A method for emergency dispatching of airport resources under emergencies

Abstract

The invention relates to an airport resource emergency dispatching method under an emergency, belongs to the technical field of resource dispatching, solves the problem that a flight parking stand allocation plan is invalid and difficult to recover due to parking stand service interruption under an emergency, and specifically comprises the following steps: step S0, constructing an initial data set; step S1, constructing a deviation preference dual-objective collaborative optimization evaluation index for an airport emergency scenario; step S2, constructing a dual-objective collaborative optimization emergency dispatching model; step S3, obtaining a single-objective optimization model; step S4, solving the single-objective optimization model to obtain an optimal feasible solution; step S5, continuing to execute steps S3 to S4 to obtain optimal feasible solutions of multiple single-objective optimization models, until the number of optimal feasible solutions reaches a set number requirement; step S6, obtaining multiple optimal solutions, generating a final aircraft parking stand dispatching plan, and executing the parking stand dispatching.

Description

Technical Field

[0001] The invention relates to the technical field of resource scheduling, and in particular to an emergency scheduling method for airport resources under emergencies. Background Art

[0002] The rapid development of civil aviation transportation business has put forward higher requirements for the use and management of existing airport infrastructure. As the core resource of the airport, the quality of the parking space plan is directly related to the operational efficiency of civil aviation. In actual operation, emergencies such as bridge equipment failure and aircraft collision on the ground occur from time to time. These incidents will cause some parking spaces to be temporarily closed, service interruption, and the original parking space scheduling plan to become invalid. Emergency scheduling of airport parking space resources is crucial. If the resource scheduling is unreasonable, it will cause aircraft collision conflicts on the ground and a chain reaction of delays, causing huge economic losses and accident risks to airports and airlines.

[0003] The Chinese invention patent application, publication number CN112163790A, invention name "Airport ground resource scheduling method, electronic device, computer readable storage medium", discloses an airport ground resource scheduling method, however, it mainly involves static ground support service allocation, and does not consider ground resource scheduling under emergencies.

[0004] The Chinese invention patent application, publication number CN106952022A, invention name "Airport flight resources and aircraft scheduling method and scheduling system", discloses a method for scheduling airport resources and aircraft, which mainly involves matching aircraft with airport transport vehicles and maintenance points, and matching by inquiring the status of aircraft and transport vehicles one by one. The information processed is very limited and the efficiency is low, which is not suitable for the comprehensive overall scheduling of large and busy airports and sudden emergency situations.

[0005] Airport parking space resource emergency scheduling has many constraints and difficult solution space search characteristics. Resource constraints make scheduling problems more difficult to solve. Some existing studies only focus on flight allocation problems under normal circumstances, but these methods do not take into account the multi-objective characteristics and high timeliness of emergency scheduling, and the generated scheduling solutions are difficult to meet the needs of complex airport operations. Summary of the invention

[0006] In view of the above analysis, an embodiment of the present invention aims to provide an emergency dispatch method for airport resources under emergencies, so as to solve the problem that the flight allocation plan becomes invalid and difficult to recover due to the closure of local aircraft stands under emergencies.

[0007] According to an embodiment of the present invention, a method for emergency dispatching of airport resources under emergency events is provided, comprising:

[0008] Step S0, constructing an initial data set, including constructing an airport parking space resource set based on the airport parking space resource data , build an aircraft resource collection based on aircraft resource data , and construct a set of original parking lot scheduling solutions ;

[0009] Step S1, based on the constructed initial data set, construct a deviation preference dual-objective collaborative optimization evaluation index for airport emergency response scenarios;

[0010] Step S2, based on the deviation preference dual-objective collaborative optimization evaluation index obtained in step S1, constructing a dual-objective collaborative optimization emergency dispatch model;

[0011] Step S3, constructing a dual-objective solution algorithm based on the ideal point method, and based on the dual-objective solution algorithm, converting the dual-objective collaborative optimization emergency dispatch model constructed in step S2 into a single-objective optimization model, wherein the dual-objective collaborative optimization emergency dispatch model obtained in step S2 is respectively subjected to single-objective optimization to obtain an optimal value, and an ideal point is formed by the optimal value;

[0012] Step S4, designing a rounded column generation algorithm to solve the single-objective optimization model obtained in step S3, and obtaining the optimal feasible solution of the single-objective optimization model;

[0013] Step S5, selecting multiple points around the ideal point determined in step S3 as new ideal points, solving them respectively, generating the objective function of a new single-objective optimization model, and continuing to perform step S4 on this objective function to obtain the optimal feasible solution of the single-objective optimization model. If the number of optimal feasible solutions obtained reaches the set number requirement, then perform step S6, otherwise return to step S3;

[0014] Step S6, obtaining multiple optimal solutions as an optimal solution set, generating a final aircraft parking stand scheduling plan based on the obtained optimal solution set, and executing parking stand scheduling according to the final aircraft parking stand scheduling plan to achieve emergency scheduling of airport resources under emergencies.

