CN115438930A - Deicing guarantee resource cooperative scheduling method in deicing operation mode and storage medium - Google Patents
Deicing guarantee resource cooperative scheduling method in deicing operation mode and storage medium Download PDFInfo
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Abstract
The invention provides a deicing guarantee resource cooperative scheduling method and a storage medium under a deicing operation mode, wherein the method comprises the following steps: constructing a flight deicing guarantee resource cooperative scheduling mechanism; constructing a flight deicing guarantee resource collaborative optimization scheduling model based on the flight deicing guarantee resource collaborative scheduling mechanism; designing a solving algorithm of the flight deicing guarantee resource collaborative optimization scheduling model; and acquiring a deicing guarantee resource cooperative scheduling result of the target airport based on guarantee resources of the target airport. By adopting the scheme, the aim of improving the operation efficiency of the airport scene on the whole can be fulfilled, and support can be provided for a controller to make a relevant decision.
Description
Technical Field
The embodiment of the invention relates to the technical field of air traffic control decision-making, in particular to a deicing guarantee resource cooperative scheduling method in a deicing operation mode and a storage medium.
Background
With the improvement of living standard of people, airplanes become more popular as transportation means for traveling. The special weather near the airport is one of the main reasons for flight delay, and particularly when the weather near the airport is ice and snow, special guarantee is needed for flight operation.
In recent years, for ice and snow weather, related scholars gradually explore a large-area flight delay early warning and emergency response mechanism mainly based on an air traffic control unit and a flight plan adjusting mechanism mainly based on an airport, and remarkable effects are achieved and are gradually perfected. However, from the perspective of guaranteeing flights and serving passengers, flow limitation and flight plan adjustment can only be passive and temporary relief measures, and basically, air traffic control units and various guarantee bodies of airports are required to continuously improve ice and snow weather guarantee capability, so that a flight scene operation scheduling method guided by flight operation requirements is perfected, a flight guarantee resource supplement and optimized scheduling mechanism guided by the flight guarantee requirements is constructed, the conditions of flow limitation and flight plan adjustment are reduced and even cancelled, and the convenient, efficient and comfortable travel requirements of mass passengers are met.
The deicing guarantee resource allocation is a classic scheduling problem with more tasks and less resources, in the scheduling problem of the deicing vehicle, the deicing requirement of a flight is a task, the deicing vehicle is an available resource, the assignment of the deicing vehicle is required to be carried out on the basis of meeting constraint conditions, the assignment process of the deicing vehicle is optimized, and the operation space-time network of the deicing vehicle is optimized while the flight deicing task is completed.
At present, related scholars develop certain preliminary studies on the aspect of flight deicing plateau assignment under the ice and snow weather condition of an airport and on the aspect of flight guarantee resource scheduling under the deicing mode, and obtain preliminary research results.
It should be noted, however, that the current research has the following disadvantages: the flight guarantee resource scheduling problem and the flight operation resource scheduling problem have strong relevance, the flight guarantee resource scheduling can achieve scientific, reasonable and feasible implementation effects on the basis of optimized scheduling of flight operation resources (runways, taxiways and parking spaces), systematic integrated optimized scheduling related to various flight operation resources is rarely researched in relation to flight operation scheduling in a deicing mode, a collaborative optimized scheduling mechanism and method of various flight guarantee resources are developed in relation to research on rarely-existing flight operation scheduling in ice and snow weather conditions, and when ice and snow weather conditions occur near airports, optimal flight operation scheduling results and flight guarantee resource scheduling results cannot be timely and efficiently obtained.
Therefore, it is necessary to provide a method for collaboratively scheduling deicing guarantee resources in a deicing operation mode, which can grasp the correlation between flight deicing guarantee resources and flight operation resource scheduling problems based on the operation practice of an airport.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method for cooperatively scheduling ice removal guarantee resources in an ice removal operation mode and a storage medium.
The embodiment of the invention provides a deicing guarantee resource cooperative scheduling method in a deicing operation mode, which comprises the following steps:
constructing a flight deicing guarantee resource cooperative scheduling mechanism;
constructing a flight deicing guarantee resource collaborative optimization scheduling model based on the flight deicing guarantee resource collaborative scheduling mechanism;
designing a solving algorithm of the flight deicing guarantee resource collaborative optimization scheduling model;
and acquiring a deicing guarantee resource cooperative scheduling result of the target airport based on guarantee resources of the target airport.
