Method, system and storage medium for optimizing the number of open airport passenger inspection channels

Abstract

The present invention provides a method, system and storage medium for optimizing the configuration of the number of open passenger inspection channels at an airport. The method comprises: establishing and initializing a configuration model for the number of open passenger inspection channels with the goal of minimizing the total number of open passenger inspection channels at the airport throughout the day; solving the configuration model for the number of open passenger inspection channels using a genetic algorithm and an SBC algorithm, obtaining the waiting time and waiting length of passengers in each time period, and judging whether constraint conditions are met respectively; if not, increasing the number of open passenger inspection channels in the corresponding time period and executing the step again; if so, calculating the total number of open passenger inspection channels at the airport throughout the day; taking the number of open passenger inspection channels in each time period with the smallest total number as the optimal configuration result, and completing the optimization of the configuration of the number of open passenger inspection channels at the airport; the present invention takes into account the non-stationary characteristics of the passenger arrival process, can quickly and accurately calculate the performance index value of the system, and can effectively reduce the operating cost and resource waste of the airport.

Description

Technical Field

[0001] The present invention relates to the field of airport command and control technology, and more specifically, to a method, system and storage medium for optimizing the configuration of the number of open airport passenger inspection channels. Background Art

[0002] The booming aviation industry presents significant challenges to airport resource allocation. With the rapid development of my country's air transport industry, China is poised to become the fastest-growing and most promising country in civil aviation. According to the "2019-2038 China Civil Aircraft Market Forecast Annual Report" released by the China Aviation Industry Development Research Center, China's passenger aircraft fleet is projected to reach 8,678 by 2038, making it the world's second-largest aviation market. However, with the rapid growth of my country's aviation industry, airlines are continuously expanding their fleets. This, particularly at large hub airports, is accompanied by a massive and highly variable passenger volume. This puts enormous pressure on the traditional, empirical approach of allocating security checkpoints. To improve airport operational efficiency and service quality, optimizing the allocation of security checkpoints is a pressing issue.

[0003] In actual service, the number of passenger inspection lanes effectively controls passenger queue lengths and wait times. Insufficient passenger inspection lanes can lead to long queues, increasing wait times and impacting passenger comfort and satisfaction. Excessive idle passenger inspection lanes reduce system utilization and lead to further waste. Numerous uncertainties exist in airport service, posing significant challenges to configuring the number of passenger inspection lanes. First, passenger arrival intervals are random, and this random nature of the arrival process significantly impacts the performance of the queuing system, increasing the difficulty of solving the problem. Second, flight departure schedules do not follow a fixed pattern, resulting in a non-stationary passenger arrival rate. Therefore, optimizing the number of available passenger inspection lanes must account for the non-stationary nature of the passenger arrival process.

[0004] The current prior art discloses a method for predicting the schedule of service desks at smart airports. The method first determines the average arrival rate, the number of empirical service desks open, the waiting time distribution, and the empirical service efficiency level; secondly, a queuing theory model is established, and the theoretical service efficiency level and the theoretical service rate are estimated based on the queuing theory model; then, the theoretical service rate is corrected based on the service rate regression hypothesis, and the regression parameters are estimated; the theoretical service efficiency level is re-estimated based on the corrected theoretical service rate; finally, the regression parameters are combined, and any two of the arrival rate, service efficiency level, and the number of service desks open are input to solve the remaining one; the method in the prior art has low prediction accuracy because it does not consider the influence of the non-stationary characteristics of the passenger arrival process. Summary of the Invention

[0005] In order to overcome the defect of the above-mentioned prior art that the non-stationary characteristics of the passenger arrival process are not taken into account, resulting in low prediction accuracy, the present invention provides a method, system and storage medium for optimizing the configuration of the number of open airport passenger inspection channels, which can quickly and accurately calculate the performance index value of the system and effectively reduce the operating costs and resource waste of the airport.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] A method for optimizing the configuration of the number of open passenger inspection channels at an airport, comprising the following steps:

[0008] S1: Considering the impact of the non-stationary characteristics of the passenger arrival process, with the goal of minimizing the total number of airport passenger inspection channels open throughout the day, constraints are set and a passenger inspection channel opening number configuration model is established;

[0009] S2: Setting the initial number of passengers and the number of open passenger inspection channels in each time period in the passenger inspection channel opening number configuration model, and using a genetic algorithm to generate a first solution set of the passenger inspection channel opening number configuration model;

[0010] S3: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each period;

[0011] S4: Determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels. If so, execute step S5. Otherwise, increase the number of open passenger inspection channels for the corresponding time period in the first solution set to obtain a second solution set, and execute steps S3-S4 again.

