Automatic personnel dispatching method for airport boarding bridge guarantee service

Abstract

The invention provides an airport boarding bridge guarantee service personnel automatic dispatching method comprising the following steps: S1, obtaining flight information in a future preset time period, and generating a boarding bridge guarantee task set K corresponding to flights; s2, obtaining available employee information at the current moment, and generating an available employee set S; s3, calculating the comprehensive matching degree between the employees and the boarding bridge guarantee tasks according to the boarding bridge guarantee task set K and the available employee set S, and constructing a weighted bipartite graph of the employees and the boarding bridge guarantee tasks according to the comprehensive matching degree; and S4, solving the weighted bipartite graph by applying a bipartite graph maximum weight matching algorithm to generate an employee dispatching result, and carrying out emergency processing on tasks which are not successfully matched in the scheme. According to the invention, automation of boarding bridge guarantee task and personnel matching is realized, and improvement of airport ground service guarantee efficiency is facilitated.

Description

Technical Field

[0001] This invention belongs to the technical field of airport scheduling methods, and in particular relates to an automatic personnel dispatching method for airport boarding bridge support services. Background Technology

[0002] Airport ground handling services are diverse and a crucial part of airport operations. Among these, boarding bridges are vital links connecting flights to boarding gates, providing passengers with convenient access to and from the aircraft. Efficient boarding bridge arrangements not only ensure passengers can board and disembark safely and on time but also improve passenger satisfaction, thereby increasing flight punctuality. Therefore, the proper scheduling of boarding bridge support personnel is an indispensable aspect of airport management.

[0003] With the continuous growth of civil aviation demand, many hub airports are accelerating their expansion, especially in the construction of terminals and aprons. Some mega-airports, such as Shanghai Pudong International Airport, have multiple terminals and a large number of parking stands and boarding bridges, which makes the scheduling of boarding bridge service personnel complex and unpredictable. Existing scheduling methods cannot efficiently solve these problems, resulting in uneven staff allocation and untimely service, affecting the overall operational efficiency of the airport.

[0004] Although the importance of boarding bridges in airport operations has gradually gained attention, research on the scheduling of boarding bridge support personnel is relatively limited. Existing research mainly focuses on the gate and parking stand allocation problem from a flight perspective, employing operations research methods to ensure that flights are successfully assigned to appropriate gates. For example, Tang et al. (2013) studied the gate allocation problem for airline-dedicated parking stands; Guépet et al. (2015) proposed the MIP model and two heuristic algorithms for parking stand allocation; Xu et al. (2017) proposed a robust parking stand allocation method to cope with random flight delays; Hagspihl et al. (2022) studied the dynamic boarding bridge configuration problem, determining the boarding gate of a flight and when to change the boarding bridge configuration, and proposed a heuristic solution method based on column generation; Zhang et al. (2022) considered maximizing the boarding bridge rate in the optimization problem; Jiang et al. (2024) studied the joint runway and parking stand assignment problem considering parking time differences and airline preferences, and proposed an improved branch pricing algorithm for solving it.

[0005] In summary, there is a lack of research on the scheduling of personnel for airport boarding bridge operations. Most studies have overlooked the complexity of personnel scheduling, and existing studies have largely failed to consider the skill requirements of boarding bridge operators, resulting in a lack of targeted and rational personnel allocation. Summary of the Invention

[0006] The purpose of this invention is to provide an automated personnel dispatching method for airport boarding bridge support services, which automates the matching of boarding bridge support tasks and personnel, thereby improving the efficiency of airport ground support. The technical solution adopted is as follows: An automatic personnel dispatching method for airport boarding bridge support services includes the following steps: Step S1: Obtain flight information within a future preset time period and generate a set K of boarding bridge support tasks corresponding to the flights; Step S2: Obtain the available employee information at the current moment and generate an available employee set S; Step S3: Calculate the overall matching degree between employees and boarding bridge support tasks based on the set of boarding bridge support tasks K and the set of available employees S, and construct a weighted bipartite graph of employees and boarding bridge support tasks based on the overall matching degree. Step S4: Apply the bipartite graph maximum weight matching algorithm to solve the weighted bipartite graph to generate employee assignment results and perform emergency handling for tasks that fail to match in the solution.

