A method, system and medium for optimizing service resource allocation at airport settlement posts

Abstract

The present invention relates to the technical field of airport service resource configuration, and discloses a method, system, and medium for optimizing the configuration of airport settlement service resources. The method comprises obtaining passenger arrival information and queue status data in the airport settlement service area to construct a dynamic data set; based on the dynamic data set, a resource configuration optimization model is constructed with minimum configuration cost as the objective function, the number of settlement stations and queue capacity of the airport settlement stations as decision variables, and average waiting time and average waiting queue length as performance constraints; and an improved gray wolf joint optimization algorithm is used to iteratively optimize and solve the resource configuration optimization model to generate an optimal resource configuration plan. By optimizing the service resource configuration of the airport settlement station, the present invention ensures the rational utilization and economy of service resources, and significantly improves the operating efficiency and resource utilization of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of airport service resource configuration, and more specifically, to an airport settlement post service resource configuration optimization method, system and medium. Background Art

[0002] In modern airport operations, international airport check-in service systems are a critical component in ensuring passengers smoothly complete their flight journeys, especially at small and medium-sized hub airports, where passenger arrival numbers fluctuate relatively little. Currently, most small and medium-sized hub airports optimize check-in services through manually generated fixed equipment configuration strategies based on historical arrival data. However, the random nature of passenger arrivals once they enter the check-in area makes the rational allocation and efficient utilization of service resources particularly important.

[0003] The international airport settlement service system integrates passenger class information and machine statistics, combined with data analysis and early warning mechanisms, to dynamically adjust the number of settlement counters available to meet passenger settlement needs. A typical process involves passengers entering the settlement area, waiting in line, settling their expenses, and then checking their vouchers before leaving. However, due to the lags in human monitoring and statistics, the existing system suffers from two major operational issues: First, insufficient settlement counters lead to queue congestion, increasing passenger wait times and reducing passenger satisfaction. Second, an oversupply of settlement counters results in idle equipment, low resource utilization, and increased operating costs. Furthermore, staff adjustments in settlement services and non-intelligent restrictions on service desk usage further exacerbate the irrational allocation of service resources. Consequently, existing technologies still have significant deficiencies in service resource utilization and efficiency. Summary of the Invention

[0004] In order to overcome the shortcomings of low service resource utilization and efficiency in existing international airport settlement services, the present invention proposes the following technical solutions:

[0005] In a first aspect, the present invention proposes a method for optimizing the allocation of service resources at an airport settlement post, comprising:

[0006] S1: Obtain passenger arrival information and queue status data in the airport settlement service area and build a dynamic dataset.

[0007] S2: Based on the dynamic data set, a resource allocation optimization model is constructed with the minimum configuration cost as the objective function, the number of checkout stations and the queue capacity of the airport checkout stations as decision variables, and the average waiting time and the average waiting queue length as performance constraints.

[0008] S3: Using the improved grey wolf joint optimization algorithm, the resource allocation optimization model is iteratively optimized and solved to generate an optimal resource allocation solution.

[0009] As a preferred technical solution, the expression of the resource allocation optimization model is as follows:

[0010]

[0011] st

[0012]

[0013] 0≤C t ≤S C

[0014]

[0015] C t ,B t ∈N +

[0016] Among them, the decision variable C I ={C1,C2,…C t} represents the number of settlement posts, decision variable B I ={B1,B2,…B t} represents the queue capacity, O(C I ,B I ) represents the decision variable C I and decision variable B I The configuration cost in the total period, t is the period variable, end represents the maximum value of the statistical period, m t It represents the configuration cost of each unit of settlement post number in the statistical period t, n t represents the configuration cost of each unit queue capacity in the statistical period t, and the performance index E{l q (C t ,B t ); δ} represents the decision variable C t , decision variable B t and random element δ, the average waiting queue length l in the statistical period q The mathematical expectation of Indicates the upper limit of the waiting queue length. q (C t ,B t ); δ} represents the decision variable C t , decision variable B t Under the action of random element δ, the average waiting time W in the statistical period q The mathematical expectation of Indicates the upper limit of the waiting time, S C represents the upper limit of the number of settlement posts, λ t represents the rate of passenger arrival during the statistical period t, N + Represents the set of positive integers.

