CN118134225A - Airport check-in personnel scheduling method, device, equipment and medium based on random event scene - Google Patents
Airport check-in personnel scheduling method, device, equipment and medium based on random event scene Download PDFInfo
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Abstract
The application provides an airport check-in personnel scheduling method, device, equipment and medium based on a random event scene, which comprises the following steps: based on the departure basic data, predicting the number of airport check-in personnel required; inputting real-time flight data and construction features into an event occurrence probability model, and predicting the occurrence probability of each event scene in the next day and the off-machine time window offset time; constructing an airport personnel scheduling mathematical model based on flight task information, airport personnel information, the required quantity of airport personnel, the time window offset time of each event scene occurrence and a constraint rule set; and constructing a multi-objective optimization function, solving a scheduling model based on the multi-objective optimization function, and determining an optimal airport personnel scheduling scheme under the condition of occurrence of an event scene. The influence of the time uncertainty of the random scene task is considered, so that a robust scheduling scheme adapting to different scenes can be generated.
Description
Technical Field
The application relates to the technical field of airport check-in personnel scheduling, in particular to an airport check-in personnel scheduling method, device, equipment and medium based on a random event scene.
Background
The airline has specified minimum and target crew numbers for each flight that are required over different time periods. The airport has a full-time staff to make detailed scheduling plans of staff scheduling in advance 1-2 days, and revising is carried out according to the next-day flight schedule and the abnormal flight condition. Typically, instead of making a new schedule every cycle, the staff schedule is modified on the same day of the week, which deterministic staff scheduling is possible if flights can be scheduled exactly, however once a flight delay occurs, the staff can take two actions: (1) The task allocation of the check-in personnel is adjusted in real time according to the latest flight information, so that the working intensity of a dispatcher is greatly increased; (2) The original scheduling plan is kept unchanged, and the time value machine staff may be in an idle state, so that the manpower resource is wasted, and the fixed rest time of the staff cannot be ensured. In order to reduce the workload of the dispatcher and improve the utilization rate of human resources. Therefore, how to implement the scheduling of airport check-in personnel for a random event scenario that takes into account time uncertainty becomes a non-trivial technical problem.
Disclosure of Invention
In view of the above, the present application aims to provide a random event scene-based airport personnel scheduling method, apparatus, device and medium, wherein an airport personnel scheduling mathematical model guarantees balance between rest time and dining time of airport personnel and risk cost of an optimal scheduling scheme through a multi-objective optimization function, so that a robust scheduling scheme adapting to different scenes can be generated by considering the influence of random scene task time uncertainty, which is helpful for reasonably utilizing human resources, controlling random risk cost, and improving staff work satisfaction and airport service level.
The embodiment of the application provides an airport check-in personnel scheduling method based on a random event scene, which comprises the following steps of:
Based on the departure basic data of the target airport, predicting the airport personnel demand quantity of the target airport in each time period of the next day;
Inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model, and predicting the occurrence probability and the on-boarding time window offset time of each event scene of the target airport on the next day; wherein the structural features are features reflecting the scene and time of the event that occurs;
Constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and the constraint rule set of the target airport;
constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and Z-type fuzzy sets of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining an optimal airport personnel scheduling scheme of the target airport under the condition of occurrence of different event scenes the next day.
In one possible implementation, the event occurrence probability model is trained by:
constructing the construction features based on a time window;
Based on the historical flight data of the target airport, determining the historical occurrence probability of each event scene and the historical on-boarding time window offset time;
inputting the construction features and the historical flight data of the target airport into a machine learning model, and predicting the predicted occurrence probability and the predicted arrival time window offset time of each event scene;
And performing iterative training on the machine learning model based on the predicted occurrence probability, the predicted machine time window offset time, the historical occurrence probability and the historical machine time window offset time, and determining the event occurrence probability model.
In one possible implementation manner, the flight task information includes a flight task start time, an boarding area where the flight task is located, the required number of airport boarding personnel required by the flight task, and the qualification level of the airport boarding personnel required by the flight task;
the airport check-in personnel information comprises the qualification grade of the airport check-in personnel and the working time of the airport check-in personnel.
