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CN-121998172-A - Civil aviation element configuration optimization method based on mixed integer dynamic programming

CN121998172ACN 121998172 ACN121998172 ACN 121998172ACN-121998172-A

Abstract

The invention discloses a civil aviation element configuration optimization method based on mixed integer dynamic programming, which comprises the following steps of S1, constructing a Chinese civil aviation core element and a carbon emission space-time characteristic database. S2, establishing a mixed integer dynamic planning model aiming at minimizing the total cost of the system in the planning period. And S3, establishing decision variables, constraint conditions and fusion solving algorithm frameworks required by the mixed integer dynamic programming model. And S4, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module based on the fusion solving algorithm framework so as to obtain a civil aviation core element dynamic configuration scheme with the optimal total cost of the system in a plurality of future periods and meeting an uncertainty scene. According to the method provided by the invention, the collaborative requirements of improving the operation efficiency and reducing the carbon emission under the view angle of carbon cost are met by optimizing the multi-source data fusion algorithm, deepening the carbon emission measuring and calculating method and adjusting the parameters of the solver.

Inventors

  • SUN LIANG

Assignees

  • 中国民航管理干部学院

Dates

Publication Date
20260508
Application Date
20260105

Claims (10)

  1. 1. The civil aviation element configuration optimization method based on mixed integer dynamic programming is characterized by comprising the following steps of: S1, constructing a Chinese civil aviation core element and carbon emission space-time characteristic database, wherein the database integrates multi-source heterogeneous data through a space-time data fusion algorithm, and the data at least comprises airport geographic information, an air line network, air fleet data, flight operation data, refined air line-model dimension carbon emission data and passenger travel demand fluctuation data; S2, establishing a mixed integer dynamic planning model aiming at minimizing the total system cost in a planning period, wherein the total system cost comprises the traditional operation cost, the embedded carbon cost, the passenger time cost and the policy compliance cost, the carbon cost is calculated by the product of the carbon emission and the dynamic carbon price, and the dynamic carbon price is related to a carbon market fluctuation coefficient; S3, establishing decision variables, constraint conditions and fusion solving algorithm frames required by the mixed integer dynamic programming model, wherein the decision variables comprise integer type-airline flight frequency allocation variables and airport resource period allocation variables, the constraint conditions comprise demand meeting constraint, airport capacity constraint, fleet availability constraint, carbon emission calculation constraint, uncertainty demand robust constraint and adjacent period dynamic association constraint; and S4, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module based on the fusion solving algorithm framework so as to obtain a civil aviation core element dynamic configuration scheme with the optimal total cost of the system in a plurality of future periods and meeting an uncertainty scene.
  2. 2. The method of claim 1, wherein the spatio-temporal data fusion algorithm in step S1 comprises correcting coordinate deviation of airport geographic information by using a spatial interpolation algorithm, eliminating outliers of flight operation data by using a time sequence smoothing algorithm, and realizing consistency matching of time-space dimensions by using a feature alignment algorithm on multi-source data.
  3. 3. The method of claim 1, wherein the refined model-to-model dimension carbon emission data in step S1 is generated by a 'bottom-up' dynamic measurement method, and the 'bottom-up' dynamic measurement method comprises the steps of calculating by using model real-time fuel efficiency and model actual distance and adopting a segmented carbon emission coefficient method, and endowing the model-to-model dimension carbon emission data with longitude, latitude and time stamp space-time attribute through a geographic information system.
  4. 4. The method of claim 1, wherein the dynamic carbon price is calculated in step S2 by multiplying a carbon market volatility factor and a policy adjustment factor based on a baseline carbon price, wherein the carbon market volatility factor is predicted by an autoregressive integral moving average time series model of historical carbon trade data.
  5. 5. The method of claim 1, wherein the traditional operating costs in step S2 include one or more of fuel costs, aircraft holding costs, crew labor costs, airport tolls, and maintenance costs.
  6. 6. The method of claim 1, wherein the uncertainty demand robust constraint in step S3 comprises that for any passenger demand fluctuation scenario, the deviation of the model decision result from the demand to be satisfied does not exceed a preset value, and the constraint is converted into a solvable linear inequality by an interval mathematical method.
  7. 7. The method according to claim 1, wherein the specific operation mode of the fusion solving algorithm framework in step S3 includes: The initialization stage, namely generating an initial solution space of a decision variable through an adaptive genetic algorithm; The iterative optimization stage is to apply deterministic constraint to the initial solution space by utilizing mixed integer dynamic programming, and screen a feasible solution; and (3) convergence judgment, namely outputting an optimal solution when the fluctuation of the objective function value continuously carrying out the preset iteration number is smaller than a preset fluctuation value.
  8. 8. The method of claim 1, wherein the carbon emission calculation constraint in step S3 comprises: Establishing a mathematical relationship between a decision variable and carbon emission E, wherein E=k×f (i, j), wherein f (i, j) is the single shift carbon emission of the model i on the route j; Embedding the mathematical relationship as an equality constraint into the mixed integer dynamic programming model, and appending an upper limit constraint on the total carbon emissions.
  9. 9. The method of claim 1, wherein the dynamic configuration of civil aviation core elements in step S4 further comprises introducing model update decision variables in the medium-long term planning period to make the scheme dynamically iterative adaptive.
  10. 10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when executing the computer program.

