Air cargo recovery method, device, computer equipment and storage medium

Abstract

The present application relates to an air cargo recovery method, device, computer equipment and storage medium, wherein the method includes: initializing a short restricted main problem; in each iteration of columns and rows, solving the linear programming relaxation of the short restricted main problem to obtain the dual value of each constraint; generating aircraft routes and cargo itineraries in the subproblems corresponding to the short restricted main problem according to the dual value; updating the short restricted main problem based on the aircraft routes and cargo itineraries of negative simplified costs to perform the next iterative calculation; when the aircraft routes or cargo itineraries of negative simplified costs are no longer generated and the aircraft routes obtained by solving the short restricted main problem are not integer solutions, integer processing is performed on the aircraft routes that are not integer solutions to update the short restricted main problem; when the iteration of columns and rows is completed, the mixed integer main problem is solved by a solver to obtain the target aircraft routes and cargo itinerary solutions. The present application realizes the rapid generation of air cargo recovery solutions and reduces recovery costs.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an air cargo recovery method, apparatus, computer equipment and storage medium. Background Art

[0002] Airlines consider the formulation of flight schedules, aircraft type allocation, route planning, crew allocation, etc. according to demand conditions. In addition, during the operation of flights, emergencies will inevitably cause flight delays or temporary cancellations, which will also have a significant impact on the airline's original flight plans, emergency transportation plans and passengers. When disruptions occur, flights cannot take off as scheduled, cargo routes are disrupted, or aircraft need to be rescheduled to adapt to changes in cargo demand. The Aviation Operations Center (AOC) is responsible for rescheduling aircraft, flights, crew members and cargo to minimize costs and resume the airline's operations. Therefore, how to quickly and effectively provide a solution to the problem of air cargo recovery is of great significance to airlines.

[0003] Currently, cargo airlines usually adopt a sequential aircraft and cargo schedule recovery approach instead of dealing with a comprehensive model due to the sudden emergence of urgent demand. A common recovery scenario is to reschedule flights and aircraft first, and cargo adjustments will be made later based on the updated flight schedule. This practice may cause more disruption to cargo operations due to the neglect of cargo diversion strategies during flight recovery. In other words, some cargo may not find a viable route and may be cancelled or transported through auxiliary capacity, resulting in additional costs. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose an air cargo recovery method, apparatus, computer equipment and storage medium to quickly generate an air cargo recovery plan and reduce recovery costs.

[0005] In order to solve the above technical problems, the embodiment of the present application provides an air cargo recovery method, comprising:

[0006] Obtaining an initial aircraft route and an initial cargo itinerary, and initializing a short-constraint master problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary;

[0007] In each column and row iteration, solving the linear programming relaxation of the short restricted master problem to obtain the dual value of each constraint;

[0008] Generate aircraft routes and cargo itineraries in the subproblems corresponding to the short-restricted master problem according to the dual values;

[0009] updating the short restricted master problem based on the aircraft route and cargo itinerary of the negative simplified cost for the next iterative calculation;

[0010] When the aircraft routes or cargo itineraries with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short restricted master problem are not integer solutions, integer processing is performed on the aircraft routes that are not integer solutions to update the short restricted master problem;

[0011] When the iteration of the columns and rows is completed, the mixed integer main problem is solved by the solver to obtain the target aircraft route and cargo itinerary plan.

[0012] In order to solve the above technical problems, the embodiment of the present application provides an air cargo recovery device, comprising:

[0013] An initialization unit, used to obtain an initial aircraft route and an initial cargo itinerary, and initialize the short-constraint master problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary;

[0014] A dual value solving unit, used for solving the linear programming relaxation of the short restricted master problem in each column and row iteration to obtain the dual value of each constraint;

[0015] An aircraft route generation unit, for generating aircraft routes and cargo itineraries in the subproblems corresponding to the short-constrained main problem according to the dual values;

[0016] A restricted master problem updating unit, used for updating the short restricted master problem based on the aircraft route and cargo itinerary of the negative simplified cost, so as to perform the next iterative calculation;

[0017] An integer processing unit, configured to perform integer processing on the aircraft routes that are not integer solutions to update the short constraint main problem when the aircraft routes or cargo itineraries with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short constraint main problem are not integer solutions;

[0018] The target solution generating unit is used for solving the mixed integer main problem through a solver when the iteration of the columns and rows is completed to obtain the target aircraft route and cargo itinerary solution.