In one possible implementation manner, the constructing an airport personnel scheduling mathematical model based on the flight task information, airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and the constraint rule set of the target airport includes:
Constructing an airport personnel scheduling mathematical model based on the flight task information of the target airport, the airport personnel information, the airport personnel demand quantity, the time window offset time of each event scene occurrence, and the flight task allocation constraint, the flight task demand constraint, the dining rule constraint, the rest rule constraint and the time conflict constraint in the constraint rule set;
The method comprises the steps that a flight task allocation constraint is that each flight task can only be allocated once, a flight task demand constraint is that airport check-in personnel demand of each flight task is met in an event scene, a meal time of each airport check-in personnel is constrained to be a plurality of minutes, a rest rule constraint is that each airport check-in personnel has two rest times before and after the meal, and a time conflict constraint is that the airport check-in personnel is allocated to the flight task in an event scene in an attendance time period, a non-rest time period and a meal time period.
In one possible implementation, the Z-type fuzzy set of each of the event scenes is determined by:
determining a risk cost expected value corresponding to each event scene in the Z-type model set based on the event risk cost value corresponding to each event scene and the risk occupation ratio weight value;
Determining standard deviation of risk cost corresponding to each event scene in the Z-type fuzzy set based on the expected value of risk cost corresponding to each event scene, the event risk cost corresponding to each event scene and the risk duty ratio weight value;
And determining the inflection point of the Z-type fuzzy set of each event scene based on the standard deviation of the risk cost corresponding to each event scene and the expected value of the risk cost corresponding to each event scene.
In one possible implementation, the multi-objective optimization function is determined by the following formula, including:
Wherein, Optimizing functions for multiple objectives,/>Is a parameter of risk aversion degree, and is a positive number [0, 1],/>Is the number of event scenes s,/>For the probability of occurrence of event scene s,/>Penalty value for flight task j,/>For the number of human shortages in event scenarios,/>For the qualification grade of airport personnel i under the aviation task j,/>Representing the assignment of airport check-in personnel i flight tasks j,/>, under the nth event scenePenalty value corresponding to dining start time h of airport personnel i in nth event scene,/>For airport check-in personnel in the nth event scene working time t is meal start time, if yes,/>Equal to 1 if no/>Equal to 0,/>Centralizing event scenario/>, for Z-type ambiguityInflection point/>Centralizing event scenarios for Z-type ambiguityRisk cost of/>Slope of event scene s in the Z-type model,/>For a flight task set,/>The airport check-in personnel set is an airport check-in personnel working time set.
In one possible implementation manner, the predicting the airport check-in personnel demand number of the target airport in each period of the next day based on the departure basic data of the target airport includes:
Fitting out a next airport check-in personnel demand prediction function based on historical check-out flight data, historical check-in personnel data and a next day check-out flight schedule in the departure basic data of the target airport;
and predicting the airport personnel demand quantity of the target airport in each period of the next day based on the airport personnel demand prediction function.
The embodiment of the application also provides an airport check-in personnel scheduling device based on the random event scene, which comprises:
the personnel demand prediction module is used for predicting the airport check-in personnel demand quantity of the target airport in each time period of the next day based on the departure basic data of the target airport;
The event scene occurrence probability prediction module is used for inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model to predict the occurrence probability and the on-boarding time window offset time of each event scene of the target airport in the next day; wherein the structural features are features reflecting the scene and time of the event that occurs;
The model construction module is used for constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel demand quantity, the time window offset time of each event scene occurrence and the constraint rule set of the target airport;
The scheme generation module is used for constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and the Z-shaped fuzzy set of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining the optimal airport personnel scheduling scheme of the target airport under the condition of different event scenes in the next day.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory are communicated through the bus when the electronic device runs, and the machine-readable instructions are executed by the processor to execute the steps of the airport personnel scheduling method based on the random event scene.
The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to execute the steps of the airport personnel scheduling method based on the random event scene.
The airport check-in personnel scheduling method, device, equipment and medium based on the random event scene provided by the embodiment of the application comprise the following steps: based on the departure basic data of the target airport, predicting the airport personnel demand quantity of the target airport in each time period of the next day; inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model, and predicting the occurrence probability and the on-boarding time window offset time of each event scene of the target airport on the next day; wherein the structural features are features reflecting the scene and time of the event that occurs; constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and the constraint rule set of the target airport; constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and Z-type fuzzy sets of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining an optimal airport personnel scheduling scheme of the target airport under the condition of occurrence of different event scenes the next day. The beneficial effect of this scheme is: the airport check-in personnel scheduling mathematical model ensures balance of rest time and dining time of airport check-in personnel and risk cost of an optimal scheduling scheme through a multi-objective optimization function, realizes that a robust scheduling scheme adapting to different scenes can be generated by considering the influence of time uncertainty of random scene tasks, and is beneficial to reasonably utilizing human resources, controlling random risk cost and improving staff work satisfaction and airport service level.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an airport check-in personnel scheduling method based on a random event scenario provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of an airport personnel scheduling device based on a random event scenario according to an embodiment of the present application;
FIG. 3 is a second schematic diagram of an airport personnel scheduling device based on a random event scenario according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment obtained by a person skilled in the art without making any inventive effort falls within the scope of protection of the present application.
