Airway time slot resource allocation method considering uncertainty multi-leg delay transportation
By constructing a route time slot resource allocation model and using the cuckoo algorithm to solve it, the problem of unreasonable resource allocation caused by flight delays is solved, and a more efficient and safe flight network operation and a better passenger experience is achieved.
Patent Information
- Application Number
- CN202411650947.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-02-18
AI Technical Summary
The prior art is difficult to effectively solve the problem of unreasonable allocation of route time slot resources caused by flight delays, which increases the uncertainty of resource allocation and the difficulty of decision-making.
By constructing a route time slot resource allocation model, multiple constraints are set, such as unique constraints, chronological constraints, capacity constraints and flight order constraints, and the solution is used to use the cuckoo algorithm to output the optimal route time slot resource allocation scheme.
This method effectively reduces the additional transportation losses caused by flight delays, improves the operation efficiency and safety of the flight network, reduces passenger waiting time, and improves passenger satisfaction and travel experience.
Smart Images
Figure CN119479377A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of air transportation system optimization in the field of aviation and aerospace, and in particular to a method for allocating route time slot resources considering uncertain multi-segment delayed transportation. Background Art
[0002] The air transport system is a complex and highly dynamic network. In modern air transport, most flights are multi-segment, that is, passengers need to transfer or transit at different airports. The delay of one flight will lead to the delay of subsequent flights, affecting the normal operation of the entire flight network. However, severe weather conditions can cause flight delays; during peak periods or special circumstances, the establishment of traffic control will affect the take-off and landing times of flights. Flight delays will lead to the need to readjust the time slot resource allocation of flights, and also increase the uncertainty of resource allocation schemes. Therefore, it is of great significance to study the route time slot resource allocation method.
[0003] In the prior art, some methods optimize the route paths of aircraft transferred under route joint ventures to reduce unnecessary flight time and costs and improve overall operational efficiency. However, these methods involve the coordination of multiple flight segments and transfer points. Transfer itself will bring additional time and costs, and does not consider issues such as flight delays, cancellations, or lost luggage. Other methods avoid waste of resources and duplicate configuration by optimizing route configuration. By accurately matching market demand and route network layout, the flight load factor and operational efficiency can be improved. However, these methods need to comprehensively consider multiple factors such as market demand, route network layout, operating costs, safety standards, etc., which increases the difficulty and uncertainty of decision-making and cannot solve flight delays.
[0004] Some methods have also built safety-aware real-time air traffic flow management models, which ensure safe spacing and avoidance between aircraft through optimization strategies, thereby improving the overall safety of the air traffic system. However, when these methods are applied to airlines with complex route networks, they need to comprehensively consider factors such as the interactive impact of multiple routes, the coordination of flight schedules, and the capacity constraints of different routes, making it difficult for the model to find a global optimal solution.
[0005] In addition, some methods apply C-band measurement data to route optimization. C-band measurement data supports high-speed air-to-ground communication needs and provides strong data support for route optimization. However, C-band measurement data is mainly used to analyze the main signal component of the air-to-ground channel, and cannot fully reflect all the information required for route optimization, such as terrain, weather, route capacity, and traffic flow. It is easy to cause route delays due to incomplete information.
[0006] Therefore, in view of the problems existing in the above methods in route optimization, the present invention proposes a route time slot resource allocation method considering uncertain multi-segment delayed transportation. Summary of the invention
[0007] In view of the shortcomings of the prior art, the present invention provides a route time slot resource allocation method for multi-segment delayed transportation taking into account uncertainty, which solves the problem of unreasonable route time slot resource allocation caused by uncertainty factors caused by flight delays.
[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a route time slot resource allocation method considering uncertain multi-segment delayed transportation, comprising the following steps:
[0009] S1. Construct a route time slot resource allocation model, taking the minimum total delay loss of multiple flight segments and the minimum average passenger delay time as the objective function;
[0010] S2. Set uniqueness constraints, time order constraints, capacity constraints, and flight order constraints;
[0011] S3. Use the cuckoo algorithm to solve the constructed route time slot resource allocation model and output the optimal route time slot resource allocation plan.
