Passenger travel recovery method, system, medium and equipment

Abstract

The invention belongs to the technical field of air transportation management and optimization, and provides a passenger travel recovery method and system, a storage medium and electronic equipment. The method comprises the steps that S1, a passenger travel recovery problem is decomposed into a main problem and a sub-problem, and a main problem limiting model and a sub-problem limiting model are established respectively; s2, establishing a disturbed passenger database, and solving a travel recovery scheme for a single passenger according to the sub-problem model and an objective function of the sub-problem model; s3, solving according to the limit main problem model to obtain a shadow price corresponding to the travel recovery scheme, and updating a target function of the sub-problem model by using the shadow price; and S4, according to the target function of the updated sub-problem model, calculating a current optimal solution or a plurality of current better solutions of the single passenger travel recovery scheme, converting the current optimal solution or the plurality of current better solutions into one column or a plurality of columns of the limiting main problem model, and adding the column or the plurality of columns into the limiting main problem model. The method can greatly shorten the solving time.

Description

Technical Field

[0001] The present invention belongs to the field of aviation transportation management and optimization technology, and in particular relates to a passenger itinerary recovery method, system, storage medium and electronic equipment. Background Art

[0002] When an involuntary flight change occurs, the existing system will unify all affected passengers on the affected ticket segment, add an IRR mark, and then all affected passengers will have their flight options uniformly set to two days before to six days after the original segment date. Since it is impossible to provide differentiated services to users in different situations, it will lead to a poor travel experience for passengers and cause a large number of customers to churn out. Therefore, it is of great practical significance to provide differentiated services to passengers through a reasonable passenger itinerary recovery system when an involuntary flight change occurs.

[0003] When flights are involuntarily changed, the airline cannot provide differentiated services to users in different situations, which will not only lead to a worse travel experience for passengers, but also cause a large number of passengers to leave, causing great losses to the airline's revenue. Take the scenario where a passenger is unable to fly due to physical reasons as an example. According to the existing system logic, the passenger can only reschedule to two days before or six days after the original flight date. The passenger's body cannot recover quickly, and he will definitely choose to refund the ticket and give up this travel plan or change the mode of travel. If the service can be differentiated and targeted, and the flight date that can be rescheduled is changed to an acceptable range, the airline will not lose this passenger, and it will also increase user stickiness, increase service experience, and get good reviews.

[0004] However, for a large airline, the exponential growth in the number of disrupted passengers and the number of feasible recovery itineraries for each disrupted passenger every day will result in a problem scale that is difficult to solve with traditional linear programming methods. Traversing the huge search space of feasible solutions cannot meet the actual needs of speed and quality. Summary of the invention

[0005] Based on this, it is necessary to provide a passenger itinerary recovery method based on large-scale mathematical programming to address the above problems, which can not only meet the differentiated services for passenger itineraries, but also achieve the goal of optimizing solution speed and result quality.

[0006] In order to solve the above technical problems, the first aspect of the present invention proposes a passenger itinerary recovery method, comprising:

[0007] S1: According to the decomposition of the passenger itinerary recovery problem into a main problem and sub-problems, a restricted main problem model and a sub-problem model are established respectively;

[0008] S2: Establish a database of disturbed passengers and solve itinerary recovery plans for individual passengers based on the subproblem model and the objective function of the subproblem model;

[0009] S3: Solve the restricted main problem model to obtain the shadow price corresponding to the itinerary recovery solution, and use the shadow price to update the objective function of the sub-problem model;

[0010] S4: Calculate the current optimal solution or multiple current better solutions of the itinerary recovery plan for a single passenger according to the objective function of the updated sub-problem model, convert the current optimal solution or multiple current better solutions into one or more columns of the restricted main problem model, and add them to the restricted main problem model;

[0011] S5: For each passenger, iterate steps S2-S4 until the global optimal solution or multiple global optimal solutions of the passenger itinerary recovery plan are obtained, and a complete recovery plan for all passengers is combined.

[0012] According to a preferred embodiment of the present invention, the step of establishing a restricted main problem model comprises:

[0013] Establish an integer programming model based on the master problem;

[0014] Define the constraints for the integer programming model.

[0015] According to a preferred embodiment of the present invention, the constraint conditions for defining the integer programming model include:

[0016] Only one recovery solution from each passenger’s recovery solution set will be selected;

[0017] The number of passengers accommodated on an aircraft cannot exceed the aircraft's cabin capacity.