First, an application event scenario to which the present application is applicable will be described. The method can be applied to the technical field of airport check-in personnel scheduling.
It has been found that airlines prescribe minimum and target crew numbers for each flight that are required over different time periods. The airport has a full-time staff to make detailed scheduling plans of staff scheduling in advance 1-2 days, and revising is carried out according to the next-day flight schedule and the abnormal flight condition. Typically, instead of making a new schedule every cycle, the staff schedule is modified on the same day of the week, which deterministic staff scheduling is possible if flights can be scheduled exactly, however once a flight delay occurs, the staff can take two actions: (1) The task allocation of the check-in personnel is adjusted in real time according to the latest flight information, so that the working intensity of a dispatcher is greatly increased; (2) The original scheduling plan is kept unchanged, and the time value machine staff may be in an idle state, so that the manpower resource is wasted, and the fixed rest time of the staff cannot be ensured. In order to reduce the workload of the dispatcher and improve the utilization rate of human resources. Therefore, how to implement the scheduling of airport check-in personnel for a random event scenario that takes into account time uncertainty becomes a non-trivial technical problem.
Based on the method, the airport personnel scheduling mathematical model guarantees balance of rest time and dining time of airport personnel and risk cost of an optimal scheduling scheme through a multi-objective optimization function, and the method realizes that a robust scheduling scheme adapting to different scenes can be generated by considering influence of task time uncertainty of the random scene, thereby being beneficial to reasonably utilizing human resources, controlling random risk cost and improving staff work satisfaction and airport service level.
Referring to fig. 1, fig. 1 is a flowchart of an airport personnel scheduling method based on a random event scenario according to an embodiment of the present application. As shown in fig. 1, the airport check-in personnel scheduling method based on a random event scene provided by the embodiment of the application includes:
s101: and predicting the airport personnel demand quantity of the target airport in each time period of the next day based on the departure basic data of the target airport.
In the step, the airport check-in personnel demand quantity of the target airport in each period of the next day is predicted according to the collected departure basic data of the target airport.
In one possible implementation manner, the predicting the airport check-in personnel demand number of the target airport in each period of the next day based on the departure basic data of the target airport includes:
A: and fitting out a next-day airport check-in personnel demand prediction function based on the historical check-out flight data, the historical check-in personnel data and the next-day check-out flight schedule in the departure basic data of the target airport.
Here, the next airport check-in personnel demand prediction function is fitted according to the historical check-out flight data, historical check-in personnel data and the next day check-out flight schedule of the target airport.
B: and predicting the airport personnel demand quantity of the target airport in each period of the next day based on the airport personnel demand prediction function.
Here, the airport personnel demand quantity of the target airport in each period of the next day is predicted according to the airport personnel demand prediction function.
The airport check-in personnel demand quantity can be displayed in the form of a manual demand graph, the abscissa of the manual demand graph is hours, and the ordinate of the manual demand graph is the airport check-in personnel demand quantity.
S102: inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model, and predicting the occurrence probability and the on-boarding time window offset time of each event scene of the target airport on the next day; wherein the structural features are features reflecting the scene and time of the event that occurs.
In the step, real-time flight data and construction features of a target airport are input into a pre-trained event occurrence probability model, and the occurrence probability of each event scene of the target airport on the next day and the off-hook time window offset time are predicted.
Wherein the construction features are features reflecting the event scene and time of occurrence.
The event occurrence probability model is obtained through iterative training of the machine learning model.
Here, the on-hook time window offset time is an offset time difference between the on-hook time and the normal on-hook time in the event scene occurs. The event scenes comprise a large-area flight delay event scene of flights due to weather reasons, a flight time delay event scene of flights caused by traffic control and other scenes.
In one possible implementation, the event occurrence probability model is trained by:
a: the construction features are constructed based on a time window.
B: and determining the historical occurrence probability of each event scene and the historical check-in time window offset time based on the historical flight data of the target airport.
Here, the passenger flow data and the actual departure data of the flights in various scenes of the airport history are collected and analyzed, and a random scene set describing different passenger flows such as large-area delay is establishedThe corresponding historical occurrence probability and the historical machine time window offset time.
