Figure 1.
Schematic Diagram of the Post-Earthquake Emergency Logistics System
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January
2022, 18(1): 45-73.
doi: 10.3934/jimo.2020142
The joint location-transportation model based on grey bi-level programming for early post-earthquake relief
| 1. | Chongqing Engineering Technology Research Center for Information Management in, Development, Chongqing Technology and Business University, Chongqing, 400067, China |
| 2. | School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China |
| 3. | Research center for economy of upper researches of the Yangtze River, Chongqing Technology, and Business University, Chongqing, 400067, China |
The initial period after the earthquake is the prime time for disaster relief. During this period, it is of great value to rationally locate the transfer facilities of relief materials and effectively arrange the transportation of relief materials. Considering the characteristics of the two-level emergency logistics system including uncertain demand, uncertain transportation time, multiple varieties of relief materials, shortage of supply, multi-transportation modes and different urgencies of relief material demand, the integrated issue with the concern of transfer facility location and relief material transportation is studied. Then, this problem is formulated as a grey mixed integer bi-level nonlinear programming in which the upper-level aims at the shortest relief material transportation time and the lower-level aims at the maximum fairness of relief material distribution. According to the characteristics of the model, a hybrid genetic algorithm is designed to solve the proposed model. Finally, a numerical simulation is carried out on the background of 5.12 Wenchuan Earthquake. In addition, the validation of the proposed model and algorithm is verified.
Keywords:
Earthquake disaster,
joint location-transportation problem,
grey bi-level programming,
facility location,
hybrid genetic algorithm.
Mathematics Subject Classification:
Primary: 90B50; Secondary: 90B06.
Citation:
Yufeng Zhou, Bin Zheng, Jiafu Su, Yufeng Li. The joint location-transportation model based on grey bi-level programming for early post-earthquake relief. Journal of Industrial and Management Optimization,
2022, 18
(1)
: 45-73.
doi: 10.3934/jimo.2020142
![]()
References:
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A. Afshar and A. Haghani,
Modeling integrated supply chain logistics in real-time large-scale disaster relief operations, Socio-Economic Plan. Sci., 46 (2012), 327-338.
doi: 10.1016/j.seps.2011.12.003. |
| [2] |
J. F. Bard,
Some properties of the bilevel programming problem, J. Optim. Theory Appl., 68 (1991), 371-378.
doi: 10.1007/BF00941574. |
| [3] |
V. Bélanger, A. Ruiz and P. Soriano,
Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles, European J. Oper. Res., 272 (2019), 1-23.
doi: 10.1016/j.ejor.2018.02.055. |
| [4] |
C. Blair,
The computational complexity of multi-level linear programs, Ann. Oper. Res., 34 (1992), 13-19.
doi: 10.1007/BF02098170. |
| [5] |
A. Bozorgi-Amiri and M. Khorsi,
A dynamic multi-objective location-routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters, The International Journal of Advanced Manufacturing Technology, 85 (2016), 1633-1648.
|
| [6] |
C. J. Cao, C. D. Li, Q. Yang, Y. Liu and T. Qu,
A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters, J. Cleaner Production, 174 (2018), 1422-1435.
doi: 10.1016/j.jclepro.2017.11.037. |
| [7] |
A. Y. Chen and T. Y. Yu,
Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response, Transport Res. B. Meth., 91 (2016), 408-423.
doi: 10.1016/j.trb.2016.06.004. |
| [8] |
Y. Dai, Z. J. Ma, D. L. Zhu and T. Fang,
Fuzzy dynamic location-routing problem in post-earthquake delivery of relief materials, Chinese Journal of Management Science, 15 (2012), 60-70.
|
| [9] |
X. T. Deng, Complexity issues in bi-level linear programming, in Multilevel Optimization: Algorithms and Applications, Vol. 20, Kluwer Acad. Publ., Dordrecht, 1998,149–164.
doi: 10.1007/978-1-4613-0307-7_6. |
| [10] |
M. Haghi, S. M. T. F. Ghomi and F. Jolai,
Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource, J. Cleaner Production, 154 (2017), 188-202.
doi: 10.1016/j.jclepro.2017.03.102. |
| [11] |
H. Hu, X. Li, Y. Y. Zhang, C. J. Shang and S. C. Zhang,
Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads, Comput. Ind. Eng., 128 (2019), 861-876.
doi: 10.1016/j.cie.2018.10.044. |
| [12] |
J. Jian, Y. Zhang, L. Jiang and J. Su, Coordination of supply chains with competing manufacturers considering fairness concerns, Complexity, 2020 (2020), 4372603.
doi: 10.1155/2020/4372603. |
| [13] |
S. Li and K. L. Teo,
Post-disaster multi-period road network repair: Work scheduling and relief logistics optimization, Ann. Oper. Res., 283 (2019), 1345-1385.
doi: 10.1007/s10479-018-3037-2. |
| [14] |
S. Li, Z. J. Ma and K. L. Teo, A new model for road network repair after natural disaster: Integrating logistics support scheduling with repair crew scheduling and routing activities, Comput. Ind. Eng., 145 (2020), 106506. |
| [15] |
S. L. Li, Z. J. Ma, B. Zheng and Y. Dai,
Fuzzy multi-objective location-multimodal transportation problem for relief delivery during the initial post-earthquake period, Chinese Journal of Management Science, 21 (2013), 144-151.
|
| [16] |
C. S. Liu, L. Luo, X. C. Zhou and F. H. Huang,
Collaborative decision-making of relief allocation-transportation in early post-earthquake: Considering both fairness and efficiency, Control and Decision, 33 (2018), 2057-2063.
|
| [17] |
C. S. Liu, G. Kou, Y. Peng and F. E. Alsaadi, Location-routing problem for relief distribution in the early post-earthquake stage from the perspective of fairness, Sustainability, 11 (2019), 3420.
doi: 10.3390/su11123420. |
| [18] |
R. Lotfian and M. Najafi,
Optimal location of emergency stations in underground mine networks using a multiobjective mathematical model, Injury Prevention, 25 (2019), 264-272.
doi: 10.1136/injuryprev-2017-042657. |
| [19] |
H. M. Rizeei, B. Pradhan and M. A. Saharkhiz, Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models, Int. J. Disast. Risk. Red., 38 (2019), 101205.
doi: 10.1016/j.ijdrr.2019.101205. |
| [20] |
A. S. Safaei, S. Farsad and M. M. Paydar,
Robust bi-level optimization of relief logistics operations, Appl. Math. Model., 56 (2018), 359-380.
doi: 10.1016/j.apm.2017.12.003. |
| [21] |
A. S. Safaei, S. Farsad and M. M. Paydar,
Emergency logistics planning under supply risk and demand uncertainty, Oper. Res., 20 (2020), 1437-1460.
doi: 10.1007/s12351-018-0376-3. |
| [22] |
F. S. Salman and E. Yücel,
Emergency facility location under random network damage: Insights from the Istanbul case, Comput. Oper. Res., 62 (2015), 266-281.
doi: 10.1016/j.cor.2014.07.015. |
| [23] |
J. Su, Q. Bai, S. Sindakis, X Zhang and T Yang, Vulnerability of multinational corporation knowledge network facing resource loss, Management Decision, 2020.
doi: 10.1108/MD-02-2019-0227. |
| [24] |
B. Vahdani, D. Veysmoradi, N. Shekari and S. M. Mousavi,
Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair, Neural Comput. Appl., 30 (2018), 835-854.
doi: 10.1007/s00521-016-2696-7. |
| [25] |
B. Vahdani, D. Veysmoradi, F. Noori and F. Mansour,
Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty, Int. J. Disast. Risk. Re., 27 (2018), 290-306.
|
| [26] |
H. J. Wang, L. J. Du and S. H. Ma,
Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake, Transport Research Part E: Logistics and Transportation Review, 69 (2014), 160-179.
doi: 10.1016/j.tre.2014.06.006. |
| [27] |
J. Xu, Z. Wang, M. Zhang and Y. Tu,
A new model for a 72-h post-earthquake emergency logistics location-routing problem under a random fuzzy environment, Transportation Letters, 8 (2016), 270-285.
doi: 10.1080/19427867.2015.1126064. |
| [28] |
M. Yahyaei and A. Bozorgi-Amiri,
Robust reliable humanitarian relief network design: An integration of shelter and supply facility location, Ann. Oper. Res., 283 (2019), 897-916.
doi: 10.1007/s10479-018-2758-6. |
| [29] |
W. Yi and L. Özdamar,
A dynamic logistics coordination model for evacuation and support in disaster response activities, European J. Oper. Res., 179 (2007), 1177-1193.
doi: 10.1016/j.ejor.2005.03.077. |
| [30] |
B. Zeng, M. Tong and X. Ma,
A new-structure grey Verhulst model: Development and performance comparison, Appl. Math. Model., 81 (2020), 522-537.
doi: 10.1016/j.apm.2020.01.014. |
| [31] |
B. Zeng, M. Zhou, X. Liu and and Z. Zhang,
Application of a new grey prediction model and grey average weakening buffer operator to forecast China's shale gas output, Energy Reports, 6 (2020), 1608-1618.
|
| [32] |
S. Zhang and H. Yi, Emergency material transportation considering the impact of secondary disasters, in Proceedings of the 2019 10th International Conference on E-business, Management and Economics, 2019,279–285.
doi: 10.1145/3345035.3345041. |
| [33] |
S. W. Zhang, H. X. Guo, K. J. Zhu, S. W. Yu and J. L. Li,
Multistage assignment optimization for emergency rescue teams in the disaster chain, Knowledge-Based Systems, 137 (2017), 123-137.
doi: 10.1016/j.knosys.2017.09.024. |
| [34] |
B. Zheng, Z. J. Ma and Y. F. Zhou,
Bi-level model for dynamic location-transportation problem for post earthquake relief distribution, J. Systems & Management, 26 (2017), 326-337.
|
| [35] |
Y. Zhou and N. Chen,
The LAP under facility disruptions during early post-earthquake rescue using PSO-GA hybrid algorithm, Fresen. Environ. Bull., 28 (2019), 9906-9914.
|
| [36] |
Y. F. Zhou, H. X. Yu, Z. Li and J. F. Su,
Robust optimization of a distribution network location-routing problem under carbon trading policies, IEEE Access, 8 (2020), 46288-46306.
doi: 10.1109/ACCESS.2020.2979259. |
| [37] |
Y. Zhou, L. Yufeng and L. Zhi, A grey target group decision method with dual hesitant fuzzy information considering decision-maker's loss aversion, Sci. Programming, 2020 (2020), 8930387.
doi: 10.1155/2020/8930387. |
show all references
References:
| [1] |
A. Afshar and A. Haghani,
Modeling integrated supply chain logistics in real-time large-scale disaster relief operations, Socio-Economic Plan. Sci., 46 (2012), 327-338.
doi: 10.1016/j.seps.2011.12.003. |
| [2] |
J. F. Bard,
Some properties of the bilevel programming problem, J. Optim. Theory Appl., 68 (1991), 371-378.
doi: 10.1007/BF00941574. |
| [3] |
V. Bélanger, A. Ruiz and P. Soriano,
Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles, European J. Oper. Res., 272 (2019), 1-23.
doi: 10.1016/j.ejor.2018.02.055. |
| [4] |
C. Blair,
The computational complexity of multi-level linear programs, Ann. Oper. Res., 34 (1992), 13-19.
doi: 10.1007/BF02098170. |
| [5] |
A. Bozorgi-Amiri and M. Khorsi,
A dynamic multi-objective location-routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters, The International Journal of Advanced Manufacturing Technology, 85 (2016), 1633-1648.
|
| [6] |
C. J. Cao, C. D. Li, Q. Yang, Y. Liu and T. Qu,
A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters, J. Cleaner Production, 174 (2018), 1422-1435.
doi: 10.1016/j.jclepro.2017.11.037. |
| [7] |
A. Y. Chen and T. Y. Yu,
Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response, Transport Res. B. Meth., 91 (2016), 408-423.
doi: 10.1016/j.trb.2016.06.004. |
| [8] |
Y. Dai, Z. J. Ma, D. L. Zhu and T. Fang,
Fuzzy dynamic location-routing problem in post-earthquake delivery of relief materials, Chinese Journal of Management Science, 15 (2012), 60-70.
|
| [9] |
X. T. Deng, Complexity issues in bi-level linear programming, in Multilevel Optimization: Algorithms and Applications, Vol. 20, Kluwer Acad. Publ., Dordrecht, 1998,149–164.
