Airline and time slot collaborative optimization method considering airline company preference
By constructing a dual-objective optimization model and a genetic algorithm, taking into account airline preferences, and scientifically allocating route and time slot resources, the problem of fairness and effectiveness in the allocation of route and time slot resources in air traffic flow management was solved. This resulted in a reduction in flight delay rates and an improvement in the fairness of resource allocation, thus promoting the efficiency and impartiality of air traffic management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FUZHOU UNIV
- Filing Date
- 2025-10-20
- Publication Date
- 2026-05-01
AI Technical Summary
Traditional air traffic flow management methods lack scientific rigor and systematicity, making it difficult to cope with complex and ever-changing air traffic environments. Furthermore, existing methods for allocating air routes and time slots ignore the differences in airline preferences, affecting the fairness and effectiveness of resource allocation.
A dual-objective optimization model is constructed, which minimizes the total delay cost and the airline fairness coefficient. An airline preference parameter is introduced, and a genetic algorithm is used to solve the route and time slot resource allocation model. Constraints are set on the uniqueness, feasibility, capacity and maximum threshold of flights and time slots to achieve the coordinated allocation of route and time slot resources.
This will effectively reduce flight delay rates, increase airlines' participation and satisfaction in collaborative decision-making, improve the fairness and efficiency of resource allocation, promote close cooperation between airlines and air traffic control departments, and build an efficient and fair air traffic management system.
Smart Images
Figure CN121963541A_ABST
Abstract
Description
Route and time slot co-optimization method considering airline preferences Technical Field
[0001] This invention relates to the field of air traffic flow management technology in the aerospace industry, specifically a method for co-optimizing routes and time slots that takes into account airline preferences. Background Technology
[0002] Traditional air traffic flow management methods rely mainly on manual scheduling and experience-based judgment, which lacks scientific rigor and systematic approach, making it difficult to cope with complex and ever-changing air traffic environments.
[0003] Existing methods for allocating air routes and time slots largely ignore the differences in airline preferences. Different airlines have varying needs and preferences regarding air routes and time slots, which to some extent affects the fairness and effectiveness of resource allocation. For example, some airlines may prefer to choose specific air routes to save fuel costs or improve flight efficiency, while others may prioritize the stability of time slots to reduce flight delay risks. Therefore, fully considering the differences in airline preferences when allocating air routes and time slots is crucial for improving the fairness and effectiveness of resource allocation.
[0004] This invention proposes a route and time slot collaborative optimization method that takes into account airline preferences. It aims to effectively reduce flight delay rates by scientifically and systematically allocating route and time slot resources, while ensuring fairness in resource acquisition among airlines, and helping to build an efficient and equitable air traffic management system. Summary of the Invention
[0005] Objective: This invention aims to address the capacity and flow imbalance problem in the allocation of airway and time slot resources in existing air traffic flow management technologies, and provides a method for coordinated optimization of airways and time slots that considers airline preferences. By constructing a dual-objective optimization model with the objectives of minimizing total delay costs and minimizing airline fairness coefficients, and by incorporating airline preference parameters, the coordinated allocation of airway and time slot resources is achieved.
[0006] This invention specifically provides a route and time slot collaborative optimization method that takes into account airline preferences, including the following steps:
[0007] Step 1: Obtain flight information, time slot information, and airspace capacity information;
[0008] Step 2: Based on flight information and time slot information, determine the objective function as minimizing the total flight delay cost and the airline fairness coefficient;
[0009] Step 3: Set flight uniqueness constraints, time slot uniqueness constraints, time slot feasibility constraints, capacity constraints, and maximum threshold constraints to complete the route and time slot resource allocation model;
[0010] Step 4: Use a genetic algorithm to solve the constructed route and time slot resource allocation model;
[0011] Step 5: Output the route and time slot resource allocation scheme.
[0012] Step 1 specifically includes: determining the scope of restricted airspace by combining airspace capacity analysis and meteorological radar data; obtaining detailed information on affected flights by combining flight schedules released by air traffic control departments, including estimated arrival time, airline, available time slots, and airspace capacity; and simultaneously accessing the airline operation database to analyze airlines' preferences for specific airspaces and routes, including airlines' route selection preferences, the acceptability of alternative routes, and providing the relative trajectory costs corresponding to different flight plans and alternative routes.