C: and inputting the construction features and the historical flight data of the target airport into a machine learning model, and predicting the predicted occurrence probability and the predicted check-in time window offset time of each event scene.
Here, the construction features and the historical flight data of the target airport are input into the machine learning model so that the predicted occurrence probability and predicted arrival time window offset time of each event scene are calculated using the machine learning model.
D: and performing iterative training on the machine learning model based on the predicted occurrence probability, the predicted machine time window offset time, the historical occurrence probability and the historical machine time window offset time, and determining the event occurrence probability model.
The method comprises the steps of taking historical occurrence probability and historical machine time window offset time as labels, carrying out iterative training on a machine learning model together with the predicted occurrence probability and the predicted machine time window offset time, and determining an event occurrence probability model so that the trained event occurrence probability model takes real-time data and constructed features as input, and predicting the momentThe following event scenarios/>Probability of occurrence and time window offset time.
S103: and constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel demand quantity, the time window offset time of each event scene occurrence and the constraint rule set of the target airport.
In the step, an airport personnel scheduling mathematical model is constructed according to flight task information, airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and a constraint rule set of a target airport.
Wherein the flight task information includes a flight task setFlight mission/>Flight mission start time/>Check-in area/>, where flight tasks are locatedAirport check-in personnel demand number/>, required for flight tasksAnd the qualification level of the personnel at the airport.
The flight task information is associated with the time window offset time of the arrival of each event scene, and the time window offset time of the arrival corresponding to different flight tasks is different under the condition of different event scenes.
Here, the airport check-in personnel information includes an airport check-in personnel setCheck-in staff/>Is a level of skill (qualification level) that is downward compatible, and airport crews with high levels may engage in low levels, otherwise not.
In one possible implementation, the flight mission information and the airport check-in personnel information include:
the flight task information comprises flight task starting time, a check-in area where the flight task is located, the required number of airport check-in personnel required by the flight task and the qualification grade of the airport check-in personnel required by the flight task; the airport check-in personnel information comprises the qualification grade of the airport check-in personnel and the working time of the airport check-in personnel.
In one possible implementation manner, the constructing an airport personnel scheduling mathematical model based on the flight task information, airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and the constraint rule set of the target airport includes:
And constructing the airport personnel scheduling mathematical model based on the flight task information of the target airport, the airport personnel information, the airport personnel demand quantity, the time window offset time of each event scene occurrence, and the flight task allocation constraint, the flight task demand constraint, the dining rule constraint, the rest rule constraint and the time conflict constraint in the constraint rule set.
The airport personnel scheduling mathematical model is constructed according to the flight task information, airport personnel information, the number of airport personnel demands, the time window offset time of each event scene occurrence, and the flight task allocation constraint, the flight task demand constraint, the dining rule constraint, the rest rule constraint and the time conflict constraint in the constraint rule set of the airport personnel scheduling mathematical model.
The method comprises the steps that a flight task allocation constraint is that each flight task can only be allocated once, a flight task demand constraint is that airport check-in personnel demand of each flight task is met in an event scene, a meal time of each airport check-in personnel is constrained to be a plurality of minutes, a rest rule constraint is that each airport check-in personnel has two rest times before and after the meal, and a time conflict constraint is that the airport check-in personnel is allocated to the flight task in an event scene in an attendance time period, a non-rest time period and a meal time period.
Here, the task allocation constraint is a schedule in which each flight task can be allocated only once to airport check-in personnel, and the expression of the task allocation constraint is:
Wherein the decision variables Representing allocation airport personnel/>For flight tasks/>。
Here, the task demand is constrained to be in an event scenarioThe express of task demand constraint is as follows:
Wherein, Representing full qualification,/>Representing partial qualification level,/>For shortage of people,/>Assigning airport check-in personnel i to flight tasks j,/>, under event scene sFor the number of human shortages in event scenarios,/>Airport check-in personnel demand number for flight task j.
Here, the meal rules are constrained to constrain the time of a meal for airport check-in personnel to beMinutes, meal start time/>In time period/>In, wherein/>The time of the check-in and check-out of the check-in personnel is respectively.