doi: 10.1007/978-1-4613-0307-7_6. |
| [10] |
M. Haghi, S. M. T. F. Ghomi and F. Jolai,
Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource, J. Cleaner Production, 154 (2017), 188-202.
doi: 10.1016/j.jclepro.2017.03.102. |
| [11] |
H. Hu, X. Li, Y. Y. Zhang, C. J. Shang and S. C. Zhang,
Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads, Comput. Ind. Eng., 128 (2019), 861-876.
doi: 10.1016/j.cie.2018.10.044. |
| [12] |
J. Jian, Y. Zhang, L. Jiang and J. Su, Coordination of supply chains with competing manufacturers considering fairness concerns, Complexity, 2020 (2020), 4372603.
doi: 10.1155/2020/4372603. |
| [13] |
S. Li and K. L. Teo,
Post-disaster multi-period road network repair: Work scheduling and relief logistics optimization, Ann. Oper. Res., 283 (2019), 1345-1385.
doi: 10.1007/s10479-018-3037-2. |
| [14] |
S. Li, Z. J. Ma and K. L. Teo, A new model for road network repair after natural disaster: Integrating logistics support scheduling with repair crew scheduling and routing activities, Comput. Ind. Eng., 145 (2020), 106506. |
| [15] |
S. L. Li, Z. J. Ma, B. Zheng and Y. Dai,
Fuzzy multi-objective location-multimodal transportation problem for relief delivery during the initial post-earthquake period, Chinese Journal of Management Science, 21 (2013), 144-151.
|
| [16] |
C. S. Liu, L. Luo, X. C. Zhou and F. H. Huang,
Collaborative decision-making of relief allocation-transportation in early post-earthquake: Considering both fairness and efficiency, Control and Decision, 33 (2018), 2057-2063.
|
| [17] |
C. S. Liu, G. Kou, Y. Peng and F. E. Alsaadi, Location-routing problem for relief distribution in the early post-earthquake stage from the perspective of fairness, Sustainability, 11 (2019), 3420.
doi: 10.3390/su11123420. |
| [18] |
R. Lotfian and M. Najafi,
Optimal location of emergency stations in underground mine networks using a multiobjective mathematical model, Injury Prevention, 25 (2019), 264-272.
doi: 10.1136/injuryprev-2017-042657. |
| [19] |
H. M. Rizeei, B. Pradhan and M. A. Saharkhiz, Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models, Int. J. Disast. Risk. Red., 38 (2019), 101205.
doi: 10.1016/j.ijdrr.2019.101205. |
| [20] |
A. S. Safaei, S. Farsad and M. M. Paydar,
Robust bi-level optimization of relief logistics operations, Appl. Math. Model., 56 (2018), 359-380.
doi: 10.1016/j.apm.2017.12.003. |
| [21] |
A. S. Safaei, S. Farsad and M. M. Paydar,
Emergency logistics planning under supply risk and demand uncertainty, Oper. Res., 20 (2020), 1437-1460.
doi: 10.1007/s12351-018-0376-3. |
| [22] |
F. S. Salman and E. Yücel,
Emergency facility location under random network damage: Insights from the Istanbul case, Comput. Oper. Res., 62 (2015), 266-281.
doi: 10.1016/j.cor.2014.07.015. |
| [23] |
J. Su, Q. Bai, S. Sindakis, X Zhang and T Yang, Vulnerability of multinational corporation knowledge network facing resource loss, Management Decision, 2020.
doi: 10.1108/MD-02-2019-0227. |
| [24] |
B. Vahdani, D. Veysmoradi, N. Shekari and S. M. Mousavi,
Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair, Neural Comput. Appl., 30 (2018), 835-854.
doi: 10.1007/s00521-016-2696-7. |
| [25] |
B. Vahdani, D. Veysmoradi, F. Noori and F. Mansour,
Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty, Int. J. Disast. Risk. Re., 27 (2018), 290-306.
|
| [26] |
H. J. Wang, L. J. Du and S. H. Ma,
Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake, Transport Research Part E: Logistics and Transportation Review, 69 (2014), 160-179.
doi: 10.1016/j.tre.2014.06.006. |
| [27] |
J. Xu, Z. Wang, M. Zhang and Y. Tu,
A new model for a 72-h post-earthquake emergency logistics location-routing problem under a random fuzzy environment, Transportation Letters, 8 (2016), 270-285.
doi: 10.1080/19427867.2015.1126064. |
| [28] |
M. Yahyaei and A. Bozorgi-Amiri,
Robust reliable humanitarian relief network design: An integration of shelter and supply facility location, Ann. Oper. Res., 283 (2019), 897-916.
doi: 10.1007/s10479-018-2758-6. |
| [29] |
W. Yi and L. Özdamar,
A dynamic logistics coordination model for evacuation and support in disaster response activities, European J. Oper. Res., 179 (2007), 1177-1193.
doi: 10.1016/j.ejor.2005.03.077. |
| [30] |
B. Zeng, M. Tong and X. Ma,
A new-structure grey Verhulst model: Development and performance comparison, Appl. Math. Model., 81 (2020), 522-537.
doi: 10.1016/j.apm.2020.01.014. |
| [31] |
B. Zeng, M. Zhou, X. Liu and and Z. Zhang,
Application of a new grey prediction model and grey average weakening buffer operator to forecast China's shale gas output, Energy Reports, 6 (2020), 1608-1618.
|
| [32] |
S. Zhang and H. Yi, Emergency material transportation considering the impact of secondary disasters, in Proceedings of the 2019 10th International Conference on E-business, Management and Economics, 2019,279–285.
doi: 10.1145/3345035.3345041. |
| [33] |
S. W. Zhang, H. X. Guo, K. J. Zhu, S. W. Yu and J. L. Li,
Multistage assignment optimization for emergency rescue teams in the disaster chain, Knowledge-Based Systems, 137 (2017), 123-137.
doi: 10.1016/j.knosys.2017.09.024. |
| [34] |
B. Zheng, Z. J. Ma and Y. F. Zhou,
Bi-level model for dynamic location-transportation problem for post earthquake relief distribution, J. Systems & Management, 26 (2017), 326-337.
|
| [35] |
Y. Zhou and N. Chen,
The LAP under facility disruptions during early post-earthquake rescue using PSO-GA hybrid algorithm, Fresen. Environ. Bull., 28 (2019), 9906-9914.
|
| [36] |
Y. F. Zhou, H. X. Yu, Z. Li and J. F. Su,
Robust optimization of a distribution network location-routing problem under carbon trading policies, IEEE Access, 8 (2020), 46288-46306.
doi: 10.1109/ACCESS.2020.2979259. |
| [37] |
Y. Zhou, L. Yufeng and L. Zhi, A grey target group decision method with dual hesitant fuzzy information considering decision-maker's loss aversion, Sci. Programming, 2020 (2020), 8930387.
doi: 10.1155/2020/8930387. |
Figure 2.
Relief materials from different temporary transfer facilities
Figure 3.
Relief materials from the same temporary transfer facility and do not need waiting
Figure 4.
Relief materials from the same temporary transfer facility and need waiting
Figure 5.
Flow Chart of Genetic Algorithm
Figure 6.
Schematic diagram of chromosome coding
Figure 7.
Operational processes of decision-making in upper-level and lower-level
Figure 8.
Schematic of arithmetic crossover operator
Figure 9.
Schematic diagram of partial matching arithmetic crossover
Figure 10.
Convergence diagram of hybrid genetic algorithm
Table 1.
The amount of relief materials supplied by collection centers
| Num. | Collection centers | Food(Units) | Daily necessities(Units) |
| Ⅰ | Chengdu Military Airport | 200000 | 90000 |
| Ⅱ | Shuangliu Airport | 800000 | 100000 |
| Ⅲ | Chengdu North Railway Station | 1200000 | 130000 |
| Num. | Collection centers | Food(Units) | Daily necessities(Units) |
| Ⅰ | Chengdu Military Airport | 200000 | 90000 |
| Ⅱ | Shuangliu Airport | 800000 | 100000 |
| Ⅲ | Chengdu North Railway Station | 1200000 | 130000 |
Table 2.
The amount of relief materials supplied by collection centers
| Candidate temporary transfer facilities | Num | Affected area | Candidate temporary transfer facilities | Num. | Affected area | ||
| Dujiangyan | (1) | ① | 800 | Pingwu | (10) | ② | 700 |
| Maoxian | (2) | ① | 800 | Jiangyou | (11) | ② | 800 |
| Pengzhou | (3) | ① | 800 | Deyang | (12) | ② | 1500 |
| Wenchuan | (4) | ① | 500 | Mianyang | (13) | ② | 1500 |
| Jiuzhaigou | (5) | ① | 1000 | Guanghan | (14) | ② | 1300 |
| Chongzhou | (6) | ① | 800 | Guangyuan | (15) | ③ | 1500 |
| Dayi | (7) | ① | 1200 | Qingchuan | (16) | ③ | 1000 |
| Shifang | (8) | ② | 1000 | Wangcang | (17) | ③ | 1000 |
| Beichuan | (9) | ② | 800 | Jiange | (18) | ③ | 1000 |
| Candidate temporary transfer facilities | Num | Affected area | Candidate temporary transfer facilities | Num. | Affected area | ||
| Dujiangyan | (1) | ① | 800 | Pingwu | (10) | ② | 700 |
| Maoxian | (2) | ① | 800 | Jiangyou | (11) | ② | 800 |
| Pengzhou | (3) | ① | 800 | Deyang | (12) | ② | 1500 |
| Wenchuan | (4) | ① | 500 | Mianyang | (13) | ② | 1500 |
| Jiuzhaigou | (5) | ① | 1000 | Guanghan | (14) | ② | 1300 |
| Chongzhou | (6) | ① | 800 | Guangyuan | (15) | ③ | 1500 |
| Dayi | (7) | ① | 1200 | Qingchuan | (16) | ③ | 1000 |
| Shifang | (8) | ② | 1000 | Wangcang | (17) | ③ | 1000 |
| Beichuan | (9) | ② | 800 | Jiange | (18) | ③ | 1000 |
Table 3.