[0018] According to a preferred embodiment of the present invention, solving a trip recovery plan for a single passenger based on the subproblem model and the objective function of the subproblem model includes:

[0019] The subproblem is a minimization problem. During the optimization iteration process, only the subproblem solution with an objective value less than zero has the potential to optimize the objective function of the main problem.

[0020] In each iteration, one or more sub-problem solutions with objective values ​​less than zero can be selected and added to the restricted main problem model for optimization.

[0021] In order to solve the above technical problems, the second aspect of the present invention proposes a passenger itinerary recovery system, the system comprising:

[0022] Model building module, database building module, sub-problem updating module, model solving module and solution completion module;

[0023] A model building module is used to build a restricted main problem model and a sub-problem model according to decomposing the passenger itinerary recovery problem into a main problem and a sub-problem;

[0024] A database establishment module is used to establish a database of disturbed passengers and solve a trip recovery plan for a single passenger based on the sub-problem model and the objective function of the sub-problem model;

[0025] A sub-problem updating module is used to solve the shadow price corresponding to the itinerary recovery solution according to the restricted main problem model, and use the shadow price to update the objective function of the sub-problem model;

[0026] A model solving module, used to calculate the current optimal solution or multiple current better solutions of the itinerary recovery plan for a single passenger according to the objective function of the updated sub-problem model, convert the current optimal solution or multiple current better solutions into one column or multiple columns of the restricted main problem model, and add them to the restricted main problem model;

[0027] The solution completion module is used to iteratively establish the database module, update the sub-problem module and solve the model module for each passenger until the global optimal solution or multiple global optimal solutions of the passenger itinerary recovery plan are obtained, and a complete recovery plan for all passengers is combined.

[0028] According to a preferred embodiment of the present invention, it comprises:

[0029] The model building module is also used to build an integer programming model based on the main problem;

[0030] The model building module is also used to define the constraints of the integer programming model.

[0031] According to a preferred embodiment of the present invention, it comprises:

[0032] The model building module is also used for each passenger, and only one recovery solution from the recovery solution set will be selected;

[0033] The model building module is also used to ensure that the number of passengers arranged on the aircraft cannot exceed the aircraft cabin capacity limit.

[0034] According to a preferred embodiment of the present invention, it comprises:

[0035] The database building module is also used when the sub-problem is a minimization problem. During the optimization iteration process, only the sub-problem solution with a target value less than zero has the potential to optimize the objective function of the main problem;

[0036] The database establishment module is also used to select one or more sub-problem solutions with target values ​​less than zero in each iteration and add them into the restricted main problem model for optimization.

[0037] In order to solve the above technical problem, the third aspect of the present invention provides an electronic device, comprising:

[0038] processor;

[0039] and a memory storing computer executable instructions, wherein the computer executable instructions, when executed, cause the processor to perform the method described in any one of the above embodiments.

[0040] In order to solve the above technical problems, the fourth aspect of the present invention proposes a computer storage medium, wherein the computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method described in any one of the above embodiments is implemented.

[0041] Compared with the prior art, the present invention has the following beneficial technical effects:

[0042] The present invention decomposes the passenger itinerary recovery problem and establishes a restricted main problem model and a sub-problem model. Each time a search is performed, only one or more feasible solutions with the potential to become the optimal solution are found among the limited feasible solutions for a certain disturbed passenger, and they are recorded in a pool of better solutions. In this way, iterations are performed until a complete recovery solution for all disturbed passengers can be combined from the pool of better solutions. The sequence of solutions obtained by solving the sub-problems can effectively improve the overall solution of the main problem, and different methods can also be selected to solve the sub-problems. This method can effectively reduce the search space and improve the solution speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of a flow chart of a passenger itinerary recovery method according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a module of a passenger itinerary recovery system according to an embodiment of the present invention;

[0046] Figure 3 The figure is a diagram showing the internal structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0048] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0049] Unless otherwise defined, all technical and scientific terms used in the specification of the present invention have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in the specification of the present invention includes any and all combinations of one or more related listed items.

[0050] Terminology explanation:

[0051] Involuntary changes: Flight cancellations, delays, etc. due to weather, passenger health, etc. that are not caused by the passenger's subjective decision.

[0052] IRR mark: an involuntary mark on the ticket. With this mark, passengers can have one chance to reschedule or refund the ticket free of charge.

[0053] Combine the following Figure 1 , Figure 2 The passenger itinerary recovery method based on large-scale mathematical programming of the present invention is described.