Here, the rest rules are constrained to constrain the first rest time of each employeeFor the second resting time, before and after dining, each resting time is at least separated from the dining time and the working and discharging time/>, respectivelyMinutes,/>For all times less than t,/>For the first rest time/>, corresponding to airport check-in personnel i, in event scene sTime before/>，/>For the time/>, before the meal start time h, corresponding to airport check-in personnel i, in the event scene sThe expression of the rest rule constraint is:
Wherein the time conflict constraint is to ensure that airport crews can only be assigned to tasks during attendance periods and not rest and dining periods in event scenario s, wherein Is an employee scheduling plan, if airport check-in personnel i is in attendance at time period t, then/>Otherwise, the constraint ensures that the task execution time, the meal time and the rest time allocated to the staff are all in the range of the staff working time and mutually exclusive, and the time conflict constraint expression is as follows:
The airport check-in personnel scheduling mathematical model is composed of the expression of the constraint rules, flight task information, airport check-in personnel information, the number of airport check-in personnel requirements and check-in time window offset time of each event scene.
S104: constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and Z-type fuzzy sets of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining an optimal airport personnel scheduling scheme of the target airport under the condition of occurrence of different event scenes the next day.
In the step, a multi-objective optimization function is constructed according to the occurrence probability of each event scene, airport personnel information and the Z-type fuzzy set of each event scene, the airport personnel scheduling mathematical model is solved according to the multi-objective optimization function, and the optimal airport personnel scheduling scheme of the next-day target airport under the condition of different event scenes is determined.
The optimal airport personnel scheduling scheme comprises the following steps: other information such as the number of airport check-in personnel required by each post area in each period, the working time period of each airport check-in personnel, check-in area allocation, rest time, dining time and the like.
In the scheme, a multi-objective optimization function is constructed with the aim of minimizing the shortage of demands, unreasonable rest time, maximizing the skill matching degree of staff and minimizing the risk cost of multi-scene random scenes.
In one possible implementation, the Z-type fuzzy set of each of the event scenes is determined by:
(1): and determining a risk cost expected value corresponding to each event scene in the Z-type model set based on the event risk cost value corresponding to each event scene and the risk ratio weight value.
Here, the risk cost price expectation value is determined by the following formula:
Wherein, For the event risk cost value corresponding to the event scene,/>For the corresponding risk duty weight value of event scene,/>The cost of risk for the event scenario is expected.
(2): And determining the standard deviation of the risk cost corresponding to each event scene in the Z-type fuzzy set based on the expected value of the risk cost corresponding to each event scene, the event risk cost corresponding to each event scene and the risk duty ratio weight value.
Here, the standard deviation of the risk cost corresponding to the event scenario is determined by the following formula:
Wherein, Standard deviation of risk cost for event scenario correspondence,/>For the event risk cost value corresponding to the event scene,/>And a risk duty ratio weight value corresponding to the event scene.
(3): And determining the inflection point of the Z-type fuzzy set of each event scene based on the standard deviation of the risk cost corresponding to each event scene and the expected value of the risk cost corresponding to each event scene.
Here, the inflection point of the Z-type ambiguity set of the event scene is determined by the following formula:
Wherein, For the inflection point of the Z-type fuzzy set of the event scene, k is the Z-type fuzzy set parameter, taken to be 1.645, corresponding to a confidence level of 95%.
In one possible implementation, the multi-objective optimization function is determined by the following formula, including:
Wherein, Optimizing functions for multiple objectives,/>Is a parameter of risk aversion degree, and is a positive number [0, 1],/>Is the number of event scenes s,/>For the probability of occurrence of event scene s,/>Penalty value for flight task j,/>For the number of human shortages in event scenarios,/>For the qualification grade of airport personnel i under the aviation task j,/>Representing the assignment of airport check-in personnel i flight tasks j,/>, under the nth event scenePenalty value corresponding to dining start time h of airport personnel i in nth event scene,/>For airport check-in personnel in the nth event scene working time t is meal start time, if yes,/>Equal to 1 if no/>Equal to 0,/>Centralizing event scenario/>, for Z-type ambiguityInflection point/>Centralizing event scenarios for Z-type ambiguityRisk cost of/>Slope of event scene s in the Z-type model,/>For a flight task set,/>The airport check-in personnel set is an airport check-in personnel working time set.
Here, the multiple objective function first part includes minimizing the weighted sum of shortages between overall staff (whether fully qualified or partially qualified) and target demand, assigning the cost of partially qualified staff and the non-ideal meal rest start time, and the second part builds FCVaR (fuzzy condition risk), which, taken into the optimization solution objective function, can more fully consider the influence of future uncertainty scene factors on the scheduling decisions, contributing to an improved risk management level of the scheduling decisions, making the scheduling more robust and adaptive, especially in extremely high risk situations, reducing the decision burden of the scheduling planner when the task time is disturbed.