[0015] Optionally, the step S1 specifically includes:

[0016] Step S1.1, evaluate the deviation of the emergency dispatch plan by calculating the spatial dispatch gap and the temporal dispatch gap. Emergency dispatch plan The expression of the deviation cost is:

[0017] (1)

[0018] in, is a collection of non-disturbed aircraft, Indicates aircraft The spatial deviation cost of Indicates aircraft The time deviation cost is Is a 0-1 variable and when the aircraft Included in the emergency dispatch plan 1 when the aircraft As the Samples, emergency dispatch plan From the parking space A collection of emergency dispatch plans ;

[0019] Step S1.2, evaluate the preference of the emergency dispatch scheme by the priority value of the aircraft assigned to the parking position. Emergency dispatch plan The expression of the preference cost is:

[0020] (6)

[0021] in, Indicates aircraft Assigned to parking space The priority value of is the collection of disturbed aircraft.

[0022] Optionally, the step S2 specifically includes:

[0023] Step S2.1, construct the deviation objective function of the emergency dispatch plan, which represents the minimum deviation cost of the emergency dispatch plan:

[0024] (7)

[0025] in, is a collection of available airport parking space resources. Is a 0-1 variable, when the parking position Adopt emergency dispatch plan 1 when it is, otherwise 0;

[0026] Step S2.2, construct the preference objective function of the emergency dispatch plan, which represents the preference cost of maximizing the emergency dispatch plan:

[0027] (8);

[0028] Step S2.3, construct the aircraft rule constraint, indicating that each aircraft needs to be dispatched to a parking space:

[0029] (9);

[0030] Step S2.4, construct parking stand rule constraints, indicating that an aircraft sequence plan is arranged for each parking stand at the airport:

[0031] (10);

[0032] Step S2.5, construct the decision variable definition domain:

[0033] (11).

[0034] Optionally, the step S3 specifically includes:

[0035] Step S3.1, determine the ideal point, perform single-objective optimization on the deviation objective function of the emergency dispatch plan and the preference objective function of the emergency dispatch plan, solve the model with the deviation objective function of the emergency dispatch plan as objective 1 and solve the model with the preference objective function of the emergency dispatch plan as objective 2, and obtain the optimal values ​​respectively. and , forming an ideal point ;

[0036] Step S3.2, based on the determined ideal point, define a distance metric, select Euclidean distance as the distance metric, the distance metric It is expressed as:

[0037] (12)

[0038] in, It means that only solving objective 1 to minimize the deviation cost of the emergency dispatch plan is used as the objective function value, It means that the preference cost of maximizing the emergency dispatch scheme obtained by solving only objective 2 is taken as the objective function value;

[0039] Step S3.3, construct a single-objective optimization model, transform the emergency dispatch model of dual-objective collaborative optimization into a single-objective optimization model, and the objective function of the single-objective optimization model is:

[0040] (13).

[0041] Optionally, the step S4 specifically includes:

[0042] Step S4.1, firstly, based on the single-objective optimization model obtained in step S3, a restricted main problem model with a preset scale of decision variables and integer constraint relaxation is constructed, and the current target value is obtained by solving it, and it is determined whether the current target value is the optimal linear solution. If it is the optimal linear solution, step S4.5 is executed, and if it is not the optimal linear solution, step S4.2 is executed;

[0043] Step S4.2, obtaining the current target value, and outputting the dual value of the current restricted main problem model, and constructing the parking position sub-problem model;

[0044] Step S4.3, design a multi-attribute labeling algorithm to solve the constructed parking slot sub-problem model, and the output result is a minimum cost path, that is, the aircraft sequence from the starting point to the end point, as the current parking slot A new emergency dispatch plan , and accordingly generate corresponding new decision variables and add them to the restricted main problem model in step S4.1;

[0045] Step S4.4, generating an optimal linear solution of the restricted main problem model by executing continuous iterations from the main problem solution of step S4.1 to the sub-problem solution of step 4.3;

[0046] Step S4.5, determine whether the optimal linear solution of the restricted main problem model is an integer solution. If it is an integer solution, execute step S5; otherwise, construct a rounded column generation algorithm and return to step S4.1.

[0047] Compared with the prior art, the method for emergency dispatching of airport resources under emergencies provided by the embodiments of the present invention has at least the following advantages.

[0048] 1. Aiming at the emergency dispatch problem of airport parking space resources under emergencies, a deviation preference dual-objective collaborative optimization model is constructed, which can effectively reduce system disturbances, improve flight dispatch efficiency, and ensure the rationality and reliability of resource dispatch under emergency scenarios.

