dynamically correct the maintenance urgency proxy objective. This "transfer-maintenance-feedback" loop is repeated, with each iteration optimizing the previous one to obtain a converged aircraft entry and transfer and hangar maintenance scheduling plan. This effectively solves the tight coupling between the two sub-problems of transfer and maintenance, significantly improving the global optimality of the scheduling plan.

[0055] In step 201, the cooperative scheduling problem is defined as follows: based on the set of aircraft to be scheduled... Hangar maintenance task set Aircraft storage and transfer task set and the preset set of deployment waves. We establish a collaborative scheduling problem model with maximizing the availability of fleet waves as the primary optimization objective.

[0056] In another exemplary embodiment of this application, with the optimization objective of minimizing transfer time and maintenance urgency, an intelligent optimization algorithm is used to solve the aircraft storage transfer sequence, which is replaced by the following steps 301-302: Step 301: For the set of inbound transfer tasks, construct a first objective function with the optimization objectives of minimizing transfer time, load balancing of transfer groups, and maintenance urgency.

[0057] Step 302: Using an intelligent optimization algorithm, an iterative search is performed among multiple aircraft entry and transfer schemes based on the first objective function to obtain an aircraft entry and transfer sequence that minimizes the first objective function. The iterative search process is as follows: the current aircraft entry and transfer sequence is selected based on the first objective function value generated in the current iteration, and the current aircraft entry and transfer sequence is fed back to the next iteration to gradually approach the optimal solution.

[0058] The formula for calculating the first objective function is: .

[0059] in, Let this be the first objective function; For airplane Entry time; The subset of aircraft awaiting repair is a set. A subset of; To ensure load balance of the dispatching group; For urgent maintenance targets; The preset normalized weight coefficients satisfy... And its value range is [0, 1].

[0060] The first objective function The four components are used to minimize the maximum transfer completion time (i.e., the maximum arrival time), prioritize the sum of arrival times of aircraft awaiting maintenance, balance the workload of the transfer group, and reduce the wave availability risk caused by maintenance delays. This optimization objective (i.e., the first objective function) focuses on the efficiency of the transfer phase and indirectly guides the transfer to improve the overall wave availability through the maintenance urgency proxy objective, focusing on the first phase optimization process.

[0061] Load balance of dispatching group It is measured by calculating the variance of the working hours of the dispatch team members.

[0062] The formula for calculating the load balance of the dispatching group is: .

[0063] in, Assemble the transport team. For the dispatch team members Total working hours; This represents the average working hours of the dispatch team members.

[0064] The formula for calculating the maintenance urgency proxy target is: .

[0065] in, For the set of dispatch waves, For waves Importance weights To plan to participate in waves A subset of aircraft The function represents the portion of the penalty value that is positive. For airplane Repair completion time, To plan to participate in waves The moment of preparation for deployment begins.

[0066] In step 202, the first phase of transfer scheduling optimization is performed: for the set of inbound transfer tasks... With the optimization objective of minimizing transfer time and balancing maintenance urgency, this study utilizes an intelligent optimization algorithm. Based on pre-defined transfer route rules, resource constraints, and aircraft maintenance schedules, it comprehensively evaluates and prioritizes potential transfer tasks. From multiple alternative transfer options, it selects the one that best achieves the optimization objective. The process involves determining the minimum aircraft handling sequence, rather than simply obtaining the sequence through pre-initialization. This process is based on dynamic programming optimization. Its iterative solution selects the optimal handling sequence based on the objective function value after each optimization, feeding the result back to the next round of evaluation. Based on this handling sequence, the optimal handling sequence for each aircraft awaiting maintenance is calculated. Actual arrival time at the hangar maintenance parking position The actual entry time , refers to airplane After completing all necessary inbound and transfer procedures, the aircraft must first dock at its designated hangar maintenance bay and meet the conditions for commencing maintenance at the earliest possible time. This time is the actual arrival time for each aircraft, calculated independently. The calculation formula is as follows: .

[0067] in, Indicates airplane The time required to complete all preceding transfer operations and arrive at the designated maintenance stop takes into account the specific transfer route, transfer tool allocation, and actual transfer time. Represented as an airplane The earliest time a designated hangar maintenance bay becomes available after being occupied. Taking into account the physical process of transport and the actual capacity of the bay, the entry time is ensured to be reasonable and accurate, thus providing a reliable starting point for subsequent maintenance scheduling.

