Multi-Vehicle Ground Support Vehicle Joint Scheduling Method and System for Large Airports

Abstract

The present invention discloses a multi-vehicle ground support vehicle joint scheduling method and system for large airports. The method includes establishing a multi-vehicle ground support vehicle joint scheduling model according to the operation mode of ground support vehicles; designing a two-stage heuristic algorithm to solve the multi-vehicle ground support vehicle joint scheduling problem. The first stage solves the ground support vehicle scheduling model of a single vehicle type; the second stage obtains the optimal scheduling plan of the airport ground support vehicles with multi-vehicle joint cooperation according to the single-vehicle scheduling results. The present invention considers the actual scenario of ground support vehicle scheduling, establishes a multi-objective optimization model from the scheduling of single-vehicle ground support vehicles to the joint scheduling of multi-vehicle mutual cooperation, and designs an improved NSGA-II algorithm and a tabu search algorithm to solve this problem, reducing the use cost of ground support vehicles and the vehicle running distance, and improving the efficiency of the airport ground support link, with practicability and applicability.

Description

Technical Field

[0001] The present invention relates to the technical fields of airport ground support, multi-objective optimization, vehicle route planning and scheduling, and specifically relates to a multi-vehicle ground support vehicle joint scheduling method and system for large airports. Background Technique

[0002] Due to the characteristics of multi-agent, time constraints, and resource limitations in airport ground support services, unreasonable scheduling methods not only increase the workload but also lead to low support efficiency. The research on single-vehicle scheduling has tended to saturation and can no longer meet the actual scheduling needs of airports. Therefore, to avoid fragmentation of scheduling research, deeply analyze the operating characteristics of different types of vehicles, and jointly schedule different types of vehicles from an overall perspective has become an urgent problem in current airport operations. In recent years, the research results of domestic and foreign scholars on airport ground support vehicle scheduling mainly focus on single-vehicle scheduling research, with less research on multi-vehicle ground support vehicle joint scheduling, and even less research considering the vehicle operation mode in the joint scheduling process. Most related research adds timing constraint conditions between different vehicle types in the scheduling model to reflect the jointness, takes the operation time of a certain vehicle type as a constraint condition for the scheduling of another vehicle type, and the independence between vehicle types is high, making it difficult to truly reflect the joint scheduling process of multi-vehicle ground support vehicles. Aiming at the problem that the continuously growing civil aviation traffic volume puts great pressure on airport ground support services, from the perspectives of surface operation efficiency and operation safety, on the basis of considering the joint services of multi-vehicle ground support vehicles with different operation modes, a scheduling model that minimizes the number of vehicle uses and the running distance is proposed to provide a more efficient ground support vehicle scheduling plan for airports to cope with the increasingly tense ground support service requirements, thus generating practical application value. Summary of the Invention

[0003] The object of the present invention is to propose a multi-vehicle ground support vehicle joint scheduling method for large airports, aiming at the joint scheduling of multi-vehicle types on the large airport surface, improving the efficiency of the airport ground support link, and providing reference guidance for the airport to make decisions related to ground support vehicle scheduling.

[0004] To achieve the above object, the technical solution provided by the present invention is:

[0005] A multi-vehicle ground support vehicle joint scheduling method for large airports, characterized by including the steps of:

[0006] S1: Establish a multi-vehicle ground support vehicle joint scheduling model for large airports;

[0007] S2: Design an adaptive NSGA-II algorithm integrating local search to solve the single-vehicle ground support vehicle scheduling model;

[0008] S3: Determine the weight of each objective function in decision-making and then select the optimal solution;

[0009] S4: Design a tabu search (TS) algorithm to solve the scheduling scheme for the joint operation of multiple vehicle types

[0010] In step S1, the operation modes of the ground support vehicles are divided into three types: continuous operation, continuous operation with resource capacity limitation, and round-trip operation. Among the three operation modes, a conveyor vehicle, a water truck, and a baggage tractor are selected as the key research vehicle types respectively. Taking the total number of ground support vehicles used and the total operating distance as the optimization objects, a joint scheduling model for multi-vehicle-type ground support vehicles is established.

[0011] In step S2, an integer coding form is adopted, and a chromosome is constructed with the flight service order of each ground support vehicle as the gene value; in the initialization stage, the savings algorithm (CW) is used to generate an initial solution that satisfies the single-vehicle-type ground support vehicle scheduling model; the design content of the adaptive NSGA-II algorithm integrating local search includes: calculation of crowding degree, selection, adaptive crossover and adaptive mutation operations, adaptive local search operations, and elite selection strategy;

[0012] In step S3, for the multi-objective optimization problem, it is necessary to evaluate the importance of the optimization objectives, and an objective assignment method based on the Pareto optimal solution set introducing the C-OWA operator is selected to determine the objective function weights and select the most suitable solution;

[0013] In step S4, by merging the chromosomes of the optimal scheduling schemes of the three vehicle types into a matrix-encoded chromosome and correcting the generated chromosome by the method of destruction and repair, a feasible initial solution for the joint scheduling of multi-vehicle-type ground support vehicles is obtained; the design content of the tabu search algorithm for the joint scheduling of multi-vehicle-type ground support vehicles includes: neighborhood generation, evaluation function and neighbor selection strategy, tabu list and special criteria;

[0014] The specific steps of the adaptive NSGA-II algorithm integrating local search used to solve the single-vehicle-type ground support vehicle scheduling model in the multi-vehicle-type ground support vehicle joint scheduling method for large airports are as follows:

[0015] Step (1): Generate an initial parental population;

[0016] Step (2): Calculate the crowding degree of the parental population. The essence of the crowding degree is to reflect the similarity degree between an individual f and other individuals. The greater the crowding degree, the smaller the similarity degree. Therefore, when performing selection operations in the same front, individuals with a greater crowding degree are more likely to be selected to maintain the diversity of the population.