[0019] In order to solve the above technical problems, a technical solution adopted by the present invention is: to provide a computer device, including one or more processors; a memory for storing one or more programs, so that the one or more processors can implement any one of the above-mentioned air cargo recovery methods.

[0020] In order to solve the above technical problems, a technical solution adopted by the present invention is: a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any one of the above-mentioned air cargo recovery methods is implemented.

[0021] The embodiment of the present invention provides an air cargo recovery method, device, computer equipment and storage medium. The method includes: obtaining an initial aircraft route and an initial cargo itinerary, and initializing a short restricted master problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary; solving the linear programming relaxation of the short restricted master problem in each column and row iteration to obtain the dual value of each constraint; generating aircraft routes and cargo itineraries in the subproblems corresponding to the short restricted master problem according to the dual value; updating the short restricted master problem based on the aircraft routes and cargo itineraries with negative simplified costs to perform the next iterative calculation; when the aircraft routes or cargo itineraries with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short restricted master problem are not integer solutions, integer processing is performed on the aircraft routes that are not integer solutions to update the short restricted master problem; when the iteration of the columns and rows ends, solving the mixed integer master problem through a solver to obtain the target aircraft routes and cargo itinerary solutions. The embodiment of the present invention gradually adds variables or constraints that are most likely to improve the solution to update the short-restricted master problem, thereby reducing the number of variables or constraints that need to be processed in each iteration, achieving rapid generation of an air cargo recovery plan, and reducing recovery costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 It is a flowchart of the implementation of the air cargo recovery method provided in the embodiment of the present application;

[0024] Figure 2 It is a flowchart of the implementation of the sub-process in the air cargo recovery method provided in the embodiment of the present application;

[0025] Figure 3 It is a flowchart of the implementation of the sub-process in the air cargo recovery method provided in the embodiment of the present application;

[0026] Figure 4 is a schematic diagram of a flight transfer network provided by an embodiment of the present application;

[0027] Figure 5 It is a flowchart of the implementation of the sub-process in the air cargo recovery method provided in the embodiment of the present application;

[0028] Figure 6 It is a flowchart of the implementation of the sub-process in the air cargo recovery method provided in the embodiment of the present application;

[0029] Figure 7 is a schematic diagram of a cargo flight delay provided in an embodiment of the present application;

[0030] Figure 8 It is a flowchart of the implementation of the sub-process in the air cargo recovery method provided in the embodiment of the present application;

[0031] Fig. 9 It is a flowchart of the implementation of the sub-process in the air cargo recovery method provided in the embodiment of the present application;

[0032] Fig.10 is a schematic diagram of an air cargo recovery device provided in an embodiment of the present application;

[0033] Fig.11 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0035] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0037] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the air cargo recovery method provided in the embodiment of the present application is generally executed by a computer device, and accordingly, the air cargo recovery device is generally configured on a computer device.

[0039] See also Figure 1 , Figure 1 A specific implementation of the air cargo recovery method is shown.

[0040] It should be noted that if there are substantially the same results, the method of the present invention is not limited to Figure 1 The process sequence shown is limited to the following steps:

[0041] S1: Obtain an initial aircraft route and an initial cargo itinerary, and initialize a short-constrained main problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary.

[0042] Specifically, the embodiments of the present application are intended to provide an improved column and row generation algorithm (CRG) for solving the comprehensive air cargo recovery problem in order to effectively respond to various types of disruptions, in order to cope with the complex challenges brought about by the increasing number of flights and the uncertain operating environment. This involves the simultaneous management of recovery flights, aircraft routes, and cargo itineraries when disruptions occur. The embodiments of the present application also incorporate flight delay decisions for aircraft and cargo, and simultaneously solve the aircraft diversion sub-problem and the cargo diversion sub-problem, thereby obtaining a high-quality recovery solution in a short period of time.

[0043] Specifically, a set of initial aircraft routes and initial cargo itineraries are obtained and used as initial variables, and time window constraints and resource constraints (such as the number of aircraft, crew restrictions, etc.) are used as initial constraints. Set the objective function, which is to minimize the air cargo recovery cost. According to the initial variables, initial constraints and objective function, a mathematical model of the short-restricted master problem is constructed to initialize the short-restricted master problem. Among them, the data model of the short-restricted master problem is a linear programming model. When constructing the data model of the short-restricted master problem, it is necessary to ensure that all variables and constraints are correctly represented and that the objective function can accurately reflect the optimization goal of the problem.