And converting the mathematical expression of the constraint rule set and the mathematical expression of the multi-objective optimization function into an integer programming form, and establishing an integer programming model. And using Gurobi to solve the model to obtain a result of the self-adaptive stochastic programming. The result considers the optimal personnel number arrangement plan of the occurrence probability of different scenes in the future, the plan comprises information such as personnel number required by each post area in each period, working time period of each personnel, personnel area allocation, rest time, dining time and the like, and the output result is displayed in a chart form and can be connected with other systems through standard interfaces to realize direct acquisition and application of the result.
According to the method, the effect of uncertainty of task time of the random scene is fully considered, a robust scheduling scheme adapting to different scenes can be generated, and the method has remarkable advantages compared with the traditional fixed scheduling scheme. The invention provides scientific and effective support for scheduling decisions in complex random scenes, is beneficial to reasonably utilizing human resources, controlling random risk cost and improving staff work satisfaction and airport service level.
The airport check-in personnel scheduling method based on the random event scene provided by the embodiment of the application comprises the following steps: based on the departure basic data of the target airport, predicting the airport personnel demand quantity of the target airport in each time period of the next day; inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model, and predicting the occurrence probability and the on-boarding time window offset time of each event scene of the target airport on the next day; wherein the structural features are features reflecting the scene and time of the event that occurs; constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and the constraint rule set of the target airport; constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and Z-type fuzzy sets of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining an optimal airport personnel scheduling scheme of the target airport under the condition of occurrence of different event scenes the next day. The airport check-in personnel scheduling mathematical model ensures balance of rest time and dining time of airport check-in personnel and risk cost of an optimal scheduling scheme through a multi-objective optimization function, realizes that a robust scheduling scheme adapting to different scenes can be generated by considering the influence of time uncertainty of random scene tasks, and is beneficial to reasonably utilizing human resources, controlling random risk cost and improving staff work satisfaction and airport service level.
Referring to fig. 2 and 3, fig. 2 is a schematic structural diagram of an airport personnel scheduling device based on a random event scenario according to an embodiment of the present application; fig. 3 is a second schematic structural diagram of an airport personnel scheduling device based on a random event scenario according to an embodiment of the present application. As shown in fig. 2, the airport personnel scheduling apparatus 200 based on a random event scenario includes:
The personnel demand prediction module 210 is configured to predict the airport personnel demand number of the target airport in each period of the next day based on the departure basic data of the target airport;
The event scene occurrence probability prediction module 220 is configured to input real-time flight data and construction features of the target airport into an event occurrence probability model, and predict probability of occurrence of each event scene of the target airport and on-board time window offset time the next day; wherein the structural features are features reflecting the scene and time of the event that occurs;
The model building module 230 is configured to build an airport personnel scheduling mathematical model based on the flight task information, airport personnel information, the number of airport personnel requirements, the time window offset time of each event scenario, and the constraint rule set of the target airport;
the scheme generating module 240 is configured to construct a multi-objective optimization function based on the probability of occurrence of each event scene, airport personnel information and the Z-type fuzzy set of each event scene, solve the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determine an optimal airport personnel scheduling scheme of the target airport under the condition of occurrence of different event scenes in the next day.
Further, as shown in fig. 3, the airport personnel scheduling device based on the random event scene further comprises a model training module 250, and the model training module 250 trains the event occurrence probability model by the following steps:
constructing the construction features based on a time window;
Based on the historical flight data of the target airport, determining the historical occurrence probability of each event scene and the historical on-boarding time window offset time;
inputting the construction features and the historical flight data of the target airport into a machine learning model, and predicting the predicted occurrence probability and the predicted arrival time window offset time of each event scene;
And performing iterative training on the machine learning model based on the predicted occurrence probability, the predicted machine time window offset time, the historical occurrence probability and the historical machine time window offset time, and determining the event occurrence probability model.
Further, the scenario generation module 240 determines the Z-type fuzzy sets of each of the event scenarios by:
determining a risk cost expected value corresponding to each event scene in the Z-type model set based on the event risk cost value corresponding to each event scene and the risk occupation ratio weight value;
Determining standard deviation of risk cost corresponding to each event scene in the Z-type fuzzy set based on the expected value of risk cost corresponding to each event scene, the event risk cost corresponding to each event scene and the risk duty ratio weight value;
And determining the inflection point of the Z-type fuzzy set of each event scene based on the standard deviation of the risk cost corresponding to each event scene and the expected value of the risk cost corresponding to each event scene.