[0054] like Figure 1 As shown, in one embodiment, a passenger itinerary recovery method based on large-scale mathematical programming includes:

[0055] S1: According to decomposing the passenger itinerary recovery problem into a main problem and sub-problems, a restricted main problem model and a sub-problem model are established respectively.

[0056] S2: Establish a database of disturbed passengers and solve the itinerary recovery plan for each passenger based on the subproblem model and the objective function of the subproblem model.

[0057] S3: Solve the restricted main problem model to obtain the shadow price corresponding to the itinerary recovery plan, and use the shadow price to update the objective function of the sub-problem model.

[0058] S4: According to the objective function of the updated sub-problem model, calculate the current optimal solution or multiple current better solutions of the itinerary recovery plan for a single passenger, convert the current optimal solution or multiple current better solutions into one or more columns of the restricted main problem model, and add them to the restricted main problem model.

[0059] S5: For each passenger, iterate steps S2-S4 until the global optimal solution or multiple global optimal solutions of the passenger itinerary recovery plan are obtained, and a complete recovery plan for all passengers is combined.

[0060] Specifically, a passenger itinerary recovery task is received and converted into a large-scale mathematical programming problem. The airline user first sets personalized requirements through the front-end interface, and then connects with the system of the present invention, and sends the affected passengers to the system of the present invention in the form of a message. The system of the present invention converts the passenger itinerary recovery task into a large-scale mathematical programming problem.

[0061] In actual production and life, many scheduling problems can be abstracted into integer programming or linear programming models, but when faced with larger problem scales, conventional mathematical solutions cannot meet the requirements in terms of speed or quality. The present invention uses column generation method and branch pricing method to optimize the solution method, which has good solution quality and efficiency.

[0062] The main problem of receiving the passenger itinerary recovery task is set as the optimal itinerary recovery plan for all passengers and a main problem model is established based on the main problem. The main problem model includes a first constraint condition. The first constraint condition is that there is only one itinerary recovery plan for a single passenger in the main problem model. The decision variable of the main problem model is whether the itinerary recovery plan for a single passenger will be selected.

[0063] The integer programming model based on the main problem is as follows:

[0064]

[0065] Where P represents the set of PNRs (“Passenger Name Record”, i.e., passenger reservation records. This is a term used in the aviation and tourism industries to uniquely identify a passenger’s reservation record. PNRs are usually used to track passengers’ travel arrangements, including flight information, seat assignments, contact information, and other passenger reservation records); i represents passengers; k represents the scheduling object of the main problem model, i.e., the passenger itinerary recovery plan; K represents the set of all possible recovery plans related to different passengers; m p represents the passenger satisfaction cost weight; m arepresents the airline revenue cost weight; represents the passenger satisfaction cost of the kth recovery solution for PNRi (the PNR record of the i-th passenger); represents the airline revenue cost of the k-th recovery plan of PNRi; represents the decision variable.

[0066] The first constraint is as follows: The above formula ensures that only one recovery plan will be selected from the recovery plan set for each passenger.

[0067] The second constraint is as follows: The above formula means that the number of passengers arranged on the aircraft cannot exceed the aircraft's cabin capacity limit.

[0068] The third constraint is as follows:

[0069] The above formula means that the shared space capacity of regular flights of connecting flights will not exceed the space capacity limit of each aircraft.

[0070] The decision variables are as follows: The above formula represents the integer constraint of the decision variable, which can determine whether the recovery plan represented by a certain variable will be selected as part of the overall recovery plan. When the decision variable is 0, the recovery plan represented by this variable will not be selected as part of the overall recovery plan; when the decision variable is 1, the recovery plan represented by this variable will be selected as part of the overall recovery plan.

[0071] The specific composition of the objective function formula (1-1) is given by formula (1-6) and formula (1-7):

[0072] in, represents the cost of cabin class change, represents the cabin variable cost, represents the trip similarity cost, represents the total time cost of the trip, Represents the trip connection time cost.

[0073] Among them, for airlines, the cost For example, it mainly consists of two parts. One part is the fixed loss cost calculated in units of time, which is usually calculated according to the regulations of the airline, namely The other part is mainly the cost of passenger cabin adjustment. Upgrading will lead to a decrease in airline revenue, while downgrading will increase revenue. Cost of changing cabin class The calculation method is expressed by formula (1-8): Di represents the set of flight segments in the original itinerary of PNRi, q dk represents the cabin class span of flight segment d in plan k, m chg Indicates the calculation base of the cost of changing the navigation position.

