Mathematical Optimization in Supply Chain Management

Dear Colleagues,

The COVID-19 pandemic provided insight in the world supply chain and the weakness due to due to expected events.  Specifically, system disruptions were significantly impacted by the closures of businesses, quarantine measures, and the disparities in the global health systems due to access inequalities and the differences in countries healthcare based on their world class ranking. As a result of the global health crisis, it is critical to identify the weaknesses in the global system and provide solutions that optimize the coordination of critical supplies across the world. This special issue is interested in research that uses probabilistic modeling or artificial intelligence to optimize the global supply chain throughput or increased resilience of the current supply chain paradigm. The probabilistic modeling approach of interests but not limited to includes both deterministic and stochastic modeling paradigms as well as static and dynamic modeling approaches. Also, journal articles that use deep learning or shallow machine learning approaches to minimize supply chain disruption, maximize supply chain resilience, or optimize the interstitial link between the system of system network.

Dr. Michael Hamilton
Dr. Niamat Ullah Ibne Hossain
Dr. Raed M. Jaradat
Guest Editors

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