市区行李值机服务移动站点优化方法

胡小兵; 张雪梅; 周航; 马一鸣

doi:10.3963/j.jssn.1674-4861.2022.03.014

市区行李值机服务移动站点优化方法

doi: 10.3963/j.jssn.1674-4861.2022.03.014

胡小兵^{1, 2,},
张雪梅^{1, 2},
周航^{1, 3, ,},
马一鸣^{1, 3}

1.
中国民航大学体系安全和智能决策实验室天津 300300
2.
中国民航大学安全科学与工程学院天津 300300
3.
中国民航大学中欧航空工程师学院天津 300300

基金项目:

国家自然科学基金项目 61472041

天津市教委科研计划项目 2020KJ037

详细信息

作者简介:
胡小兵（1975—），博士，教授. 研究方向：复杂网络系统模型，民航安全与应急管理.E-mail：huxbtg@163.com

通讯作者:
周航（1990—），博士，讲师. 研究方向：计算智能，空管智能决策，计算电磁学.E-mail：enzo-zhouhang@hotmail.com

中图分类号: U121
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出版历程
- 收稿日期: 2021-10-21
- 网络出版日期: 2022-07-25

A Method for Improved Air Luggage Check-in Service Based on Optimized Urban Mobile Stations

HU Xiaobing^{1, 2
,},
ZHANG Xuemei^{1, 2},
ZHOU Hang^{1, 3
, ,},
MA Yiming^{1, 3}

1.
Laboratory of Complex System Safety and Intelligent Decisions, Civil Aviation University of China, Tianjin 300300, China
2.
College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
3.
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 为提高航空运输的服务质量和竞争力，克服传统城市候机楼在服务范围有限、成本高和选址难度高等弊端，提出1种基于市区移动站点（UMS）的航空旅客行李值机服务模式。UMS基于乘客的实时位置分布差异来动态调配移动站点在城市的位置，因此需要解决UMS站点布局优化问题。综合考虑乘客到服务站点的平均路径长度和乘客最大可接受距离等2个重要指标，基于服务站点位置、不同时段的客源分布和站点的最大服务容量等限制因素对2个重要指标进行约束，建立基于路网的UMS布局优化的数学模型。为满足UMS服务模式对优化运算时效性的严格要求，提出1种混合智能优化算法，采用涟漪扩散算法（RSA）求解乘客与UMS站点多对多路径优化问题，采用自适应遗传算法（AGA）高效优化UMS位置分布。以天津城市路网的实际案例与随机生成测试案例对市区移动站点和城市候机楼2种模式的各服务时段的服务质量进行比较。结果显示：在相同站点数量的情况下，乘客到服务站点的平均路径长度比城市候机楼模式减小30.9%，超出乘客的可接受路径长度比城市候机楼模式减少43.7%；UMS位置分布优化使用混合算法（RSA-AGA），其平均计算时间为377 s，比城市候机楼模式所需的平均计算时间减少了41.2%；UMS服务模式在不同站点数量和随机生成测试案例中，各项优化目标均优于城市候机楼模式，更符合乘客的实时需求，验证了UMS运营模式的优越性。
- 智能交通 /
- 城市候机楼 /
- 市区移动值机站点 /
- 涟漪扩散算法 /
- 自适应遗传算法 /
- 行李值机服务
Abstract: To enhance the quality and competitiveness of air transport service and overcome the limitations of low service coverage, high costs, and complex site selection of traditional air terminals, this paper proposes a novel method for improved air luggage check-in service based on Urban Mobile Stations (UMS). Specifically, the proposed UMS can adapt the check-in locations to the real-time passenger positions, which is formulated as a UMS dynamic siting optimization problem over the road network. The average distance and the maximal acceptable distance from passengers to UMS are considered, incorporating the constraints on the locations of service, time-varying distribution of passengers, and the service capacity of stations. Then, a hybrid optimization algorithm satisfying the requirement of real-time computation is developed, which combines the ripple spreading algorithm (RSA) and the adaptive genetic algorithm (AGA). The RSA is used to solve the many-to-many path optimization problem of passenger and UMS stations, and the AGA is employed to optimize the UMS locations. Case studies based on the road network of Tianjin City and simulated random road networks are used for the comparison between the proposed method and the traditional method. The results show that the average distances from passengers to stations are reduced by 30.9%, the number of scenarios exceeding the maximum acceptable distance is decreased by 43.7%, and the average running time of solving the UMS optimization problem is shortened by 41.2% when using the proposed method. These facts show the advantages of the proposed UMS method, meeting the real-time passengers' demands.
- intelligent transportation /
- city air terminal /
- urban mobile station /
- ripple spreading algorithm /
- adaptive genetic algorithm /
- luggage check-in service

HTML全文

图 1 市区移动站点服务模式

Figure 1. Illustration of the urban mobile station service mode

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图 2 UMS服务模式在服务时段13：00—15：00的运行过程