[0068] First, after defining the problem elements such as aircraft, missions, and the preset set of sortie waves, the method enters the first stage of transfer scheduling optimization. The goal of this stage is to generate an efficient and reasonable aircraft hangar transfer sequence. Its optimization objective is complex, considering both the efficiency of the transfer operation itself (e.g., minimizing the latest arrival time of all aircraft, i.e., the maximum entry time) and the workload balance of the transfer team, avoiding some personnel being overworked while others are idle. More importantly, it initially considers the urgency of aircraft awaiting maintenance, ensuring that aircraft with serious malfunctions or mission-critical requirements can enter the hangar as early as possible. The output of this stage is a specific transfer sequence and the calculated accurate hangar entry time for each aircraft.

[0069] In another exemplary embodiment of this application, with the actual arrival time as a constraint and maximizing the availability of aircraft fleet waves as the optimization objective, an intelligent optimization algorithm is used to solve the hangar maintenance plan, which is replaced by the following steps 401 to 403: Step 401: Using the actual arrival time as an input constraint, limit the earliest time when each aircraft can begin hangar maintenance.

[0070] Step 402: For the hangar maintenance task set, construct a second objective function with the optimization objectives of maximizing the availability of aircraft fleet waves and minimizing the load balancing of maintenance personnel.

[0071] Step 403: Using an intelligent optimization algorithm, search for a hangar maintenance scheme in the feasible solution space that minimizes the second objective function.

[0072] The formula for calculating the second objective function is as follows: .

[0073] in, The second objective function is... For fleet wave availability; For the load balancing of maintenance personnel; The weighting coefficients for multi-objective optimization have a value range of [0.01, 0.2].

[0074] The formula for calculating the availability of the aircraft fleet wavelets is as follows: .

[0075] in, For the set of dispatch waves; For waves Importance weights, satisfying And for any two waves and ,like Prior to but ; The total number of aircraft; For airplane Repair completion time; For waves The moment preparations for deployment begin; This is a symbolic function, defined as follows: the function value is 1 when its input value is greater than zero, and 0 otherwise.

[0076] Maintenance personnel load balancing It is measured by calculating the variance of the working hours of maintenance personnel.

[0077] The formula for calculating the load balancing degree of maintenance personnel is: .

[0078] in, Assemble the maintenance personnel; For maintenance personnel Total working hours; This represents the average working hours of maintenance personnel.

[0079] In step 203, the second phase of maintenance scheduling optimization is performed: the actual aircraft arrival time vector determined in step 202 is received. As an input constraint, this constraint defines the earliest time when each aircraft can begin hangar maintenance. This applies to the set of hangar maintenance tasks. This phase involves constructing a maintenance task network model that includes process dependencies, resource requirements, maintenance personnel skills, and hangar space layout. Under the premise of satisfying a series of hangar resource constraints such as maintenance workstation space constraints and parallel operation constraints in the maintenance workshop, intelligent optimization techniques such as genetic algorithms are used to maximize the availability of aircraft fleet waves. and minimize the load balancing of maintenance personnel To optimize the objectives, a multi-objective optimization solution is performed. The solution process involves dynamically allocating key resources such as maintenance personnel, equipment, and workstations, and flexibly arranging task sequences. It searches for the optimal solution in the feasible solution space, obtaining a specific hangar maintenance operation sequence scheme and maintenance resource allocation scheme, and calculating the optimal solution for each aircraft. Repair completion time The repair completion time , refers to airplane The time required to complete all assigned hangar maintenance tasks and reach a ready-to-go state for the aircraft. This time is determined by the aircraft's... All maintenance task procedures are analyzed. Logical deduction and summation are performed based on the start time, duration of each procedure, its dependencies on preceding and succeeding procedures, and resource consumption. Finally, the end time of all maintenance tasks is determined. The formula for calculating the maintenance completion time is as follows: .

[0080] in, For airplane Repair completion time, For airplane A collection of hangar maintenance tasks. For maintenance tasks The start time, For maintenance tasks The actual execution time.

[0081] In another exemplary embodiment of this application, during the maintenance scheduling phase, the input constraints also include hangar resource constraints, specifically including: maintenance workstation space constraints and maintenance workshop parallel operation constraints.

[0082] The space constraint of the maintenance workstation is: at any time Assigned to any aircraft Used to perform a certain category of maintenance skills The number of maintenance personnel must not exceed the number of aircraft. The maintenance stop position is associated with the aforementioned maintenance skill category. The set available workstation space capacity .