[0049] 2. For the dual-objective optimization model, an improved rounded column generation algorithm based on the ideal point method is designed, which effectively solves the problems of large decision variables and difficult convergence of solution space in practical problems. Compared with the existing methods, it can better ensure the timeliness of airport resource scheduling under emergency situations, and can dispatch resources in a timely and efficient manner to respond to emergencies, thus avoiding airport chaos and accidents caused by emergencies.

[0050] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 The present invention is a flowchart of a method for emergency dispatching of airport resources in the event of an emergency according to an embodiment of the present invention.

[0053] Figure 2 The present invention is a flowchart of an algorithm for solving the emergency dispatch of airport resources in a method for emergency dispatch of airport resources under emergencies provided in accordance with an embodiment of the present invention.

[0054] Figure 3 A schematic diagram of a connection network for each parking stand in a method for emergency dispatch of airport resources under emergencies provided in accordance with an embodiment of the present invention.

[0055] Figure 4 A Gantt chart of a scheduling solution is generated in an example of an emergency scheduling method for airport resources under emergencies provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0058] A method for emergency dispatching of airport resources under emergencies provided by an embodiment of the present invention will be described in detail below with reference to the accompanying drawings. Figure 1 The present invention is a flowchart of a method for emergency dispatching of airport resources in the event of an emergency according to an embodiment of the present invention. Figure 2 The present invention is a flowchart of an algorithm for solving the emergency dispatch of airport resources in a method for emergency dispatch of airport resources under emergencies provided in accordance with an embodiment of the present invention. Figure 3 A schematic diagram of a connection network for each parking stand in a method for emergency dispatch of airport resources under emergencies provided in accordance with an embodiment of the present invention. Figure 4A Gantt chart of a scheduling solution is generated in an example of an emergency scheduling method for airport resources under emergencies provided in accordance with an embodiment of the present invention.

[0059] Explanation of symbols:

[0060] Airport parking space resource collection;

[0061] Parking space belong ;

[0062] A collection of available airport parking space resources;

[0063] Aircraft resource collection;

[0064] airplane belong ;

[0065] Non-disturbed aircraft collection;

[0066] disturbed aircraft collection;

[0067] = ;

[0068] A collection of original parking stand scheduling plans;

[0069] Parking space The original scheduling plan ;

[0070] Parking space A collection of emergency dispatch plans;

[0071] Parking space Emergency dispatch plan belong ;

[0072] airplane At the departure time of the original dispatch plan;

[0073] airplane At the arrival time of the original dispatch plan;

[0074] Parking space Emergency dispatch plan The deviation cost;

[0075] Parking space Emergency dispatch plan The preference cost;

[0076] No. Sample pairs cluster centers The degree of membership;

[0077] 0-1 variable, when the aircraft Included in the emergency dispatch plan 1 when it is, otherwise 0;

[0078] 0-1 variable, when the parking position Adopt emergency dispatch plan 1 when it is, otherwise 0;

[0079] 0-1 variable, when the aircraft Adaptive parking space 1 if the type is , otherwise 0;

[0080] 0-1 variable, when the aircraft The attributes of the parking space are adapted The type is 1 when it is, otherwise it is 0;

[0081] Priority matrix for aircraft dispatch at different parking positions;

[0082] airplane Assigned to parking space The priority value of .

[0083] According to one embodiment of the present invention, a method for emergency dispatch of airport resources under emergencies is provided. The method is based on the airport's planned parking stand scheduling scheme, the latest flight schedule, and the currently available parking stand information, considers the dual-objective collaborative optimization indicators of resource emergency dispatch, constructs a dual-objective emergency dispatch model, designs and improves the rounded column generation algorithm for solution, obtains the parking stand flight emergency dispatch scheme, and realizes fast and efficient emergency dispatch of airport resources under emergencies.

[0084] In this implementation, an initial data set can be constructed based on the airport parking stand resources, aircraft resources, and the original parking stand scheduling plan. Among them, the main attributes of the airport parking stand resource data may include: parking stand location, parking stand number, parking stand type (for example, bridge stand / remote stand), dockable aircraft type, current state of the parking stand (for example, whether the parking stand is open / closed), etc. The main attributes of the aircraft resource data may include: flight number, tail number, flight aircraft type, route type, execution date, flight nationality, time to arrival, planned parking stand number, planned parking stand type, planned entry time, planned exit time, departure airport, aircraft departure time, landing airport, aircraft arrival time, inbound and outbound type, number of subsequent flights, number of passengers, etc. The parking stand original scheduling plan set data includes the original scheduling plans of all parking stands, and the main attributes of each parking stand original scheduling plan may include: original aircraft position, parking stand number, parking stand location, allocated aircraft sequence, planned departure time and planned arrival time of each aircraft in the aircraft sequence, etc.

[0085] refer to Figure 1 According to an embodiment of the present invention, a method for emergency dispatching of airport resources under emergencies is provided, comprising the following steps.

[0086] Step S0, constructing an initial data set, including constructing an airport parking space resource set based on the airport parking space resource data , build an aircraft resource collection based on aircraft resource data , and construct a set of original parking lot scheduling solutions .