Optionally, the flight deicing guarantee resource cooperative scheduling mechanism specifically includes:
the comprehensive optimization scheduling process of the airport scene under the deicing operation mode is divided into two stages, wherein:
in the first stage, the scheduling of flight scene operation resources is optimized, and the deicing task of the departing flight is obtained;
and in the second stage, the assignment process of the deicing guarantee resources is optimized and scheduled, and the deicing guarantee resources are assigned to complete the deicing task of the off-site flight, and simultaneously, the space-time network of the operation of the deicing guarantee resource scene is optimized.
Optionally, the constructing a flight deicing guarantee resource collaborative optimization scheduling model specifically includes:
setting input parameters of a flight deicing guarantee resource collaborative optimization scheduling model;
setting a decision variable of a flight deicing guarantee resource collaborative optimization scheduling model;
setting constraint conditions of a flight deicing guarantee resource collaborative optimization scheduling model;
and setting an objective function of the flight deicing guarantee resource collaborative optimization scheduling model.
Optionally, the decision variables include:
deicing ensures whether resources are supplemented with deicing fluid.
Optionally, the constraint condition includes:
task assignment constraints;
deicing guarantees resource scheduling time constraints;
and limiting and restricting the capacity of the deicing fluid.
Optionally, the task assignment constraint comprises:
limiting the amount of deicing guarantee resources for any deicing task;
limiting the sequencing between any two consecutive deicing tasks;
limiting the starting and ending points of each de-icing support resource.
Optionally, the deicing guarantee resource scheduling time constraint includes:
when a first continuous deicing task and a second continuous deicing task are executed, if the deicing guarantee resource does not return to a parking position to supplement deicing fluid after the first deicing task is executed, the time interval between the deicing guarantee resource and the deicing guarantee resource for starting to execute the second deicing task and the first deicing task is greater than the sum of the time of the deicing guarantee resource for serving the first deicing task and the time of the deicing guarantee resource from the position of the first deicing task to the position of the second deicing task;
if the deicing guarantee resource returns to the parking position to supplement the deicing fluid after the first deicing task is executed, the time interval between the second deicing task and the first deicing task, which is started by the deicing guarantee resource, is greater than the sum of the time for the deicing guarantee resource to serve the first deicing task, the time for the deicing guarantee resource to travel from the position of the first deicing task to the position of the second deicing task, and the time for the deicing guarantee resource to be filled with the deicing fluid.
Optionally, the deicing fluid capacity limitation constraint comprises:
when a first continuous deicing task and a second continuous deicing task are executed, if the deicing guarantee resource does not return to a parking position to supplement deicing fluid after the first deicing task is executed, the residual deicing fluid amount of the deicing guarantee resource is larger than the required amount of the second deicing task;
and if the deicing guarantee resource returns to the parking position to supplement the deicing fluid after the first deicing task is executed, the residual quantity of the deicing fluid meets the preset deicing fluid capacity limit requirement when the deicing guarantee resource executes the second deicing task.
Optionally, the objective function includes:
and the sum of all deicing guarantee resource operation cycles of the target airport is minimum.
The embodiment of the invention provides a storage medium which stores a computer program or an instruction, and when the computer program or the instruction is executed, the method for cooperatively scheduling deicing guarantee resources in the deicing operation mode is realized.
By adopting the deicing guarantee resource cooperative scheduling method in the deicing operation mode in the embodiment of the invention, based on the relevant data provided by the airport cooperative decision making system, on the premise that the flight scheduling result is known, a scene cooperative scheduling model in the deicing operation mode is established, a Cplex solver is used for solving, the minimum time of all flight deicing operations is taken as an optimization target to perform cooperative scheduling on the airport scene operation in the deicing operation, so that the aim of integrally improving the airport scene operation efficiency can be fulfilled, and support can be provided for a controller to make relevant decisions.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating steps of a deicing guarantee resource co-scheduling method in a deicing operation mode according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a scheduling flow of a flight deicing guarantee resource scheduling mechanism according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a step of constructing a flight deicing guarantee resource optimization scheduling model according to an embodiment of the present invention.