[0083] The parallel operation constraint in the maintenance workshop is: at any given time... In a maintenance workshop where parallel operations are possible The number of maintenance tasks being performed simultaneously in the workshop shall not exceed the number of maintenance tasks being performed simultaneously in the workshop. Maximum number of parallel jobs set The repair workshop Maximum number of parallel jobs The value range is [2, 10].

[0084] The above constraints are to achieve the goal of maximizing the availability of cluster waves. and minimize the load balancing of maintenance personnel The optimization objectives are a prerequisite. They limit the parallel execution of maintenance tasks, the scope and timing of resource allocation, and can affect the maintenance completion time of each aircraft. and the actual working hours of maintenance personnel Specifically, these constraints constitute the feasible solution space of the maintenance scheduling problem. Any scheduling scheme that violates these constraints will be considered invalid and thus avoided by the intelligent optimization algorithm during the optimization process, ensuring that the generated maintenance operation sequence plan is feasible in reality. By effectively satisfying these constraints, the optimization algorithm can find the optimal solution under realistic conditions, transforming the optimization objective into an operable resource allocation and timing arrangement.

[0085] The second phase of maintenance scheduling optimization begins. This phase uses the actual aircraft arrival times output from the previous phase as a fixed, inviolable start time constraint. Under this constraint, it prioritizes and allocates resources for all maintenance tasks within the hangar. This invention employs a strategy distinct from related technologies, prioritizing maximizing fleet availability as the primary optimization objective. This metric directly measures how many aircraft can complete maintenance and be available at each pre-set mission launch time, thus closely linking scheduling decisions to mission objectives. Simultaneously, this phase also considers the load balancing of maintenance personnel.

[0086] In step 204, iterative optimization is performed: the repair completion time calculated in step 203 is used as the basis for the optimization. Feedback is sent to step 202 to revise the maintenance urgency proxy target, the maintenance completion time. The relevance to the maintenance urgency proxy objective lies in its aim to assess and quantify the risk of each aircraft being unable to participate in a pre-set sortie wave on time due to maintenance delays. Specifically, maintenance completion time... It is a proxy target for calculating maintenance urgency. The key input. If the aircraft Repair completion time Later than the wave it should have participated in The moment of preparation for deployment begins If this happens, the aircraft will negatively impact the availability of that wave and increase its urgency level.

[0087] The maintenance urgency proxy target The calculation formula can be expressed as: .

[0088] in, For the set of dispatch waves, For waves Importance weights To plan to participate in waves A subset of aircraft The function represents the portion of the penalty value that is positive, i.e., the portion that exceeds the wave preparation time.

[0089] This calculation formula quantifies the wave availability loss caused by maintenance delays. The maintenance urgency proxy target... As an optimization objective in step 202 The correction term is used. Through this feedback mechanism, the first-stage transfer scheduling can dynamically adjust the priority and sequence of aircraft transfer into the hangar based on the actual completion status of the second-stage maintenance. Priority is given to aircraft with later maintenance completion times and greater impact on wave availability, ensuring they can enter maintenance earlier and thus improving overall coordination efficiency. Steps 202 and 203 are repeated until the preset iterative convergence condition is met, ultimately outputting a coordinated optimal aircraft transfer and maintenance scheduling scheme (i.e., the converged aircraft transfer and hangar maintenance scheduling scheme).

[0090] In another exemplary embodiment of this application, the convergence condition is a first convergence condition or a second convergence condition.

[0091] The first convergence condition is: in two consecutive iterations, let the... Second and third The availability of the cluster wavelets calculated in the next iteration are as follows: and Its improvement value satisfies ,in The preset convergence threshold has a value range of [0.001, 0.01].

[0092] The second convergence condition is: the number of iterations reaches the preset maximum number of iterations. ,in, The value range is [50, 200].

[0093] During iterative convergence, the first convergence condition and the second convergence condition are judged simultaneously, and the process stops when either the first convergence condition or the second convergence condition is satisfied.

[0094] The most critical innovation of this application lies in the iterative optimization mechanism established between the two phases. After the maintenance scheduling in the second phase is completed, the estimated final completion time (i.e., maintenance completion time) for each aircraft is obtained. This time information is not the final result, but is used as a precise feedback signal and transmitted back to the first phase. In the next iteration, the transfer scheduling in the first phase will use this feedback information to more accurately assess the maintenance urgency of each aircraft, potentially generating a better transfer sequence than the previous one. This "transfer-maintenance-feedback" loop will be repeated, with each iteration optimizing based on the previous one, until the core indicator of the scheduling scheme, namely the availability of aircraft waves, no longer shows significant improvement or reaches the preset iteration limit. At this point, the system outputs the final scheduling scheme after full collaborative optimization. This scheme achieves the best balance between transfer efficiency, resource load, and task availability from a global perspective.

