Artificial IntelligenceNo Access

Research on the Process Supervision and Forecasting Model of Railway Emergency Based on GERTS

Jing Zuo

School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China

E-mail Address: 531094902@qq.com

Corresponding author.

Search for more papers by this author

Min Lyv

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, P. R. China

E-mail Address: lvminsky8800@163.com

Search for more papers by this author

Zhen-Hai Zhang

School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China

E-mail Address: zhangzhenhai@mail.lzjtu.cn

Search for more papers by this author

, and

Xiao-Qiang Wu

Key Laboratory of Mechanism and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, P. R. China

E-mail Address: wxqimun@163.com

Search for more papers by this author

https://doi.org/10.1142/S0218001419590031Cited by:3 (Source: Crossref)

Abstract

The weakness of supervision and management measures in railway emergency rescue led to difficulties of whole of monitoring and disposal on the completion time and effect. In order to solve this problem, the GERTS stochastic network schedule is introduced to the process supervision of railway emergency rescue based on the analysis of process of railway emergency rescue. With critical procedures serving as the nodes and execution process as the edges of the network, the random network model is thus formed. In case of emergency, the simulation of GERTS random network can be used to predict necessary rescue time and disposal efficiency, with previous rescue time and effect for reference. The analysis of the case shows that the simulation obtained by this method is consistent with the practical situation of the rescue. Measure standards from this method can be acquired to evaluate the work schedule and efficiency, efficiently supervising the delay of different divisions during the rescue. The proposed measure improves the efficiency and handling capacity of railway emergency rescue, having certain theoretical guidance significance and application value on railway emergency rescue.

Keywords:

References

1. D. Alem, A. Clark and A. Moreno, Stochastic network models for logistics planning in disaster relief, Eur. J. Oper. Res. 255 (1) (2016) 187–206. Web of Science, Google Scholar
2. J.-P. Chang, Z.-S. Cheng, G.-H. Zhou and Y.-L. Li, The multi-index risk evaluation of railway emergency plans based on prospect theory, J. China Railway Soc. 2 (2016) 7–18. Google Scholar
3. W. Chen, B.-H. Guo and D.-D. Lu, A GERT network model about enterprise new product development risk decisions based on Bayesian theory, Sci. Technol. Manage. Res. 250 (2) (2016) 566–578. Google Scholar
4. Y. Cheng and Z. Liang, A strategic planning model for the railway system accident rescue problem, Transp. Res. E 69 (3) (2014) 75–96. Web of Science, Google Scholar
5. H. Chi, J. Li, X. Shao and M. Gao, Timeliness evaluation of emergency resource scheduling, Eur. J. Oper. Res. (2016) 258. Web of Science, Google Scholar
6. S. Debois, T. Hildebrand and L. Sandberg, Experience report: Constraint-based modelling and simulation of railway emergency response plans, Procedia Computer Sci. 83 (2016) 1295–1300. Google Scholar
7. D. Ergu, G. Kou, Y. Peng and M. Zhang, Estimating the missing values for the incomplete decision matrix and consistency optimization in emergency management, Appl. Math. Modelling 40 (1) (2015) 254–267. Web of Science, Google Scholar
8. S. Ha, J.-Q. Zhang, E.-L. Guo, R.-N. Wu, Q.-Y. Ma and Y.-F. Wang, Study on inference model of the grassland drought and snow disaster chain based on Bayesian networks, J. Nat. Disasters 25 (4) (2016) 20–29. Google Scholar
9. X.-H. Li, F.-Q. Wang and X.-H. Chen, Support vector machine ensemble based on Choquet integral for financial distress prediction, Int. J. Pattern Recognit. Artif. Intell. 29 (4) (2015) 1550016-1–24. Link, Web of Science, Google Scholar
10. S.-H. Liu, F. Yu and P. S. P. Wang, Rank factor granules with fuzzy collaborative clustering and factor space theory, Int. J. Pattern Recognit. Artif. Intell. 31 (6) (2017) 1759008-1–21. Link, Web of Science, Google Scholar
11. P. P. Repoussis, D. C. Paraskevopoulos, A. Vazacopoulos and N. Hupert, Optimizing emergency preparedness and resource utilization in mass-casualty incidents, Eur. J. Oper. Res. 255 (2) (2016) 531–544. Web of Science, Google Scholar
12. S. J. Sadjadi, A. Makui, E. Dehghani and M. Pourmohammad, Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations, Appl. Math. Modelling 40 (1) (2015) 1419–1428. Web of Science, Google Scholar
13. L. Yu and K. Zhou, A dynamic local path planning method for outdoor robot based on characteristics extraction of laser rangefinder and extended support vector machine, Int. J. Pattern Recognit. Artif. Intell. 30 (2) (2016) 1659004-1–23. Link, Web of Science, Google Scholar
14. J. Zhang, W. Huang and J. Xu, Evaluation of the city emergency capacity based on the evidence theory, Math. Probl. Eng. 2013 (5) (2013) 1–6. Google Scholar
15. Z.-H. Zhang, X.-M. Wang, Y.-P. Zhang and H.-Y. Wang, Decision-making method of railway emergency rescue based on hierarchical agent, J. Transp. Syst. Eng. Inform. Technol. 13 (2) (2013) 141–146. Google Scholar
16. Y.-J. Zheng, H.-F. Ling, H.-H. Shi, H.-S. Chen and S.-Y. Chen, Emergency railway wagon scheduling by hybrid biogeography-based optimization, Computers Oper. Res. 43 (1) (2014) 1–8. Web of Science, Google Scholar
17. Y.-J. Zheng, M.-X. Zhang, H.-F. Ling and S.-Y. Chen, Emergency railway transportation planning using a hyper-heuristic approach, IEEE Trans. Intell. Transp. Syst. 16 (1) (2015) 321–329. Web of Science, Google Scholar
18. L. Zhou, J. Xie, X. Gu, Y. Lin, P. Ieromonachou and X. Zhang, Forecasting return of used products for remanufacturing using graphical evaluation and review technique (GERT), Int. J. Prod. Econ. 181 (2016) 315–324. Web of Science, Google Scholar
19. J. Zhu, Z.-Y. Wang and Y. Qin, Standardized management of China railway emergency plan strategic ponder, Procedia Eng. 52 (2013) 701–706. Google Scholar