A large-scale route intelligent optimization method and device

Abstract

The present invention relates to a large-scale intelligent route optimization method and device. The method comprises the following steps: S1. Using a depth-first search algorithm, searching for all aircraft paths based on an initial node on a daily basis, where the initial node is all bases; S2. Setting the number of aircraft and inputting a first set of aircraft paths into an integer programming model based on the number of aircraft; S3. Solving the integer programming model to obtain the aircraft path corresponding to each aircraft. Compared to existing technologies, the present invention utilizes a depth-first search to generate feasible aircraft paths and, in combination with an integer programming model from operations research, models flight plan optimization for known feasible aircraft paths, thereby obtaining a global optimal solution for the flight plan.

Description

Technical Field

[0001] The present invention relates to the field of route optimization, and in particular to a large-scale route intelligent optimization method and device. Background Art

[0002] When planning large-scale flights, adjustments to flight times and stopover times must be considered. Manual methods in this scenario are extremely labor-intensive and cannot guarantee optimality. Aircraft maintenance is also a key consideration in flight planning, as aircraft regularly require overnight stays at airports with maintenance facilities.

[0003] Existing flight schedule optimization methods fall into two main categories. The first relies on previous flight plans to fine-tune individual flights by manually adding, deleting, and modifying schedules. The second uses computational algorithms to model the problem and achieve optimization. The first method relies on experience and is premised on ensuring feasibility. While this method often yields good results, it is generally inefficient. While the second method may not fully meet implementation requirements, it can achieve good operational efficiency. Flight plans generated using algorithms are already highly efficient, and simple manual fine-tuning can ensure good implementation.

[0004] Current flight planning algorithms often consider fewer factors, simplifying and compromising the problem to address scale. This makes it impossible to implement aircraft maintenance while also accounting for changes in flight schedules and stopover times. Furthermore, most current algorithms utilize heuristic intelligent algorithms with poor modeling and representation capabilities, making optimal solutions unavailable for large-scale flights. Summary of the Invention

[0005] The purpose of the present invention is to provide a large-scale intelligent route optimization method and device in order to overcome the defects of the above-mentioned prior art. The method adopts depth-first search to generate feasible aircraft paths, and combines the integer programming model of operations research to model the flight plan optimization of known feasible aircraft paths, so as to obtain the global optimal solution of the flight plan.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A large-scale route intelligent optimization method, characterized in that the method comprises the following steps:

[0008] S1. Using a depth-first search algorithm, search for a set of first aircraft paths based on an initial node in units of days, where the initial node is all bases.

[0009] S2. Setting the number of aircraft, and inputting the first aircraft path set into an integer programming model established based on the number of aircraft, wherein the constraints of the integer programming model include maintenance constraints;

[0010] S3. Solve the integer programming model to obtain an aircraft path corresponding to each aircraft;

[0011] The objective function of the integer programming model is expressed as:

[0012]

[0013] Among them, F(x) is the usage of aircraft, a is the aircraft, A is the set of available aircraft, s is the departure airport, S is the set of departure airports, e is the landing airport, E is the set of landing airports, i is the aircraft path, is the set of aircraft paths on the first day with s as the departure airport and e as the landing airport, is the decision variable for aircraft path i on day 1, whether aircraft a takes off from airport s and lands at airport e;

[0014] The specific steps of S1 include:

[0015] S11. Input the flight rights time set, stopover time range, flight segment time variation range, and business logic.

[0016] S12. Enumerate all bases and connect flights. When connecting, consider whether changing the flight segment time and stopover time within the required range can facilitate the connection. If so, connect them and connect all feasible situations.

[0017] S13, determine whether the air rights are used up, if not, repeat S12, if used up, execute S14;

[0018] S14. Every day, the aircraft paths with the same take-off and landing airports are placed in a subset and numbered;

[0019] S15. Output the generated first aircraft path set.

[0020] Furthermore, the constraints of the integer programming model also include flight coverage constraints, flow balance constraints, and aircraft utilization constraints.

[0021] Furthermore, the expression of the aircraft usage constraint is:

[0022]

[0023] Where d is the date, is the decision variable for aircraft path i on day d, whether aircraft a takes off from airport s and lands at airport e.

[0024] Furthermore, the maintenance constraint is expressed as:

[0025]

[0026] Among them, D main The number of days it takes for an aircraft to return to base for maintenance after takeoff.

[0027] Furthermore, when the integer programming model is solved and no solution is found, the number of aircraft is increased, the integer programming model is updated and solved again, and the above steps are repeated until the model is solved and the aircraft path corresponding to each aircraft is obtained.

[0028] The present invention also provides a large-scale route intelligent optimization device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor performs the above steps when executing the program.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] The flight plan optimization method proposed in this invention aims to solve the optimization problem of large-scale flight plans. The method can not only adjust the flight mission time of each aircraft, but also take into account the phased maintenance of the aircraft. These problems are all represented using deterministic modeling, ensuring the optimality of the final result of the method and greatly improving the efficiency of flight planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the present invention;

[0032] Figure 2 Flowchart of the depth-first search algorithm of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0035] Example 1

[0036] The present invention provides a large-scale route intelligent optimization method, the process of the method is as follows Figure 1 The method comprises the following steps:

[0037] S1. Based on the depth-first search algorithm, search for the set of first aircraft paths based on the initial node in units of days, where the initial node is all bases.