[0074] It represents the cabin shift variable cost, which is calculated as follows:

[0075]

[0076] It represents the trip similarity cost, which is calculated as follows:

[0077]

[0078] It represents the total time cost of the trip, which is calculated as follows:

[0079] It represents the time cost of trip connection, which is calculated as follows:

[0080]

[0081] This embodiment converts the passenger itinerary recovery task into an integer programming model, namely, a main problem model. Through the constraints of the main problem model, the solution result of the main problem model is the solution to the passenger itinerary recovery problem.

[0082] Table 1 and Table 2 give the definitions and explanations of the parameters and variables involved in this embodiment.

[0083] Table 1

[0084] Table 4-1 Set definition and description

[0085]

[0086] Table 2

[0087]

[0088] According to the first constraint condition, the main problem model is branched and solved at least once to obtain a restricted main problem model. The restricted main problem model includes solutions of multiple sub-problem models after the main problem model is branched. The restricted main problem model is initialized with an initial solution, and then the solutions of the sub-problem models are added and iterated. The optimality of the solutions of the sub-problem models is judged. The decision variables of the restricted main problem model are relaxed. In each iteration, one or more solutions of sub-problem models with target values ​​less than zero can be selected and added to the restricted main problem model for optimization.

[0089] The shadow price corresponding to the itinerary recovery plan is obtained by solving the restricted main problem model, and the objective function of the sub-problem model is updated using the shadow price.

[0090] When establishing the subproblem model, considering the special connection between the selection of passenger itinerary recovery plan and the single passenger itinerary recovery problem, the passenger itinerary recovery plan k is taken as the scheduling object of the main problem, then the subproblem can be expressed as a single passenger itinerary recovery problem.

[0091] Since the main problem model containing all variables is too large, a complete main problem model is not directly constructed during the construction process. Instead, a restricted main problem model is established and an initial solution is used to initialize the model. The solution results of the sub-problems are then converted into a column of the restricted main problem and added to the restricted main problem model, and the iteration is repeated.

[0092] The main purpose of relaxing the decision variables of the restricted master problem model is to simplify the problem solving process, allowing us to deal with linear programming problems instead of more complex integer programming problems during the solution process. Linear programming problems can be solved efficiently using the CPLEX solver.

[0093] The relaxed decision variables are expressed as follows:

[0094] Setting Beta i , i∈P corresponds to the dual variable of constraint (1-2), j∈F s , q∈Q j The dual variables corresponding to constraints (1-3) are j∈F L , q∈Q j , The dual variables corresponding to constraints (1-4) are The cost of the PNRi recovery solution k is to restrict the coefficient of the corresponding decision variable in the objective function of the main problem, n i represents the number of passengers in PNRi. The optimization objective of the sub-problem is as follows:

[0095]

[0096] The cost of the recovery solution k of PNRi is the coefficient of the corresponding decision variable in the objective function of the main problem, and the calculation formula is as follows:

[0097] According to the optimality judgment conditions for solving linear programming problems, if the current solution is the optimal solution, then the objective function of the subproblem must satisfy formula (2-4):

[0098]

[0099] Therefore, the subproblem is a minimization problem. During the optimization iteration process, only the subproblem solutions with target values ​​less than zero have the potential to optimize the objective function of the main problem. In each iteration, one or more subproblem solutions with target values ​​less than zero can be selected to be added to the restricted main problem model for optimization.

[0100] In the sub-problem objective function, the decision variable is n, n∈{0,ni}. For ease of understanding, a decision variable is added Indicates whether PNRi is arranged in the q-class of flight j. The sub-problem model is constructed as follows:

[0101]

[0102] The fourth constraint condition indicates that at least one flight is selected as a recovery plan for each segment of the original PNR plan. The expression is as follows:

[0103] The fifth constraint means that if a flight has a direct predecessor flight, then one of the predecessor flights of the flight must be selected to ensure the continuity of the itinerary. The expression is as follows:

[0104]

[0105] The sixth constraint is that the sum of the space usage of multiple regular flights included in the transfer flight does not exceed the available space capacity of the transfer flight, ensuring that the space usage of the transfer flight and the regular flight will not overflow. The expressions are as follows:

[0106]

[0107] The above formula represents the value range of the decision variable, which determines the specific flight and specific cabin space to which the PNR is protected.

[0108] In this embodiment, the mathematical planner CPLEX is used to solve the restricted main problem model and complete the design of the column generation framework. In the solution process, different sub-problem solutions will be obtained by selecting different flights and cabins. Therefore, passengers are used as the scheduling objects of the sub-problems, and the scheduling scheme is solved for each passenger. The objective function of the sub-problem is updated according to the shadow price obtained by solving the restricted main problem, and then the current optimal solution of each passenger is calculated, and the optimal solution or multiple better solutions are added to the restricted main problem model. Updating and improving the solution through shadow variables (the value of the dual variable) makes the solution more accurate.