[0095] The following section uses a specific civil aviation passenger aircraft maintenance application scenario, taking the specific aircraft hangar maintenance and storage transfer collaborative scheduling process as an example to illustrate this application.

[0096] S1. Define the cooperative scheduling problem In the context of a large civil aviation maintenance center, assume three passenger aircraft, designated Aircraft 1, Aircraft 2, and Aircraft 3 (i.e., the set of aircraft to be dispatched), need to return to the maintenance hangar for in-depth maintenance after completing their flight missions. Each aircraft has its own transfer task from the external apron to the hangar for maintenance (i.e., the set of aircraft entry and transfer tasks). During maintenance, Aircraft 1 requires engine overhaul and cabin interior refurbishment; Aircraft 2 requires landing gear overhaul and flight control system upgrade; Aircraft 3 only requires routine checks and partial fuselage painting (i.e., the set of hangar maintenance tasks). Meanwhile, according to the airline's future flight schedule, there are two important operational peak periods: the first wave (peak period 1) is scheduled to start at T+12 hours, requiring Aircraft 1 and Aircraft 2 to be available to execute important routes; the second wave (peak period 2) is scheduled to start at T+20 hours, requiring Aircraft 3 to be available (i.e., the pre-set set of sortie waves). Peak period 1 is more important to the company's operations than peak period 2. Furthermore, hangar maintenance resources, such as skilled maintenance technicians, specialized tools, testing equipment, and maintenance bays, are limited, as are the ground support fleet resources responsible for aircraft towing (or transfer). This embodiment aims to establish a collaborative scheduling problem model under these complex circumstances, with the primary optimization objective of maximizing aircraft fleet availability during peak periods 1 and 2. This model ensures that aircraft can serve flight schedules as punctually as possible while meeting all maintenance and towing (or transfer) constraints.

[0097] S2. Perform the first phase of transfer scheduling optimization. In the first phase, the system will focus on the scheduling of aircraft towing (or transfer). At this time, aircraft 1, 2, and 3 are parked at remote stands or transfer areas of the airport and need to be towed (or transferred) to their reserved maintenance parking positions P1, P2, and P3 within the maintenance hangar. It is assumed that the airport has only one aircraft towing (or transfer) fleet (i.e., a dispatch team), and can only tow (or transfer) one aircraft at a time. Hanger parking positions P1, P2, and P3 are not always immediately available; for example, P1 will only be emptied and ready at T+1 hour, P2 will be available at T+0.5 hours, while P3 is immediately available.

[0098] The current optimization objectives are to minimize the maximum aircraft arrival time, minimize the total arrival time of aircraft awaiting maintenance, and balance the workload of the towing convoy (or dispatching team). In the early stages of iteration, the system will also incorporate a maintenance urgency proxy objective based on historical maintenance data or preliminary estimates of subsequent maintenance tasks. The system will utilize intelligent optimization algorithms, such as genetic algorithms, to generate a preliminary aircraft towing (or dispatching) sequence after comprehensively considering factors such as aircraft type, towing (or transfer) path length, airport ground traffic control, available time windows for the towing (or transfer) convoy, and the actual capacity of the target parking positions. For example, although aircraft 1 may be physically closer to the towing (or transfer) convoy, if its corresponding hangar parking position P1 is not yet ready, and aircraft 2's maintenance task is more urgent and its parking position P2 is available, the system may prioritize the towing (or transfer) of aircraft 2, followed by aircraft 1, and finally aircraft 3.

[0099] Once the towing (or transfer) sequence is determined, the system can calculate the actual arrival time for each aircraft. This refers to the earliest time an aircraft, after completing all necessary towing (or transfer) procedures, first docks at its designated hangar maintenance bay and meets the conditions for commencing maintenance. Taking aircraft 2 as an example, assuming its towing (or transfer) completion time is T+1 hour, and designated bay P2 becomes available in T+0.5 hours, then the arrival time of aircraft 2 is... The larger of the two values ​​will be taken, i.e., T+1 hours. Similarly, the arrival times for aircraft 1 and aircraft 3... and This logic will also be used for precise calculations. These actual entry times will form a vector, serving as a key input for the next stage of maintenance scheduling.