[0017] Step (3): Initialize the offspring population; the selection operation adopts the binary tournament principle. Randomly select two individuals from the parent population, and select the individual with a smaller non-dominated rank; if the non-dominated ranks are the same, then select the individual with a larger crowding degree;

[0018] Step (4): Perform adaptive crossover, mutation, and local search operations. The crossover operation selects the adaptive crossover operator, and the mutation operation selects the adaptive mutation operator. The core of the local search operation comes from the neighborhood search algorithm. The flights that need to be adjusted in the individual are removed through the destruction operator, and then these flights are reinserted into the vehicle route according to the repair operator to obtain a new vehicle route. There are two destruction operators adopted in the present invention, namely the worst removal destruction operator and the random destruction removal operator; there are two repair operators adopted, namely the minimum cost insertion repair operator and the maximum regret value insertion repair operator;

[0019] Step (5): Merge the parent population and the offspring population, and perform non-dominated sorting, crowding degree calculation, and deviation degree calculation on the merged population.

[0020] Step (6): Implement the elite selection strategy for the merged population. Sort the parent population in the comparison order of non-dominated rank, crowding degree, and deviation degree in turn, and select a specified number of individuals as the offspring population.

[0021] Step (7): Update the iteration count, and repeat steps (3) - (6) until the maximum iteration count is reached, then return to find the final population and stop the program running.

[0022] For the adaptive NSGA-II algorithm integrating local search, the specific steps of the elite selection strategy are as follows:

[0023] Step (1): Sort in ascending order according to the non-dominated rank. Starting from those with a non-dominated rank of 1, put the individuals in the entire front into the next-generation parent population, and then put the individuals in the next non-dominated rank until the individuals in a certain rank cannot all be put into the next-generation parent population, then go to the next step;

[0024] Step (2): Arrange the individuals in this rank in descending order of crowding degree, and successively put the individuals with a larger crowding degree into the next-generation parent population until the individuals with the same crowding degree cannot all be put into the next-generation parent population, then go to the next step;

[0025] Step (3): Sort the remaining individuals with the same crowding degree in ascending order of deviation degree, and preferentially put the individuals with a smaller deviation degree into the next-generation parent population until the next-generation parent population is filled.

[0026] The joint dispatching method for multi - vehicle ground support vehicles for large airports is as follows. The specific steps of the tabu search algorithm for the joint dispatching of multi - vehicle ground support vehicles are as follows:

[0027] Step (1): According to the optimal solutions of single - vehicle support vehicle dispatching, the chromosomes of the optimal dispatching solutions of the three vehicle types are merged into a matrix - encoded chromosome, and the generated chromosome is corrected by using the method of destruction and repair to obtain a feasible initial solution for the joint dispatching of multi - vehicle ground support vehicles. Clear the tabu list and set the tabu length.

[0028] Step (2): Perform iterative learning on the current local optimal solution X * to obtain the overall neighborhood movement set and the overall neighborhood candidate solution set according to the neighborhood generation strategy, and calculate the corresponding rating function values of each candidate solution.

[0029] Step (3): Select the neighborhood candidate solution X min with the smallest evaluation function value. If Z(X min ) < Z(X * ), then X min becomes the new local optimal solution X * , and the corresponding neighborhood movement is included in the tabu list. Then compare it with the global optimal solution X best . If Z(X min ) < Z(X best ), then X min becomes the new global optimal solution. If the neighborhood movement that can generate the global optimal solution or the local optimal solution is in the tabu list, then lift the ban on this neighborhood movement. After the comparison, update the tabu list, and judge whether the current iteration number reaches the maximum iteration step. If not, return to Step (2); if so, go to Step (4).

[0030] Step (4): Stop the search after reaching the maximum number of iterations, and output the current global optimal solution X best , which is the dispatching solution for airport ground support vehicles for multi - vehicle joint operation.

[0031] For the tabu search algorithm for the joint dispatching of multi - vehicle ground support vehicles, the neighborhood generation steps are as follows:

[0032] Step (1): First, perform neighborhood generation operations on the paths of the water trucks in continuous operation mode and optimize their paths separately; since the path planning of vehicles in continuous operation mode is not affected by coordination constraints, their optimized paths are determined first; use the neighborhood generation strategy of the "destruction and repair" concept for the corresponding chromosome X wv

[0033] ​Step (2): Generate the separate neighborhood movement sets for the baggage tractor and the conveyor belt vehicle, and merge them with the optimal neighborhood movement operations of the water truck to obtain the overall neighborhood movement set; the "destroy and repair" neighborhood generation strategy is also used for the baggage tractor and the conveyor belt vehicle.