[0007] Preferably, the boarding bridge support tasks in the boarding bridge support task set K in step S1 all define one or more job skill requirements for performing the task.

[0008] Step S3.1: Determine the employee skills Does the task meet the requirements? Skill requirements If the condition is not met, proceed to step S3.2; otherwise, proceed to step S3.3. Step S3.2, the employee With the task The overall matching degree is assigned a value of negative infinity; Step S3.3: The overall matching degree is calculated using the following formula: ; -staff The length of the idle time at the current moment; -staff Total working hours for the day; - Preset weighting coefficients used to balance the length of idle time; - Preset weighting coefficients used to balance cumulative working hours.

[0009] Preferably, the weighting coefficient and The value can be adjusted according to the airport's operational strategy; When the operational strategy prioritizes task response speed, increase... ; When the operational strategy prioritizes ensuring a balanced workload for employees, adjust... .

[0010] Preferably, the emergency treatment in step S4 includes the following steps: Step S4.1: For each task that fails to match successfully, calculate its task urgency according to the preset urgency evaluation rules; Step S4.2: Identify tasks whose calculated urgency exceeds a preset threshold as high-priority tasks; Step S4.3: Assign personnel to high-priority tasks from a pre-set emergency personnel pool containing standby personnel information, and send a dispatch instruction containing task details to their mobile terminal devices.

[0011] Preferably, the calculation model for the task urgency is as follows: ; In the formula, - Mission urgency; - Flight schedule execution time refers to the mission Planned timelines for related flights; - Current time refers to the real-time system time when the system is performing this scheduling calculation; - Preset priority values ​​for task types; It is the time threshold constant; and These are preset weighting coefficients.

[0012] Compared with the prior art, the advantages of the present invention are: 1. Based on the bipartite graph maximum weight matching algorithm, the system can automatically match flight boarding bridge support tasks with staff. During this process, the system can assign the most suitable personnel to each boarding bridge support task based on factors such as each staff member's skills, cumulative working hours, and available time.

[0013] 2. The original "comprehensive matching degree" model not only meets the hard requirement of "skills", but also balances the two goals of "employee availability" and "work fairness" through weighted parameters, making the scheduling results more humane and reasonable, thereby optimizing personnel scheduling, helping to provide airports with scientific and reasonable personnel scheduling solutions, and improving the overall work efficiency and service quality of airports.

[0014] 3. By enabling the adjustment of open weighting coefficients, this method allows managers to adjust it according to the operational priorities of different periods, making it a flexible and intelligent decision-making tool. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the automatic dispatching method for personnel providing airport boarding bridge support services; Figure 2 The flowchart shows the automatic personnel assignment method based on the bipartite graph maximum weight matching algorithm. Figure 3 This is a schematic diagram of the maximum weight matching algorithm for bipartite graphs. Figure 4 A weighted bipartite graph representing the boarding bridge support task and staff composition. Detailed Implementation

[0016] The automatic personnel dispatching method for airport boarding bridge support services of the present invention will be described in more detail below with reference to the schematic diagrams, which illustrate preferred embodiments of the invention. It should be understood that those skilled in the art can modify the invention described herein while still achieving the advantageous effects of the invention. Therefore, the following description should be understood as being of general knowledge to those skilled in the art and is not intended to limit the invention.

[0017] like Figure 1 As shown, an automatic personnel dispatching method for airport boarding bridge support services includes the following steps: Step S1: Obtain flight information within a future preset time period and generate a set K of boarding bridge support tasks corresponding to the flights.

[0018] Specifically: obtain flight information for a future preset time period from the A-CDM airport collaborative decision-making system, query the dispatch rule table, and generate a set of boarding bridge support tasks corresponding to the flight with specific skill requirements based on the airline, aircraft type, arrival / departure type and boarding bridge support task requirements corresponding to the parking stand.

