Transportation risk analysis using probabilistic model checking

Soeanu, Andrei; Debbabi, Mourad; Alhadidi, Dima; Makkawi, Makram; Allouche, Mohamad; Bélanger, Micheline; Léchevin, Nicholas

doi:10.1016/j.eswa.2014.12.052

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…Logistics outsourcing can reduce fixed costs and increase flexibility, allowing for a greater focus on a firm's core activities, a reduction of heavy asset investments and an improvement of service quality (Hsu et al, 2012). At the same time, the decision to outsource includes a number of risks related to the loss of control, long-term commitment and the failures of some LSPs to perform their duties (Farahani et al, 2011;Wang et al, 2014;Soeanu et al, 2015). Table 1 summarises some of the expected advantages and disadvantages of logistics outsourcing: …”

Section: Introductionmentioning

confidence: 99%

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

Alkhatib

Darlington

Yang

et al. 2015

Expert Systems with Applications

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The increasing importance of logistics outsourcing and availability of logistics services providers (LSPs) highlights the significance and complexity of the LSP evaluation and selection process.Most existing LSP evaluation and selection studies use historical performance data and assume independence among decision criteria. This paper proposes an integrated logistics outsourcing approach to evaluate and select LSPs based on their logistics resources and capabilities. This novel approach combines a fuzzy decision making trial, evaluation laboratory (FDEMATEL) and fuzzy techniques to order preferences by similarity to ideal solution (FTOPSIS) methods.The new multi-criteria decision making (MCDM) model addresses the impact relationships between decision criteria and ranks LSP alternatives against weighted resources and capabilities.The effectiveness of this approach is demonstrated through a real case study and a two-phase sensitivity analysis confirms its robustness.

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Section: Introductionmentioning

confidence: 99%

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

Alkhatib

Darlington

Yang

et al. 2015

Expert Systems with Applications

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show abstract

“…Therefore, probabilistic risk assessment techniques have been widely used in a variety of applications in the marine industry. For instance, Soeanu et al (2015) have recently introduced a paper conducting probabilistic risk analysis method upon transportation solution. Likewise, another paper was presented to perform probabilistic risk assessment upon tanker ship collision cases which caused accidental cargo oil flow (Goerlandt and Montewka, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Quantification of human error probability towards the gas inerting process on-board crude oil tankers

Akyüz

2015

Safety Science

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“…PRISM is one of the widely used probabilistic model checker that has been successfully applied to model and assess various reactive systems that exhibit random as well as nondeterministic behavior [29]. A variety of systems have been analyzed with the help of PRISM such as security protocols, randomized distributed algorithms, network protocols, and transportation systems [30]- [33]. A system can be broken into modules that interact with each other thereby resulting in system evolution.…”

Section: ) the Probabilistic Model Checker (Prism)mentioning

confidence: 99%

A Multi-Tiered Defense Model for the Security Analysis of Critical Facilities in Smart Cities

Mohammad

2019

IEEE Access

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The design and development of future smart city services will usher with unprecedented creativity, a plethora of functionalities and conveniences to the urban communities. The success of smart city services depends on the assurance that security is maintained and protected to meet community expectations. However, due to the variety of components involved in the design of smart city services and their complex interactions, it becomes challenging to properly assess the overall security of the system throughout the service life cycle. This paper presents a holistic approach to model the security of smart city service infrastructure at a higher level of abstraction. To match realistic security deployments, the proposed model is based on multiple tiers of defense that includes defense at component level, system level, and security operation center. For a given system configuration and component vulnerability vector, the model assesses key security parameters for a variety of attacks. The proposed model is generic and can be used for studying the security of various smart city infrastructures, deployment configurations, and attack vectors.INDEX TERMS Smart city security, threat analysis, probabilistic model checking.

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Transportation risk analysis using probabilistic model checking

Cited by 26 publications

References 19 publications

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

A novel technique for evaluating and selecting logistics service providers based on the logistics resource view

Quantification of human error probability towards the gas inerting process on-board crude oil tankers

A Multi-Tiered Defense Model for the Security Analysis of Critical Facilities in Smart Cities

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