[0017] As a preferred technical solution, the improved Grey Wolf joint optimization algorithm is used to iteratively optimize and solve the resource allocation optimization model to generate the optimal resource allocation solution, including:

[0018] S3.1: Generate and initialize individuals in the population, each of which includes a configuration combination of the number of settlement posts and queue capacity in each statistical period.

[0019] S3.2: Transfer the individual configuration combinations to the pre-built simulation model and calculate the individual configuration costs.

[0020] S3.3: Calculate the fitness score of an individual based on its configuration cost.

[0021] S3.4: Sort the individuals in the population in ascending order according to their fitness scores, delete the individuals whose fitness scores are lower than the threshold, select the first B individuals with the lowest fitness scores as guide individuals, and randomly generate an equal number of individuals to supplement the population.

[0022] S3.5: Based on the configuration combination of the guiding individual, the number of settlement posts and queue capacity of other individuals are updated.

[0023] S3.6: Determine whether the preset number of iterative optimization times has been reached. If not, jump to execute S3.2. If so, output the configuration combination of the number of settlement posts and queue capacity of the individual with the lowest fitness score as the configuration plan.

[0024] As a preferred technical solution, in S3.1, a chaotic mapping method is used to generate and initialize individuals in the population.

[0025] As a preferred technical solution, in S3.3, the individual configuration cost includes the configuration cost of the number of settlement posts and the configuration cost of the queue capacity.

[0026] The total cost of the period after accumulating the configuration cost of the number of settlement posts and the configuration cost of the queue capacity of the individual in the same period is used as the fitness score of the individual.

[0027] As a preferred technical solution, in S3.2, after the individual configuration combination is transmitted to the pre-built simulation model,

[0028] When the average waiting time exceeds the upper limit, increase the number of settlement posts.

[0029] When the average waiting queue length exceeds the upper limit, increase the queue capacity upper limit.

[0030] If the average waiting time and the average waiting queue length both exceed the upper limit, the number of settlement stations and the queue capacity upper limit will be increased at the same time.

[0031] If the average waiting time or the average waiting queue length is lower than the threshold, the queue capacity upper limit is reduced.

[0032] As a preferred technical solution, the simulation model includes:

[0033] Run management module, used to initialize, reset and stop simulation.

[0034] The random event trigger module is used to set the trigger period through the trigger and simulate the events of passenger arrival, departure and queuing at the trigger time node.

[0035] The settlement station configuration module is used to dynamically adjust the number of settlement stations within the statistical period based on the average waiting time and the average waiting queue length, and control the allocation of passengers to idle settlement stations.

[0036] The result output module is used to generate simulation result data.

[0037] As a preferred technical solution, the simulation rules set by the simulation model include one or more of the following:

[0038] Passenger arrival times and service times are generated using a second-order Erlang distribution or a negative exponential distribution.

[0039] The total duration of the simulation experiment is M hours, which is divided into multiple statistical periods.

[0040] In the initial state, the queue area of ​​the settlement system is empty and all settlement posts in the settlement system are closed.

[0041] After a passenger enters the queuing area, if an idle checkout station is detected, the passenger at the front of the queue will be assigned to the idle checkout station to receive service.

[0042] After the service is completed, the status of the current settlement post is updated to idle, and the passengers who have completed the service are removed.

[0043] In a second aspect, the present invention further provides an airport settlement service resource configuration optimization system, which is applied to the airport settlement service resource configuration optimization method as described in any solution of the first aspect, comprising:

[0044] The acquisition module is used to obtain passenger arrival information and queue status data in the airport settlement service area and build a dynamic data set.

[0045] A construction module is used to construct a resource allocation optimization model based on the dynamic data set, with the minimum configuration cost as the objective function, the number of settlement posts and the queue capacity of the airport settlement posts as decision variables, and the average waiting time and the average waiting queue length as performance constraints.

[0046] The optimization solution module is used to use the improved gray wolf joint optimization algorithm to iteratively optimize and solve the resource allocation optimization model to generate an optimal resource allocation solution.

[0047] In a third aspect, the present invention further proposes a computer-readable storage medium having a program stored thereon, and the program is executed by a processor to perform the operations performed by the airport settlement post service resource configuration optimization method as described in any of the schemes in the second aspect.