The demand for relief materials at affected points
| Affected points | Num. | Affected area | Food(units) | Daily necessities(Units) |
| Dujiangyan | 1 | ① | [224000, 336000] | [22400, 33600] |
| Guankou | 2 | ① | [42400, 63600] | [4240, 6360] |
| Qingchengshan | 3 | ① | [38880, 58320] | [3888, 5832] |
| Zipingpu | 4 | ① | [35200, 52800] | [3520, 5280] |
| Hongkou | 5 | ① | [38400, 57600] | [3840, 5760] |
| Maoxian | 6 | ① | [173200, 259800] | [17320, 25980] |
| Fushun | 7 | ① | [28800, 43200] | [2880, 4320] |
| Feihong | 8 | ① | [30400, 45600] | [3040, 4560] |
| Heihu | 9 | ① | [32000, 48000] | [3200, 4800] |
| Taiping | 10 | ① | [25600, 38400] | [2560, 3840] |
| Lixian | 11 | ① | [171200, 256800] | [17120, 25680] |
| Putouxiang | 12 | ① | [25600, 38400] | [2560, 38400] |
| Mukaxiang | 13 | ① | [16000, 24000] | [1600, 2400] |
| Tonghuaxiang | 14 | ① | [38400, 57600] | [3840, 5760] |
| Wenchuan | 15 | ① | [246800, 370200] | [24680, 37020] |
| Yingxiu | 16 | ① | [32000, 48000] | [3200, 4800] |
| Shuimo | 17 | ① | [28800, 43200] | [2880, 4320] |
| Wolong | 18 | ① | [27200, 40800] | [2720, 4080] |
| Yanmeng | 19 | ① | [27840, 41760] | [2784, 4176] |
| Sanjiang | 20 | ① | [24640, 36960] | [2464, 3696] |
| Xiaojin | 21 | ① | [20000, 30000] | [2000, 3000] |
| Heishui | 22 | ① | [49600, 74400] | [4960, 7440] |
| Songpan | 23 | ① | [19200, 28800] | [1920, 2880] |
| Anxian | 24 | ② | [191600, 287400] | [19160, 28740] |
| Xiushui | 25 | ② | [28480, 42720] | [2848, 4272] |
| Baolin | 26 | ② | [33600, 50400] | [3360, 5040] |
| Gaochuan | 27 | ② | [18240, 27360] | [1824, 2736] |
| Shifang | 28 | ② | [192000, 288000] | [19200, 28800] |
| Luoshui | 29 | ② | [30400, 45600] | [3040, 4560] |
| Shuangsheng | 30 | ② | [41600, 62400] | [4160, 6240] |
| Yinghua | 31 | ② | [38400, 57600] | [3840, 5760] |
| Mianzhu | 32 | ② | [232400, 348600] | [23240, 34860] |
| Jiannan | 33 | ② | [28160, 42240] | [2816, 4224] |
| Mianyuan | 34 | ② | [24960, 37440] | [2496, 3744] |
| Hanwang | 35 | ② | [21760, 32640] | [2176, 3264] |
| Beichuan | 36 | ② | [242800, 364200] | [24280, 36420] |
| Yong'an | 37 | ② | [31040, 46560] | [3104, 4656] |
| Yongchang | 38 | ② | [33600, 50400] | [3360, 5040] |
| Kaiping | 39 | ② | [30400, 45600] | [3040, 4560] |
| Luojiang | 40 | ② | [69600, 104400] | [6960, 10440] |
| Zhongjiang | 41 | ② | [84800, 127200] | [8480, 12720] |
| Santai | 42 | ② | [109600, 164400] | [10960, 16440] |
| Yanting | 43 | ② | [110000, 165000] | [11000, 16500] |
| Zitong | 44 | ② | [125200, 187800] | [12520, 18780] |
| Deyang | 45 | ② | [173760, 260640] | [17376, 26064] |
| Mianyang | 46 | ② | [184000, 276000] | [18400, 27600] |
| Guangyuan | 47 | ③ | [75040, 112560] | [7504, 11256] |
| Qingchuan | 48 | ③ | [216400, 324600] | [21640, 32460] |
| Walixiang | 49 | ③ | [32960, 49440] | [3296, 4944] |
| Banqiaoxiang | 50 | ③ | [39040, 58560] | [3904, 5856] |
| Qimaxiang | 51 | ③ | [36000, 54000] | [3600, 5400] |
| Yingpanxiang | 52 | ③ | [32800, 49200] | [3280, 4920] |
| Lizhou | 53 | ③ | [71200, 106800] | [7120, 10680] |
| Chaotian | 54 | ③ | [66400, 99600] | [6640, 9960] |
| Cangxi | 55 | ③ | [125200, 187800] | [12520, 18780] |
| Jiange | 56 | ③ | [62000, 93000] | [6200, 9300] |
| Yuanba | 57 | ③ | [71200, 106800] | [7120, 10680] |
| Affected points | Num. | Affected area | Food(units) | Daily necessities(Units) |
| Dujiangyan | 1 | ① | [224000, 336000] | [22400, 33600] |
| Guankou | 2 | ① | [42400, 63600] | [4240, 6360] |
| Qingchengshan | 3 | ① | [38880, 58320] | [3888, 5832] |
| Zipingpu | 4 | ① | [35200, 52800] | [3520, 5280] |
| Hongkou | 5 | ① | [38400, 57600] | [3840, 5760] |
| Maoxian | 6 | ① | [173200, 259800] | [17320, 25980] |
| Fushun | 7 | ① | [28800, 43200] | [2880, 4320] |
| Feihong | 8 | ① | [30400, 45600] | [3040, 4560] |
| Heihu | 9 | ① | [32000, 48000] | [3200, 4800] |
| Taiping | 10 | ① | [25600, 38400] | [2560, 3840] |
| Lixian | 11 | ① | [171200, 256800] | [17120, 25680] |
| Putouxiang | 12 | ① | [25600, 38400] | [2560, 38400] |
| Mukaxiang | 13 | ① | [16000, 24000] | [1600, 2400] |
| Tonghuaxiang | 14 | ① | [38400, 57600] | [3840, 5760] |
| Wenchuan | 15 | ① | [246800, 370200] | [24680, 37020] |
| Yingxiu | 16 | ① | [32000, 48000] | [3200, 4800] |
| Shuimo | 17 | ① | [28800, 43200] | [2880, 4320] |
| Wolong | 18 | ① | [27200, 40800] | [2720, 4080] |
| Yanmeng | 19 | ① | [27840, 41760] | [2784, 4176] |
| Sanjiang | 20 | ① | [24640, 36960] | [2464, 3696] |
| Xiaojin | 21 | ① | [20000, 30000] | [2000, 3000] |
| Heishui | 22 | ① | [49600, 74400] | [4960, 7440] |
| Songpan | 23 | ① | [19200, 28800] | [1920, 2880] |
| Anxian | 24 | ② | [191600, 287400] | [19160, 28740] |
| Xiushui | 25 | ② | [28480, 42720] | [2848, 4272] |
| Baolin | 26 | ② | [33600, 50400] | [3360, 5040] |
| Gaochuan | 27 | ② | [18240, 27360] | [1824, 2736] |
| Shifang | 28 | ② | [192000, 288000] | [19200, 28800] |
| Luoshui | 29 | ② | [30400, 45600] | [3040, 4560] |
| Shuangsheng | 30 | ② | [41600, 62400] | [4160, 6240] |
| Yinghua | 31 | ② | [38400, 57600] | [3840, 5760] |
| Mianzhu | 32 | ② | [232400, 348600] | [23240, 34860] |
| Jiannan | 33 | ② | [28160, 42240] | [2816, 4224] |
| Mianyuan | 34 | ② | [24960, 37440] | [2496, 3744] |
| Hanwang | 35 | ② | [21760, 32640] | [2176, 3264] |
| Beichuan | 36 | ② | [242800, 364200] | [24280, 36420] |
| Yong'an | 37 | ② | [31040, 46560] | [3104, 4656] |
| Yongchang | 38 | ② | [33600, 50400] | [3360, 5040] |
| Kaiping | 39 | ② | [30400, 45600] | [3040, 4560] |
| Luojiang | 40 | ② | [69600, 104400] | [6960, 10440] |
| Zhongjiang | 41 | ② | [84800, 127200] | [8480, 12720] |
| Santai | 42 | ② | [109600, 164400] | [10960, 16440] |
| Yanting | 43 | ② | [110000, 165000] | [11000, 16500] |
| Zitong | 44 | ② | [125200, 187800] | [12520, 18780] |
| Deyang | 45 | ② | [173760, 260640] | [17376, 26064] |
| Mianyang | 46 | ② | [184000, 276000] | [18400, 27600] |
| Guangyuan | 47 | ③ | [75040, 112560] | [7504, 11256] |
| Qingchuan | 48 | ③ | [216400, 324600] | [21640, 32460] |
| Walixiang | 49 | ③ | [32960, 49440] | [3296, 4944] |
| Banqiaoxiang | 50 | ③ | [39040, 58560] | [3904, 5856] |
| Qimaxiang | 51 | ③ | [36000, 54000] | [3600, 5400] |
| Yingpanxiang | 52 | ③ | [32800, 49200] | [3280, 4920] |
| Lizhou | 53 | ③ | [71200, 106800] | [7120, 10680] |
| Chaotian | 54 | ③ | [66400, 99600] | [6640, 9960] |
| Cangxi | 55 | ③ | [125200, 187800] | [12520, 18780] |
| Jiange | 56 | ③ | [62000, 93000] | [6200, 9300] |
| Yuanba | 57 | ③ | [71200, 106800] | [7120, 10680] |
Table 4.
Relevant parameters in upper-level transportation
| Num. | Transportation modes | Departure point( |
Destination point( |
||||
| 1 | HC | 8 | 2 | 2 | 0.15 | 100 | 0 |
| 2 | HC | 8 | 2 | 4 | 0.18 | 100 | 0 |
| 3 | HC | 8 | 2 | 9 | 0.43 | 100 | 0 |
| 4 | HC | 8 | 2 | 16 | 0.47 | 100 | 0 |
| 5 | R | 8.2 | 1 | 5 | 0.32 | 150 | 6 |
| 6 | R | 9.3 | 1 | 13 | 0.32 | 150 | 6 |
| 7 | R | 9 | 1 | 14 | 0.23 | 170 | 6 |
| 8 | R | 10.2 | 1 | 15 | 0.42 | 170 | 6 |
| 9 | A | 8.2 | 3 | 11 | 3 | 800 | 4 |
| 10 | A | 8.8 | 3 | 12 | 0.9 | 800 | 4 |
| 11 | A | 9 | 3 | 13 | 1.8 | 800 | 4 |
| 12 | A | 8.5 | 3 | 14 | 0.6 | 800 | 4 |
| 13 | A | 8.7 | 3 | 15 | 6.6 | 800 | 4 |
| 14 | HC | 8 | 1 | 1 | [1.60, 2.40] | 80 | 0 |
| 15 | HC | 8 | 1 | 3 | [1.50, 2.26] | 80 | 0 |
| 16 | HC | 8 | 1 | 12 | [1.95, 2.93] | 80 | 0 |
| 17 | HC | 8 | 1 | 13 | [3.01, 4.51] | 80 | 0 |
| 18 | HC | 8 | 1 | 11 | [5.09, 7.63] | 80 | 0 |
| 19 | HC | 8 | 1 | 8 | [1.95, 2.93] | 80 | 0 |
| 20 | HC | 8 | 1 | 14 | [2.45, 3.67] | 90 | 0 |
| 21 | HC | 8 | 1 | 15 | [6.77, 10.15] | 90 | 0 |
| 22 | HC | 8 | 1 | 16 | [9.92, 14.88] | 90 | 0 |