[0044] Among them, the "Short Restricted Master Problem" (SRMP) is a key concept in the improved column and row generation algorithm (CRG) for solving the comprehensive air cargo recovery problem. SRMP is a core component in the algorithm framework. It is a simplified and restricted form of the original problem, which is used to gradually build and optimize the solution in an iterative process.

[0045] S2: In each column and row iteration, solve the linear programming relaxation of the short restricted master problem to obtain the dual value of each constraint.

[0046] Specifically, the column and row generation algorithm is initiated by a subset of aircraft routes, cargo itineraries, and the corresponding time-related constraints in the SRMP, and in each column and row iteration, the linear programming relaxation of the short-constrained master problem is solved to obtain the dual value of each constraint.

[0047] See also Figure 2 , Figure 2 A specific implementation of step S2 is shown, which is described in detail as follows:

[0048] S21: In each iteration of columns and rows, integer constraints are removed from the short restricted master problem to obtain a relaxed linear programming model.

[0049] S22: Solving the linear programming relaxation based on the relaxed linear programming model by the solver to obtain the dual value of each constraint.

[0050] Specifically, the integer constraints in the short restricted master problem are temporarily ignored and converted into a linear programming problem. This process is called linear programming relaxation. The relaxed model is easier to solve because it no longer contains the discreteness of integer variables. Then, a suitable linear programming solver is selected according to the scale and characteristics of the short restricted master problem. A linear programming solver (such as the simplex method, interior point method, etc.) is used to solve the relaxed linear programming problem. The goal of the solver is to find the variable values ​​that satisfy all constraints (except the ignored integer constraints) so that the objective function is optimal (minimized or maximized). In a linear programming problem, each constraint corresponds to a dual value. Among them, in the column and row generation algorithm, the dual value is used to evaluate the potential impact of the newly generated columns (aircraft routes or cargo itineraries) and rows (constraints) on the objective function. Specifically, the dual value is used to construct the objective function of the subproblem to determine which newly generated columns or rows should be added to the short restricted master problem.

[0051] S3: Generate aircraft routes and cargo itineraries in the subproblems corresponding to the short-constrained main problem according to the dual values.

[0052] Specifically, the sub-problems corresponding to the short-restricted main problem include solving the problem of generating airplane flights and solving the problem of generating cargo itineraries. Among them, the objective function of solving the problem of generating airplane flights is to find a better airplane string and simplify the cost; the objective function of solving the problem of generating cargo itineraries is to find a better cargo itinerary at a lower cost. The dual value calculated above is used to construct the objective function of the sub-problem to determine which newly generated columns or rows should be added to the short-restricted main problem. In an embodiment of the present application, airplane routes and cargo itineraries are generated in the sub-problems corresponding to the short-restricted main problem according to the dual value.

[0053] See also Figure 3 and Figure 4 , Figure 3 A specific implementation of step S3 is shown. Figure 4 Schematic diagram of a flight transfer network provided by an embodiment of the present application, as described in detail as follows:

[0054] S31: In the solution to the aircraft flight generation problem, a flight connection network for each aircraft is constructed, wherein nodes in the flight connection network represent flights with originally scheduled departure and arrival times.

[0055] S32: Add the source node and the sink node to the flight connection network to obtain a target flight connection network, wherein the source node represents the airport and time available for the aircraft, the sink node represents the end of the recovery range, and the arcs of the target flight connection network represent the connections between different flights.

[0056] S33: constructing an acyclic graph based on the target flight connection network, and using a multi-label shortest path algorithm to generate the aircraft route based on the acyclic graph and the dual value.

[0057] Specifically, in solving the aircraft flight generation problem, a flight connection network for each aircraft is constructed; then the source node and the sink node are added to the flight connection network to obtain the target flight connection network; an acyclic graph is constructed based on the target flight connection network, and a multi-label shortest path algorithm is used to generate aircraft routes based on the acyclic graph and dual values. Among them, the multi-label shortest path algorithm is used to solve the sub-problems in the comprehensive cargo aviation recovery problem, especially to generate new aircraft routes.

