Simulation modeling and analysis of check-in process based on Petri network under epidemic
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摘要:
疫情下机场航站楼增设的健康检测环节使值机流程用时变久，效率低下. 对某机场航站楼值机流程现状分析，建立基于Petri网值机流程的过程模型，可靠性试验验证模型的正确性. 研究发现：1）自助值机、网上值机和柜台值机方式均使旅客托运行李的时间过长；2）与柜台值机相比，其他两种值机方式旅客等待服务的时间更短，更多旅客选择. 最后提出通过增设自助托运设备或值机柜台的方式减少旅客排队等待时间，缓解和改善值机流程中的瓶颈环节.
Abstract:Under the normalization of the epidemic, the additional health detection link in the airport terminal has made the check-in process longer and inefficient. On the basis of analysis for current situation of check-in process of an airport terminal in China, a process model based on Petri network check-in process was established, and the correctness of the model was verified by reliability test. It is found that: 1) Self-service check-in, online check-in and counter check-in all make passengers check in their baggage for too long; 2) Compared with counter check-in, the other two check-in methods have a shorter waiting time for passengers to serve and more passengers choose. Finally, it is proposed to reduce the waiting time of passengers in line by adding self-service check-in machines or check-in counters, so as to alleviate and improve the bottleneck in the check-in process.
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Key words:
- terminal building /
- Petri network /
- check-in process /
- performance analysis /
- epidemic normalization
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图 4 与柜台值机过程
${{\rm{GSPN}}}{{{'}}}$ 模型同构的MCFigure 4. MC isomorphic to counter value process
$ {{\rm{GSPN}}}{{{'}}} $ model
图 5 与自助值机过程
$ {{\rm{GSPN}}}' $ 模型同构的MCFigure 5. MC isomorphic to self-service check-in process
$ {{\rm{GSPN}}}' $ model
图 6 与网上值机过程
$ {{\rm{GSPN}}}' $ 模型同构的MC（自助打印）Figure 6. MC (self-printing) isomorphic to online check-in process
$ {{\rm{GSPN}}}' $ model
图 7 与网上值机过程
$ {{\rm{GSPN}}}' $ 模型同构的MC（人工打印）Figure 7. MC (manually printed) isomorphic to online check-in process
$ {{\rm{GSPN}}}' $ model表 1 柜台值机过程稳定状态时的概率分布
Table 1. Probability distribution at steady state of counter check-in process
变量 概率值 变量 概率值 疫情前 疫情下 疫情前 疫情下 X1 0.03563 0.02473 X36 0.00157 0.00099 X2 — 0.14548 X37 0.00734 0.01027 X3 0.00419 0.00330 X38 0.00090 0.00097 X5 0.00150 0.00165 X39 0.00074 0.00069 X6 0.00321 0.00317 X40 0.00154 0.00129 X7 0.00121 0.00211 X41 0.00093 0.00076 X8 0.00005 0.00010 X42 0.00009 0.00007 X9 0.00001 0.00004 X43 0.00006 0.00003 X10 0.000005 0.00002 X45 0.06538 0.00469 X11 0.0000004 0.000002 X46 0.00241 0.00151 X12 0.00000001 0.00000006 X47 0.09024 0.11321 X23 0.00272 0.00153 X48 0.01417 0.01756 X25 0.02219 0.01555 X49 0.00761 0.00927 X26 0.01190 0.00758 X50 0.00808 0.00915 X27 0.03485 0.02650 X51 0.00286 0.00284 X28 0.00397 0.00310 X52 0.00022 0.00020 X29 0.00311 0.00246 X53 0.00761 0.00927 X30 0.00625 0.00499 X54 0.07322 0.08595 X31 0.00376 0.00300 X55 0.25971 0.29757 X32 0.00034 0.00027 X56 0.03754 0.04291 X33 0.00016 0.00009 X57 0.05890 0.01830 X35 0.04565 0.00319 X58 0.17818 0.12366 
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表 2 柜台值机过程各库所的平均托肯
Table 2. Average tokens for each depot of counter check-in process
库所 平均托肯数 库所 平均托肯数 疫情前 疫情下 疫情前 疫情下 i 0.03563 0.02473 P21 0.170265 0.060132 P1 — 0.14548 P22 0.01909 0.01325 P2 0.010175 0.010392 P23 0.13364 0.15209 P3 0 0 P25 0.01909 0.02173 P14 0.08909 0.06498 P26 0.01908 0.02173 P16 0.05892 0.01832 P27 0.089095 0.1014 P17 0.19103 0.15846 P28 0.26726 0.304172 P18 0.43698 0.454 P30 0.03819 0.04345 P19 0.00713 0.00495 P31 0.19362 0.04338 P20 0 0 o 0.17818 0.12366
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表 3 柜台值机过程各变迁的利用率
Table 3. Utilization rates for each variation of counter check-in process
变迁 利用率 变迁 利用率 疫情前 疫情下 疫情前 疫情下 t1 0.03563 0.02473 tTC 0.13364 0.15209 t2 — 0.14548 t27 0.01909 0.02173 t3 0.010175 0.010392 t28 0.01908 0.02173 tTB 0.08909 0.06498 t29 0.089095 0.1014 t18 0.05892 0.01832 tTD 0.26726 0.304172 t19 0.08415 0.02615 t33 0.03819 0.04345 t20 0.00713 0.00495 t34 0.0589 0.0183 t23 0.01909 0.01325 tA 0.17818 0.12366
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[1] 顾广辉, 许俐, 夏蔷薇. 航站楼离港流程仿真研究[J] . 科技信息,2011,33(2):91 − 93. doi: 10.3969/j.issn.1001-9960.2011.33.408 [2] 李耐毅. 航站楼离港航班动态性仿真研究[J] . 科技前沿,2012,6(2):40 − 41. [3] SOEMON T, TOMOKI O. Simulation analysis of international departure passenger flow in an airport terminal[C]// Proceedings of the 2003 Winter Simulation Conference. New Orleans: IEEE, 2003. [4] 何川. 基于改进K近邻算法的航站楼离港旅客到达规律研究[D]. 天津: 中国民航大学, 2019. [5] 朱正月. 基于层次颜色Petri网的z仓储作业系统建模与仿真[J] . 安庆师范大学学报:自然科学版,2020,26(4):72 − 79. [6] 肖军, 邓海鸿, 高允锋, 等. 基于Petri网的工作流建模及时间性能分析[J] . 吉林大学学报:信息科学版,2009,27(1):104 − 112. [7] 王晶, 胡昊, 余萍, 等. 结合公共视图和对象Petri网的跨组织流程建模[J] . 计算机科学与探索,2014,8(1):18 − 27. doi: 10.3778/j.issn.1673-9418.1306047 [8] 文军. 基于灰色马尔可夫链模型的航空货运量预测研究[J] . 武汉理工大学学报:交通科学与工程版,2010,34(4):695 − 698. [9] 李建莲. 基于Petri网的智能控制系统建模仿真与性能分析[D]. 南昌: 江西师范大学, 2011. [10] 王洁宁, 王安国, 董健康, 等. 基于Agent的机坪服务SPN建模与仿真[J] . 计算机工程,2013,39(3):285 − 288. [11] 杨扬. 人工值机柜台动态分配策略研究[D]. 天津: 中国民航大学, 2017. [12] 陆迅, 唐小卫, 朱金福. 航站楼旅客离港流程仿真研究[J] . 西南交通大学学报,2009,44(1):135 − 140. doi: 10.3969/j.issn.0258-2724.2009.01.025 -
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