| 23 | HC | 8 | 1 | 18 | [6.53, 9.79] | 90 | 0 |
| 24 | HC | 8 | 1 | 5 | [18.88, 28.32] | 90 | 0 |
| 25 | HC | 8 | 1 | 6 | [2.08, 3.12] | 90 | 0 |
| 26 | HC | 8 | 1 | 7 | [2.08, 3.12] | 90 | 0 |
| 27 | HC | 8 | 1 | 10 | [13.44, 20.16] | 90 | 0 |
| 28 | HC | 8 | 1 | 17 | [9.68, 14.52] | 85 | 0 |
| 29 | HC | 8 | 2 | 1 | [1.60, 2.40] | 85 | 0 |
| 30 | HC | 8 | 2 | 3 | [1.52, 2.28] | 85 | 0 |
| 31 | HC | 8 | 2 | 12 | [1.92, 2.88] | 85 | 0 |
| 32 | HC | 8 | 2 | 13 | [3.04, 4.56] | 85 | 0 |
| 33 | HC | 8 | 2 | 11 | [5.28, 7.92] | 85 | 0 |
| 34 | HC | 8 | 2 | 8 | [2.27, 3.41] | 85 | 0 |
| 35 | HC | 8 | 2 | 14 | [2.77, 4.15] | 85 | 0 |
| 36 | HC | 8 | 2 | 15 | [7.09, 10.63] | 85 | 0 |
| 37 | HC | 8 | 2 | 16 | [10.24, 15.36] | 85 | 0 |
| 38 | HC | 8 | 2 | 18 | [6.85, 10.27] | 85 | 0 |
| 39 | HC | 8 | 2 | 5 | [19.20, 28.80] | 80 | 0 |
| 40 | HC | 8 | 2 | 6 | [2.40, 3.60] | 80 | 0 |
| 41 | HC | 8 | 2 | 7 | [2.40, 3.60] | 80 | 0 |
| 42 | HC | 8 | 2 | 10 | [13.76, 20.64] | 80 | 0 |
| 43 | HC | 8 | 2 | 17 | [10.00, 15.00] | 80 | 0 |
| 44 | HC | 8 | 3 | 1 | [1.36, 2.04] | 90 | 0 |
| 45 | HC | 8 | 3 | 3 | [1.26, 1.90] | 90 | 0 |
| 46 | HC | 8 | 3 | 12 | [1.71, 2.57] | 90 | 0 |
| 47 | HC | 8 | 3 | 13 | [2.77, 4.15] | 90 | 0 |
| 48 | HC | 8 | 3 | 11 | [4.85, 7.27] | 90 | 0 |
| 49 | HC | 8 | 3 | 8 | [1.71, 2.57] | 90 | 0 |
| 50 | HC | 8 | 3 | 14 | [2.21, 3.31] | 90 | 0 |
| 51 | HC | 8 | 3 | 15 | [6.53, 9.79] | 90 | 0 |
| 52 | HC | 8 | 3 | 16 | [9.68, 14.52] | 90 | 0 |
| 53 | HC | 8 | 3 | 18 | [6.29, 9.43] | 85 | 0 |
| 54 | HC | 8 | 3 | 5 | [18.64, 27.96] | 85 | 0 |
| 55 | HC | 8 | 3 | 6 | [1.84, 2.76] | 85 | 0 |
| 56 | HC | 8 | 3 | 7 | [1.84, 2.76] | 85 | 0 |
| 57 | HC | 8 | 3 | 10 | [13.20, 19.80] | 85 | 0 |
| 58 | HC | 8 | 3 | 17 | [9.44, 14.16] | 85 | 0 |
| Num. | Transportation modes | Departure point( |
Destination point( |
||||
| 1 | HC | 8 | 2 | 2 | 0.15 | 100 | 0 |
| 2 | HC | 8 | 2 | 4 | 0.18 | 100 | 0 |
| 3 | HC | 8 | 2 | 9 | 0.43 | 100 | 0 |
| 4 | HC | 8 | 2 | 16 | 0.47 | 100 | 0 |
| 5 | R | 8.2 | 1 | 5 | 0.32 | 150 | 6 |
| 6 | R | 9.3 | 1 | 13 | 0.32 | 150 | 6 |
| 7 | R | 9 | 1 | 14 | 0.23 | 170 | 6 |
| 8 | R | 10.2 | 1 | 15 | 0.42 | 170 | 6 |
| 9 | A | 8.2 | 3 | 11 | 3 | 800 | 4 |
| 10 | A | 8.8 | 3 | 12 | 0.9 | 800 | 4 |
| 11 | A | 9 | 3 | 13 | 1.8 | 800 | 4 |
| 12 | A | 8.5 | 3 | 14 | 0.6 | 800 | 4 |
| 13 | A | 8.7 | 3 | 15 | 6.6 | 800 | 4 |
| 14 | HC | 8 | 1 | 1 | [1.60, 2.40] | 80 | 0 |
| 15 | HC | 8 | 1 | 3 | [1.50, 2.26] | 80 | 0 |
| 16 | HC | 8 | 1 | 12 | [1.95, 2.93] | 80 | 0 |
| 17 | HC | 8 | 1 | 13 | [3.01, 4.51] | 80 | 0 |
| 18 | HC | 8 | 1 | 11 | [5.09, 7.63] | 80 | 0 |
| 19 | HC | 8 | 1 | 8 | [1.95, 2.93] | 80 | 0 |
| 20 | HC | 8 | 1 | 14 | [2.45, 3.67] | 90 | 0 |
| 21 | HC | 8 | 1 | 15 | [6.77, 10.15] | 90 | 0 |
| 22 | HC | 8 | 1 | 16 | [9.92, 14.88] | 90 | 0 |
| 23 | HC | 8 | 1 | 18 | [6.53, 9.79] | 90 | 0 |
| 24 | HC | 8 | 1 | 5 | [18.88, 28.32] | 90 | 0 |
| 25 | HC | 8 | 1 | 6 | [2.08, 3.12] | 90 | 0 |
| 26 | HC | 8 | 1 | 7 | [2.08, 3.12] | 90 | 0 |
| 27 | HC | 8 | 1 | 10 | [13.44, 20.16] | 90 | 0 |
| 28 | HC | 8 | 1 | 17 | [9.68, 14.52] | 85 | 0 |
| 29 | HC | 8 | 2 | 1 | [1.60, 2.40] | 85 | 0 |
| 30 | HC | 8 | 2 | 3 | [1.52, 2.28] | 85 | 0 |
| 31 | HC | 8 | 2 | 12 | [1.92, 2.88] | 85 | 0 |
| 32 | HC | 8 | 2 | 13 | [3.04, 4.56] | 85 | 0 |
| 33 | HC | 8 | 2 | 11 | [5.28, 7.92] | 85 | 0 |
| 34 | HC | 8 | 2 | 8 | [2.27, 3.41] | 85 | 0 |
| 35 | HC | 8 | 2 | 14 | [2.77, 4.15] | 85 | 0 |
| 36 | HC | 8 | 2 | 15 | [7.09, 10.63] | 85 | 0 |
| 37 | HC | 8 | 2 | 16 | [10.24, 15.36] | 85 | 0 |
| 38 | HC | 8 | 2 | 18 | [6.85, 10.27] | 85 | 0 |
| 39 | HC | 8 | 2 | 5 | [19.20, 28.80] | 80 | 0 |
| 40 | HC | 8 | 2 | 6 | [2.40, 3.60] | 80 | 0 |
| 41 | HC | 8 | 2 | 7 | [2.40, 3.60] | 80 | 0 |
| 42 | HC | 8 | 2 | 10 | [13.76, 20.64] | 80 | 0 |
| 43 | HC | 8 | 2 | 17 | [10.00, 15.00] | 80 | 0 |
| 44 | HC | 8 | 3 | 1 | [1.36, 2.04] | 90 | 0 |
| 45 | HC | 8 | 3 | 3 | [1.26, 1.90] | 90 | 0 |
| 46 | HC | 8 | 3 | 12 | [1.71, 2.57] | 90 | 0 |
| 47 | HC | 8 | 3 | 13 | [2.77, 4.15] | 90 | 0 |
| 48 | HC | 8 | 3 | 11 | [4.85, 7.27] | 90 | 0 |
| 49 | HC | 8 | 3 | 8 | [1.71, 2.57] | 90 | 0 |
| 50 | HC | 8 | 3 | 14 | [2.21, 3.31] | 90 | 0 |
| 51 | HC | 8 | 3 | 15 | [6.53, 9.79] | 90 | 0 |
| 52 | HC | 8 | 3 | 16 | [9.68, 14.52] | 90 | 0 |
| 53 | HC | 8 | 3 | 18 | [6.29, 9.43] | 85 | 0 |
| 54 | HC | 8 | 3 | 5 | [18.64, 27.96] | 85 | 0 |
| 55 | HC | 8 | 3 | 6 | [1.84, 2.76] | 85 | 0 |
| 56 | HC | 8 | 3 | 7 | [1.84, 2.76] | 85 | 0 |
| 57 | HC | 8 | 3 | 10 | [13.20, 19.80] | 85 | 0 |
| 58 | HC | 8 | 3 | 17 | [9.44, 14.16] | 85 | 0 |
Table 5.
Relevant parameters in lower-level transportation
| Depart | Destin | Depart | Destin | ||||||
| ure | ation | ure | ation | ||||||
| Num. | point | point | Num. | point | point | ||||
| ( |
( |
( |
( |
||||||
| 1 | 1 | 1 | 0 | 40 | 124 | 10 | 45 | [9.36, 14.04] | 38 |
| 2 | 1 | 2 | [0.32, 0.48] | 40 | 125 | 10 | 46 | [8.69, 13.03] | 38 |
| 3 | 1 | 3 | [0.70, 1.06] | 40 | 126 | 11 | 24 | [1.89, 2.83] | 38 |
| 4 | 1 | 4 | [0.96, 1.44] | 40 | 127 | 11 | 25 | [2.45, 3.67] | 38 |
| 5 | 1 | 5 | [1.41, 2.11] | 40 | 128 | 11 | 26 | [1.89, 2.83] | 39 |
| 6 | 1 | 21 | [10.40, 15.60] | 38 | 129 | 11 | 27 | [3.81, 5.71] | 39 |
| 7 | 2 | 6 | [0.00, 0.00] | 38 | 130 | 11 | 28 | [2.77, 4.15] | 39 |
| 8 | 2 | 7 | [0.22, 0.34] | 38 | 131 | 11 | 29 | [3.01, 4.51] | 39 |
| 9 | 2 | 8 | [0.29, 0.43] | 38 | 132 | 11 | 30 | [2.64, 3.96] | 39 |
| 10 | 2 | 9 | [0.42, 0.62] | 38 | 133 | 11 | 31 | [3.36, 5.04] | 39 |
| 11 | 2 | 10 | [0.18, 0.26] | 38 | 134 | 11 | 32 | [2.48, 3.72] | 39 |
| 12 | 2 | 11 | [6.56, 9.84] | 35 | 135 | 11 | 33 | [2.56, 3.84] | 39 |
| 13 | 2 | 12 | [7.01, 10.51] | 35 | 136 | 11 | 34 | [2.75, 4.13] | 39 |
| 14 | 2 | 13 | [4.96, 7.44] | 35 | 137 | 11 | 35 | [2.88, 4.32] | 39 |
| 15 | 2 | 14 | [4.16, 6.24] | 35 | 138 | 11 | 40 | [2.40, 3.60] | 39 |
| 16 | 2 | 15 | [11.06, 16.58] | 35 | 139 | 11 | 41 | [4.00, 6.00] | 40 |
| 17 | 2 | 16 | [7.86, 11.78] | 35 | 140 | 11 | 42 | [2.48, 3.72] | 40 |
| 18 | 2 | 17 | [9.78, 14.66] | 35 | 141 | 11 | 43 | [4.19, 6.29] | 40 |
| 19 | 2 | 18 | [9.58, 14.38] | 43 | 142 | 11 | 44 | [3.01, 4.51] | 40 |
| 20 | 2 | 19 | [11.38, 17.06] | 43 | 143 | 11 | 45 | [2.40, 3.60] | 40 |
| 21 | 2 | 20 | [10.69, 16.03] | 43 | 144 | 11 | 46 | [1.65, 2.47] | 38 |
| 22 | 2 | 22 | [7.52, 11.28] | 43 | 145 | 12 | 24 | [1.63, 2.45] | 38 |
| 23 | 2 | 23 | [7.22, 10.82] | 43 | 146 | 12 | 25 | [2.24, 3.36] | 38 |
| 24 | 3 | 1 | [1.33, 1.99] | 43 | 147 | 12 | 26 | [1.63, 2.45] | 38 |
| 25 | 3 | 2 | [1.65, 2.47] | 43 | 148 | 12 | 27 | [3.60, 5.40] | 38 |
| 26 | 3 | 3 | [2.13, 3.19] | 43 | 149 | 12 | 28 | [1.09, 1.63] | 38 |
| 27 | 3 | 4 | [2.13, 3.19] | 43 | 150 | 12 | 29 | [1.60, 2.40] | 35 |
| 28 | 3 | 5 | [2.67, 4.01] | 43 | 151 | 12 | 30 | [1.12, 1.68] | 35 |
| 29 | 3 | 21 | [11.36, 17.04] | 38 | 152 | 12 | 31 | [2.00, 3.00] | 35 |
| 30 | 4 | 6 | [2.61, 3.91] | 38 | 153 | 12 | 32 | [1.44, 2.16] | 35 |
| 31 | 4 | 7 | [10.61, 15.91] | 38 | 154 | 12 | 33 | [1.60, 2.40] | 35 |
| 32 | 4 | 8 | [4.16, 6.24] | 38 | 155 | 12 | 34 | [2.08, 3.12] | 35 |
| 33 | 4 | 9 | [4.53, 6.79] | 38 | 156 | 12 | 35 | [1.84, 2.76] | 35 |
| 34 | 4 | 10 | [4.35, 6.53] | 38 | 157 | 12 | 40 | [0.93, 1.39] | 43 |
| 35 | 4 | 11 | [5.04, 7.56] | 38 | 158 | 12 | 41 | [2.03, 3.05] | 43 |
| 36 | 4 | 12 | [5.20, 7.80] | 39 | 159 | 12 | 42 | [2.85, 4.27] | 43 |
| 37 | 4 | 13 | [3.60, 5.40] | 39 | 160 | 12 | 43 | [4.67, 7.01] | 43 |
| 38 | 4 | 14 | [3.76, 5.64] | 39 | 161 | 12 | 44 | [3.52, 5.28] | 43 |