Further, the scenario generation module 240 determines the multi-objective optimization function by the following formula:
Wherein, Optimizing functions for multiple objectives,/>Is a parameter of risk aversion degree, and is a positive number [0, 1],/>Is the number of event scenes s,/>For the probability of occurrence of event scene s,/>Penalty value for flight task j,/>For the number of human shortages in event scenarios,/>For the qualification grade of airport personnel i under the aviation task j,/>Representing the assignment of airport check-in personnel i flight tasks j,/>, under the nth event scenePenalty value corresponding to dining start time h of airport personnel i in nth event scene,/>For airport check-in personnel in the nth event scene working time t is meal start time, if yes,/>Equal to 1 if no/>Equal to 0,/>Centralizing event scenario/>, for Z-type ambiguityInflection point/>Centralizing event scenarios for Z-type ambiguityRisk cost of/>Slope of event scene s in the Z-type model,/>For a flight task set,/>The airport check-in personnel set is an airport check-in personnel working time set.
Further, when the scheme generating module 240 is used for predicting the airport check-in personnel requirement number of the target airport in each period of the next day based on the departure basic data of the target airport, the scheme generating module 240 is specifically configured to:
Fitting out a next airport check-in personnel demand prediction function based on historical check-out flight data, historical check-in personnel data and a next day check-out flight schedule in the departure basic data of the target airport;
and predicting the airport personnel demand quantity of the target airport in each period of the next day based on the airport personnel demand prediction function.
The airport check-in personnel scheduling device based on the random event scene provided by the embodiment of the application comprises: the personnel demand prediction module is used for predicting the airport check-in personnel demand quantity of the target airport in each time period of the next day based on the departure basic data of the target airport; the event scene occurrence probability prediction module is used for inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model to predict the occurrence probability and the on-boarding time window offset time of each event scene of the target airport in the next day; wherein the structural features are features reflecting the scene and time of the event that occurs; the model construction module is used for constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel demand quantity, the time window offset time of each event scene occurrence and the constraint rule set of the target airport; the scheme generation module is used for constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and the Z-shaped fuzzy set of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining the optimal airport personnel scheduling scheme of the target airport under the condition of different event scenes in the next day. The airport check-in personnel scheduling mathematical model ensures balance of rest time and dining time of airport check-in personnel and risk cost of an optimal scheduling scheme through a multi-objective optimization function, realizes that a robust scheduling scheme adapting to different scenes can be generated by considering the influence of time uncertainty of random scene tasks, and is beneficial to reasonably utilizing human resources, controlling random risk cost and improving staff work satisfaction and airport service level.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the application. As shown in fig. 4, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.
The memory 420 stores machine-readable instructions executable by the processor 410, when the electronic device 400 is running, the processor 410 communicates with the memory 420 through the bus 430, and when the machine-readable instructions are executed by the processor 410, the steps of the airport personnel scheduling method based on the random event scenario in the method embodiment shown in fig. 1 can be executed, and the specific implementation can refer to the method embodiment and will not be described herein.
The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the airport personnel scheduling method based on a random event scenario in the method embodiment shown in fig. 1 may be executed, and a specific implementation manner may refer to the method embodiment and will not be described herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. The airport check-in personnel scheduling method based on the random event scene is characterized by comprising the following steps of:
Based on the departure basic data of the target airport, predicting the airport personnel demand quantity of the target airport in each time period of the next day;
Inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model, and predicting the occurrence probability and the on-boarding time window offset time of each event scene of the target airport on the next day; wherein the structural features are features reflecting the scene and time of the event that occurs;
Constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel information, the number of airport personnel requirements, the time window offset time of each event scene occurrence and the constraint rule set of the target airport;
constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and Z-type fuzzy sets of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining an optimal airport personnel scheduling scheme of the target airport under the condition of occurrence of different event scenes the next day.
2. The random event scenario-based airport personnel scheduling method of claim 1, wherein the event occurrence probability model is trained by:
constructing the construction features based on a time window;
Based on the historical flight data of the target airport, determining the historical occurrence probability of each event scene and the historical on-boarding time window offset time;
inputting the construction features and the historical flight data of the target airport into a machine learning model, and predicting the predicted occurrence probability and the predicted arrival time window offset time of each event scene;
And performing iterative training on the machine learning model based on the predicted occurrence probability, the predicted machine time window offset time, the historical occurrence probability and the historical machine time window offset time, and determining the event occurrence probability model.
3. The airport check-in personnel scheduling method based on the random event scene as claimed in claim 1, wherein the flight task information comprises a flight task start time, a check-in area where a flight task is located, the number of airport check-in personnel required by the flight task and the qualification level of the airport check-in personnel required by the flight task;
the airport check-in personnel information comprises the qualification grade of the airport check-in personnel and the working time of the airport check-in personnel.