[0019] The "Dispatch Rules Form" includes: flight information and the corresponding boarding bridge support task.

[0020] The method for obtaining flight information for a preset future time period and generating corresponding boarding bridge support tasks for those flights involves the following steps: Assuming the current time To obtain future forecasts (Assuming) =20 minutes) within the time window, i.e., the time window [ , + [Information on flights taking off / landing within the airport / airport, and based on this information, determine the type of one or more boarding bridge support tasks required.]

[0021] Time window , + Flight information for departures / arrivals within a specified time window refers to the departure or arrival time falling within that time window. , + Flight information for flights within the specified range.

[0022] The flight information includes: flight number, airline, aircraft type, arrival / departure port, and parking position; The types of tasks that can be performed to support boarding bridges include: boarding bridge connection and removal, boarding bridge equipment charging and power disconnection, bridge-mounted air conditioning connection and removal, Chinese communication, English communication, and boarding bridge equipment maintenance.

[0023] Generate boarding bridge support tasks for all flights: Each flight corresponds to one or more boarding bridge support tasks, each task corresponds to a skill limitation, and each task requires 1 person. Multiple tasks are allowed to have the same skill limitation, such as "boarding bridge connection", "equipment charging" or "English communication", etc. All these tasks constitute the boarding bridge support task set K.

[0024] Table 1 shows the three boarding bridge support tasks generated in this embodiment.

[0025] Table 1. List of Tasks for Supporting Boarding Bridges Task Number Skills required for the task 1 boarding bridge connection 2 Boarding bridge equipment charging 3 English contact Figure 1 The T mentioned is the time variable of the system scheduling loop. begin T represents the time point at which the scheduling cycle begins. end This indicates the end time of the scheduling cycle. T+1 indicates that the time variable is incremented to the next moment, and the system will then start the next round of scheduling calculations based on the new current moment. This method executes a complete dispatching process once within each scheduling cycle.

[0026] Step S2: Obtain the current time Given the available employee information, generate an available employee set S.

[0027] Employee information should include at least the skills the employee possesses and the current time. The available employee set is generated based on the length of their idle time and the cumulative working hours for the day.

[0028] The system obtains information on all employees in a dispatchable state in real time from the airport ground staff management system.

[0029] Among them, "employee information in schedulable state" refers to: available employee information, when an employee is currently available... t In an idle state, or at the current moment t Being busy but within a time window [ t, t+Δt If an employee becomes idle within a certain timeframe, then that employee is considered available for scheduling.

[0030] At the heart of this available employee information are three key attributes relevant to the matching model of this invention: The skills possessed by employees, and the current length of their free time (from the end of the previous task to the current moment). t The time), and the cumulative working hours of the day.

[0031] All these employees form the available employee set S.

[0032] Table 2 shows the set of available employees generated in this embodiment.

[0033] Table 2 Available Employee Set S Employee ID Mastering skills Current idle time length Cumulative working hours of the day 1 Boarding bridge equipment charging, English contact 30min 200min 2 boarding bridge connection 20min 180min 3 English contact 36min 150min 4 Boarding bridge equipment charging 50min 220min 5 boarding bridge connection 40min 190min Step S3: Calculate the overall matching degree between employees and boarding bridge support tasks based on the set K of boarding bridge support tasks and the set S of available employees, and construct a weighted bipartite graph of employees and boarding bridge support tasks based on the overall matching degree.

[0034] Specifically: Based on the job skill requirements of the boarding bridge support task and the employee's skills, idle time length and cumulative working hours, a comprehensive matching degree is calculated for each task-employee pair, and a weighted bipartite graph is constructed with the task and employee as the two end nodes and the comprehensive matching degree as the weight.

[0035] in, Figure 1 , Figure 2 and Figure 4 The “task” in the text refers to the task of ensuring the smooth operation of the boarding bridge.

[0036] In the following text, for ease of description, the "boarding bridge support mission" will be referred to as the "mission".