[0034] The present invention also proposes a joint scheduling method and system for multi-type ground support vehicles for large airports, including:

[0035] A model construction module, which is used to establish a joint scheduling model for multi-type ground support vehicles, establish an optimization object with the total number of ground support vehicles used and the total operating distance, combine the unique operating characteristics of different vehicle types, and considering that some support links need to be jointly completed by different vehicle types, establish a joint scheduling model for multi-type ground support vehicles.

[0036] A solution module, which is used to design an adaptive NSGA-II algorithm integrating local search to solve the single-type ground support vehicle scheduling model, and design a tabu search algorithm for joint scheduling to obtain the joint scheduling plan for multi-type ground support vehicles;

[0037] The present invention also protects an electronic device, including:

[0038] A memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the joint scheduling method for multi-type ground support vehicles for large airports described above is implemented.

[0039] The present invention also protects a computer-readable storage medium, including:

[0040] A computer program is stored, and the computer program enables a computer to execute the joint scheduling method for multi-type ground support vehicles for large airports.

[0041] Compared with the prior art, the present invention adopts the above technical solutions and has the following technical effects:

[0042] The present invention considers the actual scenario of vehicle scheduling for multiple vehicle types at large airport aprons. According to the operating characteristics of apron vehicles, the operating modes of ground support vehicles are divided into three types: continuous operation, continuous operation with resource capacity limitation, and round-trip operation. Among the three operating modes, a conveyor vehicle, a water truck, and a baggage tractor are respectively selected as the key research vehicle types, and a joint scheduling model for ground support vehicles of multiple vehicle types is established. First, an improved NSGA-II algorithm integrating local search is proposed to solve the scheduling model of ground support vehicles for a single vehicle type. The advantage of this improved algorithm is that it uses the savings algorithm to generate high-quality initial solutions and integrates adaptive local search into the NSGA-II algorithm framework, improving the search ability of the algorithm and making it more suitable for vehicle scheduling scenarios. Subsequently, a tabu search algorithm for multi-vehicle type scheduling is adopted. The chromosomes of the optimal scheduling schemes of the three vehicle types are merged into matrix-encoded chromosomes, and the generated chromosomes are corrected by means of destruction and repair to obtain a feasible initial solution for the joint scheduling of ground support vehicles of multiple vehicle types. A neighborhood generation strategy and a rating function for destruction and repair are designed to solve the multi-vehicle type apron vehicle scheduling scheme, improving the efficiency of the airport ground support link and reducing the airport apron operation cost.

[0043] Based on the proposed joint scheduling method and system for ground support vehicles of multiple vehicle types for large airports, the T2 terminal building of a large airport is used as an example research area to design a multi-vehicle type apron vehicle scheduling scheme. Through comparative analysis with the solution results of other heuristic methods, it can be obtained that the NSGA-II algorithm integrating adaptive local search proposed in the present invention has good global optimization ability. After joint optimization by the TS algorithm, the number of vehicles used for each vehicle basically remains unchanged, but the total driving distance of some vehicle types has decreased slightly to a certain extent, indicating that the designed multi-vehicle type joint TS algorithm in this paper has the ability to continuously optimize the objective function while solving the multi-vehicle type joint scheme. In addition, after the joint scheduling of ground support vehicles of multiple vehicle types, the degree of task balance of each vehicle type is similar to that in the single vehicle type, and a relatively balanced task allocation situation is still maintained. The joint scheduling method for ground support vehicles of multiple vehicle types for large airports can provide a relatively optimal scheme for multi-vehicle type scheduling at airport aprons, has high practical value, and is of great significance for providing guidance on the joint scheduling scheme of multi-vehicle type vehicles for airports. Description of the Drawings

[0044] Figure 1 It is a flow chart of the joint scheduling method for ground support vehicles of multiple vehicle types for large airports.

[0045] Figure 2 It is a diagram of three operating modes of ground support vehicles, where (1) is the continuous operation mode, (2) is the continuous operation mode with resource capacity limitation, and (3) is the round-trip operation mode.

[0046] Figure 3Schematic diagram of the elite selection strategy.

[0047] Figure 4 Distribution map of example airport apron positions and lanes.

[0048] Figure 5 Scatter plots of the solution results of LS-ANSGAⅡ, ANSGAⅡ, TS, LNS, and FCFS, where (a) is the belt loader, (b) is the water truck, and (c) is the baggage tractor. Detailed implementation manners

[0049] The above content of the present invention will be further described in detail below in the form of embodiments. However, it should not be understood that the scope of the above subject matter of the present invention is limited to the following embodiments. All technologies implemented based on the above content of the present invention belong to the scope of the present invention.

