Modern Management
Vol. 09  No. 01 ( 2019 ), Article ID: 28754 , 15 pages
10.12677/MM.2019.91002

Data Analysis and Optimization Strategy for a Risk-Based Three-Level Inspection Channel

Chia-Hung Wang1,2, Jinman Lan1

1College of Information Science and Engineering, Fujian University of Technology, Fuzhou Fujian

2Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou Fujian

Received: Jan. 9th, 2019; accepted: Jan. 24th, 2019; published: Jan. 31st, 2019

ABSTRACT

In order to study the service management of passenger clearance and security-check process, this paper develops a simulation model for risk-based three-level inspection queuing system. In the proposed simulation model, passengers to be inspected are classified to three risk classes based on their assessed risk value, and each of them is assigned to the inspection channel corresponding to his/her risk value for security check. We evaluate the data obtained from computer simulation experiments, and conduct a sensitivity analysis on the security level and average waiting time of the studied inspection system. An executable optimization strategy is presented in the paper for selecting the model variables through a series of data analysis.

Keywords:Risk Management, Queuing System, Simulation Experiment, Security Screening, Data Analysis

1福建工程学院信息科学与工程学院，福建 福州

2福建省大数据挖掘与应用技术重点实验室，福建 福州

1. 引言

2. 基于旅客风险的三安检通道排队安检流程

Figure 1. A security-check queuing system with three-level inspection channels

$SL=100%-\left(通过常规安检与快速通关安检的“危险分子”人数占总通关旅客人数的比例\right)$

Table 1. Definition of notations

3. Arena仿真建模

1) 使用流程图工具，将三通道安检模型的具体流程表示出来；

2) 使用Arena软件相应模块，构建与三通道安检流程图相符的仿真模型；

3) 给Arena仿真模型中各个模块和运行环境设置相对应的参数；

4) 测试模型的功能性与准确性，及时更正仿真模型中的错误；

5) 查看模型的运行结果与报告；

6) 分析实验数据报告。

4. 实验方案与数据分析

4.1. 实验环境与模型参数设置

Table 2. Statistics of departure flights at Narita Airport

Table 3. Parameter setting of the queuing model for three-level inspection channels

4.2. 决策参数对系统表现指标的影响

Figure 2. Data graph of the average waiting time W with respect to the fast-pass threshold ${a}_{f}$

Figure 3. Data graph of the security level SL with respect to the fast-pass threshold ${a}_{f}$

Figure 4. Data graph of the average waiting time W with respect to the strict security-check threshold ${a}_{s}$

Figure 5. Data graph of the security level SL with respect to the strict security-check threshold ${a}_{s}$

Figure 6. Data graph of the average waiting time W with respect to the queue threshold ${Q}_{n}$ of normal inspection channel

Figure 7. Data graph of the security level SL with respect to the queue threshold ${Q}_{n}$ of normal inspection channel

Figure 8. Data graph of the average waiting time W with respect to the queue threshold ${Q}_{s}$ of strict inspection channel

Figure 9. Data graph of the security level SL with respect to the queue threshold ${Q}_{s}$ of strict inspection channel

Figure 10. Data graph of the average waiting time W with respect to the sampling probability ${P}_{n}$ of entering normal inspection channel

Figure 11. Data graph of the security level SL with respect to the sampling probability ${P}_{n}$ of entering normal inspection channel

Figure 12. Data graph of the average waiting time W with respect to the sampling probability ${P}_{s}$ of entering strict inspection channel

4.3. 实验数据分析总结

Figure 13. Data graph of the security level SL with respect to the sampling probability ${P}_{s}$ of entering strict inspection channel

5. 优化策略

1) 选择一个决策变量，结合之前预期跑过的实验数据，提出该决策变数对实验指标的影响，通过与机场实际运行环境的对比，确认决策变量的灵敏度分析范围。

2) 合理选择各组中决策变量数据点的采集间隔。过大的数据点间隔可能影响对优化结果的趋势判断，也可能会在实验流程中错过最佳值的数据点，则将对后续选取决策变量的最佳值产生影响。

3) 实验数据的运行。在完成步骤1与步骤2的基础上，进行仿真模型的输出数据记录，并观察实验结果的灵敏度分析数据图形是否有出现最佳值的趋势。

4) 观察实验指标图行变化，得出一个当前决策变量的最佳数值，同时记录该叁数值与相对应的系统表现指标，并代入下一组决策变量的选取组合中。

5) 选择下一个待定的决策变量，重复进行步骤1到步骤4，直至所有决策变量在本优化流程中运行完毕，则停止运行。

Figure 14. Flow chart of optimizing the selection of model variables

Figure 15. The optimal value of average waiting time W corresponds to the optimal selection of six decision variables

Figure 16. The optimal value of security level SL corresponds to the optimal selection of six decision variables

6. 总结与展望

Data Analysis and Optimization Strategy for a Risk-Based Three-Level Inspection Channel[J]. 现代管理, 2019, 09(01): 9-23. https://doi.org/10.12677/MM.2019.91002

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