[0109] Determine whether there is a better passenger itinerary recovery plan for the restricted main problem model. If there is no better passenger itinerary recovery plan, stop the iteration and obtain the optimal solution of the main problem model based on the restricted main problem model.

[0110] The solution of the sub-problem model is added to the restricted main problem, and the restricted main problem is solved to obtain the shadow price, and then the objective function of the sub-problem is updated. This iteration is repeated until there is no better passenger itinerary recovery plan. At this time, the optimal solution of the restricted main problem model is the optimal solution of the main problem model, that is, the passenger itinerary recovery method obtained by the present invention.

[0111] The above-mentioned passenger itinerary recovery method based on large-scale mathematical programming decomposes the passenger itinerary recovery problem according to the column generation method, establishes a restricted main problem model and a sub-problem model respectively, uses a heuristic search method based on node preprocessing to solve the sub-problem, uses the mathematical programming solver CPLEX to solve the restricted main problem model and completes the design of the column generation model, and proposes an optimization algorithm based on branch pricing to obtain an integer solution to the main problem. In the branch and bound process, a hybrid search node selection strategy is used. The column generation model and branch pricing optimization algorithm of the present invention have more advantages in the quality of solving the passenger itinerary recovery problem. In the process of limited iterations, the optimal solution for passenger itinerary recovery can be effectively found. In practical applications, a solution with better quality can be generated within an acceptable time.

[0112] like Figure 2 As shown, in one embodiment, a passenger itinerary recovery system based on large-scale mathematical programming includes: a model building module, a database building module, a sub-problem updating module, a model solving module and a solution completion module.

[0113] The model building module is specifically used to build a restricted main problem model and a sub-problem model respectively according to decomposing the passenger itinerary recovery problem into a main problem and a sub-problem.

[0114] The database establishment module is specifically used to establish a database of disturbed passengers and solve the itinerary recovery plan for a single passenger based on the sub-problem model and the objective function of the sub-problem model.

[0115] The sub-problem updating module is specifically used to solve the shadow price corresponding to the itinerary recovery plan according to the restricted main problem model, and use the shadow price to update the objective function of the sub-problem model.

[0116] The model solving module is specifically used to calculate the current optimal solution or multiple current better solutions of the itinerary recovery plan for a single passenger according to the objective function of the updated sub-problem model, convert the current optimal solution or multiple current better solutions into one or more columns of the restricted main problem model, and add them to the restricted main problem model.

[0117] The solution completion module is specifically used to iteratively establish the database module, update the sub-problem module and solve the model module for each passenger until the global optimal solution or multiple global optimal solutions of the passenger itinerary recovery plan are obtained, and a complete recovery plan for all passengers is combined.

[0118] The model building module is specifically used to build an integer programming model based on the main problem.

[0119] The model building module is specifically used to define the constraints of the integer programming model.

[0120] In the model building module, only one recovery plan from the recovery plan set for each passenger will be selected.

[0121] The model building module is specifically used to ensure that the number of passengers arranged on the aircraft cannot exceed the aircraft cabin capacity limit.

[0122] The database establishment module is specifically used for a sub-problem which is a minimization problem. During the optimization iteration process, only the sub-problem solution with a target value less than zero has the potential to optimize the objective function of the main problem.

[0123] The database establishment module is also used to select one or more sub-problem solutions with target values ​​less than zero in each iteration and add them into the restricted main problem model for optimization.

[0124] like Figure 3 As shown, an embodiment of the present invention provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140.

[0125] Memory 1130, used for storing computer programs;

[0126] The processor 1110 is configured to implement any of the above determination methods when executing a program stored in the memory 1130 .

[0127] In the electronic device provided by the embodiment of the present invention, the processor 1110 obtains the fluid flow rate of each branch in different switching states by executing the program stored in the memory 1130, and determines the initial volume flow rate of each branch; corrects the initial volume flow rate according to the pipeline parameters and fluid parameters of each branch when it is in working condition and standard condition, and obtains the standard condition volume flow rate of each branch; obtains multiple total standard condition volume flow rates according to the standard condition volume flow rates of each branch in different switching states, and determines the optimal switching state of each branch through the switching state of each branch when the total standard condition volume flow rate reaches a preset target.