[0013] The objective function formula for minimizing the total flight delay cost in step 2 is:
[0014]
[0015] Among them, t j p represents the start and end times of the time slot. ik φ represents the estimated arrival time of flight i assigned to route k, and φ is the ground delay cost coefficient. This represents the relative trajectory cost of flight i assigned to route k; when flight i is assigned to time slot j on route k, x ijk =1, otherwise x ijk =0; when flight i changes to route k, otherwise
[0016] The objective function formula for minimizing the airline's fairness coefficient in step 2 is:
[0017]
[0018]
[0019] in, q represents the average flight delay cost for airline a; A q represents the total number of airlines; a This indicates the number of airlines a.
[0020] Step 3 specifically includes the following steps:
[0021] Step 3.1: Set a unique constraint for flight allocation, so that each time slot can only be allocated to one flight to avoid conflicting allocations or resource duplication;
[0022] Step 3.2: Set a unique constraint on time slot allocation, so that each flight can only be allocated one time slot to avoid conflicting allocations or resource duplication;
[0023] Step 3.3: Set time slot feasibility constraints to ensure that the allocated time slots are later than the scheduled arrival time of the flight;
[0024] Step 3.4: Set capacity constraints to ensure that the time slot resources on the allocated air routes are less than the air route capacity limit;
[0025] Step 3.5: Set a maximum threshold constraint to ensure that the time slot resources allocated to flights do not exceed the maximum threshold, in order to prevent excessive delays and avoid the additional safety and control load caused by excessive time slot allocation delays.
[0026] The formula for setting the flight allocation uniqueness constraint in step 3.1 is as follows:
[0027] Where I represents the total number of flights; J represents the total number of time slots;
[0028] K i Indicates the total number of routes;
[0029] The formula for setting the uniqueness constraint of time slot allocation in step 3.2 is as follows:
[0030]
[0031] The formula for setting the flight allocation uniqueness constraint in step 3.3 is as follows:
[0032]
[0033] The formula for setting time slot feasibility constraints in step 3.4 is as follows:
[0034] Among them, F k This indicates the total capacity limit of route k;
[0035] The formula for setting the maximum threshold constraint in step 3.5 is as follows:
[0036] Among them, t max This indicates the maximum range of time slots that can be allocated.
[0037] Step 4 specifically includes the following steps:
[0038] Step 4.1: Extract the iteration count from the current genetic algorithm;
[0039] Step 4.2: Population initialization. Randomly generate an initial population. The population contains several chromosomes, and each chromosome represents a route time slot resource allocation scheme.
[0040] Step 4.3: Fitness assessment. Based on the objective function of the route time slot resource allocation model, calculate the fitness value of each individual to measure the quality of its solution.
[0041] Step 4.4: Selection operation. The binary tournament selection method is used to select individuals with high fitness from the current population as parent individuals for subsequent crossover and mutation operations, thus preserving superior genes.
[0042] Step 4.5, Crossover and Mutation Operations: Perform crossover operations on the selected parent individuals using the partially matched crossover operator to generate new offspring individuals; then perform mutation operations on some individuals using the exchange operator to introduce new gene combinations, enhance population diversity, and evaluate the fitness of the new individuals, retaining better solutions.
[0043] Step 4.6: Iterative evolution. Repeat the selection, crossover, mutation, and fitness evaluation process. Increment the iteration count by 1 to get the new iteration count. Continuously update the population iteratively and gradually approach the optimal solution.
[0044] Step 4.7: Termination condition judgment. When the algorithm meets the termination condition (such as reaching the maximum number of iterations or the fitness of the best individual not changing significantly for several consecutive generations), output the route time slot resource allocation scheme corresponding to the current best individual; otherwise, return to step 3 and continue iteratively solving until the optimal solution is obtained.
[0045] The fitness function expression in step 4.3 is as follows:
[0046] Fit represents the fitness value of the chromosome.
