Grant: Discovery Grants Program - Individual

Disruptions in supply chain and logistics networks are caused due to natural disasters, intentional or unintentional human actions (e.g., terrorist attacks, geopolitical conflict, labor strikes, fire, etc.), or operational contingencies (e.g., machine breakdowns or transportation/delivery delays). Disruptions due to natural disasters or the ones due to intentional or unintentional human actions often lead to severe economic impacts on the supply chains, even though their occurrences are rare. Whereas most supply chain and logistics systems are engineered to handle disruptions from random failures or from random acts of nature, with little or no degradation in performance, they are highly vulnerable to intentional threats. Furthermore, contrary to popular belief, studies have shown that a malicious and coordinated small-scale intentional attack can inflict more damage than the random acts of nature such as a major hurricane or earthquake. Moreover, the impact of supply chain disruptions caused by the COVID-19 pandemic is unprecedented. Such large-scale disruptions have compelled several businesses to take a new perspective at their supply chains, to understand their vulnerabilities, and take actions to improve resilience and robustness to reduce the risks from future disruptions. The goal of the proposed research program is to develop prescriptive models to incorporate resiliency, reliability, and robustness using risk-adverse and risk neutral models for designing and managing resilient and robust supply chains in three application areas: (i) supply chain network for e-commerce and omnichannel retailing, (ii) hub network for transportation and telecommunication systems, and (iii) service network for freight transportation. More specifically, we will study strategic design, tactical planning, and operational coordination problems that arise in these supply chain and logistics networks, seeking to understand how integrated decision models can help make better decisions. The common theme across these areas is (1) to build integrated decision models that capture complex interactions between major supply chain drivers, (2) to exploit the structure of the problem and develop innovative and tractable (exact and heuristic) solution approaches based on recent advances on large-scale optimization, decomposition methods as well as artificial intelligence (AI) and machine learning (ML) techniques, (3) to characterize the structure of the optimal solution and draw managerial insights. The insights from this research will act as a useful decision support tool for the managers in commercial business sectors as well as non-commercial settings such as military, national security, healthcare (infectious disease control and epidemic), in mitigating disruptions efficiently.