[0012] S5: Calculate the total number of airport passenger inspection channels open throughout the day based on the first solution set or the second solution set;

[0013] S6: Repeat steps S2 to S5, and take the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result to complete the configuration optimization of the number of airport passenger inspection channels open.

[0014] Preferably, the configuration model for the number of open passenger inspection channels established in step S1 is specifically:

[0015] Configure the number of passenger inspection channels in the i-th period as vector x i Taking the non-stationary characteristics of the passenger arrival process into consideration as the decision variable, and aiming to minimize the total number of airport passenger inspection channels open throughout the day, the following passenger inspection channel opening number configuration model is established:

[0016]

[0017]

[0018]

[0019]

[0020] Among them, X opt is the optimal configuration vector for the number of airport passenger inspection channels open at each time period; Z is the total number of airport passenger inspection channels open at each time period throughout the day; T is the number of time periods; L qi (x i ) is the waiting captain of passengers in the i-th period, L qmax The maximum waiting time for passengers; W qi (x i ) is the waiting time of passengers in the i-th period, W qmax is the maximum waiting time of passengers; L qi (x i ) and W qi (x i ) are all non-closed functions.

[0021] Preferably, in step S3, based on the first solution set of the passenger inspection channel opening number configuration model, the specific method of using the SBC algorithm to calculate the waiting time and waiting length of passengers in each time period is:

[0022] Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the ideal arrival rate λ in the i-th period i and service rate μ i , specifically:

[0023]

[0024]

[0025] Where λ(·) and μ(·) are the arrival rate and service rate functions at a certain moment, respectively, and T is the number of time slots;

[0026] Establish the G(t) / G(t) / C(t) non-stationary queuing model;

[0027] Define the passenger backlog b caused by congestion in the i-1th period i-1 , specifically:

[0028]

[0029] in, is the actual arrival rate in the i-1th period, the actual arrival rate in the i-th period satisfy Pi-1 (B) is the probability of passenger congestion in the i-1th period;

[0030] Calculate the probability P of passenger congestion in the i-1th period using the Erlang loss probability i-1 (B), specifically:

[0031]

[0032] Among them, c i is the number of passenger inspection channels in the i-th period;

[0033] The expected utilization rate E(U i )for:

[0034]

[0035] The expected utilization rate E(U i ) Get the corrected arrival rate for the i-th period Specifically:

[0036]

[0037] The approximate method of the G / G / C queuing model is used to solve the configuration model of the number of open passenger inspection channels, and the waiting time and waiting length of passengers in each period are obtained.

[0038] Preferably, the specific method for obtaining the waiting time of passengers in each time period is:

[0039] The waiting times for passengers in each period are as follows:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] φ1(C,ρ)=1+γ(C,ρ)

[0046] φ2(C,ρ)=1-4γ(C,ρ)

[0047]

[0048] φ4(C, ρ)=min{1, (φ1(C, ρ)+φ3(C, ρ)) / 2}

[0049]

[0050] Among them, EW is the actual waiting time of passengers in a certain period, EW(M / M / C) is the expected waiting time of passengers in a certain period, τ is the average service time of a single passenger inspection channel, ρ is the traffic intensity, μ satisfies μ={μ1, μ2,…,μ i}, c satisfies c={c1, c2, ..., c i}, and are the squared coefficients of variation of the input process distribution and the service process distribution, respectively; C is the number of open passenger inspection channels; φ, ψ, φ1, φ2, φ3, and φ4 are the first, second, third, fourth, fifth, and sixth correction factors, respectively.

[0051] Preferably, the specific method for obtaining the waiting leader of passengers in each time period is:

[0052] According to the Litt Law, the waiting captains of passengers in each time period are obtained as follows:

[0053] EQ=λ MAR EW

[0054] Among them, EQ is the actual waiting time of passengers in a certain period of time.

[0055] Preferably, after step S6, the method further includes: inputting the optimal configuration result into the airport service system, and the airport service system configures the number of open airport passenger inspection channels in each time period according to the optimal configuration result.

[0056] Preferably, in step S2, the specific method of using a genetic algorithm to generate a first solution set of the configuration model for the number of open passenger inspection channels is: using a genetic algorithm, the initial number of open passenger inspection channels is increased in value through any one or more methods of selection, crossover and mutation to obtain a value-added solution, and the initial number of open passenger inspection channels and the value-added solution are used together as the first solution set.

[0057] Preferably, in step S4, the specific method for determining whether the constraint conditions in the passenger inspection channel opening number configuration model are met is:

[0058] For the initial number of open passenger inspection channels and the value-added solution in the first solution set, it is determined whether the waiting time of passengers and the length of waiting time meet the constraints in the configuration model for the number of open passenger inspection channels.

