Airport settlement post service resource configuration optimization method and system, and medium

Abstract

The invention relates to the technical field of airport service resource allocation, and discloses an airport settlement post service resource allocation optimization method and system and a medium, and the method comprises the steps: obtaining passenger arrival information and queue state data of an airport settlement service area, and constructing a dynamic data set; on the basis of the dynamic data set, constructing a resource configuration optimization model by taking the minimum configuration cost as an objective function, taking the settlement post number and the queuing capacity of the airport settlement post as decision variables and taking the average waiting time and the average waiting queue length as performance constraint conditions; and performing iterative optimization solution on the resource configuration optimization model by using an improved grey wolf joint optimization algorithm to generate an optimal resource configuration scheme. According to the invention, by optimizing the service resource configuration of the airport settlement post, the reasonable utilization and economical efficiency of the service resources are ensured, and the operation efficiency and resource utilization rate of the system are remarkably improved.

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, the international airport settlement service system is one of the key links for passengers to successfully complete the flight process, especially in small and medium-sized hub airports, where the number of passenger arrivals fluctuates relatively little. Currently, most small and medium-sized hub airports gradually optimize settlement services by manually generating fixed equipment configuration strategies based on historical arrival information. However, the random arrival characteristics of passengers after entering the settlement area make the reasonable allocation and efficient use of service resources particularly important.

[0003] The international airport settlement service system integrates passenger grade information and machine statistics, combined with data analysis and early warning mechanisms, to dynamically adjust the number of settlement posts to meet the needs of passenger fee settlement. The typical process includes passengers entering the settlement area, waiting in line, settling fees, and leaving after checking vouchers. However, due to the lag of human monitoring and statistics, the existing system has two main problems in actual operation: First, insufficient settlement posts lead to queue congestion, increase passenger waiting time and reduce satisfaction. Second, excessive settlement posts lead to idle equipment, low resource utilization and increased operating costs. In addition, the adjustment of employee configuration in settlement services and the non-intelligent restrictions of service desk usage rules further aggravate the irrationality of service resource allocation. Therefore, the existing technology still has obvious deficiencies in service resource utilization and efficiency. Summary of the invention

[0004] In order to overcome the defects of low service resource utilization and use efficiency of 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 construct a dynamic data set.

[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 posts and the queue capacity of the airport checkout posts 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, and 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 represents the configuration cost of each unit of settlement post quantity 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. Performance indicator E{W q (C t ,B t );δ} represents the decision variable C t , decision variable B t and 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 arrivals 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 an optimal resource allocation solution, including:

[0018] S3.1: Generate and initialize individuals of 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 the individual based on its configuration cost.

[0021] S3.4: Sort the individuals in the population in ascending order according to the fitness score, delete the individuals with fitness scores below 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 S3.2. If yes, 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 of 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 current period after adding up 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 taken 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 posts 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 cycle through the trigger and simulate the events of passenger arrival, departure and queuing at the trigger time node.

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

[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] Generate passenger arrival times and service times using second-order Erlang distribution or 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, 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.

[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 proposes 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 the 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 configuration 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 plan.

[0047] In a third aspect, the present invention further proposes a computer-readable storage medium having a program stored thereon, wherein 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 the passenger arrival information and queue status, it is possible to 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 the waiting time of passengers and improves the passenger service experience. At the same time, it avoids the vacancy of equipment and the increase in 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, and further enhances 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 in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the encoding and decoding of the initialization 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 architecture diagram of the airport settlement post service resource configuration optimization system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred technical solutions. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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 only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0055] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being 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 complicated.

[0056] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0057] Example 1

[0058] This embodiment proposes a method for optimizing the allocation of airport settlement service resources. Figure 1 As shown, Figure 1 The following is a flow chart of a method for optimizing the configuration of airport settlement service resources provided in this embodiment. The method comprises the following steps:

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

[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 posts and the queue capacity of the airport checkout posts 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 the arrival information of passengers and the status of queues, it is possible to perceive the changes in passenger flow in real time, and on this basis, build an optimization model to scientifically quantify the relationship between the cost of resource allocation and service performance. The model is accurately solved by combining the 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 configuration of settlement posts in traditional methods, but also shortens the waiting time of passengers and improves the passenger service experience. At the same time, it avoids the vacancy of equipment and the increase in 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, and further enhances 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 Embodiment 1.