| 39 | 4 | 15 | 0 | 39 | 162 | 12 | 45 | 0 | 43 |
| 40 | 4 | 16 | [4.24, 6.36] | 39 | 163 | 12 | 46 | [1.55, 2.33] | 43 |
| 41 | 4 | 17 | [5.12, 7.68] | 39 | 164 | 13 | 24 | [0.67, 1.01] | 43 |
| 42 | 4 | 18 | [5.28, 7.92] | 39 | 165 | 13 | 25 | [1.23, 1.85] | 43 |
| 43 | 4 | 19 | [0.32, 0.48] | 39 | 166 | 13 | 26 | [1.01, 1.51] | 43 |
| 44 | 4 | 20 | [5.76, 8.64] | 39 | 167 | 13 | 27 | [2.59, 3.89] | 38 |
| 45 | 4 | 22 | [9.84, 14.76] | 39 | 168 | 13 | 28 | [1.81, 2.71] | 38 |
| 46 | 4 | 23 | [9.60, 14.40] | 39 | 169 | 13 | 29 | [2.08, 3.12] | 38 |
| 47 | 5 | 1 | [18.61, 27.91] | 40 | 170 | 13 | 30 | [1.68, 2.52] | 38 |
| 48 | 5 | 2 | [18.85, 28.27] | 40 | 171 | 13 | 31 | [2.40, 3.60] | 38 |
| 49 | 5 | 3 | [18.99, 28.49] | 40 | 172 | 13 | 32 | [1.52, 2.28] | 38 |
| 50 | 5 | 4 | [19.73, 29.59] | 40 | 173 | 13 | 33 | [1.68, 2.52] | 38 |
| 51 | 5 | 5 | [19.78, 29.66] | 40 | 174 | 13 | 34 | [1.52, 2.28] | 39 |
| 52 | 5 | 6 | [15.20, 22.80] | 38 | 175 | 13 | 35 | [1.95, 2.93] | 39 |
| 53 | 5 | 7 | [22.21, 33.31] | 38 | 176 | 13 | 40 | [0.99, 1.49] | 39 |
| 54 | 5 | 8 | [13.38, 20.06] | 38 | 177 | 13 | 41 | [2.88, 4.32] | 39 |
| 55 | 5 | 9 | [13.20, 19.80] | 38 | 178 | 13 | 42 | [2.08, 3.12] | 39 |
| 56 | 5 | 10 | [13.14, 19.70] | 38 | 179 | 13 | 43 | [3.81, 5.71] | 39 |
| 57 | 5 | 11 | [21.44, 32.16] | 38 | 180 | 13 | 44 | [2.56, 3.84] | 39 |
| 58 | 5 | 12 | [22.72, 34.08] | 35 | 181 | 13 | 45 | [1.55, 2.33] | 39 |
| 59 | 5 | 13 | [22.56, 33.84] | 35 | 182 | 13 | 46 | 0 | 39 |
| 60 | 5 | 14 | [22.72, 34.08] | 35 | 183 | 14 | 24 | [2.08, 3.12] | 39 |
| 61 | 5 | 15 | [17.60, 26.40] | 35 | 184 | 14 | 25 | [2.64, 3.96] | 39 |
| 62 | 5 | 16 | [21.12, 31.68] | 35 | 185 | 14 | 26 | [2.00, 3.00] | 40 |
| 63 | 5 | 17 | [22.72, 34.08] | 35 | 186 | 14 | 27 | [4.00, 6.00] | 40 |
| 64 | 5 | 18 | [22.56, 33.84] | 35 | 187 | 14 | 28 | [1.04, 1.56] | 40 |
| 65 | 5 | 19 | [19.04, 28.56] | 43 | 188 | 14 | 29 | [1.49, 2.23] | 40 |
| 66 | 5 | 20 | [24.32, 36.48] | 43 | 189 | 14 | 30 | [1.15, 1.73] | 40 |
| 67 | 5 | 21 | [16.00, 24.00] | 43 | 190 | 14 | 31 | [1.87, 2.81] | 38 |
| 68 | 5 | 22 | [17.92, 26.88] | 43 | 191 | 14 | 32 | [1.68, 2.52] | 38 |
| 69 | 5 | 23 | [8.00, 12.00] | 43 | 192 | 14 | 33 | [1.84, 2.76] | 38 |
| 70 | 6 | 1 | [1.81, 2.71] | 43 | 193 | 14 | 34 | [2.53, 3.79] | 38 |
| 71 | 6 | 2 | [1.92, 2.88] | 43 | 194 | 14 | 35 | [2.11, 3.17] | 38 |
| 72 | 6 | 3 | [1.39, 2.09] | 43 | 195 | 14 | 40 | [1.55, 2.33] | 38 |
| 73 | 6 | 4 | [2.40, 3.60] | 43 | 196 | 14 | 41 | [2.61, 3.91] | 35 |
| 74 | 6 | 5 | [2.99, 4.49] | 43 | 197 | 14 | 42 | [3.20, 4.80] | 35 |
| 75 | 6 | 21 | [11.44, 17.16] | 38 | 198 | 14 | 43 | [5.04, 7.56] | 35 |
| 76 | 6 | 22 | [16.00, 24.00] | 38 | 199 | 14 | 44 | [3.89, 5.83] | 35 |
| 77 | 6 | 23 | [16.00, 24.00] | 38 | 200 | 14 | 45 | [0.91, 1.37] | 35 |
| 78 | 7 | 1 | [2.03, 3.05] | 38 | 201 | 14 | 46 | [1.92, 2.88] | 35 |
| 79 | 7 | 2 | [2.16, 3.24] | 38 | 202 | 15 | 47 | 0 | 35 |
| 80 | 7 | 3 | [1.60, 2.40] | 38 | 203 | 15 | 48 | [5.12, 7.68] | 43 |
| 81 | 7 | 4 | [2.67, 4.01] | 38 | 204 | 15 | 49 | [5.39, 8.09] | 43 |
| 82 | 7 | 5 | [3.20, 4.80] | 39 | 205 | 15 | 50 | [4.40, 6.60] | 43 |
| 83 | 7 | 21 | [11.68, 17.52] | 39 | 206 | 15 | 51 | [4.59, 6.89] | 43 |
| 84 | 8 | 24 | [1.92, 2.88] | 39 | 207 | 15 | 52 | [3.63, 5.45] | 43 |
| 85 | 8 | 25 | [2.26, 3.38] | 39 | 208 | 15 | 53 | [0.08, 0.12] | 43 |
| 86 | 8 | 26 | [1.33, 1.99] | 39 | 209 | 15 | 54 | [1.84, 2.76] | 43 |
| 87 | 8 | 27 | [3.30, 4.94] | 39 | 210 | 15 | 55 | [3.57, 5.35] | 43 |
| 88 | 8 | 28 | 0 | 39 | 211 | 15 | 56 | [1.28, 1.92] | 43 |
| 89 | 8 | 29 | [0.83, 1.25] | 39 | 212 | 15 | 57 | [1.17, 1.75] | 43 |
| 90 | 8 | 30 | [0.48, 0.72] | 39 | 213 | 16 | 47 | [0.37, 0.55] | 38 |
| 91 | 8 | 31 | [1.20, 1.80] | 39 | 214 | 16 | 48 | 0 | 38 |
| 92 | 8 | 32 | [1.01, 1.51] | 39 | 215 | 16 | 49 | [0.88, 1.32] | 38 |
| 93 | 8 | 33 | [1.17, 1.75] | 40 | 216 | 16 | 50 | [0.72, 1.08] | 38 |
| 94 | 8 | 34 | [1.79, 2.69] | 40 | 217 | 16 | 51 | [1.01, 1.51] | 38 |
| 95 | 8 | 35 | [1.41, 2.11] | 40 | 218 | 16 | 52 | [1.60, 2.40] | 38 |
| 96 | 8 | 40 | [1.94, 2.90] | 40 | 219 | 16 | 53 | [5.09, 7.63] | 38 |
| 97 | 8 | 41 | [3.31, 4.97] | 40 | 220 | 16 | 54 | [6.66, 9.98] | 39 |
| 98 | 8 | 42 | [3.07, 4.61] | 38 | 221 | 16 | 55 | [6.75, 10.13] | 39 |
| 99 | 8 | 43 | [4.88, 7.32] | 38 | 222 | 16 | 56 | [4.53, 6.79] | 39 |
| 100 | 8 | 44 | [3.73, 5.59] | 38 | 223 | 16 | 57 | [5.60, 8.40] | 39 |
| 101 | 8 | 45 | [1.36, 2.04] | 38 | 224 | 17 | 47 | [2.16, 3.24] | 39 |
| 102 | 8 | 46 | [1.76, 2.64] | 38 | 225 | 17 | 48 | [6.48, 9.72] | 39 |
| 103 | 9 | 36 | [1.60, 2.40] | 38 | 226 | 17 | 49 | [6.56, 9.84] | 39 |
| 104 | 9 | 37 | [0.93, 1.39] | 35 | 227 | 17 | 50 | [5.79, 8.69] | 39 |
| 105 | 9 | 38 | [1.55, 2.33] | 35 | 228 | 17 | 51 | [5.97, 8.95] | 39 |
| 106 | 9 | 39 | [2.03, 3.05] | 35 | 229 | 17 | 52 | [5.01, 7.51] | 39 |
| 107 | 10 | 24 | [8.67, 13.01] | 35 | 230 | 17 | 53 | [2.29, 3.43] | 39 |
| 108 | 10 | 25 | [9.44, 14.16] | 35 | 231 | 17 | 54 | [4.00, 6.00] | 40 |
| 109 | 10 | 26 | [8.85, 13.27] | 35 | 232 | 17 | 55 | [3.55, 5.33] | 40 |
| 110 | 10 | 27 | [10.80, 16.20] | 35 | 233 | 17 | 56 | [2.83, 4.25] | 40 |
| 111 | 10 | 28 | [9.68, 14.52] | 43 | 234 | 17 | 57 | [1.20, 1.80] | 40 |
| 112 | 10 | 28 | [9.92, 14.88] | 43 | 235 | 18 | 47 | [1.49, 2.23] | 40 |
| 113 | 10 | 30 | [9.55, 14.33] | 43 | 236 | 18 | 48 | [4.53, 6.79] | 38 |
| 114 | 10 | 31 | [10.27, 15.41] | 43 | 237 | 18 | 49 | [4.40, 6.60] | 38 |
| 115 | 10 | 32 | [9.39, 14.09] | 43 | 238 | 18 | 50 | [4.13, 6.19] | 38 |
| 116 | 10 | 33 | [9.52, 14.28] | 43 | 239 | 18 | 51 | [4.32, 6.48] | 38 |
| 117 | 10 | 34 | [9.71, 14.57] | 43 | 240 | 18 | 52 | [3.36, 5.04] | 38 |
| 118 | 10 | 35 | [9.79, 14.69] | 43 | 241 | 18 | 53 | [1.47, 2.21] | 38 |
| 119 | 10 | 40 | [8.80, 13.20] | 43 | 242 | 18 | 54 | [2.82, 4.22] | 35 |
| 120 | 10 | 41 | [11.01, 16.51] | 43 | 243 | 18 | 55 | [2.69, 4.03] | 35 |
| 121 | 10 | 42 | [9.33, 13.99] | 38 | 244 | 18 | 56 | 0 | 35 |
| 122 | 10 | 43 | [11.15, 16.73] | 38 | 245 | 18 | 57 | [1.55, 2.33] | 35 |
| 123 | 10 | 44 | [9.89, 14.83] | 38 | - | - | - | - | - |
| Depart | Destin | Depart | Destin | ||||||
| ure | ation | ure | ation | ||||||
| Num. | point | point | Num. | point | point | ||||
| ( |
( |
( |
( |
||||||
| 1 | 1 | 1 | 0 | 40 | 124 | 10 | 45 | [9.36, 14.04] | 38 |
| 2 | 1 | 2 | [0.32, 0.48] | 40 | 125 | 10 | 46 | [8.69, 13.03] | 38 |
| 3 | 1 | 3 | [0.70, 1.06] | 40 | 126 | 11 | 24 | [1.89, 2.83] | 38 |
| 4 | 1 | 4 | [0.96, 1.44] | 40 | 127 | 11 | 25 | [2.45, 3.67] | 38 |
| 5 | 1 | 5 | [1.41, 2.11] | 40 | 128 | 11 | 26 | [1.89, 2.83] | 39 |
| 6 | 1 | 21 | [10.40, 15.60] | 38 | 129 | 11 | 27 | [3.81, 5.71] | 39 |
| 7 | 2 | 6 | [0.00, 0.00] | 38 | 130 | 11 | 28 | [2.77, 4.15] | 39 |
| 8 | 2 | 7 | [0.22, 0.34] | 38 | 131 | 11 | 29 | [3.01, 4.51] | 39 |
| 9 | 2 | 8 | [0.29, 0.43] | 38 | 132 | 11 | 30 | [2.64, 3.96] | 39 |
| 10 | 2 | 9 | [0.42, 0.62] | 38 | 133 | 11 | 31 | [3.36, 5.04] | 39 |
| 11 | 2 | 10 | [0.18, 0.26] | 38 | 134 | 11 | 32 | [2.48, 3.72] | 39 |
| 12 | 2 | 11 | [6.56, 9.84] | 35 | 135 | 11 | 33 | [2.56, 3.84] | 39 |
| 13 | 2 | 12 | [7.01, 10.51] | 35 | 136 | 11 | 34 | [2.75, 4.13] | 39 |
| 14 | 2 | 13 | [4.96, 7.44] | 35 | 137 | 11 | 35 | [2.88, 4.32] | 39 |
| 15 | 2 | 14 | [4.16, 6.24] | 35 | 138 | 11 | 40 | [2.40, 3.60] | 39 |
| 16 | 2 | 15 | [11.06, 16.58] | 35 | 139 | 11 | 41 | [4.00, 6.00] | 40 |
| 17 | 2 | 16 | [7.86, 11.78] | 35 | 140 | 11 | 42 | [2.48, 3.72] | 40 |
| 18 | 2 | 17 | [9.78, 14.66] | 35 | 141 | 11 | 43 | [4.19, 6.29] | 40 |
