Call for Papers: IISE Transactions (Focused Issue on Scheduling and Logistics)
Special Issue: “Modeling and Optimization of Supply Chain Resilience to Pandemics and Long-Term Crises”
Submission portal: https://think.taylorandfrancis.com/special_issues/supply-chain-resilience-pandemics/
This Special Issue intends to showcase research addressing the novel decision-making settings entailed in supply chain resilience in the wake of the COVID-19 pandemic and characterized by crisis-like environment, epistemic and deep uncertainty, and adaptability as a “new normal” instead of stability and long-term planning. Here, the epistemic and deep uncertainty refers to the uncertainty that cannot be fully described by probabilistic modeling only due to incomplete awareness about anticipated events and their likelihood. Furthermore, preparedness, recovery and adaptation decisions should be planned and deployed in the presence of concurrent disruptions when bouncing back to the “old normal” is impossible or difficult in the short or medium-term. This setting is distinct from the traditional supply chain resilience research that has been developed to manage disruptions with an instantaneous impact (e.g., earthquake) followed by post-event recovery measures to return to the “old normal” (e.g., using backup sourcing and some extra inventory pre-positioning). Typically, supply chain resilience modelling is based on disruption probability estimations (i.e., random or hazard uncertainty: known-known and known-unknown). Modeling and optimization of supply chain resilience in epistemic uncertainty can be in conflict with traditional probabilistic modeling due to simultaneous existence of different uncertainty types and difficulties in uncertainty quantification. This requires application of different techniques such as possibilistic optimization, robust convex optimization, and chance-constrained optimization, and their synthesis with probabilistic modeling and artificial intelligence-based solution algorithms.
The supply chain crisis context can be described with the following characteristics:
- Long-lasting period of turbulence with unpredictably changing supply chain structures and the environment, i.e., a deep uncertainty (unknown-unknown)
- Simultaneous disruptions in supply, demand, and logistics
- Recovery is performed in the presence of a disruption and its hardly predictable scaling (i.e., coupling of supply chain and disruption dynamics)
- Simultaneous and/or sequential openings and closures of suppliers, facilities and markets
- Cascading effects of disruptions through the supply chain networks (i.e., the ripple effect)
Important Dates
- Manuscript submission: Feb 28, 2022
- Completion of 1st round review: May 31, 2022
- Completion of 2nd round review: Oct 31, 2022
- Final manuscript submission: Dec 31, 2022
- Tentative publication date: Feb, 2023
Focus Issue Editor
- Professor Phil Kaminsky, University of California at Berkeley
Guest Editors
- Professor David W. Coit, Rutgers University
- Professor Weiwei Chen, Rutgers University
- Professor Dmitry Ivanov, Berlin School of Economics and Law
- Professor Nezih Altay, DePaul University