[0050] The present invention proposes a joint scheduling method for multi-type ground support vehicles for large airports. The flowchart is as Figure 1 shown. Considering the actual scenario of multi-type vehicle scheduling on the airport apron, the operation modes of ground support vehicles are divided into three types: continuous operation, continuous operation with resource capacity limitation, and round-trip operation. The belt loader, water truck, and baggage tractor are selected as the key research vehicle types in the three operation modes. Taking the total number of ground support vehicles used and the total running distance as the optimization objects, a joint scheduling model for multi-type ground support vehicles is established. An adaptive NSGA-II algorithm integrating local search is designed to solve the single-type ground support vehicle scheduling model. After determining the weight of each objective function in decision-making and then selecting the optimal solution, a tabu search (TS) algorithm is designed to solve the joint scheduling scheme for multi-types. It includes the following steps:

[0051] (1) Considering the actual scenario of multi-type vehicle scheduling on the airport apron, the operation modes of ground support vehicles are divided into three types: continuous operation, continuous operation with resource capacity limitation, and round-trip operation, and a joint scheduling model for multi-type ground support vehicles is established;

[0052] The specific steps in step (1) include:

[0053] 1.1, Problem description and assumptions: According to the three operation modes of ground support vehicles, as Figure 2 shown. The belt loader, water truck, and baggage tractor are selected as the key research vehicle types. Considering the computational complexity and model adaptability, information assumptions, flight assumptions, and vehicle performance assumptions are made for the joint scheduling optimization model of multi-type vehicles.

[0054] 1.2, Model establishment: In the model establishment section, the labels and parameters are first explained, and then a multi-objective optimization model is established with the first-layer decision being the ground support vehicle scheduling for a single vehicle type and the second-layer decision being the solution of the joint scheduling plan for ground support vehicles of multiple vehicle types. The objectives are to minimize the total number of support vehicles and the total operating distance.

[0055] In step 1.1, the different assumptions made for the UAV inspection model are specifically as follows:

[0056] Information assumption: The basic information of flights and vehicles is complete and error-free, and no changes will occur.

[0057] Flight assumption: Once each ground support service starts, it will not be interrupted due to any situation until the end of the support task; transit flights are divided into incoming and outgoing flights.

[0058] Vehicle performance assumption: There is no performance difference between ground support vehicles of the same type, and the working capabilities and efficiencies of relevant vehicle operators are the same. All vehicles depart from the vehicle yard, return to the vehicle yard after completing the service, and maintain a constant driving speed.

[0059] The specific steps in step 1.2 are as follows:

[0060] 1.2.1, Taking the total number of ground support vehicles used and the total operating distance as the optimization objects, combining the unique operating characteristics of different vehicle types, and considering that some support links need to be jointly completed by different vehicle types, a joint scheduling model for ground support vehicles of multiple vehicle types is established. The model objectives are as follows:

[0061]

[0062] Equation (1) represents the minimization of the number of vehicles of the three vehicle types studied in the present invention; Equation (2) represents the minimization of the total driving distance during the operation of the three vehicle types.

[0063] 1.2.2, Establishment of constraint conditions.

[0064]

[0065] Equations (3)-(8) are general constraints that all vehicle types need to follow. Equation (3) is the flight assignment uniqueness constraint, indicating that each type of service flight is only accepted once; Equations (4)-(6) are the vehicle service uniqueness constraints, indicating that ground support vehicles of each type will not repeatedly serve a certain flight; Equation (7) is the vehicle quantity constraint, indicating that the number of each type of vehicle used does not exceed the available vehicle quantity; Equation (8) is the vehicle service start time constraint, indicating that the start time of a certain vehicle type serving flight i is within its start time window.

[0066]

[0067] Equations (9) - (10) are specific constraints for the water truck. Equation (9) indicates that the water truck is in an empty state when departing from the yard; Equation (10) indicates that the remaining water volume in the water truck during operation cannot exceed the maximum volume of the water tank and cannot be less than 0.

[0068]

[0069] Equations (11) - (13) are joint operation constraints for the conveyor belt vehicle and the baggage tractor. Equation (11) indicates that the first conveyor belt vehicle for service must arrive before the first baggage tractor; Equation (12) indicates that baggage service can only start when the conveyor belt vehicle and the baggage trailer arrive at the service point simultaneously; Equation (13) indicates that the conveyor belt vehicle can end service only after all baggage tractors serving a certain flight have ended service.

[0070] (2) This problem belongs to a variant application of the VRP problem and is an NP - Hard problem. Therefore, an adaptive NSGA - Ⅱ algorithm integrating local search is first designed to solve the single - vehicle ground support vehicle scheduling model.

[0071] The specific steps of using the adaptive NSGA - Ⅱ algorithm integrating local search to solve the single - vehicle ground support vehicle scheduling model are as follows:

[0072] Step 1: Generate an initial parent population;

[0073] Step 2: Calculate the crowding degree of the parent population. The essence of the crowding degree is to reflect the similarity between an individual f and other individuals. The larger the crowding degree, the smaller the similarity. Therefore, when performing selection operations in the same front, individuals with a larger crowding degree are more likely to be selected to maintain the diversity of the population.

[0074] Step 3: Initialize the offspring population; the selection operation adopts the binary tournament principle. Randomly select two individuals from the parent population, and select the individual with a smaller non - dominated rank; if the non - dominated ranks are the same, then select the individual with a larger crowding degree;

[0075] Step 4: Perform adaptive crossover, mutation, and local search operations. The crossover operation selects an adaptive crossover operator, and the mutation operation selects an adaptive mutation operator. The core of the local search operation comes from the neighborhood search algorithm. The flights that need to be adjusted in the individual are removed through the destruction operator, and then these flights are re - inserted into the vehicle route according to the repair operator to obtain a new vehicle route. The destruction operators adopted in the present invention are two types, namely the worst - removal destruction operator and the random - removal destruction operator; the repair operators adopted are two types, namely the minimum - cost insertion repair operator and the maximum - regret - value insertion repair operator;

[0076] Step 5: Merge the parent population and the offspring population, and perform non-dominated sorting, crowding degree calculation, and deviation degree calculation on the merged population.