Detailed Description
As described in the background art, in the prior art, researches related to flight operation scheduling in a deicing mode are rarely systematic integrated optimization scheduling related to various flight operation resources, and researches related to a collaborative optimization scheduling mechanism and method for multi-type flight guarantee resources under ice and snow weather conditions are rarely developed, so that when ice and snow weather conditions occur near airports, optimal flight operation scheduling results and flight guarantee resource scheduling results cannot be timely and efficiently obtained.
In order to solve the above problems, embodiments of the present invention provide a deicing guarantee resource cooperative scheduling method in a deicing operation mode, which can implement off-site flight scene operation in a winter deicing operation mode and fast scheduling of deicing guarantee resource assignment.
So that those skilled in the art may better understand and practice the embodiments of the present invention, the concepts, schemes, principles, and advantages of the embodiments of the present invention are described in detail below with reference to the accompanying drawings by way of specific application examples.
Referring to fig. 1, an embodiment of the present invention provides a deicing guarantee resource cooperative scheduling method in a deicing operation mode, including the following steps:
s1: constructing a flight deicing guarantee resource cooperative scheduling mechanism;
in the specific implementation, flight deicing guarantee resources are of vital importance in the operation process of an airport, and flight guarantee resource scheduling personnel acquire flight scene operation scheduling time node information from an airport collaborative decision A-CDM system and assign and schedule deicing vehicles in a lump, so that each flight with deicing requirements can be served by the deicing vehicle in time, and the influence of the flight deicing guarantee resource scheduling process on flight operation is minimized.
For each departing flight, the flight scene operation resource scheduling result can give the deicing start time of the flight and the takeoff time of the flight. The flight deicing guarantee resource, namely the cooperative scheduling problem of the deicing vehicle, can be understood as meeting the deicing requirement of the flight as much as possible on the basis of the optimized scheduling result of the flight operation resource, improving the use efficiency of the deicing vehicle as much as possible on the premise of avoiding delay of the flight due to waiting of the deicing guarantee resource, reducing the idle time of the deicing vehicle and the total travel distance of the deicing vehicle, and completing the task of matching the assignment of the deicing vehicle and the deicing requirement of the off-site flight as fast and efficiently as possible. On the basis, the embodiment of the invention provides a cooperative scheduling mechanism of the deicing vehicle.
As a specific example, referring to a scheduling flow diagram of a flight deicing guarantee resource scheduling mechanism shown in fig. 2, an airport scene comprehensive optimization scheduling process in a deicing mode is divided into two stages in the embodiment of the present invention, and in the first stage, the optimized scheduling of flight scene operation resources is completed, and an off-site flight deicing demand task set is generated; in the second stage, the assignment process of the deicing vehicle is optimized and dispatched, the deicing vehicle is assigned to complete the deicing task of the departure flight, and meanwhile, the space-time network of the operation of the deicing vehicle scene is optimized, so that the aim of integrally improving the operation efficiency of the airport scene is fulfilled.
S2: constructing a flight deicing guarantee resource optimization scheduling model, and optimizing an assignment process of deicing guarantee resources and deicing tasks;
in a specific implementation, for de-icing a vehicle, the operation at an airport scene may be described as follows: the full-liquid deicing vehicle is driven out from the deicing vehicle parking place and goes to a deicing position assigned with a deicing task; after completing the deicing task of an airplane, waiting in situ or going to the next deicing station where the deicing task is assigned. The deicing process of the deicing vehicle cannot be interrupted, the deicing vehicle can stop or remove liquid after one flight task is finished, and if the residual deicing liquid of one vehicle is not enough for the next flight task, the deicing vehicle cannot go to the next task point and needs to return to the place where the deicing vehicle is parked to supplement the deicing liquid. Referring to fig. 3, a schematic diagram of steps for constructing a flight deicing guarantee resource optimization scheduling model is shown:
s2.1: setting input parameters of a flight deicing guarantee resource collaborative optimization scheduling model;
specifically, to complete the assignment task of flight deicing guarantee resources, parameters related to the deicing guarantee resources need to be set.