[0087] Step S1, based on the constructed initial data set, construct a deviation preference dual-objective collaborative optimization evaluation index for airport emergency scenarios. The deviation preference refers to the deviation cost of the emergency dispatch plan and the preference cost of the emergency dispatch plan. The emergency dispatch plan comes from the emergency plan dispatch set. This step S1 specifically includes the following steps.

[0088] Step S1.1, evaluate the deviation of the emergency scheduling plan by calculating the scheduling gap in space and time, and obtain the deviation cost of the emergency scheduling plan. For example, the scheduling gap in space can be the parking position number of the current aircraft generated in the emergency scheduling plan and the aircraft resource set of the initial data set The time scheduling gap can be the deviation between the arrival time of the current aircraft generated in the emergency scheduling plan and the planned arrival time of the aircraft in the original scheduling plan of the parking stand in the initial data set, or the deviation between the departure time of the current aircraft generated in the emergency scheduling plan and the planned departure time of the aircraft in the original scheduling plan of the parking stand in the initial data set, etc. These are all deviations of the emergency scheduling plan for airport resources. Emergency dispatch plan , the expression of deviation cost is:

[0089] (1)

[0090] in, It's a parking space Emergency dispatch plan The deviation cost, is a collection of non-disturbed aircraft, Indicates aircraft The spatial deviation cost of Indicates aircraft The time deviation cost is Is a 0-1 variable and when the aircraft Included in the emergency dispatch plan 1 when the aircraft As the Samples, emergency dispatch plan From the parking space A collection of emergency dispatch plans .

[0091] Step S1.2, evaluating the preference of the emergency dispatch plan by the priority values ​​of the aircraft assigned to different parking positions, and obtaining the preference cost of the emergency dispatch plan, specifically includes the following steps.

[0092] Step S1.2.1, based on the initial data set, construct an aircraft resource scheduling evaluation index set B, in which each aircraft is a sample, and each sample may include flight models in the aircraft resource set of the initial data set. , Route Type , Plan to allocate parking space numbers , Plan to allocate parking space type , Time from arrival to port , Port entry and exit types 、Number of subsequent flights , Number of passengers and other attributes as indicators of the sample.

[0093] Step S1.2.2, based on the indicators of the samples in the aircraft resource scheduling evaluation indicator set B, kernel principal component analysis is performed. Kernel principal component analysis maps data to a high-dimensional feature space, performs principal component analysis in this space, and then projects it back to the original low-dimensional space. First, calculate the correlation matrix R between aircraft, where each element in the matrix Expressed as

[0094] (2)

[0095] in, Indicates aircraft With airplane The correlation between the aircraft and airplane Belongs to aircraft resource set A, aircraft For the Samples, aircraft For the samples, It refers to The index of the sample The value of It refers to The average value of all indicators of samples, Indicates The index of the sample The value of Indicates The average value of all indicators of samples, Indicates the number of aircraft, indicator It belongs to the above-mentioned aircraft resource scheduling evaluation indicator set B.

[0096] Then, the eigenvalues ​​of each sample and their variance contribution rates are calculated. The eigenvalue of each sample is: , the feature vector of each sample: , eigenvalue The variance contribution rate of is:

[0097] , ( =1,2,…, ) (3).

[0098] Next, sort the samples according to their variance contribution rates, and extract the previous Eigenvalues , so that Eigenvalues The sum of is greater than the eigenvalue of the sample 80% of the total. eigenvalues ​​as the eigenvalues ​​of the aircraft represented by the sample.

[0099] Step S1.2.3, the flight-parking position priority matrix is ​​calculated using the improved fuzzy C-means clustering method. The processing flow is as follows: Figure 2 First, input the characteristic value of the aircraft obtained in the previous step; then, the design objective function is:

[0100] (4)

[0101] in, is the fuzzy coefficient, which can be taken as 2; is the number of aircraft; is the number of cluster centers, i.e., the number of available airport parking spaces; For the The cluster center represents the parking spaces; For the samples, i.e., aircraft , where each sample has eigenvalues; Indicates Sample pairs cluster centers The degree of membership; Represents the aircraft in the original parking space scheduling plan in the initial data set The original position of the aircraft; Indicates parking space location.

[0102] And each element of the membership matrix is ;

[0103] The cluster centers are: .

[0104] in, Indicates Cluster centers, Indicates Cluster centers, =1,2,3,… .

[0105] Step S1.2.4, next, initialize the above membership matrix and initialize the category data, see Figure 2 Initialize the membership matrix to be randomly generated from random values ​​between [0,1] ( OK columns), initialize the category data to .

[0106] Step S1.2.5, then calculate the membership matrix and update the cluster center .