Description

Civil aviation element configuration optimization method based on mixed integer dynamic programming Technical Field The present invention relates generally to the field of carbon emissions technology. More specifically, the invention relates to a civil aviation core element configuration optimization method based on mixed integer dynamic programming. Background Civil aviation transportation is used as a core component of a national comprehensive transportation system, and plays an irreplaceable role in promoting economic and social development, improving travel efficiency and the like. However, civil aviation is also an important field of energy consumption and carbon emission, and the carbon emission has the characteristics of wide coverage, complex space-time distribution, high emission reduction difficulty and the like. Along with the promotion of the global 'carbon neutralization' and 'carbon reaching peak' targets, the China civil aviation industry faces severe emission reduction pressure, namely according to the 'fourteen five' civil aviation green development special project, the continuous reduction of the carbon emission intensity is realized while the increase of the transportation requirement is ensured, and finally the carbon neutralization target of the whole industry is reached in 2060. The carbon emission of the civil aviation system is closely related to the configuration of core elements (airports, airlines, fleets, flight operations and the like), for example, the matching efficiency of the machine type and the airlines directly influences the fuel consumption of a single shift, the airport capacity and the network layout of the airlines determine the turnover efficiency of the flights, and the density of the flights frequently is related to the total energy consumption. However, in the configuration optimization of civil aviation core elements, due to the lack of deep fusion of multi-source heterogeneous data (including refined carbon emission and passenger demand fluctuation data), the carbon cost and the traditional operation, passenger time cost and the like are not uniformly quantized into the total cost of the system, multi-period dynamic association and uncertainty robust constraint are lacked, and the coupling of high-dimensional discrete variables and complex constraint is difficult to efficiently process by a solving algorithm, so that the global dynamic optimization of the civil aviation core elements cannot be realized, and the cooperative requirements of improving the operation efficiency and reducing the carbon emission under the view of the carbon cost are difficult to be met. In view of the foregoing, it is desirable to provide a method for dynamically optimizing civil aviation core element configuration based on mixed integer dynamic programming so as to reduce carbon emissions. Disclosure of Invention In order to solve at least one or more of the technical problems mentioned above, the present invention proposes in various aspects a method for dynamic optimization of civil aviation element configuration based on mixed integer dynamic programming. In a first aspect, the invention provides a civil aviation element configuration dynamic optimization method based on mixed integer dynamic programming, which comprises the following steps of S1, constructing a Chinese civil aviation core element and carbon emission space-time characteristic database, wherein the database integrates multi-source heterogeneous data through a space-time data fusion algorithm, and the data at least comprises airport geographic information, an air line network, air fleet data, flight operation data, refined air line-machine model dimension carbon emission data and passenger travel demand fluctuation data. S2, establishing a mixed integer dynamic planning model aiming at minimizing the total system cost in the planning period, wherein the total system cost comprises the traditional operation cost, the embedded carbon cost, the passenger time cost and the policy compliance cost, and the carbon cost is calculated by the product of the carbon emission and the dynamic carbon price, and the dynamic carbon price is related to the carbon market fluctuation coefficient. And S3, establishing decision variables, constraint conditions and a fusion solving algorithm framework required by the mixed integer dynamic programming model, wherein the decision variables comprise integer type-airline flight frequency allocation variables and airport resource period allocation variables, the constraint conditions comprise demand meeting constraint, airport capacity constraint, fleet availability constraint and carbon emission calculation constraint, uncertainty demand robust constraint and adjacent period dynamic association constraint, and the fusion solving algorithm framework is a coupling framework of mixed integer dynamic programming and an adaptive genetic algorithm, wherein the mixed integer dynamic programming proc