[0047] Beneficial effects:
[0048] 1. This invention, through the scientific and systematic allocation of air routes and time slots, with the dual core objectives of minimizing delays and improving fairness, effectively reduces flight delay rates and significantly increases airlines' participation and satisfaction in collaborative decision-making. This dual optimization mechanism promotes close cooperation between airlines and air traffic control departments, providing solid technical support and decision-making basis for building an efficient and fair air traffic management system.
[0049] 2. This invention ensures that the take-off and landing sequence of flights is reasonable and efficient by setting multiple constraints, such as flight uniqueness constraints, time slot uniqueness constraints, time slot feasibility constraints, capacity constraints, and maximum threshold constraints. These constraints work together to optimize the operational efficiency and safety of the entire flight network, effectively reduce the chain reaction caused by delays, and improve the smoothness and reliability of overall air traffic.
[0050] 3. This invention uses a genetic algorithm as the core tool for solving the route and time slot resource allocation model. With its fast convergence speed and strong global search capability, the algorithm performs well in dealing with complex multi-objective optimization problems; it significantly improves the efficiency and effectiveness of route and time slot resource allocation, and brings greater flexibility and response speed to air traffic management. Attached Figure Description
[0051] Figure 1 is a flowchart of the method of the present invention.
[0052] Figure 2 shows an example of airspace route selection.
[0053] Figure 3 is a flowchart of the genetic algorithm of the present invention. Detailed Implementation
[0054] This invention specifically provides a route and time slot collaborative optimization method that takes into account airline preferences, including the following steps:
[0055] Step 1: Obtain flight information, time slot information, and airspace capacity information;
[0056] Step 2: Based on flight information and time slot information, determine the objective function as minimizing the total flight delay cost and the airline fairness coefficient;
[0057] Step 3: Set flight uniqueness constraints, time slot uniqueness constraints, time slot feasibility constraints, capacity constraints, and maximum threshold constraints to complete the route and time slot resource allocation model;
[0058] Step 4: Use a genetic algorithm to solve the constructed route and time slot resource allocation model;
[0059] Step 5: Output the route and time slot resource allocation scheme.
[0060] Step 1 specifically includes: determining the scope of restricted airspace by combining airspace capacity analysis and meteorological radar data; obtaining detailed information on affected flights by combining flight schedules released by air traffic control departments, including estimated arrival time, airline, available time slots, and airspace capacity; and simultaneously accessing the airline operation database to analyze airlines' preferences for specific airspaces and routes, including airlines' route selection preferences, the acceptability of alternative routes, and providing the relative trajectory costs corresponding to different flight plans and alternative routes.
[0061] The objective function formula for minimizing the total flight delay cost in step 2 is:
[0062]
[0063] Among them, t j p represents the start and end times of the time slot. ik φ represents the estimated arrival time of flight i assigned to route k, and φ is the ground delay cost coefficient. This represents the relative trajectory cost of flight i assigned to route k; when flight i is assigned to time slot j on route k, x ijk =1, otherwise x ijk =0; when flight i changes to route k, otherwise
[0064] The objective function formula for minimizing the airline's fairness coefficient in step 2 is:
[0065]
[0066] in, q represents the average flight delay cost for airline a; A q represents the total number of airlines; a This indicates the number of airlines a.
[0067] Step 3 specifically includes the following steps:
[0068] Step 3.1: Set a unique constraint for flight allocation, so that each time slot can only be allocated to one flight to avoid conflicting allocations or resource duplication;
[0069] Step 3.2: Set a unique constraint on time slot allocation, so that each flight can only be allocated one time slot to avoid conflicting allocations or resource duplication;
[0070] Step 3.3: Set time slot feasibility constraints to ensure that the allocated time slots are later than the scheduled arrival time of the flight;
[0071] Step 3.4: Set capacity constraints to ensure that the time slot resources on the allocated air routes are less than the air route capacity limit;
[0072] Step 3.5: Set a maximum threshold constraint to ensure that the time slot resources allocated to flights do not exceed the maximum threshold, in order to prevent excessive delays and avoid the additional safety and control load caused by excessive time slot allocation delays.