[0059] A system for optimizing the number of open airport passenger inspection channels, applying the above-mentioned method for optimizing the number of open airport passenger inspection channels, comprises:

[0060] Model building unit: This unit considers the impact of the non-stationary characteristics of the passenger arrival process, sets constraints, and establishes a configuration model for the number of passenger inspection channels open throughout the day, with the goal of minimizing the total number of airport passenger inspection channels open throughout the day.

[0061] Initialization unit: used to set the initial number of passengers and the number of open passenger inspection channels in each period of the passenger inspection channel opening number configuration model, and use the genetic algorithm to generate the first solution set of the passenger inspection channel opening number configuration model;

[0062] SBC solution unit: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each period;

[0063] Judgment unit: used to determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model of the number of open passenger inspection channels. If the constraints are met, the calculation unit is executed. Otherwise, the number of open passenger inspection channels in the corresponding time period in the first solution set is increased to obtain the second solution set, and the SBC solution unit and judgment unit are executed again;

[0064] Calculation unit: used to calculate the total number of open airport passenger inspection channels throughout the day based on the first solution set or the second solution set;

[0065] Optimal configuration output unit: used to repeat the above steps, taking the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result, and completing the configuration optimization of the number of airport passenger inspection channels open.

[0066] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0067] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0068] The present invention provides a method, system and storage medium for optimizing the configuration of the number of open passenger inspection channels at an airport. The method comprises: considering the influence of the non-stationary characteristics of the passenger arrival process, setting constraints with the goal of minimizing the total number of open passenger inspection channels at the airport throughout the day, and establishing a configuration model for the number of open passenger inspection channels; setting the initial number of passengers and the number of open passenger inspection channels in each time period in the configuration model for the number of open passenger inspection channels, and using a genetic algorithm to generate a first solution set for the configuration model for the number of open passenger inspection channels; calculating the waiting time and the number of waiting queues of passengers in each time period using an SBC algorithm based on the first solution set of the configuration model for the number of open passenger inspection channels; determining whether the waiting time and the number of waiting queues of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels; if so, executing the next step; otherwise, increasing the number of open passenger inspection channels for the corresponding time period in the first solution set to obtain a second solution set, and executing the step again; calculating the total number of open passenger inspection channels at the airport throughout the day according to the first solution set or the second solution set; repeating the above steps, taking the first solution set or the second solution set with the smallest total number of open passenger inspection channels at the airport throughout the day as the optimal configuration result, and completing the optimization of the configuration of the number of open passenger inspection channels at the airport;

[0069] The present invention targets the queuing process of airport passenger inspection channels and proposes a method for embedding a non-stationary queuing model into a genetic algorithm. The non-stationary queuing model has greater applicability because, according to service system data analysis, the customer arrival rate of almost all service systems typically varies significantly throughout the day, and the arrival process distribution and service time distribution are usually not close to an exponential distribution. These changes have a significant impact on the performance indicators of the queuing system, so these changes must be considered when calculating system performance indicators. The extended SBC method can quickly obtain accurate system performance indicator values, thereby effectively solving the problem of optimizing the configuration of the number of open passenger inspection channels at airports, providing a scientific analysis method and decision-making basis for the configuration of the number of passenger inspection channels, and optimizing airport efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flow chart of a method for optimizing the configuration of the number of open airport passenger inspection channels provided in Example 1.

[0071] Figure 2 This is a flow chart of a method for optimizing the configuration of the number of open airport passenger inspection channels provided in Example 2.

[0072] Figure 3 This is a structural diagram of a system for optimizing the number of open airport passenger inspection channels provided in Example 3. DETAILED DESCRIPTION

[0073] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0074] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0075] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0076] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0077] Example 1

[0078] like Figure 1 As shown, this embodiment provides a method for optimizing the configuration of the number of open passenger inspection channels at an airport, including the following steps:

[0079] S1: Considering the impact of the non-stationary characteristics of the passenger arrival process, with the goal of minimizing the total number of airport passenger inspection channels open throughout the day, constraints are set and a passenger inspection channel opening number configuration model is established;

[0080] S2: Setting the initial number of passengers and the number of open passenger inspection channels in each time period in the passenger inspection channel opening number configuration model, and using a genetic algorithm to generate a first solution set of the passenger inspection channel opening number configuration model;

[0081] S3: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each period;

[0082] S4: Determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels. If so, execute step S5. Otherwise, increase the number of open passenger inspection channels for the corresponding time period in the first solution set to obtain a second solution set, and execute steps S3-S4 again.