[0058] Specifically, the multi-label shortest path algorithm is used to generate aircraft routes, including: (1) sorting all nodes (flights) in chronological order to ensure that the nodes can be processed in chronological order or logical order in subsequent steps; (2) initializing a label set for each node to store all possible paths from the source node to the node; initially, only the source node has a label, and its label represents the initial path from the source node; (3) starting from the source node, the path is gradually expanded, one node is considered at a time, and it is added to the existing path; for each node, all its possible subsequent nodes are checked, and new paths are generated according to the flight connection relationship and time constraints; (4) the concept of non-dominated labels is used to select the optimal path, that is, select those paths that have the minimum cost (or maximum utility) at the current node and are also potentially optimal at future nodes; (5) by continuously expanding and selecting non-dominated labels, a complete path from the source node to the sink node can eventually be generated; these paths represent new aircraft routes, that is, the aircraft's recovery flight plan.

[0059] like Figure 4 As shown, Figure 4 A schematic diagram showing the flight connection network for aircraft departing from Airport A. The circular and square nodes represent flights and virtual nodes, respectively. The origin and destination of each flight are shown above the node. The numbers in brackets under each node represent the scheduled departure and arrival times of each flight.

[0060] See also Figure 5 , Figure 5 A specific implementation of step S33 is shown, which is described in detail as follows:

[0061] S331: Acquire any pair of flights in the network connected to the target flight to obtain a target flight pair, wherein the target flight pair includes a first flight and a second flight.

[0062] S332: If the departure station of the second flight is the same as the arrival station of the first flight, and the departure time of the second flight is later than the departure time of the first flight in the original schedule, the second flight is connected with the first flight to construct the acyclic graph.

[0063] S333: Generate the aircraft route based on the acyclic graph and the dual value using the multi-label shortest path algorithm.

[0064] Specifically, in order to ensure that the constructed target flight connection network is a directed acyclic graph, for any pair of flight comparisons, if the departure station of the second flight is the same as the arrival station of the first flight, and the departure time of the second flight is later than the departure time of the first flight in the original schedule, the second flight is connected with the first flight. Finally, since the delayed second flight can be connected with the first flight, the original departure time of the second flight is allowed to be earlier than the arrival time of the first flight plus the transfer time.

[0065] S34: In the solution to the cargo itinerary generation problem, the multi-label shortest path algorithm is used to generate the cargo itinerary based on the dual value.

[0066] See also Figure 6 and Figure 7 , Figure 6 A specific implementation of step S34 is shown. Figure 7 Schematic diagram of cargo flight delay provided by the embodiment of the present application, as described in detail as follows:

[0067] S341: In the solution to the cargo itinerary generation problem, a flight connection network for each cargo is constructed to obtain an initial flight connection network.

[0068] S342: Add the source node and the sink node to the initial flight connection network to obtain a basic flight connection network.

[0069] S343: Generate the cargo itinerary based on the basic flight connection network and the dual value using the multi-label shortest path algorithm.

[0070] Specifically, the problem of generating cargo itineraries is solved in the embodiment of the present application, with the goal of finding a better cargo itinerary at a lower cost. In the air cargo recovery problem, the recovery decision of flight delays often has two sides for the overall recovery solution. First, the shorter the flight delay, the smaller the flight delay and cargo delay costs. In addition, for flight transfers in cargo routes, a shorter delay of the previous flight will also lead to fewer transfer interruptions. Second, in many cases, longer flight delays may show some advantages. Specifically, for consecutive flights, in order to wait for transfer cargo, consecutive flights are preferably delayed for a longer time. Therefore, unlike the aircraft route generation algorithm, the algorithm generates two departure times for consecutive flights: one that meets the turnaround time of consecutive flights operated by the same aircraft (through transfer), and the other that meets the standard transfer time sufficient for ground operations between flights operated by different aircraft. So in the embodiment of the present application, a flight connection network for each cargo is constructed to obtain an initial flight connection network; the source node and the sink node are added to the initial flight connection network to obtain a basic flight connection network; and a multi-label shortest path algorithm is used to generate cargo itineraries based on the basic flight connection network and the dual value.

[0071] The difference between the sub-problems of generating cargo itineraries and flight routes in the embodiment of the present application is that only flights arriving at the cargo destination will be connected to the sink node in the cargo network. The multi-label shortest path algorithm for generating cargo itineraries includes the following five steps: (1) node sorting (2) label set initialization (3) path expansion (4) selection of non-dominated labels (5) generation of new flight strings.

[0072] S4: updating the short restricted master problem based on the aircraft route and cargo itinerary with negative simplified cost for the next iterative calculation.

[0073] Specifically, it is determined whether the simplified costs of the above-generated aircraft routes and cargo itineraries are greater than zero. If so, the existing aircraft routes and cargo itineraries are executed through step S5; otherwise, the aircraft routes and cargo itineraries with negative simplified costs are included in the short-constrained main problem for the next iterative calculation.