[0037] The overall matching degree is determined in the following ways: Step S3.1: Determine the employee skills Does the task meet the requirements? Skill requirements If the skill requirement is not met (i.e.) If the condition is met, proceed to step S3.2; otherwise, proceed to step S3.3. Step S3.2, the employee With the task The overall matching degree is assigned a value of negative infinity.

[0038] Negative infinity indicates that the two are incompatible, and the employee must never perform this task. Figures 3-4 In this case, it is equivalent to a non-existent edge.

[0039] Step S3.3: The overall matching degree is calculated using the following formula: (1) In the formula: For employees The current moment The length of idle time; For employees The total working hours for the day, which is the total length of working time. and Preset weighting coefficients for balancing idle time length (time availability) and cumulative working time (workload); The larger the value, the more important the task. and employees The higher the compatibility or priority, the better.

[0040] calculate When that happens, only the values ​​in Table 2 are used.

[0041] In this embodiment, it is assumed that and .

[0042] This setting means that it encourages the deployment of employees with long periods of downtime. (Positive), while also not wanting employees to be overworked ( A positive value indicates a negative sign before the workload, which serves as a penalty.

[0043] Step S3.3 is for skill satisfaction ( (The situation is as follows.)

[0044] In a preferred embodiment of the present invention, the weighting coefficient and It serves as a management interface exposed to scheduling decision-makers. Airport managers can adjust it based on operational strategies (routine operational strategies, operational strategies in emergency situations such as flight delays).

[0045] Specifically: Receive externally input parameter adjustment commands and update... and The value can be adjusted to change the emphasis of the scheduling strategy on the length of idle time at the current moment or the cumulative working time.

[0046] For example, when faced with large-scale flight delays and the need to expedite operations at all costs, managers can... Increase the value (e.g.) =2), and at the same time Lower the value (e.g.) =0.01), at which point the scheduling model will be more inclined to dispatch employees who have just become available, even if they have been working for a long time.

[0047] Conversely, in daily operations, it can be maintained =1, A balanced configuration of 0.05, or appropriately increased. The value is to better achieve a balance between work and rest for employees.

[0048] Based on the comprehensive matching degree calculation formula, the comprehensive matching degree matrix of the boarding bridge support task and personnel is shown in Table 3.

[0049] Table 3 Comprehensive Matching Degree Matrix

[0050] Using the overall matching degree as the weight, the task set K and the available employee set S are the vertices at the two ends of the weighted bipartite graph, respectively, forming a weighted bipartite graph G=(K, S), as follows. Figures 3-4 As shown.

[0051] Among them, the elements in task set K are: tasks ; Available elements in the employee set S: employees .

[0052] Figure 3 In the middle, on one side is the boarding bridge support task node (task) On the other side are employee nodes (employees) ), connection task and employees The weight of the edge is the overall matching degree to be calculated.

[0053] Step S4: Apply the bipartite graph maximum weight matching algorithm to solve the weighted bipartite graph to generate employee assignment results and perform emergency handling for tasks that fail to match in the solution.

[0054] Employee assignment results: Optimal task-employee matching scheme.

[0055] Figure 2 The flowchart illustrates an automatic personnel assignment method based on the bipartite graph maximum weight matching algorithm.

[0056] After constructing the weighted bipartite graph in step S3, this invention applies a mature combinatorial optimization algorithm to find the perfect match with the largest sum of weights.

[0057] In this embodiment, the system applies the Kuhn-Munkres algorithm (bipartite graph maximum weight matching algorithm) to solve the weighted bipartite graph constructed in step S3. This algorithm can guarantee that an optimal one-to-one matching scheme is found in polynomial time, so that the sum of the comprehensive matching degree of all matched "task-employee" pairs reaches the maximum.

[0058] The specific steps are as follows: As a standard prerequisite for this algorithm, if the number of tasks and employees are not equal, virtual tasks or virtual employees can be added, and the weight of their edges with all peer nodes can be set to negative infinity to ensure smooth matching. In this embodiment, two virtual tasks are added so that the number of employees and tasks are both 5.