[0083] S5: Calculate the total number of airport passenger inspection channels open throughout the day based on the first solution set or the second solution set;

[0084] S6: Repeat steps S2 to S5, and take the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result to complete the configuration optimization of the number of airport passenger inspection channels open.

[0085] During the specific implementation process, the influence of the non-stationary characteristics of the passenger arrival process is first considered, and with the goal of minimizing the total number of passenger inspection channels open at the airport throughout the day, constraints are set and a configuration model for the number of passenger inspection channels open is established; the initial number of passengers and the number of passenger inspection channels open in each time period in the passenger inspection channel open number configuration model are set, and a genetic algorithm is used to generate a first solution set of the passenger inspection channel open number configuration model; based on the first solution set of the passenger inspection channel open number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each time period; it is determined whether the waiting time and waiting length of passengers in each time period meet the constraints in the passenger inspection channel open number configuration model; if the constraints are met, the next step is executed; otherwise, the number of passenger inspection channels open in the corresponding time period in the first solution set is increased to obtain a second solution set, and this step is executed again; the total number of passenger inspection channels open at the airport throughout the day is calculated based on the first solution set or the second solution set; the above steps are repeated, and the first solution set or the second solution set with the smallest total number of passenger inspection channels open at the airport throughout the day is taken as the optimal configuration result, completing the optimization of the airport passenger inspection channel open number configuration;

[0086] This method targets the queuing process of airport passenger inspection channels and proposes a method of embedding a non-stationary queuing model into a genetic algorithm. The non-stationary queuing model has stronger applicability because, according to service system data analysis, the customer arrival rate of almost all service systems usually changes significantly throughout the day, and the arrival process distribution and service time distribution are usually not close to the exponential distribution. These changes will have a significant impact on the performance indicators of the queuing system, so these changes must be considered when calculating the system performance indicators. The use of the extended SBC method can quickly obtain accurate system performance indicator values, thereby effectively solving the problem of optimizing the configuration of the number of open passenger inspection channels at airports, providing a scientific analysis method and decision-making basis for the configuration of the number of passenger inspection channels, and optimizing airport benefits.

[0087] Example 2

[0088] like Figure 2 As shown, this embodiment provides a method for optimizing the configuration of the number of open passenger inspection channels at an airport, including the following steps:

[0089] S1: Considering the impact of the non-stationary characteristics of the passenger arrival process, with the goal of minimizing the total number of airport passenger inspection channels open throughout the day, constraints are set and a passenger inspection channel opening number configuration model is established;

[0090] S2: Setting the initial number of passengers and the number of open passenger inspection channels in each time period in the passenger inspection channel opening number configuration model, and using a genetic algorithm to generate a first solution set of the passenger inspection channel opening number configuration model;

[0091] S3: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each period;

[0092] S4: Determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels. If so, execute step S5. Otherwise, increase the number of open passenger inspection channels for the corresponding time period in the first solution set to obtain a second solution set, and execute steps S3-S4 again.

[0093] S5: Calculate the total number of airport passenger inspection channels open throughout the day based on the first solution set or the second solution set;

[0094] S6: Repeat steps S2 to S5, and take the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result, completing the configuration optimization of the number of airport passenger inspection channels open;

[0095] The configuration model for the number of open passenger inspection channels established in step S1 is specifically as follows:

[0096] Configure the number of passenger inspection channels in the i-th period as vector x i Taking the non-stationary characteristics of the passenger arrival process into consideration as the decision variable, and aiming to minimize the total number of airport passenger inspection channels open throughout the day, the following passenger inspection channel opening number configuration model is established:

[0097]

[0098]

[0099]

[0100]

[0101] Among them, X opt is the optimal configuration vector for the number of airport passenger inspection channels open at each time period; Z is the total number of airport passenger inspection channels open at each time period throughout the day; T is the number of time periods; L qi (x i ) is the waiting captain of passengers in the i-th period, L qmax The maximum waiting time for passengers; W qi (x i ) is the waiting time of passengers in the i-th period, W qmax is the maximum waiting time of passengers; L qi (x i ) and W qi (x i ) are all non-closed functions;

[0102] In step S3, based on the first solution set of the passenger inspection channel opening number configuration model, the specific method of using the SBC algorithm to calculate the waiting time and waiting length of passengers in each time period is as follows:

[0103] Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the ideal arrival rate λ in the i-th period i and service rate μ i , specifically:

[0104]

[0105]

[0106] Where λ(·) and μ(·) are the arrival rate and service rate functions at a certain moment, respectively, and T is the number of time slots;

[0107] Establish the G(t) / G(t) / C(t) non-stationary queuing model;