[0100] S3. Perform the second phase of maintenance scheduling optimization. In the second stage, the system receives the actual aircraft arrival time vector output from step S2. For example, assuming that after the initial iteration, aircraft 1 can only be maintained at T+2 hours, aircraft 2 can start at T+1 hours, and aircraft 3 can start at T+3 hours, these time points constitute the hard constraint of the earliest start time for each aircraft's maintenance task. For each set of maintenance tasks for each aircraft, this stage will construct a detailed maintenance task network model, which includes the sequential dependencies between various maintenance procedures, the required maintenance skill types, and the estimated time for each task.

[0101] The optimization objective at this stage is to maximize the overall fleet availability during peak flight periods (i.e., maximize fleet wave availability), aiming to ensure aircraft 1 and 2 are on peak flight period 1, and aircraft 3 is on peak flight period 2. Simultaneously, it aims to minimize the load balancing of maintenance personnel, preventing some personnel from becoming overworked while others are underworked. To this end, the system employs intelligent optimization techniques, such as improved genetic algorithms or priority-based heuristic search, to schedule maintenance operations while satisfying a series of hangar resource constraints. These constraints include maintenance bay space constraints. For example, although a maintenance bay may be allocated to aircraft 1, its specific maintenance bay space, such as the area used for cabin interior refurbishment, may only allow two interior technicians to work simultaneously, even if theoretically more people could work around the aircraft. Parallel operation constraints in maintenance workshops further limit workshops sharing equipment resources. For instance, a structural repair workshop can only handle structural inspections or repairs for a maximum of two aircraft at a time; even if three aircraft are waiting, they must queue according to the workshop's maximum number of concurrent operations. By dynamically assigning maintenance tasks to appropriate maintenance personnel, tools, and workshops, the system will generate a detailed hangar maintenance operation sequence plan and maintenance resource allocation plan.

[0102] From this, the maintenance completion time for each aircraft can be calculated. This refers to the time it takes for an aircraft to complete all its assigned hangar maintenance tasks and reach a ready-to-leave state. For example, due to the complexity of the procedures and the scarcity of resources, the maintenance completion time for aircraft 1 is... It may be calculated as T+15 hours, flight 2 as T+10 hours, and flight 3 as T+18 hours.

[0103] S4. Perform iterative optimization In this core phase of coordinated scheduling, after the initial execution of S3, the system obtained the maintenance completion times for aircraft 1, 2, and 3 as T+15 hours, T+10 hours, and T+18 hours, respectively. This information is now fed back to S2 to revise the maintenance urgency proxy objective in the first phase's transfer scheduling optimization goals. For example, during peak periods, the start time for sortie preparation for aircraft 1 is T+12 hours. It was T+15 hours, 3 hours late, and The timeframe is T+10 hours, which is 2 hours earlier. This delay information will be used to calculate the risk that each aircraft will be unable to serve the flight schedule on time due to maintenance delays. The urgency value of aircraft 1 will therefore increase significantly because its completion time far exceeds the requirements of peak period 1, which may lead to flight delays or cancellations. This updated maintenance urgency proxy objective will be dynamically added as a weighting factor to the optimization objective in step S2. This means that in the next iteration, the transfer scheduling system will, in addition to considering its own transfer efficiency (i.e., minimizing transfer time and balancing the load on the towing (or transfer) fleet), prioritize the towing (or transfer) of aircraft whose maintenance completion time is later than the flight requirements and which have a significant impact on the overall fleet availability. For example, even if the towing (or transfer) route of aircraft 2 is physically more complex and takes longer, the system may adjust the transfer sequence to allow aircraft 1 to enter the maintenance area earlier because aircraft 1 is more urgent for peak period 1.

[0104] Subsequently, the system will repeat steps S2 and S3. The new transfer sequence will generate new actual arrival times, leading to adjustments in the maintenance plan and new maintenance completion times, and this process will continue in a loop. The iterative process will continue until the fleet availability improvement value calculated in two consecutive iterations is less than a preset convergence threshold (e.g., 0.005), which means that the plan has stabilized, or the number of iterations has reached the preset maximum number of iterations (e.g., 100) to prevent infinite loop calculations. Finally, the system will output an aircraft towing (or transfer) and maintenance scheduling plan that has undergone multiple rounds of collaborative optimization and balances towing (or transfer) efficiency, maintenance progress, resource utilization, and fleet availability (i.e., the converged aircraft hangar entry and transfer and hangar maintenance scheduling plan), ensuring that on-time performance and overall operational efficiency are maximized in the complex and dynamic civil aviation operating environment.