[0073] The formula for setting the flight allocation uniqueness constraint in step 3.1 is as follows:
[0074] Where I represents the total number of flights; J represents the total number of time slots;
[0075] K i Indicates the total number of routes;
[0076] The formula for setting the uniqueness constraint of time slot allocation in step 3.2 is as follows:
[0077]
[0078] The formula for setting the flight allocation uniqueness constraint in step 3.3 is as follows:
[0079]
[0080] The formula for setting time slot feasibility constraints in step 3.4 is as follows:
[0081] Among them, F k This indicates the total capacity limit of route k;
[0082] The formula for setting the maximum threshold constraint in step 3.5 is as follows:
[0083] Among them, t max This indicates the maximum range of time slots that can be allocated.
[0084] Step 4 specifically includes the following steps:
[0085] Step 4.1: Extract the iteration count from the current genetic algorithm;
[0086] Step 4.2: Population initialization. Randomly generate an initial population. The population contains several chromosomes, and each chromosome represents a route time slot resource allocation scheme.
[0087] Step 4.3: Fitness assessment. Based on the objective function of the route time slot resource allocation model, calculate the fitness value of each individual to measure the quality of its solution.
[0088] Step 4.4: Selection operation. The binary tournament selection method is used to select individuals with high fitness from the current population as parent individuals for subsequent crossover and mutation operations, thus preserving superior genes.
[0089] Step 4.5, Crossover and Mutation Operations: Perform crossover operations on the selected parent individuals using the partially matched crossover operator to generate new offspring individuals; then perform mutation operations on some individuals using the exchange operator to introduce new gene combinations, enhance population diversity, and evaluate the fitness of the new individuals, retaining better solutions.
[0090] Step 4.6: Iterative evolution. Repeat the selection, crossover, mutation, and fitness evaluation process. Increment the iteration count by 1 to get the new iteration count. Continuously update the population iteratively and gradually approach the optimal solution.
[0091] Step 4.7: Termination condition judgment. When the algorithm meets the termination condition (such as reaching the maximum number of iterations or the fitness of the best individual not changing significantly for several consecutive generations), output the route time slot resource allocation scheme corresponding to the current best individual; otherwise, return to step 3 and continue iteratively solving until the optimal solution is obtained.
[0092] The fitness function expression in step 4.3 is as follows:
[0093] Fit represents the fitness value of the chromosome.
[0094] Those skilled in the art will clearly recognize that the technical solution of this invention is not limited to theoretical concepts, but can be practically implemented by combining computer programs with their corresponding general-purpose hardware platforms. Based on this profound understanding, the core technological innovations of this invention, namely those parts that make substantial contributions to the prior art, can be fully presented and implemented in the specific form of computer programs (i.e., software products).
[0095] The route and time slot collaborative optimization method proposed in this invention, which considers airline preferences, offers diverse and flexible implementation paths and methods. The above description is merely one preferred embodiment of this invention, intended to provide a specific and feasible technical framework. However, those skilled in the art are fully capable of making various reasonable improvements and modifications to the technical solution based on the needs of actual application scenarios, without departing from the core principles and spirit of this invention. These improvements and modifications, including but not limited to algorithm optimization, parameter adjustment, and model refinement, should all be considered reasonable extensions and applications of the technology of this invention and are equally covered by the scope of protection of this invention.
Claims
1. A route and time slot co-optimization method considering airline preferences, characterized in that, Includes the following steps: Step 1: Obtain flight information, time slot information, and airspace capacity information; Step 2: Based on the flight information and time slot information, determine the objective function as minimizing the total flight delay cost and the airline fairness coefficient; Step 3: Set flight uniqueness constraints, time slot uniqueness constraints, time slot feasibility constraints, capacity constraints, and maximum threshold constraints to construct the route and time slot resource allocation model; Step 4: Use a genetic algorithm to solve the constructed route and time slot resource allocation model; Step 5: Output the route and time slot resource allocation scheme.
2. The route and time slot collaborative optimization method considering airline preferences according to claim 1, characterized in that, Step 1 specifically includes: determining the scope of restricted airspace by combining airspace capacity analysis and meteorological radar data; obtaining detailed information on affected flights by combining flight schedules released by air traffic control departments, including estimated arrival time, airline, available time slots, and airspace capacity; and simultaneously accessing the airline operation database to analyze airlines' preferences for specific airspaces and routes, including airlines' route selection preferences, the acceptability of alternative routes, and providing the relative trajectory costs corresponding to different flight plans and alternative routes.