For a set of de-icing vehicles that can be scheduled, for each de-icing vehicle
The following parameters are given: the upper limit W of the capacity of the deicing fluid of the deicing vehicle; default speed k of the de-icing vehicle.
In a planning time period, an off-site flight deicing task set epsilon = { e | e needing to be completed 1 ,e 2 ,e 3 ,…,e |ε| }，e 0 And e | ε + +1 Respectively representing the position of the deicing vehicle from the stop position of the deicing vehicle to the 1 st deicing task and a virtual ending task, wherein the virtual ending task represents that the deicing vehicle finishes all tasks in the whole optimization process, exits the task list and has epsilon + ＝ε∪{e 0 ,e |ε|+1 Are aimed at
Wherein the number of the deicing vehicles required by the e tasks is X e Because the aim of the model is to utilize deicing guarantee resources to the maximum extent and maximize the service efficiency of the deicing vehicle, each deicing vehicle participates in the deicing operation process, and each vehicle has a virtual knotBundle task, so when e = e 0 Shi chi e The number of the ice removing vehicles is the total number,
same reason is e = e |ε|+1 When the temperature of the water is higher than the set temperature,
distance gamma between the positions of the e tasks and the positions of the e' tasks ee' When e = e 0 When, gamma ee' The distance eta between the ice removing vehicle parking position and the position of the e-th task e ', when e = e |ε|+1 When, gamma is ee' Distance, γ, for the end of the virtual mission of the ice-removing vehicle ee' =0; the task completion time required for the e task is phi e When e = e 0 Time phi e The time for the deicing vehicle to travel from the deicing vehicle parking place to the position of the 1 st deicing task is provided; when e = e |ε|+1 While, U e For the deicing vehicle to virtually finish the walking distance of the task, phi e And =0. For the purpose of
The maximum deicing fluid volume of each deicing vehicle required by the e task is psi e The time for the flight of the e task to start occupying the deicing station is Y e 。
S2.2: setting a decision variable of a flight deicing guarantee resource collaborative optimization scheduling model;
in specific implementation, for a flight guarantee resource collaborative optimization scheduling model, assignment variables of deicing tasks and deicing vehicles are considered, decision variables of task execution sequence and walking distance of the deicing vehicles are considered according to the operation characteristics of the scene of the deicing vehicles, interval variables are used in the model to record the change condition of the volume of deicing liquid, and the decision variables of whether the deicing vehicles carry out liquid supplementing or not are increased.
As a specific example, 1. Assume that there is a precedence order for task development, i.e., for
If the vehicle v is executingExecuting the e 'th task after the e' th task is finished
Is 1, otherwise is 0.
2. Determining the time t at which the de-icing vehicle v arrives at the location of the e-th task ve 。
3. Setting interval variables to record the change condition of the deicing fluid aiming at the deicing fluid in the deicing vehicle,
indicating the volume of deicing fluid remaining after the deicing vehicle v has performed the e-th task.
4. If the deicing vehicle v goes to the stopping point of the deicing vehicle to supplement the deicing fluid after the e-th task is executed, then
Otherwise it is 0.
S2.3: setting constraint conditions of a flight deicing guarantee resource collaborative optimization scheduling model;
in a specific implementation, the flight deicing guarantee resource optimization scheduling model mainly comprises deicing task assignment constraints, same vehicle scheduling time interval constraints, flight operation time window constraints and flight safety interval constraints.
As a specific example, the following constraints may be established:
(1) Task assignment constraints
Equation (1) limits any one task e to have and can only have χ e Performing deicing by using a deicing vehicle; equations (2) - (3) restrict the two tasks e and e' performed consecutively to have and can only have one precedence for the same deicing vehicle; the restrictions of the equations (4) - (5) are that for the same deicing vehicle, each deicing vehicle needs to start from the parking position of the deicing vehicle and finally return to the virtual terminal.