[0107] Step S1.2.6, then, calculate the difference between the current objective function value and the previous objective function value, and determine whether the difference between the current objective function value and the previous objective function value is less than the set threshold; if it is not less than the set threshold, return to step S1.2.5, recalculate the membership matrix and update the cluster center ; If it is less than the set threshold, stop the algorithm and execute step S1.2.7.

[0108] Step S1.2.7, obtain the current membership matrix, use the membership matrix as the priority value matrix of the aircraft at the parking position, and obtain the preference cost of the emergency scheduling plan based on the priority value matrix. The membership matrix is ​​the priority value matrix of aircraft scheduling at different parking positions. , specifically expressed as:

[0109] (5)

[0110] in, Indicates aircraft Assigned to parking space The priority value of .

[0111] Based on the above calculations, the expression of the preference cost of each emergency dispatch scheme is:

[0112] (6)

[0113] in, It's a parking space Emergency dispatch plan The preference cost, is the collection of disturbed aircraft.

[0114] Step S2, based on the deviation preference dual-objective collaborative optimization evaluation index obtained in step S1, constructing a dual-objective collaborative optimization emergency dispatch model. Constructing the emergency dispatch model includes constructing a deviation objective function of the emergency dispatch plan and a preference objective function of the emergency dispatch plan.

[0115] In this step, the deviation objective function of the emergency dispatch plan and the preference objective function of the emergency dispatch plan are constructed based on the deviation cost of the emergency dispatch plan and the preference cost of the emergency dispatch plan obtained in the above step S1, and the airport resource emergency dispatch plan is constructed as a mixed integer programming model based on set partitioning. Step S2 specifically includes the following steps.

[0116] Step S2.1, construct the deviation objective function of the emergency dispatch plan, which represents the minimum deviation cost of the emergency dispatch plan:

[0117] (7)

[0118] in, is a collection of available airport parking space resources. Is a 0-1 variable, when the parking position Adopt emergency dispatch plan 1 if the value is 0, otherwise it is 0.

[0119] Step S2.2, construct the preference objective function of the emergency dispatch plan, which represents the preference cost of maximizing the emergency dispatch plan:

[0120] (8).

[0121] Step S2.3, construct the aircraft rule constraint, indicating that each aircraft needs to be dispatched to a parking space:

[0122] (9).

[0123] Step S2.4, construct parking stand rule constraints, indicating that an aircraft sequence plan is arranged for each parking stand at the airport:

[0124] (10).

[0125] Step S2.5, construct decision variables Domain:

[0126] (11).

[0127] The deviation objective function of the emergency dispatch scheme and the preference objective function of the emergency dispatch scheme constructed above constitute a dual-objective collaborative optimization emergency dispatch model.

[0128] Step S3, constructing a dual-objective solution algorithm based on the ideal point method, and based on the dual-objective solution algorithm, converting the dual-objective collaborative optimization emergency dispatch model constructed in step S2 into a single-objective optimization model.

[0129] Step S3.1, determine the ideal point. Perform single-objective optimization on the deviation objective function of the emergency dispatch plan and the preference objective function of the emergency dispatch plan obtained in step S2, that is, solve the model with the deviation objective function of the emergency dispatch plan as objective 1 and solve the model with the preference objective function of the emergency dispatch plan as objective 2, and obtain the optimal values ​​respectively. and , forming an ideal point .

[0130] Step S3.2, based on the determined ideal point, define a distance metric. Select Euclidean distance as the distance metric. It is expressed as:

[0131] (12)

[0132] in, It means that only solving objective 1 (step S2.1) to minimize the deviation cost of the emergency dispatch plan is used as the objective function value, It means that the preference cost of maximizing the emergency dispatch plan obtained by solving only objective 2 (step S2.2) is used as the objective function value.

[0133] Step S3.3, construct a single-objective optimization model. The emergency dispatch model of dual-objective collaborative optimization is transformed into a single-objective optimization model, and the objective function of the single-objective optimization model is:

[0134] (13).

[0135] Step S4, designing a rounded column generation algorithm to solve the single-objective optimization model obtained in step S3, specifically includes the following steps.

[0136] In this step, the column generation algorithm is used to solve the linear relaxation problem of the model, and then a rounded column generation algorithm is designed based on the linear solution to quickly obtain the integer solution.

[0137] Step S4.1, firstly, based on the single-objective optimization model obtained in step S3, a decision variable of a preset scale and a restricted main problem model with integer constraint relaxation are constructed, and the current target value is obtained by solving the problem, and it is determined whether the current target value is the optimal linear solution. If it is the optimal linear solution, step S4.5 is executed, and if it is not the optimal linear solution, step S4.2 is executed. The calculation can be completed by using, for example, the Gurobi solver. The certain variable scale can be, for example, the decision variable The number of such constraints is less than 1000. In the application, the number of such constraints can be selected separately according to the needs. Integer constraint relaxation means adjusting the domain of the constraint decision variable in step S2.5 to a continuity constraint, that is, ,in Indicates the original A subset of .