[0012] Preferably, the step S1 specifically includes the following steps:
[0013] S1.1, the objective function of the first stage of the time slot resource allocation model is constructed with the minimum total delay transportation loss of multiple flight segments;
[0014] S1.2. The minimum average passenger delay time considering uncertain multi-segment delayed transportation is selected as the second-stage objective function of the route time slot resource allocation model.
[0015] Preferably, the objective function formula of the first stage in step S1.1 is:
[0016]
[0017] in, With q s They represent the unit time delay costs of temporary route k and planned route respectively, and x y and They represent the decision variables for assigning flight i to time slot j and choosing the planned route and temporary route k. With t j They represent the planned arrival time and the start time of the time slot respectively.
[0018] Preferably, the objective function formula of the second stage in step S1.2 is:
[0019]
[0020] Among them, n irepresents the number of passengers carried by flight i.
[0021] Preferably, the step S2 specifically includes the following steps:
[0022] S2.1. Set a unique constraint on time slot resource allocation to avoid resource duplication or conflicting allocation. A flight can only be allocated one time slot.
[0023] S2.2, set time sequence constraints to ensure that flights are carried out in time sequence;
[0024] S2.3, set feasibility constraints to ensure that the allocated time slot resources are within a reasonable time range;
[0025] S2.4. Set capacity constraints to ensure that the allocated route time slot resources are less than the route capacity limit;
[0026] S2.5. Set flight order constraints to specify the priority or order of certain specific flights.
[0027] Preferably, the formula for setting the uniqueness constraint of time slot resource allocation in step S2.1 is:
[0028] Where I represents the total number of flights;
[0029] The formula for the timing constraint in step S2.2 is:
[0030] t j ≥t ij r ij , where t ij With r ij They represent the time when flight i enters the flow-restricted area within route j and the decision variables respectively;
[0031] The formula for the feasibility constraint in step S2.3 is:
[0032]
[0033] In the step S2.4, the capacity constraint of the time slot resource allocation is set. The constraint formula of the allocated route time slot resource needs to be less than the route capacity limit is as follows:
[0034] Among them, t bj With t fj They represent the time slots of the first flight b and the last flight f of route j, respectively. j Indicates the total capacity limit of the route;
[0035] The formula for the flight sequence constraint in step S2.5 is:
[0036] μi =min j∈J t ij .
[0037] Preferably, the step S3 specifically includes the following steps:
[0038] S3.1, population initialization, randomly generate several cuckoo populations, each population contains several individuals;
[0039] S3.2, evaluate the fitness value of each individual;
[0040] S3.3, conduct global exploration, update population individuals, and retain individuals with better fitness values;
[0041] S3.4, conduct local development, generate new populations, update fitness values, and retain better solutions;
[0042] S3.5, continue to iterate until the optimal solution is obtained;
[0043] S3.6. When the optimal individual no longer changes, the optimal solution for route time slot resource allocation is output. If it is not satisfied, return to step S3.2 and continue to solve the route time slot resource allocation model until the optimal solution is output.
[0044] Preferably, the fitness function expression in step S3.2 is as follows:
[0045] F=minO1+minO2
[0046] In step S3.3, the expression for the global exploration of cuckoo flight based on the Levy algorithm is:
[0047] in, With x b They represent the current nest position and the optimal nest position respectively, α and ξ represent the step length control factor and the flight step length respectively, and Levy represent dot product and Levy probability distribution respectively.
[0048] The present invention provides a route time slot resource allocation method for transport with uncertain multi-segment delays.
[0049] It has the following beneficial effects:
[0050] 1. The present invention establishes a route time slot resource allocation model that takes into account uncertain multi-segment delayed transportation. The model comprehensively considers the actual carrying capacity and restriction conditions of the route to ensure that the capacity limit of the route is not exceeded when allocating time slot resources, thereby effectively avoiding resource waste and allocation conflicts.
[0051] 2. The present invention makes the allocation scheme more flexible and real-time by dynamically adjusting the allocation of route time slot resources, which helps to reduce the additional transportation losses caused by delays and improve economic benefits. By taking the minimum average passenger delay time as the objective function, it can minimize the waiting time of passengers and improve passenger satisfaction and travel experience.