(2) De-icing vehicle scheduling time constraints
Equation (6) limits that if two tasks e and e 'are executed by the same deicing vehicle v, the execution sequence of the task e is immediately before the task e', and the deicing vehicle does not return to the deicing vehicle parking place for supplementing deicing fluid after executing the task e, the time interval between the start of the task e 'and the start of the task e by the deicing vehicle v should be greater than the sum of the time of the service e task of the deicing vehicle and the time of the deicing vehicle traveling from the position of the e-th task to the position of the e' -th task. And (7) limiting that if two tasks e and e ' are executed by the same deicing vehicle v, the execution sequence of the task e is immediately before the task e ', the vehicle returns to the deicing vehicle after executing the task e and is parked as the supplementary deicing fluid, and the time interval between the task e ' starting execution of the deicing vehicle v and the task e starting execution of the deicing vehicle v is greater than the time of the task e of the deicing vehicle serviceThe sum of the time of the deicing vehicle from the position of the e-th task to the position of the e' -th task and the time of the deicing vehicle being filled with the deicing fluid. In order to ensure that the flight deicing task is smoothly carried out, the formula (8) limits the time t for the deicing vehicle executing the task e to reach the task position ve Before the flight begins to occupy the deicing stations, unnecessary flight delay caused by untimely scheduling of flight guarantee resources is prevented.
(3) Deicing fluid volume limit constraint
The formula (9) is the deicing fluid capacity limit constraint of the deicing vehicle; the formula (10) limits that after any deicing vehicle executes the task e, if the deicing vehicle does not return to the deicing vehicle parking position to supplement the deicing fluid, the residual quantity of the deicing fluid of the deicing vehicle is larger than the deicing requirement of the deicing vehicle executing the next task e'; and (3) after the deicing vehicle finishes the task e, if the deicing vehicle returns to the parking place of the deicing vehicle for supplementing the deicing fluid, the residual quantity of the deicing fluid of the deicing vehicle meets the requirement of limiting the capacity of the deicing fluid after the deicing vehicle executes the next task e'.
S2.4: setting an objective function of a flight deicing guarantee resource collaborative optimization scheduling model;
in specific implementation, in the process of scheduling flight guarantee resources, airport scheduling personnel hope to reduce the total length of a walking path of the deicing vehicle as much as possible, improve the service efficiency of the deicing vehicle and reduce the idle time of the deicing vehicle, so that the minimum sum of the running periods of all the deicing vehicles is used as an optimization target of a model.
As a specific example, the objective function of the setting is as shown in equation 12:
wherein,
the time when the v-th deicing vehicle reaches the position of the last virtual task, namely the time when the v-th deicing vehicle completes all tasks assigned to the v-th deicing vehicle,
for the time when the v-th deicing vehicle is driven out of the stopping position of the deicing station,
and (4) working period of the v-th deicing vehicle in the planning time period.
S3: designing a solving algorithm of a combined rolling time domain control strategy;
in specific implementation, for the task assignment problem of the ice-removing vehicle, a description is given to relevant symbols of a solution algorithm (RHC-Cplex) of the joint rolling time domain control strategy in the embodiment of the present invention. Psi (k) is assigned deicing task set after the kth optimized scheduling stage is completed; lambda (k) is a deicing task set which participates in scheduling but has not finished assignment after the kth optimized scheduling stage is finished; ζ (k) is the set of deicing tasks assigned for the kth stage;
is Y e In [ T ] 0 (k-1)+C·H SY ,T 0 (k)+C·H SY ) Set of deicing tasks for an interval, H SY C is the time length of the rolling time domain, and C is the number of the rolling time domains; Γ (k) is the sum of the working cycles of the ice removing vehicle completing the assignment in the kth stage; Δ (k) is the set of ice trolleys required for the kth phase to complete the assigned deicing task. Aiming at deicing task e ∈ epsilon, Y e (k) Indicating the starting time Y of a deicing task e e On the kth time domain; omega e (k) Indicating the end time omega of the deicing task e e On the kth time domain; t is t ve (k) The moment when the deicing vehicle v reaches the position of the deicing task e is shown in the kth time domain, and the steps of the algorithm flow are as follows:
s3.1: obtaining flight deicing task information in a planning time period, generating an initial deicing task queue from small to large according to the Ye sequence, setting k =0, initializing C, and enabling Y of a first deicing task e Is set to T 0 (0) Initialize Ψ (k), ζ (k), and
。
s3.2: after the deicing task assignment of the kth stage is completed, omega is satisfied e (k)≤T 0 (k)+H SY And (3) placing the conditional deicing tasks into a set psi (k), and placing deicing vehicles required by the k stage to finish the assigned deicing tasks into a set delta (k). And Γ (k) is calculated with reference to equation 15.