4. The airport personnel scheduling method based on random event scenes according to claim 1, wherein the constructing an airport personnel scheduling mathematical model based on the flight task information, airport personnel information, the number of airport personnel needs, the time window offset time of each event scene occurrence and the constraint rule set of the target airport comprises:
Constructing an airport personnel scheduling mathematical model based on the flight task information of the target airport, the airport personnel information, the airport personnel demand quantity, the time window offset time of each event scene occurrence, and the flight task allocation constraint, the flight task demand constraint, the dining rule constraint, the rest rule constraint and the time conflict constraint in the constraint rule set;
The method comprises the steps that a flight task allocation constraint is that each flight task can only be allocated once, a flight task demand constraint is that airport check-in personnel demand of each flight task is met in an event scene, a meal time of each airport check-in personnel is constrained to be a plurality of minutes, a rest rule constraint is that each airport check-in personnel has two rest times before and after the meal, and a time conflict constraint is that the airport check-in personnel is allocated to the flight task in an event scene in an attendance time period, a non-rest time period and a meal time period.
5. The random event scenario-based airport personnel scheduling method of claim 1, wherein the Z-type fuzzy sets for each of the event scenarios are determined by:
determining a risk cost expected value corresponding to each event scene in the Z-type model set based on the event risk cost value corresponding to each event scene and the risk occupation ratio weight value;
Determining standard deviation of risk cost corresponding to each event scene in the Z-type fuzzy set based on the expected value of risk cost corresponding to each event scene, the event risk cost corresponding to each event scene and the risk duty ratio weight value;
And determining the inflection point of the Z-type fuzzy set of each event scene based on the standard deviation of the risk cost corresponding to each event scene and the expected value of the risk cost corresponding to each event scene.
6. The random event scenario-based airport personnel scheduling method of claim 1, wherein determining the multi-objective optimization function by the following formula comprises:
Wherein, Optimizing functions for multiple objectives,/>Is a parameter of risk aversion degree, and is a positive number [0, 1],/>Is the number of event scenes s,/>For the probability of occurrence of event scene s,/>Penalty value for flight task j,/>For the number of human shortages in event scenarios,/>For the qualification grade of airport personnel i under the aviation task j,/>Representing the assignment of airport check-in personnel i flight tasks j,/>, under the nth event scenePenalty value corresponding to dining start time h of airport personnel i in nth event scene,/>For airport check-in personnel in the nth event scene working time t is meal start time, if yes,/>Equal to 1 if no/>Equal to 0,/>Centralizing event scenario/>, for Z-type ambiguityInflection point/>Centralizing event scenario/>, for Z-type ambiguityRisk cost of/>Slope of event scene s in the Z-type model,/>For a flight task set,/>The airport check-in personnel set is an airport check-in personnel working time set.
7. The airport personnel scheduling method based on random event scene as set forth in claim 1, wherein predicting the airport personnel demand of the target airport at each time of the next day based on the departure basic data of the target airport comprises:
Fitting out a next airport check-in personnel demand prediction function based on historical check-out flight data, historical check-in personnel data and a next day check-out flight schedule in the departure basic data of the target airport;
and predicting the airport personnel demand quantity of the target airport in each period of the next day based on the airport personnel demand prediction function.
8. An airport check-in personnel scheduling device based on a random event scene, which is characterized by comprising:
the personnel demand prediction module is used for predicting the airport check-in personnel demand quantity of the target airport in each time period of the next day based on the departure basic data of the target airport;
The event scene occurrence probability prediction module is used for inputting the real-time flight data and the construction features of the target airport into an event occurrence probability model to predict the occurrence probability and the on-boarding time window offset time of each event scene of the target airport in the next day; wherein the structural features are features reflecting the scene and time of the event that occurs;
The model construction module is used for constructing an airport personnel scheduling mathematical model based on the flight task information, the airport personnel demand quantity, the time window offset time of each event scene occurrence and the constraint rule set of the target airport;
The scheme generation module is used for constructing a multi-objective optimization function based on the occurrence probability of each event scene, airport personnel information and the Z-shaped fuzzy set of each event scene, solving the airport personnel scheduling mathematical model based on the multi-objective optimization function, and determining the optimal airport personnel scheduling scheme of the target airport under the condition of different event scenes in the next day.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via said bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the random event scenario based airport personnel scheduling method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the random event scenario based airport personnel scheduling method according to any of claims 1 to 7.
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