[0108] Define the passenger backlog b caused by congestion in the i-1th period i-1 , specifically:

[0109]

[0110] in, is the actual arrival rate in the i-1th period, the actual arrival rate in the i-th period satisfy P i-1 (B) is the probability of passenger congestion in the i-1th period;

[0111] Calculate the probability P of passenger congestion in the i-1th period using the Erlang loss probability i-1 (B), specifically:

[0112]

[0113] Among them, c i is the number of passenger inspection channels in the i-th period;

[0114] The expected utilization rate E(U i )for:

[0115]

[0116] The expected utilization rate E(U i ) Get the corrected arrival rate for the i-th period Specifically:

[0117]

[0118] The approximate method of the G / G / C queuing model is used to solve the configuration model for the number of open passenger inspection channels, and the waiting time and number of waiting queues of passengers in each period are obtained;

[0119] The specific method for obtaining the waiting time of passengers in each period is:

[0120] The waiting times for passengers in each period are as follows:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] φ1(C,ρ)=1+γ(C,ρ)

[0127] φ2(C,ρ)=1-4γ(C,ρ)

[0128]

[0129] φ4(C, ρ)=min{1, (φ1(C, ρ)+φ3(C, ρ)) / 2}

[0130]

[0131] Among them, EW is the actual waiting time of passengers in a certain period, EW(M / M / C) is the expected waiting time of passengers in a certain period, τ is the average service time of a single passenger inspection channel, ρ is the traffic intensity, μ satisfies μ={μ1, μ2,…,μ i}, c satisfies c={c1, c2, ..., c i}, and are the squared coefficients of variation of the input process distribution and the service process distribution, respectively; C is the number of open passenger inspection channels; φ, ψ, φ1, φ2, φ3, and φ4 are the first, second, third, fourth, fifth, and sixth correction factors, respectively;

[0132] The specific method to obtain the waiting captain of passengers in each period is:

[0133] According to the Litt Law, the waiting captains of passengers in each time period are obtained as follows:

[0134] EQ=λ MAR EW

[0135] Among them, EQ is the actual waiting time of passengers in a certain period;

[0136] After step S6, the method further includes: inputting the optimal configuration result into the airport service system, and the airport service system configuring the number of open airport passenger inspection channels in each time period according to the optimal configuration result;

[0137] In step S2, the specific method for generating the first solution set of the passenger inspection channel opening number configuration model using the genetic algorithm is as follows: using the genetic algorithm, increasing the initial number of passenger inspection channel openings by any one or more methods of selection, crossover, and mutation to obtain an increased-value solution, and the initial number of passenger inspection channel openings and the increased-value solution are collectively used as the first solution set;

[0138] In step S4, the specific method for determining whether the constraint conditions in the passenger inspection channel opening number configuration model are met is:

[0139] For the initial number of open passenger inspection channels and the value-added solution in the first solution set, it is determined whether the waiting time of passengers and the length of waiting time meet the constraints in the configuration model for the number of open passenger inspection channels.

[0140] In the specific implementation process, the impact of the non-stationary characteristics of the passenger arrival process is first considered. With the goal of minimizing the total number of airport passenger inspection channels open throughout the day, a passenger inspection channel opening number configuration model is established. Specifically,

[0141] Configure the number of passenger inspection channels in the i-th period as vector x i Taking the non-stationary characteristics of the passenger arrival process into consideration as the decision variable, and aiming to minimize the total number of airport passenger inspection channels open throughout the day, the following passenger inspection channel opening number configuration model is established:

[0142]

[0143]

[0144]

[0145]

[0146] Among them, X opt is the optimal configuration vector for the number of airport passenger inspection channels open at each time period; Z is the total number of airport passenger inspection channels open at each time period throughout the day; T is the number of time periods; L qi (x i ) is the waiting captain of passengers in the i-th period, L qmax The maximum waiting time for passengers; W qi (x i) is the waiting time of passengers in the i-th period, W qmax is the maximum waiting time of passengers; L qi (x i ) and W qi (x i ) are all non-closed functions;

[0147] An initial number of passengers and an initial number of open passenger inspection channels in each time period of a passenger inspection channel opening number configuration model are set, and a first solution set is generated using a genetic algorithm. The specific method includes: using the genetic algorithm to increase the initial number of open passenger inspection channels through any one or more methods of selection, crossover, and mutation to obtain an increased-value solution, and the initial number of open passenger inspection channels and the increased-value solution are collectively used as the first solution set;

[0148] Based on the first solution set of the passenger inspection channel opening number configuration model, the specific method of using the SBC algorithm to calculate the waiting time and waiting length of passengers in each period is as follows:

[0149] Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the ideal arrival rate λ in the i-th period i and service rate μ i , specifically:

[0150]

[0151]

[0152] Where λ(·) and μ(·) are the arrival rate and service rate functions at a certain moment, respectively, and T is the number of time slots;

[0153] Establish the G(t) / G(t) / C(t) non-stationary queuing model;

[0154] Define the passenger backlog b caused by congestion in the i-1th period i-1 , the passenger backlog of the previous period will be transferred to the next period, specifically:

[0155]

[0156] in, is the actual arrival rate in the i-1th period, the actual arrival rate in the i-th period satisfy P i-1 (B) is the probability of passenger congestion in the i-1th period;

[0157] Calculate the probability P of passenger congestion in the i-1th period using the Erlang loss probability i-1 (B), specifically:

[0158]

[0159] Among them, c i is the number of passenger inspection channels in the i-th period;

[0160] The expected utilization rate E(U i )for:

[0161]

[0162] The expected utilization rate E(U i ) Get the corrected arrival rate for the i-th period Specifically:

[0163]

[0164] The approximate method of the G / G / C queuing model is used to solve the configuration model for the number of open passenger inspection channels, and the waiting time and number of waiting queues of passengers in each period are obtained;

[0165] The specific method for obtaining the waiting time of passengers in each period is:

[0166] The waiting times for passengers in each period are as follows:

[0167]

[0168]

[0169]

[0170]

[0171]

[0172] φ1(C,ρ)=1+γ(C,ρ)

[0173] φ2(C,ρ)=1-4γ(C,ρ)

[0174]

[0175] φ4(C, ρ)=min{1, (φ1(C, ρ)+φ3(C, ρ)) / 2}

[0176]

[0177] Among them, EW is the actual waiting time of passengers in a certain period, EW(M / M / C) is the expected waiting time of passengers in a certain period, τ is the average service time of a single passenger inspection channel, ρ is the traffic intensity, μ satisfies μ={μ1, μ2,…,μ i}, c satisfies c={c1, c2, ..., c i}, and are the squared coefficients of variation of the input process distribution and the service process distribution, respectively; C is the number of open passenger inspection channels; φ, ψ, φ1, φ2, φ3, and φ4 are the first, second, third, fourth, fifth, and sixth correction factors, respectively;

[0178] The specific method to obtain the waiting captain of passengers in each period is:

[0179] According to the Litt Law, the waiting captains of passengers in each time period are obtained as follows:

[0180] EQ=λ MAR EW

[0181] Among them, EQ is the actual waiting time of passengers in a certain period;

[0182] For the initial number of open passenger inspection channels and the value-added solution, determine whether the passenger waiting time and waiting time length meet the constraints of the passenger inspection channel opening number configuration model. If the constraints are met, proceed to the next step; otherwise, increase the number of open passenger inspection channels for the corresponding time period of the first solution set and execute this step again;

[0183] Calculate the total number of airport passenger inspection channels open throughout the day based on the first solution set or the second solution set;

[0184] Repeat the above steps and take the first or second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result to complete the configuration optimization of the number of airport passenger inspection channels open;

[0185] Since the genetic algorithm is an algorithm for finding the maximum value, in order to achieve the goal of finding the minimum total number, such as Figure 2 As shown, in this embodiment, the first or second solution set is set to a negative value. At this time, the larger the actual total number of solutions, the smaller the value in the genetic algorithm. Therefore, in the process of finding the actual minimum total number, it is necessary to eliminate the solutions with smaller objective functions in the solution set of the genetic algorithm.

[0186] Finally, the optimal configuration result is input into the airport service system, which then configures the number of open airport passenger inspection channels at each time period based on the optimal configuration result.

[0187] This method targets the queuing process of airport passenger inspection channels and proposes a method of embedding a non-stationary queuing model into a genetic algorithm. The non-stationary queuing model has stronger applicability because, according to service system data analysis, the customer arrival rate of almost all service systems usually changes significantly throughout the day, and the arrival process distribution and service time distribution are usually not close to the exponential distribution. These changes will have a significant impact on the performance indicators of the queuing system, so these changes must be considered when calculating the system performance indicators. The use of the extended SBC method can quickly obtain accurate system performance indicator values, thereby effectively solving the problem of optimizing the configuration of the number of open passenger inspection channels at airports, providing a scientific analysis method and decision-making basis for the configuration of the number of passenger inspection channels, and optimizing airport benefits.