[0065] In this embodiment, considering that the number of settlement posts and the setting of queue capacity both have an impact on the enterprise's operating costs, this embodiment establishes the overall configuration cost under the joint configuration strategy in the total period as the objective function based on these two decision variables, and strives to minimize the target total cost. Therefore, the expression of the resource configuration 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 means solving the overall allocation cost in the total period, and the decision variable C I ={C1,C2,…C t} represents the number of settlement posts, and 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 represents the configuration cost of each unit of settlement post quantity 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. Performance indicator E{W q (C t ,B t );δ} represents the decision variable C t , decision variable B t and 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 As the decision variables themselves are subject to constraints, and since the queue capacity determines the degree of passenger congestion, a constraint relationship is added between the buffer area and the queue leader to ensure that the queue leader 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 resource allocation optimization model is iteratively optimized and solved using the improved gray wolf joint optimization algorithm to generate the optimal resource allocation solution, including:

[0078] S3.1: Generate and initialize individuals of the population Each entity 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 encoding and decoding of the individual initialization in the embodiment of the present invention. When the number of settlement posts and queue capacity are jointly optimized, 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 number of settlement posts configuration for 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, in which 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 in each time period to ensure the diversity and rationality of the individual distribution. After the 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 divided into two parts according to the statistical period, namely the number of settlement posts (C) and the queue capacity (B). When the traversal loop reaches the last statistical period dimension (Row), the system automatically identifies the separation mechanism, generates the final queue capacity configuration through the set upper and lower boundary values, and completes the decoding. Figure 1 The yellow box in the middle indicates that the number of settlement posts generated under the current time period dimension has reached the upper limit of the system performance index, indicating that the individual constraints need to be adjusted through subsequent steps. The orange box indicates that the queue capacity configuration generated in the current time period does not meet the performance index constraints and needs to be optimized and corrected in subsequent iterative updates. Through this encoding and decoding process, the initial individual can adapt to different time period parameter requirements in the multi-time period joint optimization problem, laying the foundation for further using intelligent algorithms to iteratively generate the optimal resource allocation plan.

[0081] S3.2: Transfer the individual configuration combination to the pre-built simulation model, calculate the individual configuration cost, and use the end to split the configuration situation and update the configuration 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 posts and the upper limit of queue capacity 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 lower than the upper limit by 50%, the queue capacity upper limit will be reduced.

[0086] After the simulation update results are returned to the receiving function established in the algorithm in a serial format, the split operation is performed to generate C′ (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 the 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 current period after the accumulation 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 taken as the fitness score F of the individual. (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 configuration results of the number of settlement posts and queue capacity of each individual in each time period dimension C″ (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 elimination and update of individuals in an embodiment of the present invention, combined with Figure 3 From the process shown in the figure, we can clearly see the specific mechanism of elimination and update.

[0092] During the update process, the values ​​of the two decision variables (i.e., settlement post configuration and queue capacity configuration) of each individual in all time periods are first accumulated to calculate their respective total costs. Then, the two total costs are 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 meet 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 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 capabilities 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 updating the configuration of the individual, 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 S3.2. If yes, 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 through Tecnomatix Plant Simulation, which specifies the simulation running time and the number of running experiments, and sets multiple groups of experimental cases according to actual conditions. The following modules are designed for the simulation model of the time-varying settlement service system:

[0097] The operation management module is used to initialize, reset and stop the simulation, including the setting of simulation operation control for 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 post configuration module is used to dynamically adjust the number of settlement posts within the statistical period according to the average waiting time and the average waiting queue length, and control the allocation of passengers to idle settlement posts.

