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考虑非平稳随机过程的机场旅检通道服务资源配置优化

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针对机场旅客到达过程的非平稳性和随机性造成旅检通道需求不确定且具备高时变性、配置难度大这一问题,提出一种非平稳排队模型嵌入遗传算法的方法.建立具有多个并行服务台的单阶段服务系统的非平稳排队模型,将输入过程和服务过程拟合为一般分布,并提出拓展的积压后移平稳(stationary backlog-carryover,SBC)近似,以快速求解系统性能指标值.提出一种将拓展的SBC近似嵌入遗传算法的优化方法,设计实验算例和优化算例.通过解析结果与仿真结果进行对比,验证拓展SBC近似的精确性以及优化方法的有效性,并分析非平稳性对系统性能以及优化结果的影响.将该方法应用在实际案例中,结果表明,优化后的配置方案比实际配置方案减少了14.80%的旅检通道数量,这证明该方法是有效的.
Optimization of Service Resource Allocation for Airport Security Checkpoints Considering Non-stationary Random Processes
The non-stationarity and randomness of airport passenger arrivals result in uncertain and highly time-varying requirements for security checkpoints,increasing the difficulty of resource allocation.To address this issue,this paper proposes a method embedding a non-stationary queuing model into a genetic algorithm.Initially,a non-stationary queuing model for a single-stage service system with multiple parallel service desks is established,where the input and service processes are fitted to general distributions.Furthermore,an extended stationary backlog-carryover(SBC)approximation is proposed to quickly solve system performance metrics.Then,the extended SBC approximation is embedded into the genetic algorithm for optimization.Finally,experimental and optimization examples are designed.The accuracy of the extended SBC approximation and the effectiveness of the optimization method are verified by comparing the analytical results with the simulation results.The impact of non-stationarity on system performance and optimization results is also analyzed.Results of applying this method to a practical case show that the optimized allocation scheme reduces the number of security checkpoints by 14.80%compared to the actual allocation scheme,proving its effectiveness.

optimization of airport security checkpoint allocationnon-stationary queuing modelingservice desk allocationstationary backlog-carryover(SBC)approximationgenetic algorithm

张惠煜、严李洲、陈庆新、毛宁

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广东工业大学 广东省计算机集成制造系统重点实验室,广东 广州,510006

机场安检通道配置优化 非平稳排队建模 服务台配置 积压后移平稳近似 遗传算法

国家自然科学基金资助项目广东省基础与应用基础研究基金资助项目广东省基础与应用基础研究基金资助项目广州市基础研究计划资助项目

619730892022A15150111752022A15150109912023A04J0406

2024

10.3969/j.issn.1007-7375.230017

工业工程
广东工业大学

工业工程

CSTPCDCHSSCD
影响因子：0.691
ISSN：1007-7375
年,卷(期)：2024.27(5)