[0077] Step 6: Apply the elitist selection strategy to the merged population. Sort the parent population in sequence according to the comparison order of non-dominated rank, crowding degree, and deviation degree, and select a specified number of individuals as the offspring population.

[0078] Step 7: Update the iteration count, and repeat Steps 3 - 6 until the maximum iteration count is reached. Return the final population found and stop the program.

[0079] The specific steps in the said Step (2) include:

[0080] 2.1, Generation of the initial solution. To ensure the quality of the initial solution and the iteration efficiency of the single-vehicle type scheduling, the present invention uses the CW algorithm to generate the initial solution that meets the ground support vehicle scheduling model of the single-vehicle type.

[0081] 2.2, Solve the ground support vehicle scheduling model of the single-vehicle type using the adaptive NSGA-II algorithm integrated with local search.

[0082] In the said Step 2.1:

[0083] Assume that H vehicles serve H flights, which will generate H loops. Calculate the reduction value of the transportation distance after merging any two loops, and merge the two loops with the largest reduction value until no other loops can be merged.

[0084] For the airport ground support vehicle scheduling of the single-vehicle type, the present invention adopts the method of inserting a single flight into the vehicle path according to the savings value in path merging. The specific process is as follows: Whenever a new path set VC is generated, a savings value matrix sav[i][k][n][d] of size n*4 will be obtained simultaneously. The meaning of each row in sav is that the selected flight i is inserted into the nth gap of path k, and the resulting path length savings value is d. Each insertion operation represented by each row satisfies the time window constraint condition, and the entire sav is sorted in descending order according to d. Subsequently, the insertion operation represented by the first row in sav will be used to generate the predicted new path set pre_VC, and it will be judged whether this path set meets the specific constraint conditions of each vehicle type. If relevant constraint conditions are violated, the insertion operation represented by the next row in sav will be selected until an insertion operation that meets all constraints is found and the new path set VC is generated. After several loop operations, when a certain VC can no longer generate sav, it indicates that the CW algorithm can no longer merge paths, and thus this VC will be used as the initial solution for the support vehicle scheduling of the corresponding vehicle type.

[0085] The specific steps in the said Step (3) are:

[0086] 3.1. Standardize the objective function values. For the bi-objective problem, use the heuristic algorithm to obtain the Pareto optimal solution set of the problem, number the individual solutions in the obtained Pareto optimal solution set, from top to bottom as 1, 2,..., f,..., g. Divide the objective function values Z1(f) and Z2(f) of the number of vehicles used and the driving distance of each individual solution by the maximum values of the number of vehicles used and the driving distance in the optimal solution set and complete the standardization;

[0087] 3.2. Calculate the combined weight of the objective function values of each individual and the weight of each objective;

[0088] 3.3. Multiply the standardized objective function values of all individuals by their respective corresponding objective function values to obtain the weighted objective function values and generate a weighted matrix;

[0089] 3.4. Find the maximum and minimum values among each weighted objective function value, and then calculate the distances of each individual from the optimal level and the worst level. The greater the distance, the closer it is to the optimal level of optimization, and the corresponding vehicle type scheduling is the optimal solution.

[0090] The specific steps in step 3.2 are as follows:

[0091] 3.2.1. Calculation of the weights and combined weights of each objective function:

[0092] The calculation formula for the combined weight of each objective function is as follows:

[0093]

[0094] where C represents the combination number.

[0095] The C-OWA operator is a weighted average method for processing continuous data. Introduce the C-OWA operator to calculate the weights of each objective function. The calculation formula is as follows::

[0096]

[0097] where w1' and w2' respectively represent the absolute weights corresponding to the two objective functions; w1 and w2 in formula (4-20) represent the relative weights corresponding to the two objective functions.

[0098] The specific steps in step 3.4 are as follows:

[0099] 3.4.1. Calculation of the distances of the optimal level and the worst level:

[0100] The calculation formula for the weighted objective function value is as follows:

[0101]

[0102] In the formula, Y1(f) and Y2(f) respectively represent the weighted objective function values of the number of vehicles in use and the total driving distance.

[0103] The specific steps in step 3.3 are as follows:

[0104] 3.3.1, Calculation of weighted objective function value:

[0105] The calculation formula for the distance between the optimal level and the worst level is as follows:

[0106]

[0107] (4) Design a tabu search (TS) algorithm to solve the scheduling scheme for the joint operation of multiple vehicle types

[0108] The specific steps in step (4) are as follows:

[0109] 4.1, Construct a feasible initial solution for the joint scheduling of ground support vehicles for multiple vehicle types

[0110] 4.2, Perform iterative learning on the current local optimal solution X * to obtain the overall neighborhood movement set and the overall neighborhood candidate solution set according to the neighborhood generation strategy, and calculate the corresponding rating function values of each candidate solution.