3. The route and time slot collaborative optimization method considering airline preferences according to claim 2, characterized in that, The objective function formula for minimizing the total flight delay cost in step 2 is: Among them, t j p represents the start and end times of the time slot. ik φ represents the estimated arrival time of flight i assigned to route k, and φ is the ground delay cost coefficient. This represents the relative trajectory cost of flight i assigned to route k; when flight i is assigned to time slot j on route k, x ijk =1, otherwise x ijk =0; when flight i changes to route k, otherwise 4. The route and time slot collaborative optimization method considering airline preferences according to claim 2, characterized in that, The objective function formula for minimizing the airline fairness coefficient in step 2 is: in, q represents the average flight delay cost for airline a; A q represents the total number of airlines; a This indicates the number of airlines a.
5. The route and time slot collaborative optimization method considering airline preferences according to claim 1, characterized in that, Step 3 specifically includes the following steps: Step 1, setting a unique constraint on flight allocation, ensuring that each time slot can only be allocated to one flight to avoid conflicting allocations or resource duplication; Step 2, setting a unique constraint on time slot allocation, ensuring that each flight can only be allocated one time slot to avoid conflicting allocations or resource duplication; Step 3, setting a time slot feasibility constraint to ensure that the allocated time slot is later than the scheduled arrival time of the flight; Step 4, setting a capacity constraint to ensure that the time slot resources on the allocated route are less than the route capacity limit; Step 5, setting a maximum threshold constraint to ensure that the time slot resources allocated to the flight do not exceed the maximum threshold, in order to prevent excessive delays and avoid the additional safety and control load caused by excessive time slot allocation delays.
6. The route and time slot collaborative optimization method considering airline preferences according to claim 5, characterized in that, The formula for setting the flight allocation uniqueness constraint in step 1 is as follows: Where I represents the total number of flights; J represents the total number of time slots; K i This represents the total number of air routes; the formula for setting the uniqueness constraint of time slot allocation in step 2 is: The formula for setting the flight allocation uniqueness constraint in step 3 is as follows: The formula for setting time slot feasibility constraints in step 4 is: Among them, F k This represents the total capacity limit of route k; the formula for setting the maximum threshold constraint in step 5 is: Among them, t max This indicates the maximum range of time slots that can be allocated.
7. The route and time slot collaborative optimization method considering airline preferences according to claim 1, characterized in that, Step 4 specifically includes the following steps: Step 1, extracting the iteration count from the current genetic algorithm; Step 2, population initialization, randomly generating an initial population containing several chromosomes, each chromosome representing a route time slot resource allocation scheme; Step 3, fitness evaluation, calculating the fitness value of each individual based on the objective function of the route time slot resource allocation model to measure the quality of its solution; Step 4, selection operation, using a binary tournament selection method to select individuals with high fitness from the current population as parent individuals for subsequent crossover and mutation operations, preserving superior genes; Step 5, crossover and mutation operation, applying a partially matched crossover operator to the selected parent individuals. Step 6: Perform crossover to generate new offspring individuals; then, use the exchange operator to mutate some individuals, introducing new gene combinations to enhance population diversity, and evaluate the fitness of the new individuals, retaining the better solution; Step 7: Iterative evolution, repeating the selection, crossover, mutation, and fitness evaluation process, incrementing the iteration count by 1 to obtain the new iteration count, continuously iterating and updating the population, gradually approaching the optimal solution; Step 8: Termination condition judgment, when the algorithm meets the termination condition (such as reaching the maximum number of iterations, or the fitness of the optimal individual not changing significantly for several consecutive generations), output the route time slot resource allocation scheme corresponding to the current optimal individual; otherwise, return to step 3 and continue iteratively solving until the optimal solution is obtained.
8. The route and time slot collaborative optimization method considering airline preferences according to claim 7, characterized in that, The fitness function expression in step 3 is as follows: Fit represents the fitness value of the chromosome.