[0128] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0129] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0130] The memory 1130 may include a random access memory 1130 (RAM), or may include a non-volatile memory 1130 (non-volatile memory), such as at least one disk memory 1130. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0131] The above-mentioned processor 1110 can be a general-purpose processor 1110, including a central processing unit 1110 (CPU), a network processor 1110 (NP), etc.; it can also be a digital signal processor 1110 (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0132] An embodiment of the present invention provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors 1110 to implement the determination method of any of the above embodiments.

[0133] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidStateDisk (SSD)), etc.

[0134] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A passenger itinerary recovery method, characterized in that: include: S1: According to the decomposition of the passenger itinerary recovery problem into a main problem and sub-problems, a restricted main problem model and a sub-problem model are established respectively; S2: Establish a database of disturbed passengers and solve itinerary recovery plans for individual passengers based on the subproblem model and the objective function of the subproblem model; S3: Solve the restricted main problem model to obtain the shadow price corresponding to the itinerary recovery solution, and use the shadow price to update the objective function of the sub-problem model; S4: Calculate the current optimal solution or multiple current better solutions of the itinerary recovery plan for a single passenger according to the objective function of the updated sub-problem model, convert the current optimal solution or multiple current better solutions into one or more columns of the restricted main problem model, and add them to the restricted main problem model; S5: For each passenger, iterate steps S2-S4 until the global optimal solution or multiple global optimal solutions of the passenger itinerary recovery plan are obtained, and a complete recovery plan for all passengers is combined.

2. The passenger itinerary recovery method according to claim 1, characterized in that: The establishing of the restriction main problem model comprises: Establish an integer programming model based on the master problem; Define the constraints for the integer programming model.

3. The passenger itinerary recovery method according to claim 2, characterized in that: The constraints for defining the integer programming model include: Only one recovery solution from each passenger’s recovery solution set will be selected; The number of passengers accommodated on an aircraft cannot exceed the aircraft's cabin capacity.

4. The passenger itinerary recovery method according to claim 1, characterized in that: The method of solving a trip recovery plan for a single passenger based on the subproblem model and the objective function of the subproblem model includes: The subproblem is a minimization problem. During the optimization iteration process, only the subproblem solution with an objective value less than zero has the potential to optimize the objective function of the main problem. In each iteration, one or more sub-problem solutions with objective values ​​less than zero can be selected and added to the restricted main problem model for optimization.

5. A passenger itinerary recovery system, characterized in that: The system comprises: a model building module, a database building module, a sub-problem updating module, a model solving module and a solution completing module; A model building module is used to build a restricted main problem model and a sub-problem model according to decomposing the passenger itinerary recovery problem into a main problem and a sub-problem; A database establishment module is used to establish a database of disturbed passengers and solve a trip recovery plan for a single passenger based on the sub-problem model and the objective function of the sub-problem model; A sub-problem updating module is used to solve the shadow price corresponding to the itinerary recovery solution according to the restricted main problem model, and use the shadow price to update the objective function of the sub-problem model; A model solving module, used to calculate the current optimal solution or multiple current better solutions of the itinerary recovery plan for a single passenger according to the objective function of the updated sub-problem model, convert the current optimal solution or multiple current better solutions into one column or multiple columns of the restricted main problem model, and add them to the restricted main problem model; The solution completion module is used to iteratively establish the database module, update the sub-problem module and solve the model module for each passenger until the global optimal solution or multiple global optimal solutions of the passenger itinerary recovery plan are obtained, and a complete recovery plan for all passengers is combined.

6. The passenger itinerary recovery system according to claim 5, characterized in that: include: The model building module is also used to build an integer programming model based on the main problem; The model building module is also used to define the constraints of the integer programming model.

7. The passenger itinerary recovery system according to claim 6, characterized in that: include: The model building module is also used for each passenger, and only one recovery solution from the recovery solution set will be selected; The model building module is also used to ensure that the number of passengers arranged on the aircraft cannot exceed the aircraft cabin capacity limit.

8. The passenger itinerary recovery system according to claim 5, characterized in that: include: The database building module is also used when the sub-problem is a minimization problem. During the optimization iteration process, only the sub-problem solution with a target value less than zero has the potential to optimize the objective function of the main problem; The database establishment module is also used to select one or more sub-problem solutions with target values ​​less than zero in each iteration and add them into the restricted main problem model for optimization.

9. An electronic device, characterized in that: include: processor; and a memory storing computer executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1 to 4.

10. A computer storage medium, characterized in that: in, The computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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