[0111] 4.3, Select the neighborhood candidate solution X with the smallest evaluation function value min , if Z(X min ) < Z(X * ), then X min becomes the new local optimal solution X * , and the corresponding neighborhood movement is included in the tabu list. Subsequently, compare it with the global optimal solution X best , if Z(X min ) < Z(X best ), then X min becomes the new global optimal solution. If the neighborhood movement that can generate the global optimal solution or the local optimal solution is in the tabu list, then lift the ban on this neighborhood movement. After the comparison, update the tabu list, and judge whether the current iteration number has reached the maximum iteration step. If not, return to step 4.2; if so, go to step 4.4.

[0112] 4.4, Stop the search after reaching the maximum number of iterations, and output the current global optimal solution X best , and this solution is the scheduling scheme for airport ground support vehicles for the joint operation of multiple vehicle types.

[0113] The specific steps in step 4.1 are as follows:

[0114] 4.1.1, Initial solution construction and initialization:

[0115] According to the optimal vehicle scheduling plan for single vehicle types, the chromosomes of the optimal scheduling plans for three vehicle types are merged into matrix-encoded chromosomes, and the generated chromosomes are corrected using the method of destruction and repair to obtain a feasible initial solution for the joint scheduling of multi-vehicle ground support vehicles. Clear the taboo list and set the taboo length.

[0116] The specific steps in step 4.2 are as follows:

[0117] 4.2.1, Neighborhood generation action: The neighborhood generation steps are as follows:

[0118] First, perform neighborhood generation operations on the vehicle paths of pipeline refueling vehicles and water trucks, and optimize the paths of these two types of vehicles separately. Since the path planning of these two types of vehicles is not affected by collaborative constraints, their optimized paths can be determined first. Use the neighborhood generation strategy of the chromosome corresponding to the above two types of vehicles and the concept of "destruction and repair".

[0119] Secondly, generate separate neighborhood movement sets for baggage tractors and conveyor belt vehicles, and merge them with the optimal neighborhood movement operations of pipeline refueling vehicles and water trucks to obtain the overall neighborhood movement set. Also use the neighborhood generation strategy of "destruction and repair" for baggage tractors and conveyor belt vehicles.

[0120] 4.2.2 Calculation of the evaluation function: The calculation formula of the evaluation function in the tabu search process of multi-vehicle collaborative scheduling is as follows:

[0121]

[0122] In the formula, Z(X) is the evaluation function, which consists of a multi-objective single compromise function and a penalty function; X best is the global optimal solution in the current iteration; Z1(X) and Z2(X) respectively represent the two objective function values corresponding to the solution X; and are the comprehensive weights of the two objective functions Z1 and Z2 respectively; violate(X) is the number of times the conveyor belt vehicle violates the collaborative constraint generated by the solution X, and α is the penalty coefficient.

[0123] To sum up, according to the characteristics of the operating modes of the above three types of vehicles, the present invention separately establishes a single scheduling model for each vehicle type. Subsequently, the single scheduling models of each vehicle type are integrated into a multi-vehicle joint ground support vehicle scheduling model, which takes into account the joint operation constraints between baggage tractors and conveyor belt vehicles, and sets a joint objective function to regard the scheduling models of all vehicle types as a whole.

[0124] For the solution algorithm of airport ground support vehicle scheduling, it is necessary to comprehensively understand whether the currently available ground support vehicle resources can complete the ground support services for all served flights under the maximum utilization degree. Therefore, the present invention designs an adaptive NSGA-II integrated with local search to solve the ground support vehicle scheduling model for a single vehicle type, uses local search operations and adaptive crossover and mutation operators to enhance both global and local search capabilities simultaneously, and then fuses the obtained ground support vehicle scheduling model for a single vehicle type and inputs it into a tabu search algorithm for joint scheduling of multi-vehicle-type ground support vehicles, and finally obtains an airport ground support vehicle scheduling plan that meets various constraint conditions.

[0125] On the basis of the above research, the present invention uses the real flight data of a large domestic airport as an example for verification and analysis. The T2 terminal building of this airport is selected as the research space range, and October 2, 2019, 10:00 - 18:00 is selected as the research time range. The results of the example verification show that the scheduling model and algorithm proposed by the present invention can effectively schedule airport ground support vehicles and thus improve the efficiency of the airport ground support link, and have practicability and applicability.