[0048] The beneficial effects of the present invention include at least:

[0049] The present invention significantly improves the operating efficiency and resource utilization of the system by optimizing the service resource configuration of the airport settlement post. By dynamically analyzing passenger arrival information and queue status, it can perceive the changes in passenger flow in real time, and build an optimization model based on this to scientifically quantify the relationship between the cost of resource configuration and service performance. The model is accurately solved by combining an intelligent optimization algorithm, and the number of settlement posts and queue capacity are dynamically adjusted to achieve efficient matching of resources. This solution not only solves the queue congestion problem caused by insufficient settlement post configuration in traditional methods, but also shortens passenger waiting time and improves passenger service experience. At the same time, it avoids equipment vacancy and increased operating costs caused by over-configuration, ensuring the rational use and economy of service resources. Through intelligent and dynamic optimization methods, the present invention makes settlement services more flexible and efficient, further enhancing the overall level of airport operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of the airport settlement post service resource configuration optimization method provided by an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the encoding and decoding principles of the initialized individual in an embodiment of the present invention.

[0052] Figure 3 This is a schematic diagram of the principle of eliminating and updating individuals in an embodiment of the present invention.

[0053] Figure 4 This is an architectural diagram of the airport settlement service resource configuration optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will describe embodiments of the present invention with reference to the accompanying drawings and preferred technical solutions. Those skilled in the art will readily understand other advantages and benefits of the present invention from the contents disclosed in this specification. The present invention may also be implemented or applied through different specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred technical solutions are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0055] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0056] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0057] Example 1

[0058] This embodiment proposes a method for optimizing the allocation of service resources at an airport settlement station, such as Figure 1 As shown, Figure 1 This is a flow chart of a method for optimizing the configuration of airport settlement service resources provided by this embodiment. The method includes the following steps:

[0059] S1: Obtain passenger arrival information and queue status data in the airport settlement service area and build a dynamic dataset.

[0060] S2: Based on the dynamic data set, a resource allocation optimization model is constructed with the minimum configuration cost as the objective function, the number of checkout stations and the queue capacity of the airport checkout stations as decision variables, and the average waiting time and the average waiting queue length as performance constraints.

[0061] S3: Using the improved grey wolf joint optimization algorithm, the resource allocation optimization model is iteratively optimized and solved to generate an optimal resource allocation solution.

[0062] It is understandable that by dynamically analyzing passenger arrival information and queue status, it is possible to perceive changes in passenger flow in real time, and based on this, build an optimization model to scientifically quantify the relationship between the cost of resource allocation and service performance. By combining intelligent optimization algorithms to accurately solve the model, the number of settlement posts and queue capacity are dynamically adjusted to achieve efficient matching of resources. This solution not only solves the queue congestion problem caused by insufficient settlement post configuration in traditional methods, but also shortens passenger waiting time and improves passenger service experience. At the same time, it avoids equipment vacancy and increased operating costs caused by over-configuration, ensuring the rational use and economy of service resources. Through intelligent and dynamic optimization methods, the present invention makes settlement services more flexible and efficient, further enhancing the overall level of airport operation management.

[0063] Example 2

[0064] This embodiment makes improvements based on the airport settlement post service resource configuration optimization method proposed in Example 1.

[0065] In this embodiment, considering that both the number of settlement stations and the queue capacity have an impact on the enterprise's operating costs, this embodiment establishes the overall allocation cost under the joint allocation strategy within the total time period as the objective function based on these two decision variables, striving to minimize the target total cost. Therefore, the expression of the resource allocation optimization model is as follows:

[0066]

[0067] st

[0068]

[0069] 0≤C t ≤S C

[0070]