[0052] 3. The present invention ensures that the take-off and landing sequence of flights is reasonable by setting uniqueness constraints, time sequence constraints, capacity constraints and flight sequence constraints, optimizes the operating efficiency and safety of the entire flight network, and reduces the chain reaction caused by delays.
[0053] 4. The present invention uses the cuckoo algorithm, which has a faster convergence speed and stronger global search capability, and can find a near-optimal solution in a complex multi-objective optimization problem, thereby improving the efficiency and effect of time slot resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS
[0054] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION
[0055] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0056] Example:
[0057] Please see attached Figure 1 The embodiment of the present invention provides a route time slot resource allocation method considering uncertain multi-segment delayed transportation, comprising the following steps:
[0058] S1. Construct a route time slot resource allocation model, taking the minimum total delay loss of multiple flight segments and the minimum average passenger delay time as the objective function;
[0059] S2. Set uniqueness constraints, time order constraints, capacity constraints, and flight order constraints;
[0060] S3. Use the cuckoo algorithm to solve the constructed route time slot resource allocation model and output the optimal route time slot resource allocation plan.
[0061] Specifically, in step S1, by constructing the objective function of minimizing the total delay loss of multiple flight segments, the additional transportation costs caused by flight delays can be effectively reduced. At the same time, the objective function of minimizing the average passenger delay time can be used to minimize the waiting time of passengers and improve passenger satisfaction.
[0062] By reducing the additional costs caused by delays, the operational efficiency of airlines can be improved. At the same time, the delay time of passengers can be optimized, and the travel experience and satisfaction of passengers can be improved.
[0063] This solves the problem in the prior art that multi-segment delays and passenger delay time are not effectively considered, resulting in low flight network operation efficiency and poor passenger experience.
[0064] Step S2 sets a variety of constraints to ensure that the allocated time slot resources are not reused and are reasonable and effective. It also improves the safety and efficiency of flight operations by constraining the time sequence and flight sequence.
[0065] Avoid resource waste: Ensure that each time slot can only be allocated to one flight to avoid duplicate or conflicting resource allocation.
[0066] Improve operational efficiency: Improve the overall operational efficiency of the flight network through reasonable sequence and capacity constraints.
[0067] The problem that resource allocation in the prior art often results in duplication or conflict, resulting in low operating efficiency and poor security is solved.
[0068] The cuckoo algorithm in step S3 has a fast convergence speed and can quickly find a solution close to the optimal one. The global search and local development capabilities of the cuckoo algorithm improve the efficiency and effect of model solving.
[0069] Improve solution efficiency: Through the optimization capability of the cuckoo algorithm, complex multi-objective optimization problems can be solved efficiently.
[0070] Ensure the optimality of the solution: Through the iterative optimization of the algorithm, the optimality of the route time slot resource allocation plan is guaranteed.
[0071] The invention solves the problem in the prior art that the algorithm has low solving efficiency and is difficult to find the global optimal solution, resulting in a less than ideal allocation solution.
[0072] The S1 step specifically includes the following steps:
[0073] S1.1, the objective function of the first stage of the time slot resource allocation model is constructed with the minimum total delay transportation loss of multiple flight segments;
[0074] S1.2. The minimum average passenger delay time considering uncertain multi-segment delayed transportation is selected as the second-stage objective function of the route time slot resource allocation model.
[0075] The objective function formula of the first stage in step S1.1 is:
[0076]
[0077] in, With q s They represent the unit time delay costs of temporary route k and planned route respectively, and x y and They represent the decision variables for assigning flight i to time slot j and choosing the planned route and temporary route k. With t j They represent the planned arrival time and the start time of the time slot respectively.
[0078] The objective function formula of the second stage in step S1.2 is:
[0079]
[0080] Among them, n i represents the number of passengers carried by flight i.