For omega e (k)＞T 0 (k)+H SY After the existing scheduling result is frozen, the deicing task is put into the set lambda (k). Updating T 0 (k+1)＝T 0 (k)+H SY And is combined with Y e In [ T ] 0 (k)+C·H SY ,T 0 (k+1)+C·H SY ) Inter-zone deicing tasks put into
In (1), the constraint is updated with reference to equation (16).
S3.3: optimizing deicing task assignment information for the k +1 th phase using Cplex, i.e. for
The internal de-icing task is assigned as shown in equation (19).
S3.4: let k = k +1, determine whether or not it satisfies
If yes, go to step S3.5, otherwise return to S3.2.
S3.5: the objective function value Γ is counted as shown in equation 20.
Γ＝Γ(k) (20)
S4: and acquiring a deicing guarantee resource cooperative scheduling result of the target airport based on guarantee resources of the target airport.
Specifically, based on the guarantee resource of the target airport, the solution algorithm of the joint rolling time domain control strategy is utilized to solve the flight deicing guarantee resource optimal scheduling model, and the deicing guarantee resource cooperative scheduling result of the target airport is obtained.
In order to make the embodiments of the present invention better understood and implemented by those skilled in the art, the following description explains embodiments of the present invention in a specific application scenario.
The embodiment is based on the real flight guarantee capacity of the deicing operation in 2019 winter of the great airport, and has 26 deicing vehicles, the upper limit W of the capacity of the deicing liquid of the deicing vehicle is 5160 liters, and the deicing liquid required by various types of flights to deice is counted in different snow situations as shown in table 1.
TABLE 1 deicing and deicing fluid for various types of flights in different snow conditions
The walking distance of the deicing vehicles among the deicing positions of the three deicing plateaus and the parking positions of the deicing vehicles are arranged in eachAnd the walking distance of the deicing station is used as model input. Taking the rolling time domain time length H of the RHC-Cplex algorithm SY And =1800s and 2400s, taking the number of rolling time domains C =2 and 3, and comparing the solution result of the RHC-Cplex algorithm with the solution result of the flight guarantee resource by using a single Cplex and a traditional First Come First Served (FCFS), wherein the results are shown in table 2. Compared with the RHC-Cplex and other two algorithms, the RHC-Cplex algorithm has obviously longer operation time, but can greatly reduce the target function gamma by 7.8 percent, 7.9 percent and 7.2 percent respectively, and can obtain a better solution within an acceptable time range; compared with the method of singly using Cplex, the RHC-Cplex algorithm has better solving results, under the optimal parameter setting (thickening), the target function gamma can be respectively reduced by 1.8%, 1.6% and 1.3%, although the target function has less obvious reduction, the optimal solution can be obtained in a limited and shorter time, and the solving efficiency is higher. Under the same typical daily flight operation scene, the rolling time domain time length H of the RHC-Cplex algorithm SY And the rolling time domain number C parameter can directly influence the algorithm solving precision and solving speed, and a decision maker can select the optimal algorithm parameter combination according to the actual scheduling scene.
TABLE 2 comparison of results of the RHC-Cplex algorithm of the present invention with other algorithms
The embodiment of the invention also provides a storage medium which stores a computer program or an instruction, and when the computer program or the instruction is executed, the method for cooperatively scheduling the deicing guarantee resources in the deicing operation mode in any embodiment is realized.
Although the embodiments of the present invention have been disclosed, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A deicing guarantee resource cooperative scheduling method in a deicing operation mode is characterized by comprising the following steps:
constructing a flight deicing guarantee resource cooperative scheduling mechanism;
constructing a flight deicing guarantee resource collaborative optimization scheduling model based on the flight deicing guarantee resource collaborative scheduling mechanism;
designing a solving algorithm of the flight deicing guarantee resource collaborative optimization scheduling model;
and acquiring a deicing guarantee resource cooperative scheduling result of the target airport based on guarantee resources of the target airport.