[0071] C t ,B t ∈N +

[0072] Among them, the expression of the resource allocation optimization model is to solve the overall allocation cost in the total period, and the decision variable C I ={C1,C2,…C t} represents the number of settlement posts, decision variable B I ={B1,B2,…B t} represents the queue capacity, O(C I ,B I ) represents the decision variable C I and decision variable B IThe configuration cost in the total period, t is the period variable, end represents the maximum value of the statistical period, m t It represents the configuration cost of each unit of settlement post number in the statistical period t, n t represents the configuration cost of each unit queue capacity in the statistical period t, and the performance index E{l q (C t ,B t ); δ} represents the decision variable C t , decision variable B t and random element δ, the average waiting queue length l in the statistical period q The mathematical expectation of Indicates the upper limit of the waiting queue length. q (C t ,B t ); δ} represents the decision variable C t , decision variable B t Under the action of random element δ, the average waiting time W in the statistical period q The mathematical expectation of E{l q (C I ,B I )} and E{W q (C I ,B I )} Mainly through TCP communication, the number of settlement posts and queue capacity results of the corresponding period are transmitted to the simulation model, and then constraint verification is performed to update the results. and The decision variables themselves are subject to constraints. Since the queue capacity determines the degree of passenger congestion, a constraint relationship is added between the buffer area and the queue length to ensure that the queue length in each time period is within the upper limit. Indicates the upper limit of the waiting time, S C represents the upper limit of the number of settlement posts, λ t represents the rate of passenger arrivals during the statistical period t, N + Represents the set of positive integers.

[0073] The improved Grey Wolf Joint Optimization Algorithm of this embodiment has made innovative improvements in individual encoding and decoding, and individual elimination and updating. As shown in Table 1, Table 1 is the overall framework of the improved Grey Wolf Joint Optimization Algorithm of this embodiment.

[0074] Table 1 Overall framework of the improved gray wolf joint optimization algorithm

[0075]

[0076]

[0077] As shown in Table 1, the improved Grey Wolf joint optimization algorithm is used to iteratively optimize and solve the resource allocation optimization model to generate the optimal resource allocation solution, including:

[0078] S3.1: Generate and initialize individuals of the population Each individual includes a configuration combination of the number of settlement posts and queue capacity within each statistical period.

[0079] It should be noted that if Figure 2 As shown, Figure 2 This is a schematic diagram of the principle of initializing the encoding and decoding of individuals in an embodiment of the present invention. When jointly optimizing the number of settlement posts and queue capacity, the individual initialization generation method needs to be adjusted to meet the needs of multi-time period joint optimization. Since the changes in passenger arrival rate in each statistical time period need to be comprehensively considered during the smooth queuing process, combined with the number of settlement posts and queue capacity configuration in different time periods, the initialization process first covers the total time period dimension by expanding the individual coding structure. During the initialization process, the individual code will be divided into two parts according to the statistical time period: the first part is the configuration of the number of settlement posts in each statistical time period, and the second part is the corresponding queue capacity configuration. In this way, the linkage and consistency of resource configuration in each time period can be achieved.

[0080] Figure 1 The top of the figure shows the structure of the initial gray wolf individuals, where the individual codes have been segmented according to the statistical time period, and the number of settlement posts and queue capacity configuration of the total time period are combined in series. When generating the initialization individuals, Matlab is used to execute the optimization algorithm, and the Hent chaotic mapping method is used to encode and decode the number of settlement post configurations and queue capacity initialization individuals for each time period to ensure the diversity and rationality of the individual distribution. After initialization is completed, the individuals need to be decoded so that they can be iteratively updated in the subsequent optimization process. The specific process is as follows Figure 1 As shown in the figure, the initialized individuals are split into two parts based on the statistical period: the number of settlement stations (C) and the queue capacity (B). When the traversal loop reaches the last statistical period dimension (Row), the system automatically identifies the partitioning mechanism, generates the final queue capacity configuration based on the set upper and lower boundary values, and completes the decoding. Figure 1 The yellow box indicates that the number of settlement posts generated for the current time period has reached the upper limit of the system's performance indicator, indicating that individual constraints need to be adjusted in subsequent steps. The orange box indicates that the queue capacity configuration generated for the current time period does not meet the performance indicator constraints and requires optimization and correction in subsequent iterative updates. Through this encoding and decoding process, the initial individual can adapt to the different time period parameter requirements in the multi-time period joint optimization problem, laying the foundation for further iterative generation of optimal resource allocation solutions using intelligent algorithms.

[0081] S3.2: Transfer the individual configuration combinations to the pre-built simulation model, calculate the individual configuration costs, and use the end to split the configurations and update the configurations according to the following rules:

[0082] When the average waiting time W q When the upper limit is exceeded, the number of settlement posts will be increased by 1.

[0083] When the average waiting queue length l q When the upper limit is exceeded, the queue capacity limit is increased.