[0059] like Figure 4 The diagram shows the matching of boarding bridge support tasks and employees. There are 3 boarding bridge support tasks and 5 employees. After adding 2 virtual tasks, the matching is performed based on the bipartite graph maximum weight matching algorithm.

[0060] like Figure 4 As shown, the peer node is the employee node.

[0061] In this embodiment, the optimal matching scheme obtained by the algorithm is: Task 1 is assigned to employee 5 (overall matching score 30.5), Task 2 is assigned to employee 4 (overall matching score 39), and Task 3 is assigned to employee 3 (overall matching score 28.5).

[0062] Employee 2 and Employee 1 are on standby in this round of scheduling.

[0063] In a preferred embodiment of the present invention, the system does not end after a single dispatch.

[0064] The system will continuously monitor the airport's real-time data stream.

[0065] For example, when the dispatching system detects through the A-CDM airport collaborative decision-making system that the estimated arrival time of a flight is delayed by 30 minutes, this change will serve as a trigger condition.

[0066] The dispatching system will take the flight's calculated departure / landing time as input, re-execute step S1 to generate new boarding bridge support task requirements, and then execute steps S3 and S4 again to recalculate the optimal matching for the affected tasks, thereby dynamically adjusting the dispatching plan.

[0067] The meaning of "using the flight's delayed departure / arrival time as input" is: Flights falling within the time window in step S1 may change.

[0068] Specifically: The dispatching system first synchronizes and updates the flight's departure / arrival time in the local database, and then uses the current time... Based on this, step S1 is re-executed to scan flight information within a future preset time period, generating a new set of boarding bridge support tasks K that includes the delayed flight.

[0069] Similarly, if an employee becomes unavailable due to sudden illness, the dispatch system will remove them from the available employee set S. Specifically, the system will lock the employee ID, remove it from set S, reset the status of future tasks already assigned to that employee, and release them back to the task pool for rescheduling.

[0070] When the number of tasks exceeds the number of employees, the emergency handling in step S4 includes the following steps: Step S4.1: For each task that fails to match successfully, calculate its task urgency according to the preset urgency evaluation rules.

[0071] The task urgency is calculated by combining the difference between the scheduled execution time of the flight associated with the unmatched task and the current time, as well as the priority value preset for that task type. Mission urgency The comprehensive calculation is achieved through the following formula: (2) In the formula, The urgency of the task indicates the task's urgency. The current urgency of overall processing. The larger this value, the higher the priority of system resource allocation, and the earlier it should be processed by the scheduling algorithm; This refers to the task The planned timeframes for related flights are typically derived from real-time updates of data from the A-CDM airport collaborative decision-making system. It is the current moment. t This refers to the real-time system time when the system performs this scheduling calculation; It is the remaining time window, which is the time remaining before the task must start; It is a preset priority value for the task type. For example, the priority value for large aircraft flight support tasks can be set to 10, and for small aircraft flights to be set to 5, or international flights > domestic flights. This reflects the inherent importance of the task itself. This is a time threshold constant, set to a very small positive number (e.g., 0.01), when the task has already been delayed (i.e., ...). When the value is extremely close to the denominator, the max function forcibly locks the denominator to 0. This ensured the level of urgency. At this point, a maximum saturation value will be reached. + This ensures that delayed tasks always maintain the highest priority without compromising computational stability, and avoids mathematically negative values ​​or infinity. and These are preset weighting coefficients. It is used to adjust the remaining time window. Regarding the urgency of the task Preset weights of influence It is used to adjust the task type to the task urgency. Preset weights for the impact.

[0072] Step S4.2: Identify tasks whose calculated urgency exceeds a preset threshold as high-priority tasks; Step S4.3: Assign personnel to high-priority tasks from a pre-set emergency personnel pool containing standby personnel information, and send a dispatch instruction containing task details to their mobile terminal devices.