[0138] Based on this, the restricted main problem model is constructed

[0139] (14)

[0140]

[0141]

[0142] .

[0143] Step S4.2, obtain the current target value from step S4.1, and output the dual value of the current restricted master problem model and , construct the parking position sub-problem model. The above solution to the restricted main problem model can be performed by calling the dual value function GetDuals of the Gurobi solver and obtaining the dual value. Construct the parking position sub-problem model, and its objective function is:

[0144] (15).

[0145] The above objective function can be refactored as:

[0146] (16)

[0147] in, Represents a collection of aircraft resources; represents the dual value corresponding to the aircraft rule constraint of step S2.3; Represents a 0-1 variable, when the aircraft Included in the scheduling plan 1 when it is, otherwise 0; Represents the dual value corresponding to the parking space rule constraint in step S2.4.

[0148] The constraints of the parking slot problem model include: (1) Aircraft spacing constraints: ,in, Indicates the arrival time of the aircraft in the original scheduling plan. represents the departure time of the aircraft in the original scheduling plan; (2) Model constraint: ,in is a 0-1 variable. Adaptive parking space 1 if the aircraft is of type, otherwise 0; (3) Aircraft attribute matching requirements: ,in is a 0-1 variable. The attributes of the parking space are adapted The type is 1 when the variable is set to 0; (4) Variable definition ,in is a 0-1 variable. Included in the scheduling plan 1 if the value is 0, otherwise it is 0.

[0149] Step S4.3, design a multi-attribute labeling algorithm to solve the constructed parking slot sub-problem model, and the output result is a minimum cost path, that is, a set of aircraft from the starting point to the end point.

[0150] refer to Figure 3 ,First, construct a connected network whose nodes include | | planes, virtual starting points and virtual end points, and the edges between the nodes represent the connections between the planes, between the plane and the virtual starting point, and between the plane and the virtual end point. Use labels Describes the current node status

[0151] (17)

[0152] Among them, the status Represents the cumulative deviation cost of the current node. If the current node is a non-disturbed aircraft node, then , if the current node is a disturbed aircraft node, then ;state Represents the cumulative preference cost of the current node, that is .

[0153] A non-dominant rule is designed to identify and remove inferior labels. The non-dominant rule is: for two labels and ,if , And one of the groups is a strict inequality, then the label Occupancy .Label Starting from the virtual starting point, continue to expand backwards. When expanding to the end point, the backtracking label Generate the shortest path (i.e., minimum cost path) as the best aircraft sequence, i.e. [ …], and use the best aircraft sequence obtained as the current parking position A new emergency dispatch plan , and generate corresponding new variables Add to the restricted master problem model of step S4.1, that is, Figure 1 As shown, add new columns to restrict the main problem.

[0154] Step S4.4, by executing continuous iterations from the main problem solution of step S4.1 to the sub-problem solution of step 4.3 (i.e., sub-problem search), an optimal linear solution of the restricted main problem model is generated.

[0155] Step S4.5, determine whether the optimal linear solution of the restricted main problem model is an integer solution. If it is an integer solution, execute step S5, otherwise, construct a rounded column generation algorithm. Constructing a rounded column generation algorithm includes: based on the currently obtained optimal linear solution, select decision variables The variable with the largest value is rounded and fixed, that is, , and return to step S4.1. For example, when the maximum value of the variable is , it is fixed to , then return to step S4.1 to step S4.4 to solve, and continue iterating until If there is no fractional solution for the variable with the largest value, the iteration is stopped and the optimal feasible solution of the single-objective optimization model in step S3 is obtained.

[0156] Step S5: increasing or adjusting the step length based on the ideal point determined in step S3 , forming a new set of ideal points, that is, four ideal points can be generated: , , , . Use one of the above adjustment strategies in turn, and then update the distance metric , generate the objective function of a new single-objective optimization model, and then execute step S4 to obtain the optimal feasible solution of the single-objective optimization model. If the number of optimal feasible solutions obtained meets the set number requirement, execute step S6; if not, return to step S3. Figure 1 A flowchart showing the overall solution framework.

[0157] Step S6, multiple optimal solutions are obtained as an optimal solution set, and a final aircraft parking stand scheduling plan is generated based on the obtained optimal solution set. The parking stand scheduling is performed according to the final aircraft parking stand scheduling plan to realize the emergency scheduling of airport resources under emergencies. The final aircraft parking stand scheduling plan can be generated by the airport operation decision maker selecting a set of solutions from the obtained optimal solution set according to actual needs.

[0158] Figure 4 A Gantt chart of a scheduling solution is generated in an example of an emergency scheduling method for airport resources under emergencies provided in accordance with an embodiment of the present invention.