[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 embodiment, for each group of cases, after generating the configuration individuals, the TCP communication interface between the simulation end and the optimization algorithm end is constructed, the initialization configuration individual results are transmitted to the simulation model and the corresponding performance indicators are solved, and the solved performance indicators are fed back to the improved gray wolf joint optimization algorithm executed by Matlab, and the optimization iteration operation is performed using the set objective function, and the individual with the lowest fitness score is selected as the optimal configuration strategy for the current iteration. After the iteration is completed, the number of settlement post configurations and the queue capacity required to be set for each time period under the current example input parameter setting 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, the 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 for comparison 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 post service resource configuration optimization system, which is applied to the airport settlement post 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] Among them, the acquisition module 100 is used to obtain the passenger arrival information and queue status data of the airport settlement service area to construct a dynamic data set. The construction module 200 is used to construct a resource configuration 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. The optimization solution module 300 is used to use the improved gray wolf joint optimization algorithm to iteratively optimize and solve the resource configuration optimization model to generate the optimal resource configuration plan.

[0111] It should be noted that the aforementioned explanation of the implementation method 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, which, when executed by a processor, implements the above-mentioned method for optimizing the configuration of airport settlement post service resources.

[0114] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. 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 are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0115] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0116] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

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

[0118] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, 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 clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the allocation of service resources at an airport settlement post, characterized in that: include: S1: Obtain passenger arrival information and queue status data in the airport settlement service area and build a dynamic data set; 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 posts and the queue capacity of the airport checkout posts as decision variables, and the average waiting time and the average waiting queue length as performance constraints; 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.

2. The method for optimizing the allocation of airport settlement service resources according to claim 1, characterized in that: The expression of the resource configuration optimization model is as follows: st 0≤C t ≤S C C t ,B t ∈N + Among them, the decision variable C I ={C1,C2,…C t } represents the number of settlement posts, and 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 represents the configuration cost of each unit of settlement post quantity 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; performance indicator E{W q (C t ,B t ); δ} represents the decision variable C t , decision variable B t and 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 arrivals during the statistical period t, N + Represents the set of positive integers.

3. The method for optimizing the allocation of airport settlement service resources according to claim 2, characterized in that: The resource allocation optimization model is iteratively optimized and solved using the improved gray wolf joint optimization algorithm to generate the optimal resource allocation solution, including: S3.1: Generate and initialize individuals of the population, each of which includes a configuration combination of the number of settlement posts 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 according to its configuration cost; S3.4: Sort the individuals in the population in ascending order according to the fitness score, delete the individuals with fitness scores below 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; S3.5: Based on the configuration combination of the guiding individual, the number of settlement posts and queue capacity of other individuals are updated; S3.6: Determine whether the preset number of iterative optimization times has been reached. If not, jump to S3.

2. If yes, 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.

4. The method for optimizing the allocation of service resources at an airport settlement post according to claim 3, characterized in that: In S3.1, the chaotic mapping method is used to generate and initialize individuals in the population.

5. The method for optimizing the allocation of airport settlement service resources according to claim 3, 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 current period after adding up 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 taken as the fitness score of the individual.

6. The method for optimizing the allocation of airport settlement service resources according to claim 3, characterized in that: In S3.2, after the individual configuration combinations are transferred 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 upper limit; If the average waiting time and the average waiting queue length both exceed the upper limit, the number of settlement posts and the queue capacity upper 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.

7. The method for optimizing the allocation of service resources at an airport settlement post according to any one of claims 3 to 6, characterized in that: 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 cycle through the trigger and simulate the arrival, departure and queuing events of passengers at the trigger time node; The settlement station configuration module is used to dynamically adjust the number of settlement stations within the statistical period according to the average waiting time and the average waiting queue length, and control the allocation of passengers to idle settlement stations; The result output module is used to generate simulation result data.

8. The method for optimizing the allocation of service resources at an airport settlement post according to claim 7, characterized in that: The simulation model setting simulation rules include one or more of the following: Generate passenger arrival times and service times using second-order Erlang distribution or negative exponential distribution; The total duration of the simulation experiment is M hours, which is 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 check-in station is detected, the passenger at the front of the queue will be assigned to the empty check-in 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.

9. An airport settlement service resource configuration optimization system, characterized in that: include: The acquisition module is used to obtain the passenger arrival information and queue status data of the airport settlement service area and build a dynamic data set; A construction module is used to construct a resource configuration 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; 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 plan.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and the program is used by the processor to execute the operations performed by the airport settlement post service resource configuration optimization method as described in any one of claims 1 to 8.

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