[0105] This application aims to solve the complex coupled scheduling problem of deep maintenance and preceding transfer processes for large-scale aircraft fleets. The method establishes a two-stage iterative optimization framework. In the first stage, scheduling optimization is performed on the aircraft arrival and transfer, aiming to minimize transfer time and maintenance urgency proxy objectives while balancing the load of the transport group. This generates an aircraft arrival and transfer sequence and determines the actual arrival time. In the second stage, using the actual arrival time determined in the first stage as a constraint, hangar maintenance scheduling optimization is performed. Its core objective is to maximize the availability of aircraft waves for a series of future tasks, while also considering the load balancing of maintenance personnel. The key to this invention is the establishment of an iterative feedback mechanism between the two stages. The aircraft maintenance completion time calculated in the second stage is fed back to the first stage to dynamically correct the maintenance urgency proxy objective of the transfer decision. Through iterative iteration until convergence, a collaboratively optimal scheduling scheme is ultimately generated. This invention elevates the scheduling objective from a simple efficiency indicator to mission-oriented aircraft availability. Through an innovative iterative optimization framework, it effectively solves the tight coupling between the transfer and maintenance sub-problems, significantly improving the global optimality of the scheduling scheme.

[0106] Based on the same inventive concept, this application also provides an aircraft hangar maintenance and inbound transfer coordinated scheduling device for implementing the aforementioned aircraft hangar maintenance and inbound transfer coordinated scheduling method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more aircraft hangar maintenance and inbound transfer coordinated scheduling device embodiments provided below can be found in the limitations of the aircraft hangar maintenance and inbound transfer coordinated scheduling method described above, and will not be repeated here.

[0107] In one exemplary embodiment, such as Figure 3 As shown, an aircraft hangar maintenance and inbound transfer collaborative scheduling device is provided, including: a problem modeling module 1, a transfer scheduling module 2, a maintenance scheduling module 3, and a collaborative optimization module 4.

[0108] Problem modeling module 1 is used to establish a collaborative scheduling problem model based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft transfer tasks, and the preset set of sortie waves. The collaborative scheduling problem model is decomposed into a transfer scheduling stage and a maintenance scheduling stage for iterative optimization.

[0109] The transfer scheduling module 2 is used to solve the aircraft entry and transfer sequence using an intelligent optimization algorithm during the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency proxy objective, and to determine the actual entry time of each aircraft. The transfer time includes the maximum entry time of all aircraft and the total entry time of the aircraft to be maintained. The maintenance urgency proxy objective is a weighted delay penalty based on the maintenance completion time.

[0110] The transfer scheduling module 2 focuses on maximizing arrival time, prioritizing the sum of arrival times for aircraft awaiting maintenance, and balancing the load of transfer groups. It indirectly guides wave availability by incorporating maintenance urgency proxy targets. Specifically, this module uses heuristic rules to solve for aircraft arrival and transfer sequences that meet the current optimization objectives, and accurately calculates the availability of each aircraft awaiting maintenance based on the transfer sequence and parking space availability. Arrival time at the hangar maintenance parking position .

[0111] The maintenance scheduling module 3 is used to solve the hangar maintenance plan and determine the maintenance completion time of each aircraft by using intelligent optimization algorithms, with the actual arrival time as a constraint and the optimization objective of maximizing the availability of aircraft waves, during the maintenance scheduling phase. The availability of aircraft waves is the weighted average of the ratio of the number of aircraft performing each wave task to the total number of aircraft planned to participate in each wave in the set of sortie waves.

[0112] The maintenance scheduling module 3 receives the calculated aircraft arrival time vector provided by the transfer scheduling module 2. As a hard constraint for initiating maintenance, maintenance scheduling can be optimized based on fine-grained time information. This module further optimizes maintenance scheduling based on the hangar maintenance task set and maximizes fleet wave availability. and minimize the load balancing of maintenance personnel To optimize the objectives, a dynamic schedule and resource allocation plan for hangar maintenance operations (i.e., a hangar maintenance plan) is generated, and the maintenance completion time is calculated. .

[0113] The collaborative optimization module 4 is used to feed back the maintenance completion time of the maintenance scheduling phase to the transfer scheduling phase to dynamically correct the maintenance urgency proxy target. It is iterated repeatedly until convergence, and the converged aircraft storage transfer and hangar maintenance scheduling scheme is output.