[0074] See also Figure 8 , Figure 8 A specific implementation of step S4 is shown, which is described in detail as follows:

[0075] S41: Add the negative simplified cost aircraft routes and cargo itineraries as new columns.

[0076] S42: Determine whether to generate a new duplicate aircraft flight and obtain a determination result.

[0077] S43: If the judgment result is to generate the new duplicate aircraft flight, then add the flight constraints corresponding to the new duplicate flight, and update the short-constrained main problem according to the flight constraints and the new column to perform the next iterative calculation.

[0078] S44: If the judgment result is that the new duplicate aircraft flight is not generated, the new column is included in the short-constrained main problem to perform the next iterative calculation.

[0079] Specifically, the negative simplified cost airplane routes and cargo itineraries are taken as new columns. It is determined whether the generated airplane routes or cargo itineraries introduce a new departure time to determine whether a new duplicate airplane flight is generated, and a determination result is obtained. If a new departure time is introduced, a new duplicate airplane flight is generated; if no new departure time is introduced, a new duplicate airplane flight is not generated. If it is determined that the new duplicate airplane flight is generated, the flight constraints corresponding to the new duplicate flight are added, and the short-restricted main problem is updated according to the flight constraints and the new column, and then the step S2 is returned to perform the next iterative calculation.

[0080] S5: When the aircraft routes or cargo itineraries with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short restricted main problem are not integer solutions, integer processing is performed on the aircraft routes that are not integer solutions to update the short restricted main problem.

[0081] See also Fig. 9 , Fig. 9 A specific implementation of step S5 is shown, which is described in detail as follows:

[0082] S51: When the aircraft routes or cargo itineraries with negative simplification costs are no longer generated, the simplified restricted main problem is solved based on the existing aircraft routes and cargo itineraries to obtain a solution result.

[0083] S52: Incorporating the integer solution in the solution result into the mixed integer main problem.

[0084] S53: Using the branch and bound method, the aircraft routes that are not integer solutions in the solution are processed as integers to update the short-constrained main problem.

[0085] Specifically, when the flight routes or cargo itineraries with negative simplified costs are no longer generated, the simplified restricted main problem is solved based on the existing flight routes and cargo itineraries to obtain a solution result, and the integer solution in the solution result is incorporated into the mixed integer main problem. Then, the flight routes that are not integer solutions in the solution result are integerized using the branch and bound method to update the short restricted main problem, and then the execution returns to step S2 to perform the next iterative calculation.

[0086] Among them, the branch and bound method is an algorithm commonly used in optimization problems and decision problems, and is particularly suitable for solving integer programming, combinatorial optimization and other problems. It is an algorithm framework that systematically enumerates all candidate solutions, and cuts off some paths by maintaining a global upper bound (or lower bound) to reduce the amount of calculation, thereby improving the efficiency of the solution. The steps of integer processing using the branch and bound method include solving RMIP (Related Master Interger Program), checking the integer nature of the solution, applying the branch and bound method, obtaining an integer solution, and verifying and outputting the solution.

[0087] S6: When the iteration of the columns and rows is completed, the mixed integer main problem is solved by the solver to obtain the target aircraft route and cargo itinerary plan.

[0088] Specifically, when no further favorable aircraft routes or cargo itineraries are found in the above column and row iterations, the column and row iterations are terminated, and the mixed integer master problem is solved by the solver to obtain the target aircraft routes and cargo itinerary solutions. The mixed integer master problem includes all favorable aircraft routes and cargo itineraries generated in the previous iterations, as well as related constraints, such as aircraft availability, cargo transfer requirements, and flight time connections.

[0089] The purpose of solving this mixed integer master problem is to find an integer solution that satisfies all constraints and has the optimal objective function (minimizing the recovery cost), that is, to determine the specific flight sequence of each aircraft and the specific transportation itinerary of each batch of cargo.