[0188] Example 3

[0189] like Figure 3 As shown, this embodiment provides a system for optimizing the number of open airport passenger inspection channels, applying the method for optimizing the number of open airport passenger inspection channels described in Embodiment 1 or 2, including:

[0190] Model building unit 301: Considering the impact of the non-stationary characteristics of the passenger arrival process, setting constraints and establishing a configuration model for the number of passenger inspection channels open throughout the day with the goal of minimizing the total number of airport passenger inspection channels open throughout the day;

[0191] Initialization unit 302: used to set the initial number of passengers and the number of open passenger inspection channels in each time period in the passenger inspection channel opening number configuration model, and generate a first solution set of the passenger inspection channel opening number configuration model using a genetic algorithm;

[0192] SBC solving unit 303: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each time period;

[0193] Determination unit 304: used to determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels. If so, the calculation unit is executed. Otherwise, the number of open passenger inspection channels for the corresponding time period in the first solution set is increased to obtain a second solution set, and the SBC solution unit and determination unit are executed again.

[0194] Calculation unit 305: used to calculate the total number of open airport passenger inspection channels throughout the day according to the first solution set or the second solution set;

[0195] The optimal configuration output unit 306 is used to repeat the above steps, and take the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result, thereby completing the configuration optimization of the number of airport passenger inspection channels open.

[0196] In the specific implementation process, first, the model building unit 301 considers the influence of the non-stationary characteristics of the passenger arrival process, sets constraints with the goal of minimizing the total number of passenger inspection channels open at the airport throughout the day, and establishes a passenger inspection channel opening number configuration model; the initialization unit 302 sets the initial number of passengers and passenger inspection channels open in each time period in the passenger inspection channel opening number configuration model, and uses a genetic algorithm to generate a first solution set of the passenger inspection channel opening number configuration model; the SBC solution unit 303 calculates the waiting time and waiting length of passengers in each time period based on the first solution set of the passenger inspection channel opening number configuration model using the SBC algorithm; the judgment unit Element 304 determines whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels. If so, the next step is executed. Otherwise, the number of open passenger inspection channels for the corresponding time period in the first solution set is increased to obtain a second solution set, and the step is executed again. Calculation unit 305 calculates the total number of open passenger inspection channels at the airport throughout the day based on the first solution set or the second solution set. Optimal configuration output unit 306 repeats the above steps and uses the first solution set or the second solution set with the smallest total number of open passenger inspection channels at the airport throughout the day as the optimal configuration result, completing the optimization of the number of open passenger inspection channels at the airport.

[0197] This system aims at the queuing process of airport passenger inspection channels and proposes a method of embedding a non-stationary queuing model into a genetic algorithm. The non-stationary queuing model has stronger applicability because, according to the analysis of service system data, the customer arrival rate of almost all service systems usually changes significantly throughout the day, and the arrival process distribution and service time distribution are usually not close to the exponential distribution. These changes will have a significant impact on the performance indicators of the queuing system, so these changes must be considered when calculating the system performance indicators. The use of the extended SBC method can quickly obtain accurate system performance indicator values, thereby effectively solving the problem of optimizing the configuration of the number of open passenger inspection channels at airports, providing a scientific analysis method and decision-making basis for the configuration of the number of passenger inspection channels, and optimizing airport benefits.

[0198] The same or similar reference numerals correspond to the same or similar components;

[0199] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0200] 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 number of open airport passenger inspection channels, characterized in that: The following steps are involved: S1: Considering the impact of the non-stationary characteristics of the passenger arrival process, with the goal of minimizing the total number of airport passenger inspection channels open throughout the day, constraints are set and a passenger inspection channel opening number configuration model is established; The specific configuration model for the number of open passenger inspection channels is as follows: Configure the number of passenger inspection channels in the i-th period as a vector Taking the non-stationary characteristics of the passenger arrival process into consideration as the decision variable, and aiming to minimize the total number of airport passenger inspection channels open throughout the day, the following passenger inspection channel opening number configuration model is established: in, Configure vectors for the optimal number of open airport passenger inspection channels at each time period; The total number of airport passenger inspection channels open at various times throughout the day; is the number of time periods; is the waiting captain of passengers in the i-th period, The maximum number of passengers waiting for the captain; is the waiting time of passengers in the i-th period, The maximum waiting time for passengers; and All are non-closed functions; S2: Setting the initial number of passengers and the number of open passenger inspection channels in each time period in the passenger inspection channel opening number configuration model, and using a genetic algorithm to generate a first solution set of the passenger inspection channel opening number configuration model; S3: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each period, specifically: Based on the first solution set of the passenger inspection channel opening number configuration model, the ideal arrival rate in the i-th period is calculated using the SBC algorithm. and service rate , specifically: in, and They are the arrival rate and service rate functions at a certain moment, is the number of time periods; Establish the G(t) / G(t) / C(t) non-stationary queuing model; Define the passenger backlog caused by congestion in the i-1th period , specifically: in, is the actual arrival rate in the i-1th period, the actual arrival rate in the i-th period satisfy ; is the probability of passenger congestion in the i-1th period; Calculate the probability of passenger congestion in the i-1th period using the Erlang loss probability , specifically: in, is the number of passenger inspection channels in the i-th period; The expected utilization rate of the G(t) / G(t) / C(t) non-stationary queue model in the i-th period for: Utilize the expected utilization rate in period i Get the corrected arrival rate for the i-th period , specifically: The approximate method of the G / G / C queuing model is used to solve the configuration model for the number of open passenger inspection channels, and the waiting time and number of waiting queues of passengers in each period are obtained; S4: Determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model for the number of open passenger inspection channels. If so, execute step S5. Otherwise, increase the number of open passenger inspection channels in the corresponding time period in the first solution set to obtain a second solution set, and execute steps S3-S4 again. S5: Calculate the total number of airport passenger inspection channels open throughout the day based on the first solution set or the second solution set; S6: Repeat steps S2 to S5, and take the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result to complete the configuration optimization of the number of airport passenger inspection channels open.