[0081] Specifically, flight delays have many uncertain factors, such as weather, mechanical failure, traffic control, etc., which lead to uncertainty in flight information. Minimizing the total delay transportation loss can help airlines reduce additional costs and improve economic benefits; minimizing the average passenger delay time focuses on passenger experience and satisfaction. Therefore, taking the total delay transportation loss and the average passenger delay time as the objective function, the impact of these uncertain factors on the allocation of route time slot resources is comprehensively considered.
[0082] Since there is a significant difference in the operating costs between temporary routes and planned routes, the cost of flights flying on temporary routes is higher. The objective function of the first stage of the time slot resource allocation model is constructed with the minimum total delay loss of multiple segments, and its expression is as follows:
[0083]
[0084] in, With q s They represent the unit time delay costs of temporary route k and planned route respectively, and x y and They represent the decision variables for assigning flight i to time slot j and choosing the planned route and temporary route k. With t j They represent the planned arrival time and the start time of the time slot respectively.
[0085] When there are multiple flight delays and route congestion due to uncertainties, the passenger delay time increases. The minimum average passenger delay time for multiple flight delays is selected as the second-stage objective function of the route time slot resource allocation model, and its expression is as follows:
[0086]
[0087] Among them, n i represents the number of passengers carried by flight i.
[0088] The S2 step specifically includes the following steps:
[0089] S2.1. Set a unique constraint on time slot resource allocation to avoid resource duplication or conflicting allocation. A flight can only be allocated one time slot.
[0090] S2.2, set time sequence constraints to ensure that flights are carried out in time sequence;
[0091] S2.3, set feasibility constraints to ensure that the allocated time slot resources are within a reasonable time range;
[0092] S2.4. Set capacity constraints to ensure that the allocated route time slot resources are less than the route capacity limit;
[0093] S2.5. Set flight order constraints to specify the priority or order of certain specific flights.
[0094] The formula for setting the uniqueness constraint of time slot resource allocation in step S2.1 is:
[0095] Where I represents the total number of flights;
[0096] The formula for the timing constraint in step S2.2 is:
[0097] t j ≥t ij r ij , where t ij With r ij They represent the time when flight i enters the flow-restricted area within route j and the decision variables respectively;
[0098] The formula for the feasibility constraint in step S2.3 is:
[0099]
[0100] In step S2.4, the capacity constraint of time slot resource allocation is set. The constraint formula of the allocated route time slot resource needs to be less than the route capacity limit is as follows:
[0101] Among them, t bj With t fj They represent the time slots of the first flight b and the last flight f of route j, respectively. j Indicates the total capacity limit of the route;
[0102] The formula for the flight sequence constraint in step S2.5 is:
[0103] μ i =min j∈J t ij .
[0104] Specifically, considering the uncertain multi-segment delayed transportation, the route time slot resource allocation model is constructed, and the constraints are set as follows:
[0105] (1) To ensure that each time slot can only be allocated to one flight, a unique constraint on time slot resource allocation is set to avoid resource duplication or conflicting allocation. A flight can only be allocated one time slot:
[0106] Where I represents the total number of flights.
[0107] (2) Flight operations are subject to timing constraints, that is, there is a sequence between flights:
[0108] t j ≥t ij r ij , where t ij With r ij They represent the time when flight i enters the flow-restricted area within route j and the decision variables respectively.
[0109] (3) Set feasibility constraints for time slot resource allocation to ensure the rationality of the allocated time slot resources:
[0110]
[0111] (4) Set the capacity constraint for time slot resource allocation. The allocated route time slot resources need to be less than the route capacity limit. The constraint is as follows:
[0112] Among them, t bj With t fj They represent the time slots of the first flight b and the last flight f of route j, respectively. j Indicates the total capacity limit of the route.
[0113] (5) The take-off and landing order of flights will affect the operating efficiency and safety of the entire flight network. Flight sequence constraints can be used to specify the priority or order of certain specific flights to ensure the smooth operation of the flight system:
[0114]
[0115] The S3 step specifically includes the following steps:
[0116] S3.1, population initialization, randomly generate several cuckoo populations, each population contains several individuals;
[0117] S3.2, evaluate the fitness value of each individual;
[0118] S3.3, conduct global exploration, update population individuals, and retain individuals with better fitness values;
[0119] S3.4, conduct local development, generate new populations, update fitness values, and retain better solutions;
[0120] S3.5, continue to iterate until the optimal solution is obtained;
[0121] S3.6. When the optimal individual no longer changes, the optimal solution for route time slot resource allocation is output. If it is not satisfied, return to step S3.2 and continue to solve the route time slot resource allocation model until the optimal solution is output.
