Abdullah Al Khaled
Mississippi State University
14 Papers
5 Citations
Abdullah Al Khaled is an academic researcher from Mississippi State University. The author has contributed to research in topics: Imperialist competitive algorithm & Supply chain. The author has an hindex of 8, co-authored 14 publications. Previous affiliations of Abdullah Al Khaled include University of Alabama at Birmingham & Bangladesh University of Engineering and Technology.
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Papers
Resilient supplier selection and optimal order allocation under disruption risks
Seyed Mohsen Hosseini,Nazanin Morshedlou,Dmitry Ivanov,M.D. Sarder,Kash Barker,Abdullah Al Khaled +5 more
TL;DR: A stochastic bi-objective mixed integer programming model is proposed to support the decision-making in how and when to use both proactive and reactive strategies in supplier selection and order allocation and can benefit suppliers to find the optimal set of operational decisions that enhance their resilience capabilities.
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A hybrid ensemble and AHP approach for resilient supplier selection
TL;DR: This paper first seeks to explore the resilience criteria for supplier selection based on the notion of resilience capacities which can be divided into three categories: absorptive capacity, adaptive capacity, and restorative capacity.
146
A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer
TL;DR: In this paper, the authors explored the key drivers that contribute to the design of resilient supply chains based on the notion of absorptive, adaptive and restorative capacities and introduced a generic conceptual framework comprising five key phases: threat analysis, resilience capacity design, resilience cost evaluation, resilience quantification, and resilience improvement.
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Freight flow optimization to evaluate the criticality of intermodal surface transportation system infrastructures
TL;DR: In this paper, an optimization model that facilitates all OD-specific traffic flow with optimum cost, utilizing an integrated intermodal network consisting of three surface transportation modes: highway, railway, and waterway, is presented.
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Applying Deep Learning to Public Health: Using Unbalanced Demographic Data to Predict Thyroid Disorder
Yasser Attiga,Shih-Yin Chen,John LaGue,Anaelia Ovalle,Nathan Stott,Tom Brander,Abdullah Al Khaled,Gaurika Tyagi,Patricia Francis-Lyon +8 more
- 01 Nov 2018
TL;DR: The results suggest that deep learning may be successfully employed to select candidates for early intervention for improved health outcomes, utilizing a large dataset with only minimal demographic variables, similar to datasets that are held by the marketing arms of healthcare providers.
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