[0084] If the average waiting time W q and the average waiting queue length l q When both exceed the upper limit, the number of settlement stations and the queue capacity upper limit will be increased at the same time.

[0085] If the average waiting time W q Or the average waiting queue length l q If it is 50% lower than the upper limit, the queue capacity limit will be reduced.

[0086] After the simulation results are updated, the receiving function established in the algorithm is returned in a serial format, the splitting operation is performed, and C′ is generated. (i.j) and B′ (i.j) (i=1,2,…,pop;j=1,2,…dim), t and dim values ​​are equal.

[0087] S3.3: Calculate the fitness score of an individual based on its configuration cost.

[0088] In this embodiment, the configuration cost of an individual includes the configuration cost of the number of settlement posts and the configuration cost of the queue capacity. The total cost of the configuration cost of the number of settlement posts and the configuration cost of the queue capacity of an individual in the same period is accumulated as the individual's fitness score F (i) (i=1,2,…,pop).

[0089] S3.4: According to the fitness score, the fitness score F of individuals in the population is calculated. (i) Sort in ascending order, delete individuals whose fitness scores are lower than the threshold, and set the lowest fitness score as BestF (CaB) , select the first B individuals with the lowest fitness scores as guide individuals, and randomly generate an equal number of individuals to supplement the population.

[0090] S3.5: Based on the configuration combination of the leading individual, the number of settlement posts and queue capacity of other individuals are updated to obtain the updated settlement post number configuration result and queue capacity configuration result C″ for each individual in each time period dimension. (i.j) and B″ (i.j)(i=1,2,…,pop; j=1,2,…dim).

[0091] Among them, when using the improved gray wolf joint optimization algorithm for elimination and update, the settlement post configuration cost and queue capacity configuration cost of each individual are summed up respectively, and the total cost of the two is ranked in ascending order, so as to eliminate individuals with low fitness ranking. At the same time, in order to maintain the diversity of the population and the stability of the algorithm convergence, a corresponding number of new individuals are randomly generated to make up for it. The experimental results show that setting the elimination ratio to 15% can effectively balance the optimization speed and the quality of the solution. Specifically, after each iteration, the elimination update operation is as follows: Figure 3 As shown, Figure 3 This is a schematic diagram of the principle of eliminating and updating individuals in an embodiment of the present invention, combined with Figure 3 The process shown in the figure clearly shows the specific mechanism of elimination and update.

[0092] During the update process, the values ​​of each individual's two decision variables (i.e., settlement station configuration and queue capacity configuration) in all time periods are accumulated to calculate their respective total costs. The two total costs are then summed to obtain the overall configuration cost of the individual. Figure 3 The process of ranking all individuals by total cost is shown in . For example, Figure 3 The total number of settlement post configurations of individual 2 ranks high among all individuals (17%), indicating that its settlement post configuration is better, but its total number of queue capacity configurations ranks poorly (90%), resulting in the overall total cost It ranks only 87% among all individuals. According to the elimination rule, the total cost of individual 2 does not reach the good fitness standard of the top 85% of the population, so the individual will be eliminated and replaced by a new individual randomly generated for use in the next round of iteration.

[0093] This elimination and update strategy can dynamically adjust the population structure while retaining high-quality individuals, effectively avoiding falling into local optimality, while improving the algorithm's global optimization ability for resource allocation problems. Figure 2 The reorganization result on the right clearly reflects the specific implementation of this updating method, that is, by sorting and comprehensively ranking the two decision variables of the individual in stages, the elimination and supplement operations are closely combined, laying the foundation for the next step of optimization.

[0094] After the individual configuration is updated, the fitness score F′ of the updated individual is obtained at the same time (i) , by judging F′ (i) With BestF (CaB) Update the top three levels of C, B and BestF′ (CaB) .

[0095] S3.6: Determine whether the preset number of iterative optimization times has been reached. If not, jump to execute S3.2. If so, output the configuration combination of the number of settlement posts and queue capacity of the individual with the lowest fitness score as the configuration plan.

[0096] In this embodiment, simulation modeling is completed using Tecnomatix Plant Simulation. The simulation run time and the number of experiments are specified, and multiple groups of experiments are set according to the actual situation. The following modules are designed for the time-varying settlement service system simulation model:

[0097] The operation management module is used to initialize, reset and stop simulation, including the setting of simulation operation control mechanisms such as simulation experiment initialization, simulation experiment reset and simulation operation stop.