[0159] The final solution is selected according to the actual needs of the airport. An intelligent parking space allocation platform can be established based on technologies such as HTML, CSS, and JS. The Gantt chart technology is used to generate a scheduling plan. The resulting Gantt chart diagram is shown below: Figure 4 As shown, it provides guidance for airport operation decisions.

[0160] The emergency dispatch method for airport resources under emergencies provided by the implementation method of the present invention has at least the following advantages compared with the prior art: for the emergency dispatch problem of airport parking space resources under emergencies, a deviation preference dual-objective collaborative optimization model is established, which can effectively reduce system disturbances, improve flight dispatch efficiency, and ensure the fairness of resource dispatch under emergencies; for the dual-objective optimization model, an improved rounded column generation algorithm based on the ideal point method is designed, which effectively solves the problems of large scale of decision variables and difficult convergence of solution space in practical problems. Compared with the existing methods, the emergency dispatch method for airport resources under emergencies provided by the implementation method of the present invention can better ensure the timeliness of airport resource dispatch under emergencies, and ensure the operation order and safety of the airport under complex and changeable conditions.

[0161] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0162] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0163] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for emergency dispatching of airport resources under emergencies, characterized in that: include: Step S0, constructing an initial data set, including constructing an airport parking space resource set based on the airport parking space resource data , build an aircraft resource collection based on aircraft resource data , and construct a set of original parking lot scheduling solutions ; Step S1, based on the constructed initial data set, construct a deviation preference dual-objective collaborative optimization evaluation index for airport emergency response scenarios; Step S2, based on the deviation preference dual-objective collaborative optimization evaluation index obtained in step S1, constructing a dual-objective collaborative optimization emergency dispatch model; Step S3, constructing a dual-objective solution algorithm based on the ideal point method, and based on the dual-objective solution algorithm, converting the dual-objective collaborative optimization emergency dispatch model constructed in step S2 into a single-objective optimization model, wherein the dual-objective collaborative optimization emergency dispatch model obtained in step S2 is respectively subjected to single-objective optimization to obtain an optimal value, and an ideal point is formed by the optimal value; Step S4, designing a rounded column generation algorithm to solve the single-objective optimization model obtained in step S3, and obtaining the optimal feasible solution of the single-objective optimization model; Step S5, selecting multiple points around the ideal point determined in step S3 as new ideal points, solving them respectively, generating the objective function of a new single-objective optimization model, and continuing to perform step S4 on this objective function to obtain the optimal feasible solution of the single-objective optimization model. If the number of optimal feasible solutions obtained reaches the set number requirement, then perform step S6, otherwise return to step S3; Step S6, obtaining multiple optimal solutions as an optimal solution set, generating a final aircraft parking stand scheduling plan based on the obtained optimal solution set, and executing parking stand scheduling according to the final aircraft parking stand scheduling plan to achieve emergency scheduling of airport resources under emergencies; Wherein, the step S1 specifically includes: Step S1.1, evaluate the deviation of the emergency dispatch plan by calculating the spatial dispatch gap and the temporal dispatch gap. Emergency dispatch plan The expression of the deviation cost is: （1） in, is a collection of non-disturbed aircraft, Indicates aircraft The spatial deviation cost of Indicates aircraft The time deviation cost is Is a 0-1 variable and when the aircraft Included in the emergency dispatch plan 1 when the aircraft As the Samples, emergency dispatch plan From the parking space A collection of emergency dispatch plans ; Step S1.2, evaluate the preference of the emergency dispatch scheme by the priority value of the aircraft assigned to the parking position. Emergency dispatch plan The expression of the preference cost is: （6） in, Indicates aircraft Assigned to parking space The priority value of is the collection of disturbed aircraft; The step S1.2 specifically includes: Step S1.2.1, constructing an aircraft resource scheduling evaluation index set B based on the initial data set; Step S1.2.2, based on the indicators of the samples in the above aircraft resource scheduling evaluation indicator set B, kernel principal component analysis is performed, including, first, calculating the correlation matrix R between aircraft, where each element in the matrix Expressed as （2） in, Indicates aircraft With airplane The correlation between the aircraft and airplane Belongs to aircraft resource set A, aircraft For the Samples, aircraft For the samples, It refers to The index of the sample The value of It refers to The average value of all indicators of samples, Indicates The index of the sample The value of Indicates The average value of all indicators of samples, Indicates the number of aircraft, indicator Belongs to the above-mentioned aircraft resource scheduling evaluation indicator set B; Then, the eigenvalues ​​of each sample and their variance contribution rates are calculated. The eigenvalue of each sample is , the feature vector of each sample is , eigenvalue The variance contribution rate of is: , ( =1,2,…, ) （3） Next, sort the samples according to their variance contribution rates, and extract the previous Eigenvalues , so that Eigenvalues The sum of is greater than the eigenvalue of the sample 80% of the total, the extracted sample eigenvalues, as the