Claims

1. A joint scheduling method for multi-type ground support vehicles for large airports, characterized in that It includes the following steps: S1: Establish a joint scheduling model for multi-type ground support vehicles at large airports; S2: Design an adaptive NSGA-II algorithm integrating local search to solve the single-type ground support vehicle scheduling model; S3: Determine the weight of each objective function in decision-making and then select the optimal solution; S4: Design a tabu search algorithm to solve the optimal solution for the joint scheduling of multi-type ground support vehicles; In step S1, the operation modes of ground support vehicles are divided into three types: continuous operation, continuous operation with resource capacity limitation, and round-trip operation. A conveyor belt vehicle, a water truck, and a baggage tractor are selected as the research vehicle types in the three operation modes respectively. Taking the total number of ground support vehicles used and the total running distance as the optimization objects, a joint scheduling model and constraint conditions for multi-type ground support vehicles are established; In step S2, an integer coding form is adopted, and a chromosome is constructed with the flight service order of each ground support vehicle as the gene value; In the initialization stage, the savings algorithm is used to generate an initial solution that satisfies the single-type ground support vehicle scheduling model; The design content of the adaptive NSGA-II algorithm integrating local search includes: calculation of crowding degree, selection, adaptive crossover and adaptive mutation operations, adaptive local search operations, and elitist selection strategy; In step S3, for the multi-objective optimization problem, it is necessary to evaluate the importance of the optimization objectives, and an objective assignment method based on the Pareto optimal solution set introducing the C-OWA operator is selected to determine the objective function weights and select the most suitable solution; In step S4, by merging the chromosomes of the optimal scheduling solutions of the three vehicle types into a matrix-encoded chromosome and correcting the generated chromosome by the method of destruction and repair, a feasible initial solution for the joint scheduling of multi-type ground support vehicles is obtained. The design content of the tabu search algorithm for the joint scheduling of multi-type ground support vehicles includes: neighborhood generation, evaluation function and neighbor selection strategy, tabu list, and special rules.

2. The joint scheduling method for multi-type ground support vehicles for large airports according to claim 1, wherein, The objective function of the model in step S1 is as follows: Equation (1) represents the minimization of the number of the three vehicle types used; Equation (2) represents the minimization of the total driving distance during the operation of the three vehicle types; Among them, type is the type of ground support vehicle, bv represents the baggage tractor, cv represents the conveyor belt vehicle, and wv represents the fresh water vehicle; is a 0-1 decision variable indicating whether the k-th vehicle of type type is used; is a 0-1 decision variable indicating whether the k-th vehicle of type type serves flight i and flight j in sequence; is the transfer distance of the vehicle of type type from flight i to flight j; The constraint conditions include: Equations (3)-(8) are general constraints that all vehicle types need to follow; Equation (3) is the flight assignment uniqueness constraint, indicating that each type of service flight is only accepted once; Equations (4)-(6) are vehicle service uniqueness constraints, indicating that the ground support vehicles of each vehicle type will not serve a certain flight repeatedly; Equation (7) is the vehicle quantity constraint, indicating that the number of vehicles of each vehicle type used does not exceed the available vehicle quantity; Equation (8) is the vehicle service start time constraint, indicating that the start time of a certain vehicle type serving flight i is within its start time window; Equations (9)-(10) are specific constraints for the water truck; Equation (9) indicates that the water truck is in an empty state when leaving the vehicle yard; Equation (10) indicates that the remaining water volume of the water truck during operation cannot exceed the maximum volume of the water tank and cannot be less than 0; Equations (11) to (13) are the joint operation constraints for the conveyor belt vehicle and the baggage tractor; Equation (11) means that the first conveyor belt vehicle for service must arrive before the first baggage tractor; Equation (12) means that baggage service can only start when the conveyor belt vehicle and the baggage trailer arrive at the service point simultaneously; Equation (13) means that the conveyor belt vehicle can end the service only after all the baggage tractors serving a certain flight have ended the service; Among them, H is the set of flights; H type is the set of flights that need to be served by type vehicles; N type is the set of type vehicles; Δ + Δ(i) is the set of arcs starting from node i; Δ - Δ(j) is the set of arcs returning to node j; E is the set of arcs; is the transfer distance of type vehicles from flight i to flight j; MAX type is the total number of type vehicles; is the time when type vehicle k arrives at the service point of flight i; ET i type is the earliest start time of type vehicle service for flight i; LT i type is the latest start time of type vehicle service for flight i; is the time when type vehicle k starts serving flight i; is the time when type vehicle k finishes serving flight i; is the remaining fresh water capacity of fresh water vehicle k after serving flight i; cap is the maximum fresh water capacity of the fresh water vehicle.

3. The joint scheduling method for multi-type ground support vehicles for large airports according to claim 1, characterized in that In step S2, the adaptive NSGA-II algorithm integrating local search is used to solve the single-vehicle ground support vehicle scheduling model, and the specific steps are as follows: Step (1): Generate an initial parent population; Step (2): Calculate the crowding degree of the parent population; the crowding degree reflects the similarity between individual f and other individuals. The greater the crowding degree, the smaller the similarity. Therefore, when performing selection operations in the same front, individuals with a greater crowding degree are more likely to be selected to maintain the diversity of the population; Step (3): Initialize the offspring population; the selection operation adopts the binary tournament principle. Randomly select two individuals from the parent population and select the individual with a smaller non-dominated rank; if the non-dominated ranks are the same, select the individual with a greater crowding degree; Step (4): Perform adaptive crossover, mutation, and local search operations; the crossover operation selects the adaptive crossover operator, and the mutation operation selects the adaptive mutation operator; the core of the local search operation comes from the neighborhood search algorithm. The flights to be adjusted in the individual are removed through the destruction operator, and then these flights are reinserted into the vehicle route according to the repair operator to obtain a new vehicle route; there are two adopted destruction operators, namely the worst removal destruction operator and the random destruction removal operator; there are two adopted repair operators, namely the minimum cost insertion repair operator and the maximum regret value insertion repair operator; Step (5): Merge the parent population and the offspring population, and perform non-dominated sorting, crowding degree calculation, and deviation degree calculation on the merged population; Step (6): Perform the elite selection strategy on the merged population; Sort the parent population in sequence according to the comparison order of non-dominated rank, crowding degree, and deviation degree, and select a specified number of individuals as the offspring population; Step (7): Update the iteration times, and repeat steps (3) to (6) until the maximum iteration times are reached. Return the final population found and stop the program operation.