[0122] The fitness function expression in step S3.2 is as follows:
[0123] F=minO1+minO2
[0124] In step S3.3, the expression for the global exploration of cuckoo flight based on Levy algorithm is:
[0125] in, With x b They represent the current nest position and the optimal nest position respectively, α and ξ represent the step length control factor and the flight step length respectively, and Levy represent dot product and Levy probability distribution respectively.
[0126] Specifically, due to the uncertainty of flight information, it is impossible to obtain the optimal resource allocation plan. At the same time, considering that there are multiple objectives in the flight delay problem, including reducing the total delay transportation loss and the average passenger delay time, the cuckoo algorithm is suitable for multi-objective optimization problems and can find an optimal solution that balances all objectives. This method has a fast convergence speed, can quickly search the solution space, and find a solution close to the optimal solution.
[0127] Assume that the number of flights and optional routes with uncertain multi-segment delays are represented by i and B respectively, and the number of allocable time slots contained in each route is M. Considering the constraints set by the model, each flight needs to select the best route time slot resource allocation scheme among BM possible time slots. The cuckoo algorithm introduces the discovery probability and solves the constructed time slot resource allocation model from two aspects: global random exploration and local random development.
[0128] The expression of the discovery probability distribution of the cuckoo algorithm is as follows:
[0129]
[0130] Among them, p a,i and represent the probability of finding an egg in nest i and the average probability of finding a cuckoo egg, respectively. n and f i They represent the number of bird nests and the fitness value of the route time slot resource allocation model respectively.
[0131] According to the route time slot resource allocation model, the probability p a,i The solution result of the model is locally developed. By combining global exploration with local development, the search speed of the route resource time slot allocation model is improved. The algorithm is based on the Levy algorithm, and the expression of the global exploration of cuckoo flight is as follows:
[0132] in, With x b They represent the current nest position and the optimal nest position respectively, α and ξ represent the step length control factor and the flight step length respectively, and Levy represent dot product and Levy probability distribution respectively.
[0133] Based on the discovery probability p a , the expression for solving the local development of the route time slot resource allocation model is as follows:
[0134]
[0135] Among them, r and H represent the scaling factor and Heaviside function respectively. Represents the different route time slot resource allocation schemes during the random walk process.
[0136] The cuckoo algorithm combines global exploration and local development, and uses parallel search to improve the search capability for the optimal solution of route time slot resource allocation. The process of using the cuckoo algorithm to solve the route time slot resource allocation model is as follows:
[0137] (1) Population initialization
[0138] M cuckoo populations are randomly generated, and each population contains N individuals.
[0139] (2) Evaluate fitness value
[0140] The fitness function expression for route time slot resource allocation is as follows:
[0141] F=minO1+minO2
[0142] (3) Combine multiple populations to conduct global exploration of the optimal solution for route time slot resource allocation. Use the above formula for global exploration of cuckoo flight to perform global search, calculate the individual fitness values in the update process, and retain individuals with better individual fitness values.
[0143] (4) Carry out local development of the multiple populations generated in the previous step. Based on the discovery probability p a ,use Generate a new population through the greedy search method. In the iterative process, the individual fitness values are continuously updated to retain the better solutions. Through the above process, the evolution of the cuckoo population is completed.
[0144] (5) Using the above process, the elite population is continuously updated and the best individuals are continuously preserved in the elite population.
[0145] (6) When the optimal individual no longer changes, the optimal solution for route time slot resource allocation is output. If it is not satisfied, return to step (2) and continue to solve the route time slot resource allocation model until the optimal solution is output.