[0090] In the embodiment of the present application, the initial aircraft route and the initial cargo itinerary are obtained, and the short restricted main problem is initialized based on the preset objective function, the initial aircraft route and the initial cargo itinerary; in each iteration of columns and rows, the linear programming relaxation of the short restricted main problem is solved to obtain the dual value of each constraint; according to the dual value, the aircraft route and the cargo itinerary are generated in the sub-problem corresponding to the short restricted main problem; the short restricted main problem is updated based on the aircraft route and the cargo itinerary of the negative simplified cost to perform the next iterative calculation; when the aircraft route or the cargo itinerary of the negative simplified cost is no longer generated and the aircraft route obtained by solving the short restricted main problem is not an integer solution, the aircraft route that is not an integer solution is integer processed to update the short restricted main problem; when the iteration of the columns and rows is completed, the mixed integer main problem is solved by the solver to obtain the target aircraft route and cargo itinerary plan. The embodiment of the present invention gradually adds the variables or constraints that are most likely to improve the solution to update the short restricted main problem, reduces the number of variables or constraints that need to be processed in each iteration, realizes the rapid generation of air cargo recovery plans, and reduces recovery costs.

[0091] Please refer to Fig.10, as a response to the above Figure 1 The present application provides an embodiment of an air cargo recovery device, and the device embodiment is similar to Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0092] like Fig.10 As shown, the air cargo recovery device of this embodiment includes: an initialization unit 71, a dual value solving unit 72, an aircraft route generating unit 73, a restricted master problem updating unit 74, an integer processing unit 75 and a target solution generating unit 76, wherein:

[0093] An initialization unit 71 is used to obtain an initial aircraft route and an initial cargo itinerary, and initialize a short-constrained master problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary;

[0094] A dual value solving unit 72, used for solving the linear programming relaxation of the short restricted master problem in each column and row iteration to obtain the dual value of each constraint;

[0095] An aircraft route generation unit 73, configured to generate aircraft routes and cargo itineraries in the subproblems corresponding to the short-constrained main problem according to the dual values;

[0096] A restricted master problem updating unit 74, used to update the short restricted master problem based on the aircraft route and cargo itinerary with negative simplified cost, so as to perform the next iterative calculation;

[0097] An integer processing unit 75, configured to perform integer processing on the aircraft routes that are not integer solutions to update the short constraint main problem when the aircraft routes or cargo routes with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short constraint main problem are not integer solutions;

[0098] The target solution generating unit 76 is used to solve the mixed integer main problem through a solver when the iteration of the columns and rows is completed to obtain the target aircraft route and cargo itinerary solution.

[0099] Furthermore, the sub-problems corresponding to the short-constraint main problem include solving the problem of generating airplane flights and solving the problem of generating cargo itineraries; the airplane route generation unit 73 includes:

[0100] A first network construction unit, configured to construct a flight connection network for each aircraft in the solution to the aircraft flight generation problem, wherein nodes in the flight connection network represent flights with original scheduled departure and arrival times;

[0101] a node adding unit, configured to add a source node and a sink node to the flight connection network to obtain a target flight connection network, wherein the source node represents an airport and time available for an aircraft, the sink node represents the end of a recovery range, and the arcs of the target flight connection network represent connections between different flights;

[0102] An aircraft route generation unit, configured to construct an acyclic graph based on the target flight connection network, and generate the aircraft route based on the acyclic graph and the dual value using a multi-label shortest path algorithm;

[0103] A cargo itinerary generating unit is used to generate the cargo itinerary based on the dual value by using the multi-label shortest path algorithm in solving the cargo itinerary generating problem.

[0104] Furthermore, the aircraft route generation unit includes:

[0105] A flight pair acquisition unit, configured to acquire any pair of flights in the target flight connection network to obtain a target flight pair, wherein the target flight pair includes a first flight and a second flight;

[0106] an acyclic graph construction unit, configured to connect the second flight with the first flight if the departure station of the second flight is the same as the arrival station of the first flight and the departure time of the second flight is later than the departure time of the first flight in the original schedule, so as to construct the acyclic graph;

[0107] A route generating unit is used to generate the aircraft route based on the acyclic graph and the dual value by using the multi-label shortest path algorithm.

[0108] Furthermore, the cargo itinerary generating unit includes:

[0109] A second network construction unit is used to construct a flight connection network for each cargo in solving the cargo itinerary generation problem to obtain an initial flight connection network;

[0110] A basic flight connection network construction unit, configured to add the source node and the sink node to the initial flight connection network to obtain a basic flight connection network;

[0111] The itinerary generating unit is used to generate the cargo itinerary based on the basic flight connection network and the dual value by using the multi-label shortest path algorithm.