Figure 2. Operation process of UMS service mode during service time 13:00—15:00

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图 3 RSA计算多对多路径优化问题流程图

Figure 3. Flowchart of RSA for calculating many-to-many optimization problem

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图 4 RSA求解多对多路径优化问题的过程

Figure 4. The process of RSA solving many-to-many path optimization problem

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图 5 1条染色体结构图（例如：N_UMSt = 3并且选择6、9和21标记的节点作为UMS位置）

Figure 5. Structure of one chromosome (e. g. N_UMSt = 3 and the nodes marked by 6, 9, and 21 are chosen as the UMS locations)

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图 6 两阶段自适应遗传算法的结构

Figure 6. Illustration of the two-stage adaptive genetic algorithm

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图 7 改变突变和均匀交叉可能性的两阶段自适应遗传算法的流程图

Figure 7. Flowchart of the two-stage adaptive genetic algorithm varying the mutation and uniform crossover possibilities

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图 8 2019年4月25日天津市路网及客源位置示意图

Figure 8. Schematic diagram of Tianjin's urban route network and passenger source location on April 25, 2019

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图 9 随机生成网络和乘客分布的测试中UMS和CAT的最佳站点位置

Figure 9. Optimal station locations of UMS and CAT in a test with randomly generated network and passenger distribution

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表 1 3种混合方法的平均计算时间

Table 1. Average calculation time of the three hybrid methods

类型	平均计算时间/(s)
类型	RSA-GA	D-GA	A^*-GA
多对多路径优化算法	1 058.0	3 942.0	7 726.0
GA	21.5	23.9	29.1

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表 2 AGA和SA在不同乘客分布情况下进行100次试验结果

Table 2. Results of AGA and SA with 100 different passenger source distributions

类型	C_BOF^f /km	C₁ /km	C₂ /km	计算时间/s
AGA	8.89	6.52	2 370.58	557.85
SA	10.23	6.71	3 529.14	1 612.06

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表 3 具有恒定数量的UMS（N_UMS = 3）的结果

Table 3. Results with a constant number of UMS(N_UMS = 3)

服务时段	C_BOF^f /km	C₁ /km	C₂ /km	计算时间/s
05：00—07：00	4.16	3.08	1 081	389
07：00—09：00	2.78	2.39	392	393
09：00—11：00	2.36	1.79	574	391
11：00—13：00	2.78	2.27	512	378
13：00—15：00	2.23	1.97	262	386
15：00—17：00	3.16	1.76	1 397	392
17：00—19：00	3.19	2.06	1 126	417
19：00—21：00	2.80	2.53	268	387

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表 4 CAT不同站点数量情况下的结果

Table 4. Results of cases with different station numbers of CAT

站点数量	C_BOF^f /km	C₁ /km	C₂ /km	计算时间/s
3	4.48	3.23	1245	663
4	3.07	2.46	613	648
5	2.67	2.38	289	597
6	2.28	2.05	225	617
7	2.00	1.89	111	634
8	1.77	1.71	59	652
9	1.69	1.68	6	679

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表 5 UMS可变数量的结果（C_BOF^f < 3 km和C1 < 2 km）

Table 5. Results with a variable number of UMS (C_BOF^f < 3 km and C₁ < 2 km)

服务时间段	站点数量	C_BOF^f/km	C₁ /km	C₂ /km	计算时间/s
05：00—07：00	6	1.94	1.92	16	369
07：00—09：00	4	1.93	1.81	115	353
09：00—11：00	3	2.36	1.79	569	382
11：00—13：00	4	1.96	1.58	384	411
13：00—15：00	3	2.20	1.94	261	396
15：00—17：00	4	1.71	1.42	288	393
17：00—19：00	4	2.10	1.77	329	362
19：00—21：00	5	1.91	1.91	0	347

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表 6 UMS和CAT的调查和运营成本

Table 6. Investigation and operation costs of UMS and CAT

类型	城市候机楼		市区移动站点
资本投资（单个站点）/百万元	38.1~317.9（建造成本：5 000~40 000 m²）	6.4~25.6（年租金：2 000~10 000 m²）	2（特种车辆价格）
年度运营成本（单个站点）/百万元	6.4~63.6	3.2~16.4	≈0.6
站点数量	3~6	3~6	12~18
总成本（20年）/百万元	498.3~9 539.4	576.0~5 040.0	168.0~252.0

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表 7 具有恒定数量的UMS和随机乘客分布的结果(N=3)

Table 7. Results with constant number of UMS with random passenger distributions (N=3)

模式		C_OF /(km)	C₁ /(km)	C₂ /(km)	计算时间/(s)
市区移动站点	服务时段1	5.70	4.48	1215	639
	服务时段2	5.61	4.31	1298	627
	服务时段3	5.67	4.34	1330	637
	服务时段4	5.82	4.45	1371	641
	服务时段5	5.55	4.31	1236	648
	服务时段6	5.57	4.34	1229	638
	服务时段7	5.35	4.15	1204	631
	服务时段8	5.53	4.29	1239	637
	平均	5.60	4.33	1265	637
城市候机楼		7.48	4.56	2918	681

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