[0146] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A route time slot resource allocation method considering uncertain multi-segment delayed transportation, characterized in that: The following steps are involved: S1. Construct a route time slot resource allocation model, taking the minimum total delay loss of multiple flight segments and the minimum average passenger delay time as the objective function; S2. Set uniqueness constraints, time order constraints, capacity constraints, and flight order constraints; S3. Use the cuckoo algorithm to solve the constructed route time slot resource allocation model and output the optimal route time slot resource allocation plan.
2. The method for allocating route time slot resources for uncertain multi-segment delayed transportation according to claim 1, characterized in that: The S1 step specifically includes the following steps: S1.1, the objective function of the first stage of the time slot resource allocation model is constructed with the minimum total delay transportation loss of multiple flight segments; S1.
2. The minimum average passenger delay time considering uncertain multi-segment delayed transportation is selected as the second-stage objective function of the route time slot resource allocation model.
3. The method for allocating route time slot resources for multi-segment delayed transportation considering uncertainty according to claim 2, characterized in that: The objective function formula of the first stage in step S1.1 is: in, With q s They represent the unit time delay costs of temporary route k and planned route respectively, and x y and They represent the decision variables for assigning flight i to time slot j and choosing the planned route and temporary route k. With t j They represent the planned arrival time and the start time of the time slot respectively.
4. The method for allocating route time slot resources for multi-segment delayed transportation considering uncertainty according to claim 2, characterized in that: The objective function formula of the second stage in step S1.2 is: Among them, n i represents the number of passengers carried by flight i.
5. The method for allocating route time slot resources for uncertain multi-segment delayed transportation according to claim 1, characterized in that: The S2 step specifically includes the following steps: S2.
1. Set a unique constraint on time slot resource allocation to avoid resource duplication or conflicting allocation. A flight can only be allocated one time slot. S2.2, set time sequence constraints to ensure that flights are carried out in time sequence; S2.3, set feasibility constraints to ensure that the allocated time slot resources are within a reasonable time range; S2.
4. Set capacity constraints to ensure that the allocated route time slot resources are less than the route capacity limit; S2.
5. Set flight order constraints to specify the priority or order of certain specific flights.
6. The method for allocating route time slot resources for uncertain multi-segment delayed transportation according to claim 5, characterized in that: The formula for setting the uniqueness constraint of time slot resource allocation in step S2.1 is: Where I represents the total number of flights; The formula for the timing constraint in step S2.2 is: t j ≥t ij r ij , where t ij With r ij They represent the time when flight i enters the flow-restricted area within route j and the decision variables respectively; The formula for the feasibility constraint in step S2.3 is: In the step S2.4, the capacity constraint of the time slot resource allocation is set. The constraint formula of the allocated route time slot resource needs to be less than the route capacity limit is as follows: Among them, t bj With t fj They represent the time slots of the first flight b and the last flight f of route j, respectively. j Indicates the total capacity limit of the route; The formula for the flight sequence constraint in step S2.5 is: m i =min j∈J t ij 。 7. The method for allocating route time slot resources for multi-segment delayed transportation considering uncertainty according to claim 1, characterized in that: The S3 step specifically includes the following steps: S3.1, population initialization, randomly generate several cuckoo populations, each population contains several individuals; S3.2, evaluate the fitness value of each individual; S3.3, conduct global exploration, update population individuals, and retain individuals with better fitness values; S3.4, conduct local development, generate new populations, update fitness values, and retain better solutions; S3.5, continue to iterate until the optimal solution is obtained; S3.
6. When the optimal individual no longer changes, the optimal solution for route time slot resource allocation is output. If it is not satisfied, return to step S3.2 and continue to solve the route time slot resource allocation model until the optimal solution is output.
8. The method for allocating route time slot resources for multi-segment delayed transportation considering uncertainty according to claim 7, characterized in that: The fitness function expression in step S3.2 is as follows: F=minO1+minO2 In step S3.3, the expression for the global exploration of cuckoo flight based on the Levy algorithm is: in, With x b They represent the current nest position and the optimal nest position respectively, α and ξ represent the step length control factor and the flight step length respectively, and Levy represent dot product and Levy probability distribution respectively.