[0112] Further, the limiting main question updating unit 74 includes:

[0113] A new column generating unit, used for taking the negative simplified cost aircraft route and cargo itinerary as a new column;

[0114] A judgment unit, used for judging whether to generate a new duplicate aircraft flight and obtaining a judgment result;

[0115] A first updating unit, configured to add a flight constraint corresponding to the new duplicate flight if the judgment result is to generate the new duplicate flight, and update the short-constrained main problem according to the flight constraint and the new column to perform the next iterative calculation;

[0116] The second updating unit is used to include the new column into the short-constraint main problem for the next iterative calculation if the judgment result is that the new duplicate aircraft flight is not generated.

[0117] Further, the dual value solving unit 72 includes:

[0118] An integer constraint removal unit, used for removing integer constraints from the short restricted master problem in each column and row iteration to obtain a relaxed linear programming model;

[0119] A linear programming relaxation solving unit is used to solve the linear programming relaxation based on the relaxed linear programming model through the solver to obtain the dual value of each constraint.

[0120] Furthermore, the integer processing unit 75 comprises:

[0121] A solution result generating unit, used for solving the simplified restricted main problem based on the existing aircraft routes and cargo itineraries to obtain a solution result when the aircraft routes or cargo itineraries with negative simplified costs are no longer generated;

[0122] An integer solution incorporating unit, used to incorporate the integer solution in the solution result into the mixed integer main problem;

[0123] The short-constraint main problem updating unit is used to use the branch and bound method to integer-process the aircraft routes that are not integer solutions in the solution results to update the short-constraint main problem.

[0124] To solve the above technical problems, the present application also provides a computer device. Fig.11 , Fig.11 This is a basic structural block diagram of the computer device in this embodiment.

[0125] The computer device 8 includes a memory 81, a processor 82, and a network interface 83 which are interconnected through a system bus. It should be noted that: Fig.11Only a computer device 8 having three components, a memory 81, a processor 82, and a network interface 83, is shown in the figure, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0126] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0127] The memory 81 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 81 can be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 81 can also be an external storage device of the computer device 8, such as a plug-in hard disk equipped on the computer device 8, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 81 can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as the program code of the air cargo recovery method, etc. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or are to be output.

[0128] The processor 82 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to run the program code stored in the memory 81 or process data, such as running the program code of the above-mentioned air cargo recovery method to implement various embodiments of the air cargo recovery method.

[0129] The network interface 83 may include a wireless network interface or a wired network interface, and the network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0130] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores a computer program, and the computer program can be executed by at least one processor to enable the at least one processor to perform the steps of an air cargo recovery method as described above.

[0131] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0132] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An air cargo recovery method, characterized in that: include: Obtaining an initial aircraft route and an initial cargo itinerary, and initializing a short-constraint master problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary; In each column and row iteration, solving the linear programming relaxation of the short restricted master problem to obtain the dual value of each constraint; Generate aircraft routes and cargo itineraries in the subproblems corresponding to the short-restricted master problem according to the dual values; updating the short restricted master problem based on the aircraft route and cargo itinerary of the negative simplified cost for the next iterative calculation; When the aircraft routes or cargo itineraries with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short restricted master problem are not integer solutions, integer processing is performed on the aircraft routes that are not integer solutions to update the short restricted master problem; When the iteration of the columns and rows is completed, the mixed integer main problem is solved by the solver to obtain the target aircraft route and cargo itinerary plan; The sub-problems corresponding to the short-constraint main problem include solving the problem of generating airplane flights and solving the problem of generating cargo itineraries; Generating the flight routes and cargo itineraries in the subproblems corresponding to the short restricted main problem according to the dual values ​​includes: In the solving of the aircraft flight generation problem, a flight connection network for each aircraft is constructed, wherein nodes in the flight connection network represent flights with original scheduled departure and arrival times; Adding a source node and a sink node to the flight connection network to obtain a target flight connection network, wherein the source node represents the airport and time at which the aircraft is available, the sink node represents the end of the recovery range, and the arcs of the target flight connection network represent connections between different flights; Constructing an acyclic graph based on the target flight connection network, and generating the aircraft route based on the acyclic graph and the dual value using a multi-label shortest path algorithm; In the solution to the cargo itinerary generation problem, a flight connection network for each cargo is constructed to obtain an initial flight connection network; Adding the source node and the sink node to the initial flight connection network to obtain a basic flight connection network; The cargo itinerary is generated based on the basic flight connection network and the dual value using the multi-label shortest path algorithm.