[0073] This embodiment provides an automatic personnel dispatching system for airport boarding bridge support services, used to implement the aforementioned automatic personnel dispatching method for airport boarding bridge support services. The system is coupled to an airport flight information database and an employee status database, and connected to one or more mobile terminal devices held by employees. The system includes the following modules: The task generation module is used to obtain flight information for a future preset time period from the airport flight information database and generate corresponding boarding bridge support task data. The employee screening module is used to retrieve available employee information from the employee status database and generate available employee data. The matching calculation module is configured to receive task data from the task generation module and employee data from the employee screening module. This module has a built-in configurable weight coefficient storage unit and calculates the weight coefficients stored in the unit based on the weight coefficients. , The overall matching degree between tasks and employees is calculated using a preset formula, and then a weighted bipartite graph is constructed. The optimal solution module is used to solve weighted bipartite graphs by applying the maximum weight matching algorithm for bipartite graphs, generate the optimal task-employee matching scheme, package the successfully matched parts of the scheme into scheduling instructions and send them to the corresponding employee mobile terminal devices through the connection, and transmit the unmatched tasks to the emergency processing module. The emergency response module receives unmatched tasks from the optimal solution module, identifies high-priority tasks based on preset urgency assessment rules, matches personnel from a pre-set emergency personnel database, and sends scheduling instructions containing task details to them.

[0074] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the present invention. Any equivalent substitutions or modifications made by those skilled in the art to the technical solutions and content disclosed in the present invention without departing from the scope of the present invention shall be deemed to have remained within the protection scope of the present invention.

Claims

1. A method for automatically dispatching personnel to provide airport boarding bridge support services, characterized in that, Includes the following steps: Step S1: Obtain flight information within a future preset time period and generate a set K of boarding bridge support tasks corresponding to the flights; Step S2: Obtain the available employee information at the current moment and generate an available employee set S; Step S3: Calculate the overall matching degree between employees and boarding bridge support tasks based on the set of boarding bridge support tasks K and the set of available employees S, and construct a weighted bipartite graph of employees and boarding bridge support tasks based on the overall matching degree. Step S4: Apply the bipartite graph maximum weight matching algorithm to solve the weighted bipartite graph to generate employee assignment results and perform emergency handling for tasks that fail to match in the solution. Step S3, which calculates the overall matching degree, includes: Step S3.1: Determine the employee skills Does the task meet the requirements? Skill requirements ; If the condition is not met, proceed to step S3.2; otherwise, proceed to step S3.

3. Step S3.2, the employee With the task The overall matching degree is assigned a value of negative infinity; Step S3.3: The overall matching degree is calculated using the following formula: ； -staff The length of the idle time at the current moment; -staff Total working hours for the day; - Preset weighting coefficients used to balance the length of idle time; - Preset weighting coefficients used to balance cumulative working hours.

2. The automatic personnel dispatching method for airport boarding bridge support services according to claim 1, characterized in that, The boarding bridge support tasks in the boarding bridge support task set K mentioned in step S1 all define one or more job skill requirements required to perform the task.

3. The method for automatic personnel dispatching for airport boarding bridge support services according to claim 1, characterized in that, The weighting coefficient and The value can be adjusted according to the airport's operational strategy; When the operational strategy prioritizes task response speed, increase... ; When the operational strategy prioritizes ensuring a balanced workload for employees, adjust... .

4. The method for automatic personnel dispatching for airport boarding bridge support services according to claim 1, characterized in that, The emergency response in step S4 includes the following steps: Step S4.1: For each task that fails to match successfully, calculate its task urgency according to the preset urgency evaluation rules; Step S4.2: Identify tasks whose calculated urgency exceeds a preset threshold as high-priority tasks; Step S4.3: Assign personnel to high-priority tasks from a pre-set emergency personnel pool containing standby personnel information, and send a dispatch instruction containing task details to their mobile terminal devices.

5. The method for automatic personnel dispatching for airport boarding bridge support services according to claim 4, characterized in that, The calculation model for the urgency of the task is as follows: ； In the formula, - Mission urgency; - Flight schedule execution time refers to the mission Planned timelines for related flights; - Current time refers to the real-time system time when the system is performing this scheduling calculation; - Preset priority values ​​for task types; It is the time threshold constant; and These are preset weighting coefficients.

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