2. The method for optimizing the number of open airport passenger inspection channels according to claim 1, characterized in that: The specific method for obtaining the waiting time of passengers in each period is: The waiting times for passengers in each period are as follows: in, The actual waiting time of passengers in a certain period of time, is the expected waiting time of passengers in a certain period of time, is the average service time of a single passenger inspection channel, is the traffic intensity, ; satisfy , satisfy , and are the squared coefficients of variation of the input process distribution and the service process distribution, respectively. is the number of open passenger inspection channels, These are the first, second, third, fourth, fifth and sixth correction factors respectively.

3. A method for optimizing the number of open airport passenger inspection channels according to claim 1 or 2, characterized in that: The specific method to obtain the waiting captain of passengers in each period is: According to the Litt Law, the waiting captains of passengers in each time period are obtained as follows: in, The actual waiting team leader for passengers during a certain period of time.

4. The method for optimizing the number of open airport passenger inspection channels according to claim 1 is characterized in that: After step S6, the method further includes: inputting the optimal configuration result into the airport service system, and the airport service system configures the number of open airport passenger inspection channels in each time period according to the optimal configuration result.

5. The method for optimizing the number of open airport passenger inspection channels according to claim 4, characterized in that: In step S2, the specific method of using the genetic algorithm to generate the first solution set of the passenger inspection channel opening number configuration model is: using the genetic algorithm, the initial passenger inspection channel opening number is increased by any one or more methods of selection, crossover and mutation to obtain a value-added solution, and the initial passenger inspection channel opening number and the value-added solution are used together as the first solution set.

6. The method for optimizing the number of open airport passenger inspection channels according to claim 5, characterized in that: In step S4, the specific method for determining whether the constraint conditions in the passenger inspection channel opening number configuration model are met is: For the initial number of open passenger inspection channels and the value-added solution in the first solution set, it is determined whether the waiting time of passengers and the length of waiting time meet the constraints in the configuration model for the number of open passenger inspection channels.

7. A system for optimizing the number of open passenger inspection channels at an airport, applying the method for optimizing the number of open passenger inspection channels at an airport as described in any one of claims 1 to 6, characterized in that: include: Model building unit: This unit considers the impact of the non-stationary characteristics of the passenger arrival process, sets constraints, and establishes a configuration model for the number of passenger inspection channels open throughout the day, with the goal of minimizing the total number of airport passenger inspection channels open throughout the day. Initialization unit: used to set the initial number of passengers and the number of open passenger inspection channels in each period of the passenger inspection channel opening number configuration model, and use the genetic algorithm to generate the first solution set of the passenger inspection channel opening number configuration model; SBC solution unit: Based on the first solution set of the passenger inspection channel opening number configuration model, the SBC algorithm is used to calculate the waiting time and waiting length of passengers in each time period; Judgment unit: used to determine whether the waiting time and waiting length of passengers in each time period meet the constraints in the configuration model of the number of open passenger inspection channels. If the constraints are met, the calculation unit is executed. Otherwise, the number of open passenger inspection channels in the corresponding time period in the first solution set is increased to obtain the second solution set, and the SBC solution unit and judgment unit are executed again; Calculation unit: used to calculate the total number of open airport passenger inspection channels throughout the day based on the first solution set or the second solution set; Optimal configuration output unit: used to repeat the above steps, taking the first solution set or the second solution set with the smallest total number of airport passenger inspection channels open throughout the day as the optimal configuration result, and completing the configuration optimization of the number of airport passenger inspection channels open.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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