| 19 | 2 | 18 | [9.58, 14.38] | 43 | 142 | 11 | 44 | [3.01, 4.51] | 40 |
| 20 | 2 | 19 | [11.38, 17.06] | 43 | 143 | 11 | 45 | [2.40, 3.60] | 40 |
| 21 | 2 | 20 | [10.69, 16.03] | 43 | 144 | 11 | 46 | [1.65, 2.47] | 38 |
| 22 | 2 | 22 | [7.52, 11.28] | 43 | 145 | 12 | 24 | [1.63, 2.45] | 38 |
| 23 | 2 | 23 | [7.22, 10.82] | 43 | 146 | 12 | 25 | [2.24, 3.36] | 38 |
| 24 | 3 | 1 | [1.33, 1.99] | 43 | 147 | 12 | 26 | [1.63, 2.45] | 38 |
| 25 | 3 | 2 | [1.65, 2.47] | 43 | 148 | 12 | 27 | [3.60, 5.40] | 38 |
| 26 | 3 | 3 | [2.13, 3.19] | 43 | 149 | 12 | 28 | [1.09, 1.63] | 38 |
| 27 | 3 | 4 | [2.13, 3.19] | 43 | 150 | 12 | 29 | [1.60, 2.40] | 35 |
| 28 | 3 | 5 | [2.67, 4.01] | 43 | 151 | 12 | 30 | [1.12, 1.68] | 35 |
| 29 | 3 | 21 | [11.36, 17.04] | 38 | 152 | 12 | 31 | [2.00, 3.00] | 35 |
| 30 | 4 | 6 | [2.61, 3.91] | 38 | 153 | 12 | 32 | [1.44, 2.16] | 35 |
| 31 | 4 | 7 | [10.61, 15.91] | 38 | 154 | 12 | 33 | [1.60, 2.40] | 35 |
| 32 | 4 | 8 | [4.16, 6.24] | 38 | 155 | 12 | 34 | [2.08, 3.12] | 35 |
| 33 | 4 | 9 | [4.53, 6.79] | 38 | 156 | 12 | 35 | [1.84, 2.76] | 35 |
| 34 | 4 | 10 | [4.35, 6.53] | 38 | 157 | 12 | 40 | [0.93, 1.39] | 43 |
| 35 | 4 | 11 | [5.04, 7.56] | 38 | 158 | 12 | 41 | [2.03, 3.05] | 43 |
| 36 | 4 | 12 | [5.20, 7.80] | 39 | 159 | 12 | 42 | [2.85, 4.27] | 43 |
| 37 | 4 | 13 | [3.60, 5.40] | 39 | 160 | 12 | 43 | [4.67, 7.01] | 43 |
| 38 | 4 | 14 | [3.76, 5.64] | 39 | 161 | 12 | 44 | [3.52, 5.28] | 43 |
| 39 | 4 | 15 | 0 | 39 | 162 | 12 | 45 | 0 | 43 |
| 40 | 4 | 16 | [4.24, 6.36] | 39 | 163 | 12 | 46 | [1.55, 2.33] | 43 |
| 41 | 4 | 17 | [5.12, 7.68] | 39 | 164 | 13 | 24 | [0.67, 1.01] | 43 |
| 42 | 4 | 18 | [5.28, 7.92] | 39 | 165 | 13 | 25 | [1.23, 1.85] | 43 |
| 43 | 4 | 19 | [0.32, 0.48] | 39 | 166 | 13 | 26 | [1.01, 1.51] | 43 |
| 44 | 4 | 20 | [5.76, 8.64] | 39 | 167 | 13 | 27 | [2.59, 3.89] | 38 |
| 45 | 4 | 22 | [9.84, 14.76] | 39 | 168 | 13 | 28 | [1.81, 2.71] | 38 |
| 46 | 4 | 23 | [9.60, 14.40] | 39 | 169 | 13 | 29 | [2.08, 3.12] | 38 |
| 47 | 5 | 1 | [18.61, 27.91] | 40 | 170 | 13 | 30 | [1.68, 2.52] | 38 |
| 48 | 5 | 2 | [18.85, 28.27] | 40 | 171 | 13 | 31 | [2.40, 3.60] | 38 |
| 49 | 5 | 3 | [18.99, 28.49] | 40 | 172 | 13 | 32 | [1.52, 2.28] | 38 |
| 50 | 5 | 4 | [19.73, 29.59] | 40 | 173 | 13 | 33 | [1.68, 2.52] | 38 |
| 51 | 5 | 5 | [19.78, 29.66] | 40 | 174 | 13 | 34 | [1.52, 2.28] | 39 |
| 52 | 5 | 6 | [15.20, 22.80] | 38 | 175 | 13 | 35 | [1.95, 2.93] | 39 |
| 53 | 5 | 7 | [22.21, 33.31] | 38 | 176 | 13 | 40 | [0.99, 1.49] | 39 |
| 54 | 5 | 8 | [13.38, 20.06] | 38 | 177 | 13 | 41 | [2.88, 4.32] | 39 |
| 55 | 5 | 9 | [13.20, 19.80] | 38 | 178 | 13 | 42 | [2.08, 3.12] | 39 |
| 56 | 5 | 10 | [13.14, 19.70] | 38 | 179 | 13 | 43 | [3.81, 5.71] | 39 |
| 57 | 5 | 11 | [21.44, 32.16] | 38 | 180 | 13 | 44 | [2.56, 3.84] | 39 |
| 58 | 5 | 12 | [22.72, 34.08] | 35 | 181 | 13 | 45 | [1.55, 2.33] | 39 |
| 59 | 5 | 13 | [22.56, 33.84] | 35 | 182 | 13 | 46 | 0 | 39 |
| 60 | 5 | 14 | [22.72, 34.08] | 35 | 183 | 14 | 24 | [2.08, 3.12] | 39 |
| 61 | 5 | 15 | [17.60, 26.40] | 35 | 184 | 14 | 25 | [2.64, 3.96] | 39 |
| 62 | 5 | 16 | [21.12, 31.68] | 35 | 185 | 14 | 26 | [2.00, 3.00] | 40 |
| 63 | 5 | 17 | [22.72, 34.08] | 35 | 186 | 14 | 27 | [4.00, 6.00] | 40 |
| 64 | 5 | 18 | [22.56, 33.84] | 35 | 187 | 14 | 28 | [1.04, 1.56] | 40 |
| 65 | 5 | 19 | [19.04, 28.56] | 43 | 188 | 14 | 29 | [1.49, 2.23] | 40 |
| 66 | 5 | 20 | [24.32, 36.48] | 43 | 189 | 14 | 30 | [1.15, 1.73] | 40 |
| 67 | 5 | 21 | [16.00, 24.00] | 43 | 190 | 14 | 31 | [1.87, 2.81] | 38 |
| 68 | 5 | 22 | [17.92, 26.88] | 43 | 191 | 14 | 32 | [1.68, 2.52] | 38 |
| 69 | 5 | 23 | [8.00, 12.00] | 43 | 192 | 14 | 33 | [1.84, 2.76] | 38 |
| 70 | 6 | 1 | [1.81, 2.71] | 43 | 193 | 14 | 34 | [2.53, 3.79] | 38 |
| 71 | 6 | 2 | [1.92, 2.88] | 43 | 194 | 14 | 35 | [2.11, 3.17] | 38 |
| 72 | 6 | 3 | [1.39, 2.09] | 43 | 195 | 14 | 40 | [1.55, 2.33] | 38 |
| 73 | 6 | 4 | [2.40, 3.60] | 43 | 196 | 14 | 41 | [2.61, 3.91] | 35 |
| 74 | 6 | 5 | [2.99, 4.49] | 43 | 197 | 14 | 42 | [3.20, 4.80] | 35 |
| 75 | 6 | 21 | [11.44, 17.16] | 38 | 198 | 14 | 43 | [5.04, 7.56] | 35 |
| 76 | 6 | 22 | [16.00, 24.00] | 38 | 199 | 14 | 44 | [3.89, 5.83] | 35 |
| 77 | 6 | 23 | [16.00, 24.00] | 38 | 200 | 14 | 45 | [0.91, 1.37] | 35 |
| 78 | 7 | 1 | [2.03, 3.05] | 38 | 201 | 14 | 46 | [1.92, 2.88] | 35 |
| 79 | 7 | 2 | [2.16, 3.24] | 38 | 202 | 15 | 47 | 0 | 35 |
| 80 | 7 | 3 | [1.60, 2.40] | 38 | 203 | 15 | 48 | [5.12, 7.68] | 43 |
| 81 | 7 | 4 | [2.67, 4.01] | 38 | 204 | 15 | 49 | [5.39, 8.09] | 43 |
| 82 | 7 | 5 | [3.20, 4.80] | 39 | 205 | 15 | 50 | [4.40, 6.60] | 43 |
| 83 | 7 | 21 | [11.68, 17.52] | 39 | 206 | 15 | 51 | [4.59, 6.89] | 43 |
| 84 | 8 | 24 | [1.92, 2.88] | 39 | 207 | 15 | 52 | [3.63, 5.45] | 43 |
| 85 | 8 | 25 | [2.26, 3.38] | 39 | 208 | 15 | 53 | [0.08, 0.12] | 43 |
| 86 | 8 | 26 | [1.33, 1.99] | 39 | 209 | 15 | 54 | [1.84, 2.76] | 43 |
| 87 | 8 | 27 | [3.30, 4.94] | 39 | 210 | 15 | 55 | [3.57, 5.35] | 43 |
| 88 | 8 | 28 | 0 | 39 | 211 | 15 | 56 | [1.28, 1.92] | 43 |
| 89 | 8 | 29 | [0.83, 1.25] | 39 | 212 | 15 | 57 | [1.17, 1.75] | 43 |
| 90 | 8 | 30 | [0.48, 0.72] | 39 | 213 | 16 | 47 | [0.37, 0.55] | 38 |
| 91 | 8 | 31 | [1.20, 1.80] | 39 | 214 | 16 | 48 | 0 | 38 |
| 92 | 8 | 32 | [1.01, 1.51] | 39 | 215 | 16 | 49 | [0.88, 1.32] | 38 |
| 93 | 8 | 33 | [1.17, 1.75] | 40 | 216 | 16 | 50 | [0.72, 1.08] | 38 |
| 94 | 8 | 34 | [1.79, 2.69] | 40 | 217 | 16 | 51 | [1.01, 1.51] | 38 |
| 95 | 8 | 35 | [1.41, 2.11] | 40 | 218 | 16 | 52 | [1.60, 2.40] | 38 |
| 96 | 8 | 40 | [1.94, 2.90] | 40 | 219 | 16 | 53 | [5.09, 7.63] | 38 |
| 97 | 8 | 41 | [3.31, 4.97] | 40 | 220 | 16 | 54 | [6.66, 9.98] | 39 |
| 98 | 8 | 42 | [3.07, 4.61] | 38 | 221 | 16 | 55 | [6.75, 10.13] | 39 |
| 99 | 8 | 43 | [4.88, 7.32] | 38 | 222 | 16 | 56 | [4.53, 6.79] | 39 |
| 100 | 8 | 44 | [3.73, 5.59] | 38 | 223 | 16 | 57 | [5.60, 8.40] | 39 |
| 101 | 8 | 45 | [1.36, 2.04] | 38 | 224 | 17 | 47 | [2.16, 3.24] | 39 |
| 102 | 8 | 46 | [1.76, 2.64] | 38 | 225 | 17 | 48 | [6.48, 9.72] | 39 |
| 103 | 9 | 36 | [1.60, 2.40] | 38 | 226 | 17 | 49 | [6.56, 9.84] | 39 |
| 104 | 9 | 37 | [0.93, 1.39] | 35 | 227 | 17 | 50 | [5.79, 8.69] | 39 |
| 105 | 9 | 38 | [1.55, 2.33] | 35 | 228 | 17 | 51 | [5.97, 8.95] | 39 |
| 106 | 9 | 39 | [2.03, 3.05] | 35 | 229 | 17 | 52 | [5.01, 7.51] | 39 |
| 107 | 10 | 24 | [8.67, 13.01] | 35 | 230 | 17 | 53 | [2.29, 3.43] | 39 |
| 108 | 10 | 25 | [9.44, 14.16] | 35 | 231 | 17 | 54 | [4.00, 6.00] | 40 |
| 109 | 10 | 26 | [8.85, 13.27] | 35 | 232 | 17 | 55 | [3.55, 5.33] | 40 |
| 110 | 10 | 27 | [10.80, 16.20] | 35 | 233 | 17 | 56 | [2.83, 4.25] | 40 |
| 111 | 10 | 28 | [9.68, 14.52] | 43 | 234 | 17 | 57 | [1.20, 1.80] | 40 |
| 112 | 10 | 28 | [9.92, 14.88] | 43 | 235 | 18 | 47 | [1.49, 2.23] | 40 |
| 113 | 10 | 30 | [9.55, 14.33] | 43 | 236 | 18 | 48 | [4.53, 6.79] | 38 |
| 114 | 10 | 31 | [10.27, 15.41] | 43 | 237 | 18 | 49 | [4.40, 6.60] | 38 |
| 115 | 10 | 32 | [9.39, 14.09] | 43 | 238 | 18 | 50 | [4.13, 6.19] | 38 |
| 116 | 10 | 33 | [9.52, 14.28] | 43 | 239 | 18 | 51 | [4.32, 6.48] | 38 |
| 117 | 10 | 34 | [9.71, 14.57] | 43 | 240 | 18 | 52 | [3.36, 5.04] | 38 |
| 118 | 10 | 35 | [9.79, 14.69] | 43 | 241 | 18 | 53 | [1.47, 2.21] | 38 |
| 119 | 10 | 40 | [8.80, 13.20] | 43 | 242 | 18 | 54 | [2.82, 4.22] | 35 |
| 120 | 10 | 41 | [11.01, 16.51] | 43 | 243 | 18 | 55 | [2.69, 4.03] | 35 |
| 121 | 10 | 42 | [9.33, 13.99] | 38 | 244 | 18 | 56 | 0 | 35 |
| 122 | 10 | 43 | [11.15, 16.73] | 38 | 245 | 18 | 57 | [1.55, 2.33] | 35 |
| 123 | 10 | 44 | [9.89, 14.83] | 38 | - | - | - | - | - |
Table 6.