[0114] The collaborative optimization module 4 implements the iterative process by controlling the information feedback and cyclical calls between the transfer scheduling module 2 and the maintenance scheduling module 3, and determines whether the iteration terminates based on the convergence condition, ultimately outputting the collaboratively optimal scheduling scheme. Specifically, this module uses the aircraft maintenance completion time calculated by the maintenance scheduling module 3... The precise feedback is sent to the transfer scheduling module 2. This feedback is used by the transfer scheduling module 2 to dynamically adjust the maintenance urgency proxy objective in its optimization goals, enabling the transfer scheduling to prioritize aircraft that are critical to the overall fleet availability in the next iteration. This targeted and refined feedback mechanism greatly improves the efficiency and convergence speed of collaborative optimization, ensuring that the system can quickly converge to the optimal or near-optimal scheduling scheme in complex dynamic environments.

[0115] The aircraft hangar maintenance and inbound transfer collaborative scheduling device of this application, through modular functional design, automatically executes the collaborative scheduling process and outputs a high-quality and reliable scheduling solution.

[0116] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data on the coordinated scheduling of aircraft hangar maintenance and inbound transfer. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for coordinated scheduling of aircraft hangar maintenance and inbound transfer.

[0117] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0118] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0119] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0120] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0122] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0123] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for coordinated scheduling of aircraft hangar maintenance and inbound transfer, characterized in that, include: Based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft transfer tasks, and the set of preset sortie waves, a collaborative scheduling problem model is established, and the collaborative scheduling problem model is decomposed into a process of iterative optimization of the transfer scheduling stage and the maintenance scheduling stage. During the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency as the proxy goal, an intelligent optimization algorithm is used to solve the aircraft entry and transfer sequence and determine the actual entry time of each aircraft; the transfer time includes the maximum entry time of all aircraft and the total entry time of the aircraft to be maintained. The maintenance urgency proxy objective is a weighted delay penalty based on maintenance completion time; During the maintenance scheduling phase, with the actual arrival time as a constraint and maximizing the availability of aircraft waves as the optimization objective, an intelligent optimization algorithm is used to solve the hangar maintenance plan and determine the maintenance completion time for each aircraft. The availability of aircraft waves is the weighted average of the ratio of the number of aircraft performing each wave task to the total number of aircraft planned to participate in each wave in the set of sortie waves. The maintenance completion time in the maintenance scheduling phase is fed back to the transfer scheduling phase to dynamically adjust the maintenance urgency proxy target. This process is repeated iteratively until convergence, and the converged aircraft hangar transfer and hangar maintenance scheduling plan is output.

2. The aircraft hangar maintenance and inbound transfer collaborative scheduling method according to claim 1, characterized in that, With the optimization objectives of minimizing transfer time and balancing maintenance urgency, an intelligent optimization algorithm is used to solve for the aircraft storage transfer sequence, specifically including: For the set of inbound transfer tasks, a first objective function is constructed with the optimization objectives of minimizing transfer time, load balancing of transfer groups, and maintenance urgency. Using an intelligent optimization algorithm, an iterative search is performed on multiple aircraft entry and transfer schemes based on a first objective function to obtain an aircraft entry and transfer sequence that minimizes the first objective function. The iterative search process is as follows: the current aircraft entry and transfer sequence is selected based on the first objective function value generated in the current iteration, and the current aircraft entry and transfer sequence is fed back to the next iteration to gradually approach the optimal solution. The formula for calculating the first objective function is: ； in, Let this be the first objective function; For airplane Entry time; The subset of aircraft awaiting repair is a set. A subset of; To ensure load balance of the dispatching group; For urgent repair needs; The preset normalized weighting coefficients satisfy... And their values ​​are all in the range of [0, 1]; The formula for calculating the load balance of the dispatching group is: ； in, Assemble the transport team. For the dispatch team members Total working hours; This refers to the average working hours of the dispatch team members; The formula for calculating the maintenance urgency proxy target is: ； in, For the set of dispatch waves, For waves Importance weights To plan to participate in waves A subset of aircraft The function represents the portion of the penalty value that is positive. For airplane Repair completion time, To plan to participate in waves The moment of preparation for deployment begins.