4. The joint scheduling method for multi-vehicle ground support vehicles for large airports according to claim 1, characterized in that The specific steps of the elite selection strategy in step S2 are as follows: Step (1): Sort according to the non-dominated rank from small to large. Starting from the non-dominated rank of 1, put the individuals in the entire front into the next-generation parent population, and then put the individuals in the next non-dominated rank until the individuals in a certain rank cannot all be put into the next-generation parent population, and then go to the next step; Step (2): Arrange the individuals in this rank in descending order of crowding degree, and sequentially put the individuals with a greater crowding degree into the next-generation parent population until the individuals with the same crowding degree cannot all be put into the next-generation parent population, and then go to the next step; Step (3): Sort the remaining individuals with the same degree of crowding in ascending order of the degree of deviation. Prioritize putting the individuals with a smaller degree of deviation into the next-generation parent population until the next-generation parent population is filled.

5. The joint scheduling method for multi-vehicle ground support vehicles for large airports according to claim 1, wherein The specific steps of step S3 are as follows: Step (1): Standardize the objective function values; for the bi-objective problem, use a heuristic algorithm to obtain the Pareto optimal solution set of the problem, number the individual solutions in the obtained Pareto optimal solution set, from top to bottom as 1, 2,..., f,..., g; divide the objective function values Z1(f) and Z2(f) of the number of vehicles used and the driving distance for each individual solution by the maximum values of the number of vehicles used and the driving distance in the optimal solution set and complete the standardization; Step (2): Calculate the combined weight of the objective function values of each individual and the weights of each objective; The calculation formula for the combined weight of each objective function is as follows: Where C represents the combination number; The C-OWA operator is a weighted average method for processing continuous data. Use the C-OWA operator to calculate the weights of each objective function. The calculation formula is as follows: Where w1' and w2' respectively represent the corresponding absolute weights of the two objective functions; w1 and w2 represent the corresponding relative weights of the two objective functions; Step (3): Multiply the standardized values of the objective functions of all individuals by their respective corresponding objective function values to obtain the weighted objective function values and generate a weighted matrix; The calculation formula for the weighted objective function value is as follows: In the formula, Y1(f) and Y2(f) respectively represent the weighted objective function values of the number of vehicles used and the total driving distance; Step (4); Find the maximum and minimum values in each weighted objective function value, and then calculate the distance of each individual from the optimal level and the worst level. The greater the distance, the closer it is to the optimal level of optimization, and the corresponding vehicle type scheduling is the optimal solution; The calculation formula for the distance between the optimal level and the worst level is as follows:

6. The joint scheduling method for multi-type ground support vehicles for large airports according to claim 1, characterized in that, The specific steps of the tabu search algorithm in step S4 are as follows: Step (1): According to the optimal solution of the single-vehicle type ground support vehicle scheduling, merge the chromosomes of the optimal scheduling solutions of the three vehicle types into a matrix-encoded chromosome, and correct the generated chromosome by using the method of destruction and repair to obtain a feasible initial solution for the joint scheduling of multi-vehicle type ground support vehicles. Clear the tabu list and set the tabu length; Step (2): Iteratively learn the current local optimal solution X * to obtain the overall neighborhood movement set and the overall neighborhood candidate solution set according to the neighborhood generation strategy, and calculate the corresponding rating function values of each candidate solution; Step (3): Select the neighborhood candidate solution X with the minimum evaluation function value min , if Z(X min ) < Z(X * ), then X min becomes the new local optimal solution X * , and the corresponding neighborhood move is recorded in the taboo list, where Z(X) is the evaluation function; then compare it with the global optimal solution X best , if Z(X min ) < Z(X best ), then X min becomes the new global optimal solution; if the neighborhood move that generates the global optimal solution or the local optimal solution is in the taboo list, then lift the ban on this neighborhood move; after the comparison, update the taboo list, and judge whether the current iteration number reaches the maximum iteration step. If not, return to Step (2); if so, go to Step (4); Step (4): Stop the search after reaching the maximum number of iterations, and output the current global optimal solution X best , and the global optimal solution X best is a multi-vehicle ground support vehicle scheduling plan for airports.

7. The joint scheduling method for multi-type ground support vehicles for large airports according to claim 1, characterized in that The neighborhood generation steps are as follows: Step (1): First, perform neighborhood generation operation on the paths of the water tank trucks in continuous operation mode and optimize their paths separately; since the path planning of the vehicles in continuous operation mode is not affected by collaborative constraints, their optimized paths are determined first; for the corresponding chromosome X wv Use the neighborhood generation strategy of the "destroy and repair" concept; Step (2): Generate separate neighborhood movement sets for the baggage tractor and the conveyor belt vehicle, and merge them with the optimal neighborhood movement operations of the water truck to obtain an overall neighborhood movement set; Use the "destruction and repair" neighborhood generation strategy for the baggage tractor and the conveyor belt vehicle as well.

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