[0098] The random event trigger module is used to set the trigger cycle through the trigger, simulate the events of passenger arrival, departure and queuing at the trigger time node, and set the statistical result event frequency determined by the trigger time.

[0099] The settlement station configuration module is used to dynamically adjust the number of settlement stations within the statistical period based on the average waiting time and the average waiting queue length, and control the allocation of passengers to idle settlement stations.

[0100] The result output module is used to output the simulation model running results and use a table to store the result values ​​in a specified format, thereby verifying the effectiveness of the queuing approximation model.

[0101] In this example, for each case, after generating individual configurations, a TCP communication interface is established between the simulation and optimization algorithm. The initial configuration results are transmitted to the simulation model, where the corresponding performance indicators are solved. The solved performance indicators are then fed back to the improved Grey Wolf Joint Optimization algorithm executed in Matlab. The algorithm then iterates the optimization using the set objective function, selecting the individual with the lowest fitness score as the optimal configuration strategy for the current iteration. After the iteration is complete, the number of settlement stations configured and the queue capacity required for each time period based on the input parameters of the current case are output.

[0102] In this embodiment, the simulation rules set by the simulation model include:

[0103] Under the condition that the squared coefficient of variation remains stable, passenger arrival time and service time are generated using the second-order Erlang distribution or negative exponential distribution.

[0104] The total settlement service time for each complete simulation experiment is 18 hours, which is divided into multiple statistical periods according to the needs of the statistical unit. The experiment is repeated 1000 times, and the simulation is performed to obtain the average value to compare the simulation result with the approximate result.

[0105] In the initial state, the queue area of ​​the settlement system is empty and all settlement posts in the settlement system are closed.

[0106] After a passenger enters the queuing area, when an idle checkout station is detected, the passenger at the front of the queue will be assigned to the idle checkout station to receive service.

[0107] After the service is completed, the status of the current settlement post is updated to idle, and the passengers who have completed the service are removed from entering the waiting system or security inspection system. The overall settlement system is open.

[0108] Example 3

[0109] like Figure 4 As shown, this embodiment proposes an airport settlement service resource configuration optimization system, which is applied to the airport settlement service resource configuration optimization method as described in the above embodiment, including: an acquisition module 100, a construction module 200 and an optimization solution module 300.

[0110] The acquisition module 100 is used to acquire passenger arrival information and queue status data from the airport's checkout service area to construct a dynamic dataset. The construction module 200 is used to construct a resource allocation optimization model based on this dynamic dataset, using minimum allocation cost as the objective function, the number of checkout stations and queue capacity at the airport as decision variables, and average waiting time and average waiting queue length as performance constraints. The optimization solution module 300 is used to iteratively optimize and solve the resource allocation optimization model using an improved Grey Wolf joint optimization algorithm to generate an optimal resource allocation solution.

[0111] It should be noted that the above explanation of the embodiment of the airport settlement post service resource configuration optimization method is also applicable to the airport settlement post service resource configuration optimization system of this embodiment, and will not be repeated here.

[0112] Example 4

[0113] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned method for optimizing the configuration of service resources at an airport settlement post is implemented.

