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Manuscript deadline
28 February 2022

Cover image - IISE Transactions

IISE Transactions

Special Issue Editor(s)

David W. Coit, Rutgers University
[email protected]

Weiwei Chen, Rutgers University
[email protected]

Dmitry Ivanov, Berlin School of Economics and Law
[email protected]

Nezih Altay, DePaul University
[email protected]

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Modeling and Optimization of Supply Chain Resilience to Pandemics and Long-Term Crises

IISE Transactions: Focused Issue on Scheduling and Logistics

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)

We are seeking papers that fit their problem settings and methodology into this novel context. We do not limit our Special Issue to the COVID-19 pandemic only and encourage submissions looking at other possible supply chain crises that can happen in future, e.g., long-term political crises, global financial crises, recessions, and climate change. Creating relevant knowledge for these future challenges now is imperative and of vital importance. There is a pressing need to develop modeling and optimization techniques that account for crisis settings and guide firms in building adaptable, reconfigurable, resilient, and viable supply chains. Topics of interests include, but are not limited to:

  • modeling and optimization of supply chains under epistemic and deep uncertainty
  • multi-objective optimization of network designs with consideration of efficiency, adaptability, resilience, and viability
  • contingency planning for and reactive deployment of recovery under epistemic and deep uncertainty
  • design and management of supply chains based on inherent adaptability and ability to change as a “normal,” and not as a reaction to exceptional events
  • design and management of global supply chain networks given situational availability of some regions for production/logistics activities due to quarantines or severe and long natural disasters
  • utilization of digital technology and data analytics to enhance supply chain resilience and viability
  • response and recovery strategies taking potential crisis recurrence and setbacks into consideration

For these research domains, we expect novel and innovative contributions, ideally motivated by a practical context. We invite papers that go beyond stochastic modelling of resilience and encounter for the specifics of long-term supply chain crises.

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
[email protected]

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Submission Instructions

Papers must be submitted through http://mc.manuscriptcentral.com/iietransactions and prepared according to the journal’s Instructions for authors. Select “Special Issue” for the question “Please select the Focus Issue to which the paper is most related” at Step 1 in the submission process, and select the specific special issue at Step 6.

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