Selim Bora
Texas A&M University at Qatar
9 Papers
8 Citations
Selim Bora is an academic researcher from Texas A&M University at Qatar. The author has contributed to research in topics: Job shop scheduling & Supply chain network. The author has an hindex of 3, co-authored 9 publications. Previous affiliations of Selim Bora include Texas A&M University.
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Papers
Models and computational algorithms for maritime risk analysis: a review
TL;DR: A detailed literature review of over 180 papers about different threats, their consequences pertinent to the maritime industry, and a discussion on various risk assessment models and computational algorithms are provided.
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A quantitative approach for assessment and improvement of network resilience
TL;DR: A conceptual framework featuring the ability of the network system to adopt alternative plans when a component is disrupted is introduced and an optimization model is further introduced to maximize the network resilience under budget constraint through reinforcing the weakest components in the network.
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Liquefied Natural Gas Ship Route Planning Model Considering Market Trend Change
Jaeyoung Cho,Gino J. Lim,Taofeek Biobaku,Selim Bora,Hamid R. Parsaei +4 more
- 21 Oct 2014
TL;DR: In this paper, the authors considered a new biannual LNG ship routing and scheduling problem and a stochastic extension under boil-off gas (BOG) uncertainty while serving geographically dispersed multiple customers using a fleet of heterogeneous vessels.
Literature Survey on Underwater Threat Detection
Taofeek Biobaku,Gino J. Lim,Jaeyoung Cho,Selim Bora,Hamid R. Parsaei +4 more
- 20 Apr 2015
TL;DR: In this article, the authors present a concise review on the literature in threat detection within the maritime domain with specific emphasis on solution methodologies, sensing technologies, regional affiliations of authors and research contributions related to underwater threat detection.
An optimal sonar placement approach for detecting underwater threats under budget limitations
TL;DR: Numerical results suggest a new optimization model using hexagonal grid systems delivers acceptable detection coverage under a multi-period placement scheme.
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