Transportation of relief materials from collection centers to opened temporary transfer facilities
| Collection centers | Opened temporary transfer facility | Transportation mode | Food (units) | Collection centers | Opened temporary transfer facility | Transportation mode | Daily necessities (units) |
| facility | facility | ||||||
| Ⅱ | (1) | H | 54849 | Ⅰ | (1) | H | 15869 |
| Ⅲ | (1) | H | 71647 | Ⅱ | (12) | H | 13904 |
| Ⅲ | (17) | H | 80471 | Ⅰ | (16) | H | 13037 |
| Ⅲ | (12) | A | 384669 | Ⅰ | (17) | H | 9487 |
| Ⅲ | (13) | A | 304150 | Ⅲ | (12) | A | 49057 |
| Ⅲ | (14) | A | 153849 | Ⅲ | (13) | A | 80943 |
| Ⅲ | (15) | A | 205214 | Ⅰ | (14) | R | 25568 |
| Ⅰ | (13) | R | 182926 | Ⅰ | (15) | R | 26039 |
| Ⅰ | (15) | R | 17074 | Ⅱ | (2) | HC | 12652 |
| Ⅱ | (2) | HC | 113786 | Ⅱ | (4) | HC | 18159 |
| Ⅱ | (4) | HC | 80529 | Ⅱ | (9) | HC | 5805 |
| Ⅱ | (9) | HC | 77345 | Ⅱ | (16) | HC | 11686 |
| Ⅱ | (6) | HC | 107638 | - | - | - | - |
| Collection centers | Opened temporary transfer facility | Transportation mode | Food (units) | Collection centers | Opened temporary transfer facility | Transportation mode | Daily necessities (units) |
| facility | facility | ||||||
| Ⅱ | (1) | H | 54849 | Ⅰ | (1) | H | 15869 |
| Ⅲ | (1) | H | 71647 | Ⅱ | (12) | H | 13904 |
| Ⅲ | (17) | H | 80471 | Ⅰ | (16) | H | 13037 |
| Ⅲ | (12) | A | 384669 | Ⅰ | (17) | H | 9487 |
| Ⅲ | (13) | A | 304150 | Ⅲ | (12) | A | 49057 |
| Ⅲ | (14) | A | 153849 | Ⅲ | (13) | A | 80943 |
| Ⅲ | (15) | A | 205214 | Ⅰ | (14) | R | 25568 |
| Ⅰ | (13) | R | 182926 | Ⅰ | (15) | R | 26039 |
| Ⅰ | (15) | R | 17074 | Ⅱ | (2) | HC | 12652 |
| Ⅱ | (2) | HC | 113786 | Ⅱ | (4) | HC | 18159 |
| Ⅱ | (4) | HC | 80529 | Ⅱ | (9) | HC | 5805 |
| Ⅱ | (9) | HC | 77345 | Ⅱ | (16) | HC | 11686 |
| Ⅱ | (6) | HC | 107638 | - | - | - | - |
Table 7.
Transportation of relief materials from opened temporary transfer facilities to affected points
| Opened temporary transfer facility | Affected point | Food (units) | Opened temporary transfer facility | Affected point | Daily necessities (units) |
| (1) | 1 | 71036 | (1) | 1 | 8753 |
| (1) | 2 | 13446 | (1) | 2 | 1657 |
| (1) | 3 | 12330 | (1) | 3 | 1519 |
| (1) | 4 | 11163 | (1) | 4 | 1376 |
| (1) | 5 | 12178 | (1) | 5 | 1501 |
| (2) | 6 | 54926 | (2) | 6 | 6768 |
| (2) | 7 | 9133 | (2) | 7 | 1126 |
| (2) | 8 | 9641 | (2) | 8 | 1188 |
| (2) | 9 | 10148 | (2) | 9 | 1251 |
| (2) | 10 | 8119 | (2) | 10 | 1001 |
| (4) | 11 | 54292 | (4) | 11 | 6690 |
| (4) | 12 | 8119 | (4) | 12 | 1001 |
| (4) | 13 | 5074 | (4) | 13 | 626 |
| (4) | 14 | 12178 | (4) | 14 | 1501 |
| (4) | 15 | 78267 | (4) | 15 | 9644 |
| (4) | 16 | 10148 | (4) | 16 | 1251 |
| (4) | 17 | 9133 | (4) | 17 | 1125 |
| (4) | 18 | 8626 | (4) | 18 | 1063 |
| (4) | 19 | 8829 | (4) | 19 | 1088 |
| (4) | 20 | 7814 | (4) | 20 | 963 |
| (1) | 21 | 6343 | (1) | 21 | 1063 |
| (2) | 22 | 15730 | (2) | 22 | 1938 |
| (2) | 23 | 6089 | (2) | 23 | 719 |
| (13) | 24 | 113026 | (13) | 24 | 18783 |
| (13) | 25 | 16801 | (13) | 25 | 2792 |
| (13) | 26 | 19821 | (13) | 26 | 3294 |
| (13) | 27 | 10760 | (13) | 27 | 1789 |
| (14) | 28 | 113262 | (14) | 28 | 18822 |
| (14) | 29 | 17934 | (14) | 29 | 2981 |
| (12) | 30 | 24541 | (12) | 30 | 4078 |
| (14) | 31 | 22653 | (14) | 31 | 3765 |
| (12) | 32 | 137095 | (12) | 32 | 22782 |
| (12) | 33 | 16612 | (12) | 33 | 2761 |
| (13) | 34 | 14725 | (13) | 34 | 2447 |
| (12) | 35 | 12837 | (12) | 35 | 2134 |
| (9) | 36 | 143230 | (9) | 36 | 23802 |
| (9) | 37 | 18311 | (9) | 37 | 3043 |
| (9) | 38 | 19821 | (9) | 38 | 3294 |
| (9) | 39 | 17934 | (9) | 39 | 2981 |
| (12) | 40 | 41058 | (12) | 40 | 6643 |
| (12) | 41 | 50024 | (12) | 41 | 7529 |
| (13) | 42 | 64654 | (13) | 42 | 10744 |
| (13) | 43 | 64890 | (13) | 43 | 10783 |
| (13) | 44 | 73856 | (13) | 44 | 12273 |
| (12) | 45 | 102502 | (12) | 45 | 17034 |
| (13) | 46 | 108543 | (13) | 46 | 18038 |
| (15) | 47 | 48232 | (15) | 47 | 5687 |
| (16) | 48 | 139090 | (16) | 48 | 16399 |
| (16) | 49 | 21185 | (16) | 49 | 2498 |
| (16) | 50 | 25093 | (16) | 50 | 2959 |
| (16) | 51 | 23139 | (16) | 51 | 2728 |
| (16) | 52 | 21082 | (16) | 52 | 2486 |
| (15) | 53 | 45764 | (15) | 53 | 5396 |
| (15) | 54 | 42678 | (15) | 54 | 4862 |
| (17) | 55 | 80471 | (17) | 55 | 9487 |
| (15) | 56 | 39850 | (15) | 56 | 4698 |
| (15) | 57 | 45764 | (15) | 57 | 5396 |
| Opened temporary transfer facility | Affected point | Food (units) | Opened temporary transfer facility | Affected point | Daily necessities (units) |
| (1) | 1 | 71036 | (1) | 1 | 8753 |
| (1) | 2 | 13446 | (1) | 2 | 1657 |
| (1) | 3 | 12330 | (1) | 3 | 1519 |
| (1) | 4 | 11163 | (1) | 4 | 1376 |
| (1) | 5 | 12178 | (1) | 5 | 1501 |
| (2) | 6 | 54926 | (2) | 6 | 6768 |
| (2) | 7 | 9133 | (2) | 7 | 1126 |
| (2) | 8 | 9641 | (2) | 8 | 1188 |
| (2) | 9 | 10148 | (2) | 9 | 1251 |
| (2) | 10 | 8119 | (2) | 10 | 1001 |
| (4) | 11 | 54292 | (4) | 11 | 6690 |
| (4) | 12 | 8119 | (4) | 12 | 1001 |
| (4) | 13 | 5074 | (4) | 13 | 626 |
| (4) | 14 | 12178 | (4) | 14 | 1501 |
| (4) | 15 | 78267 | (4) | 15 | 9644 |
| (4) | 16 | 10148 | (4) | 16 | 1251 |
| (4) | 17 | 9133 | (4) | 17 | 1125 |
| (4) | 18 | 8626 | (4) | 18 | 1063 |
| (4) | 19 | 8829 | (4) | 19 | 1088 |
| (4) | 20 | 7814 | (4) | 20 | 963 |
| (1) | 21 | 6343 | (1) | 21 | 1063 |
| (2) | 22 | 15730 | (2) | 22 | 1938 |
| (2) | 23 | 6089 | (2) | 23 | 719 |
| (13) | 24 | 113026 | (13) | 24 | 18783 |
| (13) | 25 | 16801 | (13) | 25 | 2792 |
| (13) | 26 | 19821 | (13) | 26 | 3294 |
| (13) | 27 | 10760 | (13) | 27 | 1789 |
| (14) | 28 | 113262 | (14) | 28 | 18822 |
| (14) | 29 | 17934 | (14) | 29 | 2981 |
| (12) | 30 | 24541 | (12) | 30 | 4078 |
| (14) | 31 | 22653 | (14) | 31 | 3765 |
| (12) | 32 | 137095 | (12) | 32 | 22782 |
| (12) | 33 | 16612 | (12) | 33 | 2761 |
| (13) | 34 | 14725 | (13) | 34 | 2447 |
| (12) | 35 | 12837 | (12) | 35 | 2134 |
| (9) | 36 | 143230 | (9) | 36 | 23802 |
| (9) | 37 | 18311 | (9) | 37 | 3043 |
| (9) | 38 | 19821 | (9) | 38 | 3294 |
| (9) | 39 | 17934 | (9) | 39 | 2981 |
| (12) | 40 | 41058 | (12) | 40 | 6643 |
| (12) | 41 | 50024 | (12) | 41 | 7529 |
| (13) | 42 | 64654 | (13) | 42 | 10744 |
| (13) | 43 | 64890 | (13) | 43 | 10783 |
| (13) | 44 | 73856 | (13) | 44 | 12273 |
| (12) | 45 | 102502 | (12) | 45 | 17034 |
| (13) | 46 | 108543 | (13) | 46 | 18038 |
| (15) | 47 | 48232 | (15) | 47 | 5687 |
| (16) | 48 | 139090 | (16) | 48 | 16399 |
| (16) | 49 | 21185 | (16) | 49 | 2498 |
| (16) | 50 | 25093 | (16) | 50 | 2959 |
| (16) | 51 | 23139 | (16) | 51 | 2728 |
| (16) | 52 | 21082 | (16) | 52 | 2486 |
| (15) | 53 | 45764 | (15) | 53 | 5396 |
| (15) | 54 | 42678 | (15) | 54 | 4862 |
| (17) | 55 | 80471 | (17) | 55 | 9487 |
| (15) | 56 | 39850 | (15) | 56 | 4698 |
| (15) | 57 | 45764 | (15) | 57 | 5396 |
Table 8.