eigenvalues ​​of the aircraft represented by the sample; Step S1.2.3, using the improved fuzzy C-means clustering method to calculate the flight-parking stand priority value matrix, including first inputting the characteristic value of the aircraft obtained in the previous step; Next, the objective function is designed as: （4） in, is the fuzzy coefficient, which is 2 here; is the number of aircraft; is the number of cluster centers, i.e., the number of available airport parking spaces; For the The cluster center represents the parking spaces; For the samples, i.e., aircraft , where each sample has eigenvalues; Indicates Sample pairs cluster centers The degree of membership; Represents the aircraft in the original parking space scheduling plan in the initial data set The original position of the aircraft; Indicates parking space location; And each element of the membership matrix is ; The cluster centers are: in, Indicates Cluster centers, Indicates Cluster centers, =1,2,3,… ; Step S1.2.4, initialize the membership matrix to be randomly generated from random values ​​between [0,1] ( OK columns), initialize the category data to ; Step S1.2.5, calculate the membership matrix and update the cluster center ; Step S1.2.6, calculate the difference between the current objective function value and the previous objective function value, and determine whether the difference between the current objective function value and the previous objective function value is less than the set threshold. If it is not less than the set threshold, return to step S1.2.5, recalculate the membership matrix and update the cluster center ; If it is less than the set threshold, stop the algorithm and execute step S1.2.7; Step S1.2.7, obtain the current membership matrix, use the membership matrix as the priority value matrix of the aircraft at the parking position, and obtain the preference cost of the emergency scheduling plan based on the priority value matrix. The membership matrix is ​​the priority value matrix of the aircraft scheduling at different parking positions. , specifically expressed as: （5） in, Indicates aircraft Assigned to parking space The priority value of Based on the above calculations, the expression of the preference cost of each emergency dispatch scheme is: （6）。 2. The method for emergency dispatch of airport resources under emergency events according to claim 1, characterized in that: The step S2 specifically includes: Step S2.1, construct the deviation objective function of the emergency dispatch plan, which represents the minimum deviation cost of the emergency dispatch plan: （7） in, is a collection of available airport parking space resources. Is a 0-1 variable, when the parking position Adopt emergency dispatch plan 1 when it is, otherwise 0; Step S2.2, construct the preference objective function of the emergency dispatch plan, which represents the preference cost of maximizing the emergency dispatch plan: （8）； Step S2.3, construct the aircraft rule constraint, indicating that each aircraft needs to be dispatched to a parking space: （9）； Step S2.4, construct parking stand rule constraints, indicating that an aircraft sequence plan is arranged for each parking stand at the airport: （10）； Step S2.5, construct the decision variable definition domain: （11）。 3. The method for emergency dispatch of airport resources under emergency events according to claim 2, characterized in that: The step S3 specifically includes: Step S3.1, determine the ideal point, perform single-objective optimization on the deviation objective function of the emergency dispatch plan and the preference objective function of the emergency dispatch plan, solve the model with the deviation objective function of the emergency dispatch plan as objective 1 and solve the model with the preference objective function of the emergency dispatch plan as objective 2, and obtain the optimal values ​​respectively. and , forming an ideal point ; Step S3.2, based on the determined ideal point, define a distance metric, select Euclidean distance as the distance metric, the distance metric It is expressed as: （12） in, It means that only solving objective 1 to minimize the deviation cost of the emergency dispatch plan is used as the objective function value, It means that the preference cost of maximizing the emergency dispatch scheme obtained by solving only objective 2 is taken as the objective function value; Step S3.3, construct a single-objective optimization model, transform the emergency dispatch model of dual-objective collaborative optimization into a single-objective optimization model, and the objective function of the single-objective optimization model is: （13）。 4. The method for emergency dispatch of airport resources under emergency events according to claim 3, characterized in that: The step S4 specifically includes: Step S4.1, firstly, based on the single-objective optimization model obtained in step S3, a restricted main problem model with a preset scale of decision variables and integer constraint relaxation is constructed, and the current target value is obtained by solving it, and it is determined whether the current target value is the optimal linear solution. If it is the optimal linear solution, step S4.5 is executed, and if it is not the optimal linear solution, step S4.2 is executed; Step S4.2, obtaining the current target value, and outputting the dual value of the current restricted main problem model, and constructing the parking position sub-problem model; Step S4.3, design a multi-attribute labeling algorithm to solve the constructed parking slot sub-problem model, and the output result is a minimum cost path, that is, the aircraft sequence from the starting point to the end point, as the current parking slot A new emergency dispatch plan , and accordingly generate corresponding new decision variables and add them to the restricted main problem model in step S4.1; Step S4.4, generating an optimal linear solution of the restricted main problem model by executing continuous iterations from the main problem solution of step S4.1 to the sub-problem solution of step 4.3; Step S4.5, determine whether the optimal linear solution of the restricted main problem model is an integer solution. If it is an integer solution, execute step S5; otherwise, construct a rounded column generation algorithm and return to step S4.1.

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