2. The scheduling method according to claim 1, wherein the flight deicing guarantee resource cooperative scheduling mechanism specifically comprises:
the comprehensive optimization scheduling process of the airport scene in the deicing operation mode is divided into two stages, wherein:
in the first stage, the scheduling of flight scene operation resources is optimized, and the deicing task of the departing flight is obtained;
and in the second stage, optimizing and scheduling an assignment process of the deicing guarantee resources, and optimizing a space-time network for operation of the deicing guarantee resource scene while assigning the deicing guarantee resources to complete the deicing task of the departing flight.
3. The scheduling method according to claim 1, wherein the constructing of the flight deicing guarantee resource collaborative optimization scheduling model specifically comprises:
setting input parameters of a flight deicing guarantee resource collaborative optimization scheduling model;
setting a decision variable of a flight deicing guarantee resource collaborative optimization scheduling model;
setting constraint conditions of a flight deicing guarantee resource collaborative optimization scheduling model;
and setting an objective function of the flight deicing guarantee resource collaborative optimization scheduling model.
4. The scheduling method of claim 3, wherein the decision variable comprises:
deicing ensures whether resources are supplemented with deicing fluid.
5. The scheduling method of claim 3, wherein the constraint condition comprises:
task assignment constraints;
deicing guarantees resource scheduling time constraints;
and limiting and restricting the capacity of the deicing fluid.
6. The scheduling method of claim 5 wherein the task assignment constraints comprise:
limiting the amount of deicing guarantee resources for any deicing task;
limiting the sequencing between any two consecutive deicing tasks;
limiting the starting and ending points of each de-icing support resource.
7. The scheduling method of claim 5, wherein the de-icing guarantee resource scheduling time constraint comprises:
when a first continuous deicing task and a second continuous deicing task are executed, if the deicing guarantee resource does not return to a parking position to supplement deicing fluid after the first deicing task is executed, the time interval between the deicing guarantee resource and the deicing guarantee resource for starting to execute the second deicing task and the first deicing task is greater than the sum of the time of the deicing guarantee resource for serving the first deicing task and the time of the deicing guarantee resource from the position of the first deicing task to the position of the second deicing task;
if the deicing guarantee resource returns to the parking position to supplement the deicing fluid after the first deicing task is executed, the time interval between the deicing guarantee resource and the second deicing task is larger than the sum of the time of the deicing guarantee resource for serving the first deicing task, the time of the deicing guarantee resource from the position of the first deicing task to the position of the second deicing task and the time of the deicing guarantee resource for filling the deicing fluid.
8. The scheduling method of claim 5, wherein the deicing fluid capacity limit constraint comprises:
when a first deicing task and a second deicing task are continuously executed, if the deicing guarantee resource does not return to a parking position to supplement deicing fluid after the first deicing task is executed, the residual deicing fluid amount of the deicing guarantee resource is larger than the required amount of the second deicing task;
and if the deicing guarantee resource returns to the parking position to supplement the deicing fluid after the first deicing task is executed, the residual amount of the deicing fluid meets the limit requirement of the capacity of the preset deicing fluid when the deicing guarantee resource executes the second deicing task.
9. The scheduling method of claim 3, wherein the objective function comprises:
and the sum of all deicing guarantee resource operation cycles of the target airport is minimum.
10. A storage medium, storing a computer program or instructions which, when executed, implement the method of any one of claims 1 to 9.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116206463A (en) * | 2023-03-06 | 2023-06-02 | 吉林大学 | Public road operation vehicle dispatch system |
| CN117250955A (en) * | 2023-09-19 | 2023-12-19 | 中国民航工程咨询有限公司 | Formation generation method, system and storage medium for airport pavement snow removal collaborative operation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116206463A (en) * | 2023-03-06 | 2023-06-02 | 吉林大学 | Public road operation vehicle dispatch system |
| CN116206463B (en) * | 2023-03-06 | 2024-04-26 | 吉林大学 | Public road operation vehicle dispatch system |
| CN117250955A (en) * | 2023-09-19 | 2023-12-19 | 中国民航工程咨询有限公司 | Formation generation method, system and storage medium for airport pavement snow removal collaborative operation |
| CN117250955B (en) * | 2023-09-19 | 2024-09-27 | 中国民航工程咨询有限公司 | Formation generation method, system and storage medium for airport pavement snow removal collaborative operation |
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