[0038] S1 uses a depth-first algorithm to search daily flight paths, minimizing the cost of finding all feasible flight paths. Subsequent optimization models only require specifying decision variables to complete the optimization. The depth-first algorithm can accurately find all feasible flight paths without duplication or omission based on the rules. Using this method to search daily flight paths can, to a certain extent, guarantee a globally optimal solution for flight plans.

[0039] The flowchart of the depth-first algorithm of the present invention is as follows: Figure 2 The specific steps of the depth-first algorithm of the present invention are:

[0040] S11. Input the flight rights time set, stopover time range, flight segment time variation range, and business logic.

[0041] S12. Enumerate all bases and connect flights. When connecting, consider whether changing the flight segment time and stopover time within the required range can facilitate the connection. If so, connect them and connect all feasible situations.

[0042] S13, determine whether the air rights are used up, if not, repeat S12, if used up, execute S14;

[0043] S14. Every day, the aircraft paths with the same take-off and landing airports are placed in a subset and numbered;

[0044] S15. Output the generated first aircraft path set.

[0045] In S14, all aircraft paths are flown by one aircraft, without considering maintenance constraints.

[0046] S2. Set the number of aircraft and input the first aircraft path set into an integer programming model established based on the number of aircraft.

[0047] S3. Solve the integer programming model to obtain the aircraft path corresponding to each aircraft.

[0048] The reason why aircraft maintenance is not considered in the aircraft route search is that the number of days designated for aircraft maintenance is not fixed and needs to be determined based on the specific aircraft and weather conditions. Therefore, this factor is taken into account in the integer programming model.

[0049] The objective function of the integer programming model is expressed as:

[0050]

[0051] Among them, F(x) is the usage of aircraft, a is the aircraft, A is the set of available aircraft, s is the departure airport, S is the set of departure airports, e is the landing airport, E is the set of landing airports, i is the aircraft path, is the set of aircraft paths on the first day with s as the departure airport and e as the landing airport, is the decision variable for aircraft path i on day 1, whether aircraft a takes off from airport s and lands at airport e.

[0052] The constraints of the integer programming model include flight coverage constraints, flow balance constraints, aircraft usage constraints, and maintenance constraints. In the constraints, d is the date, is the decision variable for whether plane a takes off from airport s and lands at airport e on day d. main The number of days it takes for an aircraft to return to base for maintenance after takeoff.

[0053] 1. Flight coverage constraints

[0054]

[0055] The flight coverage constraint states that each flight number must be executed only once per day. All flights in the input rights for each day must be included in the final flight plan, and each flight can only be flown once by one aircraft, ensuring that the routes of each aircraft do not overlap.

[0056] 2. Flow balance constraints

[0057]

[0058] The flow balance constraint states that aircraft must depart from the same airport they landed at the previous day for the next day's mission. The last airport an aircraft landed at on a given day must be the airport it departs from the next day. This constraint ensures that aircraft routes remain continuous between consecutive days, ensuring the consistency of the flight schedule and facilitating the fulfillment of aircraft maintenance constraints.

[0059] 3. Aircraft Usage Restrictions

[0060]

[0061] The aircraft usage constraint states that each aircraft is used only once per day. Since the decision variables represent a flight route per day, each aircraft is used only once per day to complete a flight route. This prevents the model from using an aircraft multiple times on the same day.

[0062] 4. Maintenance constraints

[0063]

[0064] Maintenance constraints require a flight to a maintenance base every certain number of days. Each aircraft must land at a suitable airport within Dmain days of flight to meet maintenance requirements. This may increase the number of aircraft in use, but since this is a hard constraint, it cannot be violated and must be met.

[0065] In S3, by defining the objective function and using various constraints to describe the business scenario of flight planning, the optimization model can be directly solved using optimization solvers such as ortools and SCIP to obtain a flight plan result. The result provides takeoff and landing times for all flights and assigns them to specific aircraft.

[0066] The present invention mainly proposes a two-stage flight plan optimization method, which aims to solve the optimization problem of large-scale flight plans. The method can not only adjust the flight mission time of each aircraft, but also take into account the periodic maintenance of the aircraft. These problems are represented by deterministic modeling, which ensures the optimality of the final result of the method. The algorithm proposed by the present invention is to first use the depth-first algorithm to search the aircraft paths every day, so as to find all feasible aircraft paths at a relatively low cost. When using the subsequent optimization model, only the decision variables need to be specified to complete the optimization. The depth-first algorithm can find all feasible aircraft paths without duplication or omission according to the rules. Using this method to complete the daily aircraft route search can, to a certain extent, ensure that the global optimal solution of the flight plan is obtained.

