Transportation risk analysis using probabilistic model checking

被引：27

作者：

Soeanu, Andrei ^{[1
]}

Debbabi, Mourad ^{[1
]}

Alhadidi, Dima ^{[1
]}

Makkawi, Makram ^{[1
]}

Allouche, Mohamad ^{[2
]}

Belanger, Micheline ^{[2
]}

Lechevin, Nicholas ^{[2
]}

机构：

[1] Concordia Univ, Montreal, PQ, Canada

[2] Def Res & Dev Canada, Valcartier, PQ, Canada

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2015年 / 42卷 / 09期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Transport; Risk management; Decision making; Model checking;

D O I：

10.1016/j.eswa.2014.12.052

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Transportation and supply chain activities represent essential components in many endeavors covering both public and private domains. However, the underlying transport networks are complex and potentially fragile due to weather, natural disasters or other risk factors. Thus, assessing transportation related risk represents a key decision support capability along with the ability to evaluate contingency options for risk mitigation. In this paper, we address these issues by adopting probabilistic model checking to evaluate the risk and contingency options related to transportation tasks. In this pursuit, risk related properties are assessed for behavioral models capturing the transport system. Moreover, we show the usefulness of constructing decision trees that can provide insightful means of risk appraisal. The proposed approach can help decision makers evaluate contingency options and determine lower and upper cost bounds for risky transportation tasks such as those involved in humanitarian aid provision. The proposed approach is also illustrated with a case study. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：4410 / 4421

页数：12

共 38 条

[1]

Abraham E., 2010, Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems (QEST 2010), P37, DOI 10.1109/QEST.2010.13

[2]

[Anonymous], 1998, On algorithmic verification methods for probabilistic systems

[3]

Aziz A, 1996, LNCS, P269, DOI [DOI 10.1007/3-540-61474-5_75, DOI 10.1007/3-540-61474-5]

[4]

Barbot B, 2011, LECT NOTES COMPUT SC, V6605, P128, DOI 10.1007/978-3-642-19835-9_12

[5]

Beasley M., 2005, Journal of Accounting and Public Policy, V25, P521, DOI DOI 10.1016/J.JACCPUBPOL.2005.10.001

[6] Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains [J].

Ben-Tal, Aharon ;

Do Chung, Byung ;

Mandala, Supreet Reddy ;

Yao, Tao .

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2011, 45 (08) :1177-1189

[7]

Berger P., 2004, INT J MANAGEMENT SCI

[8]

Besnard F, 2007, THESIS KTH SCH ELECT

[9]

Caulkins J., 2007, OPTIMIZING INVESTMEN

[10]

Chand Mahesh, 2015, International Journal of Business Information Systems, V18, P67, DOI 10.1504/IJBIS.2015.066128

← 1 2 3 4 →