2. The air cargo recovery method according to claim 1, characterized in that: The step of constructing an acyclic graph based on the target flight connection network and generating the aircraft route based on the acyclic graph and the dual value using a multi-label shortest path algorithm includes: Acquire any pair of flights in the target flight connection network to obtain a target flight pair, wherein the target flight pair includes a first flight and a second flight; If the departure station of the second flight is the same as the arrival station of the first flight, and the departure time of the second flight is later than the departure time of the first flight in the original schedule, the second flight is connected with the first flight to construct the acyclic graph; The multi-label shortest path algorithm is used to generate the aircraft route based on the acyclic graph and the dual value.

3. The air cargo recovery method according to claim 1, characterized in that: The aircraft route and cargo itinerary based on the negative simplified cost are updated to the short restricted main problem for the next iterative calculation, including: Add the negative simplified cost of the aircraft route and cargo itinerary as new columns; Determine whether to generate a new duplicate flight and obtain a determination result; If the judgment result is to generate the new duplicated flight, then add the flight constraints corresponding to the new duplicated flight, and update the short-constrained main problem according to the flight constraints and the new column to perform the next iterative calculation; If the judgment result is that the new duplicate aircraft flight is not generated, the new column is included in the short-constraint main problem to perform the next iterative calculation.

4. The air cargo recovery method according to any one of claims 1 to 3, characterized in that: In each column and row iteration, the linear programming relaxation of the short restricted master problem is solved to obtain the dual value of each constraint, including: In each column and row iteration, integer constraints are removed from the short restricted master problem to obtain a relaxed linear programming model; The linear programming relaxation is solved by the solver based on the relaxed linear programming model to obtain the dual value of each constraint.

5. The air cargo recovery method according to any one of claims 1 to 3, characterized in that: When the negative simplified cost aircraft route or cargo itinerary is no longer generated and the aircraft route obtained by solving the short restricted main problem is not an integer solution, integer processing is performed on the aircraft route that is not an integer solution to update the short restricted main problem, including: When the aircraft routes or cargo itineraries with negative simplified costs are no longer generated, the short-constraint main problem is solved based on the existing aircraft routes and cargo itineraries to obtain a solution result; Incorporating integer solutions in the solution results into the mixed integer main problem; The aircraft routes that are not integer solutions in the solution are processed into integers by using a branch and bound method to update the short-constrained main problem.

6. An air cargo recovery device, characterized in that: include: An initialization unit, used to obtain an initial aircraft route and an initial cargo itinerary, and initialize the short-constraint master problem based on a preset objective function, the initial aircraft route and the initial cargo itinerary; A dual value solving unit, used for solving the linear programming relaxation of the short restricted master problem in each column and row iteration to obtain the dual value of each constraint; An aircraft route generation unit, for generating aircraft routes and cargo itineraries in the subproblems corresponding to the short-constrained main problem according to the dual values; A restricted master problem updating unit, used for updating the short restricted master problem based on the aircraft route and cargo itinerary of the negative simplified cost, so as to perform the next iterative calculation; An integer processing unit, configured to perform integer processing on the aircraft routes that are not integer solutions to update the short constraint main problem when the aircraft routes or cargo itineraries with negative simplified costs are no longer generated and the aircraft routes obtained by solving the short constraint main problem are not integer solutions; A target solution generating unit, used for solving the mixed integer master problem by a solver to obtain a target aircraft route and cargo itinerary solution when the iteration of the columns and rows is completed; The sub-problems corresponding to the short-constraint main problem include solving the problem of generating airplane flights and solving the problem of generating cargo itineraries; The aircraft route generation unit comprises: A first network construction unit, configured to construct a flight connection network for each aircraft in the solution to the aircraft flight generation problem, wherein nodes in the flight connection network represent flights with original scheduled departure and arrival times; a node adding unit, configured to add a source node and a sink node to the flight connection network to obtain a target flight connection network, wherein the source node represents an airport and time available for an aircraft, the sink node represents the end of a recovery range, and the arcs of the target flight connection network represent connections between different flights; An aircraft route generation unit, configured to construct an acyclic graph based on the target flight connection network, and generate the aircraft route based on the acyclic graph and the dual value using a multi-label shortest path algorithm; A second network construction unit is used to construct a flight connection network for each cargo in solving the cargo itinerary generation problem to obtain an initial flight connection network; A basic flight connection network construction unit, configured to add the source node and the sink node to the initial flight connection network to obtain a basic flight connection network; The itinerary generating unit is used to generate the cargo itinerary based on the basic flight connection network and the dual value by using the multi-label shortest path algorithm.

7. A computer device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the air cargo recovery method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the air cargo recovery method according to any one of claims 1 to 5 is implemented.

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