[0067] In the depth-first algorithm of the present invention, the specific flight rights times, stopover time ranges, and flight segment time ranges vary among different airlines and should be considered on a case-by-case basis. The specific connection logic can be customized as needed, making it suitable for all types of routes and aircraft.

[0068] The present invention also provides a large-scale route intelligent optimization device, including a memory and a processor, wherein a computer program is stored in the memory, and the above steps are performed when the processor executes the program.

[0069] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A large-scale route intelligent optimization method, characterized in that: The method comprises the following steps: S1. Using a depth-first search algorithm, search for a set of first aircraft paths based on an initial node in units of days, where the initial node is all bases. S2. Setting the number of aircraft, and inputting the first aircraft path set into an integer programming model established based on the number of aircraft, wherein the constraints of the integer programming model include maintenance constraints; S3. Solve the integer programming model to obtain an aircraft path corresponding to each aircraft; The objective function of the integer programming model is expressed as: Among them, F(x) is the usage of aircraft, a is the aircraft, A is the set of available aircraft, s is the departure airport, S is the set of departure airports, e is the landing airport, E is the set of landing airports, i is the aircraft path, is the set of aircraft paths on the first day with s as the departure airport and e as the landing airport, is the decision variable for aircraft path i on day 1, whether aircraft a takes off from airport s and lands at airport e; The specific steps of S1 include: S11. Input the flight rights time set, stopover time range, flight segment time variation range, and business logic. S12. Enumerate all bases and connect flights. When connecting, consider whether changing the flight segment time and stopover time within the required range can facilitate the connection. If so, connect them and connect all feasible situations. S13, determine whether the air rights are used up, if not, repeat S12, if used up, execute S14; S14. Every day, the aircraft paths with the same take-off and landing airports are placed in a subset and numbered; S15. Output the generated first aircraft path set.

2. A large-scale route intelligent optimization method according to claim 1, characterized in that: The constraints of the integer programming model also include flight coverage constraints, flow balance constraints, and aircraft utilization constraints.

3. A large-scale route intelligent optimization method according to claim 2, characterized in that: The expression of the aircraft usage constraint is: Where d is the date, is the decision variable for aircraft path i on day d, whether aircraft a takes off from airport s and lands at airport e.

4. A large-scale route intelligent optimization method according to claim 2, characterized in that: The expression of the maintenance constraint is: Among them, D main The number of days it takes for an aircraft to return to base for maintenance after takeoff.

5. The large-scale route intelligent optimization method according to claim 1, characterized in that: When the integer programming model is solved and no solution is found, the number of aircraft is increased, the integer programming model is updated and solved again, and the above steps are repeated until the model is solved and the aircraft path corresponding to each aircraft is obtained.

6. A large-scale route intelligent optimization device, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that: When the processor executes the program, the following steps are performed: S1. Using a depth-first search algorithm, search for a set of first aircraft paths based on an initial node in units of days, where the initial node is all bases. S2. Setting the number of aircraft, and inputting the first aircraft path set into an integer programming model established based on the number of aircraft, wherein the constraints of the integer programming model include maintenance constraints; S3. Solve the integer programming model to obtain an aircraft path corresponding to each aircraft; The objective function of the integer programming model is expressed as: Among them, F(x) is the usage of aircraft, a is the aircraft, A is the set of available aircraft, s is the departure airport, S is the set of departure airports, e is the landing airport, E is the set of landing airports, i is the aircraft path, is the set of aircraft paths on the first day with s as the departure airport and e as the landing airport, is the decision variable for aircraft path i on day 1, whether aircraft a takes off from airport s and lands at airport e; The specific steps of S1 include: S11. Input the flight rights time set, stopover time range, flight segment time variation range, and business logic. S12. Enumerate all bases and connect flights. When connecting, consider whether changing the flight segment time and stopover time within the required range can facilitate the connection. If so, connect them and connect all feasible situations. S13, determine whether the air rights are used up, if not, repeat S12, if used up, execute S14; S14. Every day, the aircraft paths with the same take-off and landing airports are placed in a subset and numbered; S15. Output the generated first aircraft path set.

7. A large-scale route intelligent optimization device according to claim 6, characterized in that: The constraints of the integer programming model also include flight coverage constraints, flow balance constraints, and aircraft utilization constraints.

8. A large-scale route intelligent optimization device according to claim 7, characterized in that: The expression of the aircraft usage constraint is: Where d is the date, is the decision variable for aircraft path i on day d, whether aircraft a takes off from airport s and lands at airport e.

9. The large-scale route intelligent optimization device according to claim 7, characterized in that: The expression of the maintenance constraint is: Among them, D main The number of days it takes for an aircraft to return to base for maintenance after takeoff.

10. The large-scale route intelligent optimization device according to claim 6, characterized in that: When the integer programming model is solved and no solution is found, the number of aircraft is increased, the integer programming model is updated and solved again, and the above steps are repeated until the model is solved and the aircraft path corresponding to each aircraft is obtained.

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