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PDF(973 KB)
基于仿真模型的排队规则遗传优化算法研究
Research on genetic optimization algorithm of queue rules based on simulation model
排队规则是排队系统的重要组成部分, 直接关系到排队系统的运行效率. 本文以具有复杂结构的排队系统为研究对象, 构建了排队系统仿真模型; 进一步地, 考虑排队规则的多样性特征, 提出了基于排队系统仿真建模的排队规则遗传优化算法, 并进行了算法的优化设计. 基于眼科医院实际数据的分析结果表明, 与先来先服务(FCFS)排队规则相比, 本文提出的方法所得到的排队规则可以将平均住院等待人数减少1/2以上, 并可实现系统的长期稳定性. 论文工作实现了系统仿真与遗传优化算法的有效结合, 对优化复杂系统的排队规则、提高排队系统的运行效率具有积极意义.
Queue rules are an important component of the queuing system. They directly influence the operational efficiency of the queue system. This paper constructs the queue system simulation model with the complicated queue system as a research object. Further, considering the diversity of queue rules, we propose a genetic optimization algorithm based on queuing system simulation model, which is further modified. Analyzing the eye hospital actual data, it shows that comparing with first come first service (FCFS) rule, the queue rule designed in this paper can reduce more than half of the average hospital waiting patients and ensure the long-term stability of the system. This paper effectively combines the simulation model and the genetic optimization algorithm. It is of decisive significance to optimize queue rules of a complex system and improve the operational efficiency of a queue system.
排队系统 / 仿真模型 / 排队规则 / 遗传优化算法 / 实例分析 {{custom_keyword}} /
queuing system / simulation model / queue rules / genetic optimization algorithm / case study {{custom_keyword}} /
[1] 徐光辉. 随机服务系统[M]. 科学出版社, 1988.
[2] 岳德权,孙妍平. 带有止步和中途退出的M/M/C/N部分服务员同步多重休假排队系统的等待时间[J]. 系统工程理论与实践, 2008, 28(2): 89-97.Yue D Q, Sun Y P. The waiting time of M/M/C/N queuing system with balking, reneging and multiple synchronous vacations of partial servers[J]. Systems Engineering — Theory & Practice, 2008, 28(2): 89-97.
[3] 余玄少妙,唐应辉. 离散时间有限缓冲空间GI/Geom/1/N工作休假排队系统稳态概率算法及性能分析[J]. 系统工程理论与实践, 2009, 29(9): 99-107.Yu M M, Tang Y H. Steady-state probability algorithm and performance analysis of the finite buffer discrete-time GI/Geom/1/N queueing system with working vacations[J]. Systems Engineering — Theory & Practice, 2009, 29(9): 99-107.
[4] Alfa A S, He Q. Algorithmic analysis of the discrete time GIX/GY/1 queuing system[J]. Performance Evaluation, 2008, 65(9): 623-640.
[5] Luh H. Derivation of the N-step interdeparture time distribution in GI/G/1 queuing systems[J]. European Journal of Operational Research, 1999, 118(1): 194-212.
[6] Kramer S B, Assad A A. Alternating priority versus FCFS scheduling in a two-class queuing system[J]. Operations Research Letters, 2012, 40(6): 506-509.
[7] 冯允成,邹志红,周泓. 离散系统仿真[M].北京:机械工业出版社, 1998.
[8] Cochran J K, Bharti A. Stochastic bed balancing of an obstetrics hospital[J]. Health Care Management Science, 2006, 9(1): 31-45.
[9] Akcali E, Côté M J, Lin C. A network flow approach to optimizing hospital bed capacity decisions[J]. Health Care Management Science, 2006, 9(4): 391-404.
[10] Ni Z W, Lu X C, Liu D Y. Simulation of queuing systems with different queuing disciplines based on any logic[C]// International Conference on Electronic Commerce and Business Intelligence, IEEE, 2009: 164-167.
[11] Wang T, Guinet A, Belaidi A, et al. Modeling and simulation of emergency services with ARIS and Arena case study: The emergency department of Saint Joseph and Saint Luc Hospital[J]. Production Planning and Control, 2009, 20(6): 484-495.
[12] Goldsmith D, Siegel M. Improving health care management through the use of dynamic simulation modeling and health information systems[J]. International Journal of Information Technologies and Systems Approach, 2012, 5(1): 19-36.
[13] Holland J H. Adaptation in natural and artificial systems[M]. Michigan: University of Michigan Press, 1975.
国家自然科学基金(91224007)
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