Transportation of relief materials from opened temporary transfer facilities to affected points
| Affected point | Affected point | ||||
| food | Daily necessities | food | Daily necessities | ||
| 1 | 10 | 10 | 30 | 11.1 | 11.8 |
| 2 | 10.4 | 10.4 | 31 | 11.44 | 11.57 |
| 3 | 10.88 | 10.88 | 32 | 22.3 | 23 |
| 4 | 11.2 | 11.2 | 33 | 11.7 | 12.4 |
| 5 | 11.76 | 11.76 | 34 | 12.7 | 12.7 |
| 6 | 8.15 | 16.3 | 35 | 12 | 12.7 |
| 7 | 8.43 | 16.58 | 36 | 30.86 | 39.29 |
| 8 | 8.51 | 16.66 | 37 | 18.02 | 26.45 |
| 9 | 8.67 | 16.82 | 38 | 18.8 | 27.23 |
| 10 | 8.37 | 16.52 | 39 | 19.4 | 27.83 |
| 11 | 35.26 | 22.66 | 40 | 10.86 | 11.56 |
| 12 | 22.86 | 22.86 | 41 | 12.24 | 12.94 |
| 13 | 20.86 | 20.86 | 42 | 18.6 | 18.6 |
| 14 | 21.06 | 21.06 | 43 | 25.08 | 25.08 |
| 15 | 16.36 | 16.36 | 44 | 20.4 | 20.4 |
| 16 | 21.66 | 21.66 | 45 | 9.7 | 10.4 |
| 17 | 22.76 | 22.76 | 46 | 10.8 | 10.8 |
| 18 | 22.96 | 22.96 | 47 | 15.3 | 10.62 |
| 19 | 16.76 | 16.76 | 48 | 16.94 | 20.4 |
| 20 | 23.56 | 23.56 | 49 | 18.04 | 21.5 |
| 21 | 23 | 23 | 50 | 17.84 | 21.3 |
| 22 | 17.55 | 25.7 | 51 | 18.2 | 21.66 |
| 23 | 17.17 | 25.32 | 52 | 18.94 | 22.4 |
| 24 | 15 | 15 | 53 | 15.4 | 10.72 |
| 25 | 12.34 | 12.34 | 54 | 17.6 | 12.92 |
| 26 | 12.06 | 12.06 | 55 | 33.12 | 33.42 |
| 27 | 14.04 | 14.04 | 56 | 16.9 | 12.22 |
| 28 | 15.6 | 15.73 | 57 | 16.76 | 12.08 |
| 29 | 10.96 | 11.09 | - | - | - |
| Affected point | Affected point | ||||
| food | Daily necessities | food | Daily necessities | ||
| 1 | 10 | 10 | 30 | 11.1 | 11.8 |
| 2 | 10.4 | 10.4 | 31 | 11.44 | 11.57 |
| 3 | 10.88 | 10.88 | 32 | 22.3 | 23 |
| 4 | 11.2 | 11.2 | 33 | 11.7 | 12.4 |
| 5 | 11.76 | 11.76 | 34 | 12.7 | 12.7 |
| 6 | 8.15 | 16.3 | 35 | 12 | 12.7 |
| 7 | 8.43 | 16.58 | 36 | 30.86 | 39.29 |
| 8 | 8.51 | 16.66 | 37 | 18.02 | 26.45 |
| 9 | 8.67 | 16.82 | 38 | 18.8 | 27.23 |
| 10 | 8.37 | 16.52 | 39 | 19.4 | 27.83 |
| 11 | 35.26 | 22.66 | 40 | 10.86 | 11.56 |
| 12 | 22.86 | 22.86 | 41 | 12.24 | 12.94 |
| 13 | 20.86 | 20.86 | 42 | 18.6 | 18.6 |
| 14 | 21.06 | 21.06 | 43 | 25.08 | 25.08 |
| 15 | 16.36 | 16.36 | 44 | 20.4 | 20.4 |
| 16 | 21.66 | 21.66 | 45 | 9.7 | 10.4 |
| 17 | 22.76 | 22.76 | 46 | 10.8 | 10.8 |
| 18 | 22.96 | 22.96 | 47 | 15.3 | 10.62 |
| 19 | 16.76 | 16.76 | 48 | 16.94 | 20.4 |
| 20 | 23.56 | 23.56 | 49 | 18.04 | 21.5 |
| 21 | 23 | 23 | 50 | 17.84 | 21.3 |
| 22 | 17.55 | 25.7 | 51 | 18.2 | 21.66 |
| 23 | 17.17 | 25.32 | 52 | 18.94 | 22.4 |
| 24 | 15 | 15 | 53 | 15.4 | 10.72 |
| 25 | 12.34 | 12.34 | 54 | 17.6 | 12.92 |
| 26 | 12.06 | 12.06 | 55 | 33.12 | 33.42 |
| 27 | 14.04 | 14.04 | 56 | 16.9 | 12.22 |
| 28 | 15.6 | 15.73 | 57 | 16.76 | 12.08 |
| 29 | 10.96 | 11.09 | - | - | - |
Table 9.
Sensitivity analysis of $ P $
| P | Amount of opened facilities | Opened temporary transfer facilities (the optimal solution) | Objective function value | Running time(s) | ||||||
| MIN | MAX | AVG | Optimal | AVG | SD | AVG | minimum | SD | ||
| 4 | 4 | 4 | 4 | (5), (9), (12), (15) | 13273776.83 | 14868939.06 | 1451416.62 | 53.48 | 16.16 | 42.31 |
| 5 | 5 | 5 | 5 | (2), (3), (9), (13), (15) | 8848462.92 | 9220754.33 | 302415.93 | 207.17 | 165.37 | 25.97 |
| 6 | 6 | 6 | 6 | (1), (4), (9), (12), (13), (15) | 6853198.43 | 7271162.90 | 290586.68 | 244.28 | 183.08 | 36.24 |
| 7 | 7 | 7 | 7 | (1), (2), (4), (9), (13), (14), (15) | 5781612.49 | 6227580.83 | 273625.21 | 257.40 | 234.93 | 12.80 |
| 8 | 8 | 8 | 8 | (1), (2), (4), (9), (12), (13), (15), (16) | 5072974.53 | 5295173.25 | 224424.74 | 244.43 | 178.19 | 49.48 |
| 9 | 9 | 9 | 9 | (1), (2), (4), (9), (13), (14), (15), (16), (17) | 4858475.72 | 5124041.30 | 150278.08 | 244.43 | 178.19 | 49.48 |
| 10 | 9 | 10 | 9.8 | (1), (2), (4), (9), (12), (13), (14), (15), (16), (17) | 4782950.76 | 4924828.55 | 113448.04 | 233.81 | 193.92 | 26.61 |
| 11 | 11 | 11 | 11 | (1), (2), (3), (4), (9), (12), (13), (14), (15), (16), (17) | 4778328.99 | 4925983.68 | 107442.10 | 228.31 | 213.40 | 16.51 |
| 12 | 11 | 12 | 11.2 | (1), (2), (4), (6), (9), (12), (13), (14), (15), (16), (18) | 4776395.25 | 4878544.65 | 110360.54 | 232.67 | 198.17 | 27.35 |
| 13 | 12 | 13 | 12.4 | (1), (2), (3), (4), (5), (9), (10), (12), (13), (14), (15), (16), (18) | 4783358.20 | 4892302.40 | 102522.06 | 233.92 | 221.46 | 7.82 |
| 14 | 11 | 14 | 12.5 | (1), (2), (4), (6), (9), (12), (13), (14), (15), (16), (17) | 4757147.13 | 4856777.97 | 106004.73 | 237.36 | 224.33 | 12.72 |
| P | Amount of opened facilities | Opened temporary transfer facilities (the optimal solution) | Objective function value | Running time(s) | ||||||
| MIN | MAX | AVG | Optimal | AVG | SD | AVG | minimum | SD | ||
| 4 | 4 | 4 | 4 | (5), (9), (12), (15) | 13273776.83 | 14868939.06 | 1451416.62 | 53.48 | 16.16 | 42.31 |
| 5 | 5 | 5 | 5 | (2), (3), (9), (13), (15) | 8848462.92 | 9220754.33 | 302415.93 | 207.17 | 165.37 | 25.97 |
| 6 | 6 | 6 | 6 | (1), (4), (9), (12), (13), (15) | 6853198.43 | 7271162.90 | 290586.68 | 244.28 | 183.08 | 36.24 |
| 7 | 7 | 7 | 7 | (1), (2), (4), (9), (13), (14), (15) | 5781612.49 | 6227580.83 | 273625.21 | 257.40 | 234.93 | 12.80 |
| 8 | 8 | 8 | 8 | (1), (2), (4), (9), (12), (13), (15), (16) | 5072974.53 | 5295173.25 | 224424.74 | 244.43 | 178.19 | 49.48 |
| 9 | 9 | 9 | 9 | (1), (2), (4), (9), (13), (14), (15), (16), (17) | 4858475.72 | 5124041.30 | 150278.08 | 244.43 | 178.19 | 49.48 |
| 10 | 9 | 10 | 9.8 | (1), (2), (4), (9), (12), (13), (14), (15), (16), (17) | 4782950.76 | 4924828.55 | 113448.04 | 233.81 | 193.92 | 26.61 |
| 11 | 11 | 11 | 11 | (1), (2), (3), (4), (9), (12), (13), (14), (15), (16), (17) | 4778328.99 | 4925983.68 | 107442.10 | 228.31 | 213.40 | 16.51 |
| 12 | 11 | 12 | 11.2 | (1), (2), (4), (6), (9), (12), (13), (14), (15), (16), (18) | 4776395.25 | 4878544.65 | 110360.54 | 232.67 | 198.17 | 27.35 |
| 13 | 12 | 13 | 12.4 | (1), (2), (3), (4), (5), (9), (10), (12), (13), (14), (15), (16), (18) | 4783358.20 | 4892302.40 | 102522.06 | 233.92 | 221.46 | 7.82 |
| 14 | 11 | 14 | 12.5 | (1), (2), (4), (6), (9), (12), (13), (14), (15), (16), (17) | 4757147.13 | 4856777.97 | 106004.73 | 237.36 | 224.33 | 12.72 |
Table 10.
Performance comparison between genetic algorithm (GA) and immune optimization algorithm (IOA)
| Algorithm | Time(AVG) | |||
| 9 | GA | 4858475.72 | 5124041.30 | 244.43 |
| IOA | 4936050.86 | 5244041.02 | 250.67 | |
| 10 | GA | 4782950.76 | 4924828.55 | 233.81 |
| IOA | 4821963.21 | 4959828.55 | 230.14 | |
| 11 | GA | 4778328.99 | 4925983.68 | 228.31 |
| IOA | 4790375.49 | 4965983.68 | 225.63 | |
| 12 | GA | 4776395.25 | 4878544.65 | 232.67 |
| IOA | 4795203.36 | 4879121.35 | 229.43 | |
| 13 | GA | 4783358.20 | 4892302.40 | 233.92 |
| IOA | 4790522.90 | 4898580.12 | 228.45 | |
| 16 | GA | 4757147.13 | 4856777.97 | 237.36 |
| IOA | 4767658.14 | 4848676.16 | 233.97 |
| Algorithm | Time(AVG) | |||
| 9 | GA | 4858475.72 | 5124041.30 | 244.43 |
| IOA | 4936050.86 | 5244041.02 | 250.67 | |
| 10 | GA | 4782950.76 | 4924828.55 | 233.81 |
| IOA | 4821963.21 | 4959828.55 | 230.14 | |
| 11 | GA | 4778328.99 | 4925983.68 | 228.31 |
| IOA | 4790375.49 | 4965983.68 | 225.63 | |
| 12 | GA | 4776395.25 | 4878544.65 | 232.67 |
| IOA | 4795203.36 | 4879121.35 | 229.43 | |
| 13 | GA | 4783358.20 | 4892302.40 | 233.92 |
| IOA | 4790522.90 | 4898580.12 | 228.45 | |
| 16 | GA | 4757147.13 | 4856777.97 | 237.36 |
| IOA | 4767658.14 | 4848676.16 | 233.97 |
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