[0114] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0116] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0117] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0118] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0119] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the allocation of service resources at an airport settlement station, characterized in that: include: S1: Obtain passenger arrival information and queue status data in the airport settlement service area and construct a dynamic dataset; S2: Based on the dynamic data set, a resource allocation optimization model is constructed with the minimum configuration cost as the objective function, the number of checkout stations and the queue capacity of the airport checkout stations as decision variables, and the average waiting time and the average waiting queue length as performance constraints. The expression of the resource allocation optimization model is as follows: Among them, the decision variables Indicates the number of settlement posts, decision variables represents the queue capacity, Represents decision variables and decision variables The configuration cost in the total period, t is the time period variable, Indicates the maximum value of the statistical period. Configuration cost per unit of settlement post quantity, Indicates the statistical period t Configuration cost per unit queue capacity, performance index Indicates that the decision variables , decision variables and random elements The average waiting queue length during the statistical period under the effect of The mathematical expectation of Indicates the upper limit of the waiting queue length; performance indicator Indicates that the decision variables , decision variables and random elements The average waiting time during the statistical period under the effect of The mathematical expectation of Indicates the upper limit of the waiting time. Indicates the upper limit of the number of settlement posts. Indicates the statistical period t The rate of passenger arrivals, represents the set of positive integers; S3: Using the improved Grey Wolf joint optimization algorithm, the resource allocation optimization model is iteratively optimized to generate the optimal resource allocation solution, including: S3.1: Generate and initialize individuals in the population, each of which includes a configuration combination of the number of settlement stations and queue capacity in each statistical period; S3.2: Transfer the individual configuration combinations to the pre-built simulation model and calculate the individual configuration costs; S3.3: Calculate the fitness score of the individual based on its configuration cost; S3.4: Sort the individuals in the population in ascending order according to their fitness scores, delete the individuals whose fitness scores are lower than the threshold, and select the individuals with the lowest fitness scores. B Individuals are used as guide individuals, and an equal number of individuals are randomly generated to supplement the population; S3.5: Based on the configuration combination of the guiding individual, the number of settlement stations and queue capacity of other individuals are updated; S3.6: Determine whether the preset number of iterative optimizations has been reached. If not, jump to S3.

2. If so, output the configuration combination of the number of settlement stations and queue capacity of the individual with the lowest fitness score as the configuration solution; Wherein, the simulation model includes: Run management module, used to initialize, reset and stop simulation; The random event trigger module is used to set the trigger period through the trigger and simulate the events of passenger arrival, departure and queuing at the trigger time node; The checkout station configuration module is used to dynamically adjust the number of checkout stations within the statistical period based on the average waiting time and the average waiting queue length, and control the allocation of passengers to available checkout stations; The result output module is used to generate simulation result data.

2. The method for optimizing the allocation of service resources at an airport settlement post according to claim 1, characterized in that: In S3.1, the chaotic mapping method is used to generate and initialize individuals in the population.

3. The method for optimizing the allocation of service resources at an airport settlement post according to claim 1, characterized in that: In S3.3, the individual configuration cost includes the configuration cost of the number of settlement posts and the configuration cost of the queue capacity; The total cost of the period after accumulating the configuration cost of the number of settlement posts and the configuration cost of the queue capacity of the individual in the same period is used as the fitness score of the individual.

4. The method for optimizing the allocation of service resources at an airport settlement post according to claim 1, characterized in that: In S3.2, after transferring the individual configuration combination to the pre-built simulation model, When the average waiting time exceeds the upper limit, increase the number of settlement posts; When the average waiting queue length exceeds the upper limit, increase the queue capacity limit; If both the average waiting time and the average waiting queue length exceed the upper limit, the number of settlement stations and the queue capacity limit will be increased at the same time; If the average waiting time or the average waiting queue length is lower than the threshold, the queue capacity upper limit is reduced.

5. The method for optimizing the allocation of service resources at an airport settlement post according to claim 4, characterized in that: The simulation model setting simulation rules include one or more of the following: Generate passenger arrival times and service times using a second-order Erlang distribution or negative exponential distribution; The total duration of the simulation experiment is M Hours, and divided into multiple statistical periods; In the initial state, the queuing area of ​​the settlement system is empty, and all settlement posts in the settlement system are closed; After a passenger enters the queuing area, if an empty checkout station is detected, the passenger at the front of the queue will be assigned to the empty checkout station for service; After the service is completed, the status of the current settlement post is updated to idle, and the passengers who have completed the service are removed.

6. An airport settlement service resource configuration optimization system, applied to the airport settlement service resource configuration optimization method according to any one of claims 1 to 5, characterized in that: include: The acquisition module is used to obtain passenger arrival information and queue status data in the airport settlement service area and build a dynamic data set; A construction module is used to construct a resource allocation optimization model based on the dynamic data set, with minimum configuration cost as the objective function, the number of checkout stations and queue capacity of the airport checkout stations as decision variables, and the average waiting time and average waiting queue length as performance constraints; The optimization solution module is used to use the improved gray wolf joint optimization algorithm to iteratively optimize and solve the resource allocation optimization model to generate an optimal resource allocation solution.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, and the processor executes the operations performed by the airport settlement post service resource configuration optimization method as described in any one of claims 1 to 5.

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