3. The aircraft hangar maintenance and inbound transfer collaborative scheduling method according to claim 1, characterized in that, Using actual arrival time as a constraint and maximizing fleet availability as the optimization objective, an intelligent optimization algorithm is used to solve the hangar maintenance plan, specifically including: Using the actual arrival time as an input constraint, the earliest time when each aircraft can begin hangar maintenance is limited; For the hangar maintenance task set, a second objective function is constructed with the optimization objectives of maximizing fleet availability and minimizing maintenance personnel load balancing. Using intelligent optimization algorithms, a hangar maintenance scheme that minimizes the second objective function is searched in the feasible solution space; The formula for calculating the second objective function is as follows: ； in, The second objective function is... For fleet wave availability; For the load balancing of maintenance personnel; The weighting coefficients for multi-objective optimization have a value range of [0.01, 0.2]. The formula for calculating the availability of the aircraft fleet wavelets is as follows: ； in, For the set of dispatch waves; For waves Importance weights, satisfying And for any two waves and ,like Prior to but ; The total number of aircraft; For airplane Repair completion time; For waves The moment preparations for deployment begin; This is a sign function, defined as follows: the function value is 1 when its input value is greater than zero, and 0 otherwise; The formula for calculating the load balancing degree of maintenance personnel is: ； in, Assemble the maintenance personnel; For maintenance personnel Total working hours; This represents the average working hours of maintenance personnel.

4. The aircraft hangar maintenance and inbound transfer collaborative scheduling method according to claim 1, characterized in that, During the maintenance scheduling phase, the constraints also include hangar resource constraints, specifically including: maintenance workstation space constraints and parallel operation constraints in the maintenance workshop; The space constraint of the maintenance workstation is: at any time Assigned to any aircraft Used to perform a certain category of maintenance skills The number of maintenance personnel must not exceed the number of aircraft. The maintenance stop position is associated with the aforementioned maintenance skill category. The set available workstation space capacity ; The parallel operation constraint in the maintenance workshop is: at any given time... In a maintenance workshop where parallel operations are possible The number of maintenance tasks being performed simultaneously in the workshop shall not exceed the number of maintenance tasks being performed simultaneously in the workshop. Maximum number of parallel jobs set The repair workshop Maximum number of parallel jobs The value range is [2, 10].

5. The aircraft hangar maintenance and inbound transfer collaborative scheduling method according to claim 1, characterized in that, The convergence condition adopts either the first convergence condition or the second convergence condition; The first convergence condition is: in two consecutive iterations, let the... Second and third The availability of the cluster wavelets calculated in the next iteration are as follows: and Its improvement value satisfies ,in The preset convergence threshold has a value range of [0.001, 0.01]. The second convergence condition is: the number of iterations reaches the preset maximum number of iterations. ,in, The value range is [50, 200].

6. The aircraft hangar maintenance and inbound transfer collaborative scheduling method according to claim 1, characterized in that, The formula for calculating the repair completion time is: ； in, For airplane Repair completion time, For airplane A collection of hangar maintenance tasks. For maintenance tasks The start time, For maintenance tasks The actual execution time.

7. A collaborative scheduling device for aircraft hangar maintenance and inbound transfer, characterized in that, include: The problem modeling module is used to establish a collaborative scheduling problem model based on the set of aircraft to be scheduled, the set of hangar maintenance tasks, the set of aircraft entering and transferring tasks, and the preset set of sortie waves. The collaborative scheduling problem model is decomposed into a transfer scheduling stage and a maintenance scheduling stage for iterative optimization. The transfer scheduling module is used to solve the aircraft entry and transfer sequence using intelligent optimization algorithms during the transfer scheduling phase, with the optimization objective of minimizing transfer time and maintenance urgency as the proxy goal, and to determine the actual entry time of each aircraft; the transfer time includes the maximum entry time of all aircraft and the total entry time of the aircraft to be maintained. The maintenance urgency proxy objective is a weighted delay penalty based on maintenance completion time; The maintenance scheduling module is used to solve hangar maintenance plans and determine the maintenance completion time for each aircraft during the maintenance scheduling phase, with the actual arrival time as a constraint and the optimization objective of maximizing the availability of aircraft waves. The availability of aircraft waves is the weighted average of the ratio of the number of aircraft performing each wave task to the total number of aircraft planned to participate in each wave in the set of sortie waves. The collaborative optimization module is used to feed back the maintenance completion time from the maintenance scheduling phase to the transfer scheduling phase to dynamically correct the maintenance urgency proxy target. This process is repeated iteratively until convergence, and the converged aircraft hangar transfer and hangar maintenance scheduling scheme is output.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the aircraft hangar maintenance and inbound transfer coordinated scheduling method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the aircraft hangar maintenance and inbound transfer collaborative scheduling method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the aircraft hangar maintenance and inbound transfer collaborative scheduling method as described in any one of claims 1-6.

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