{"id":656,"date":"2021-06-04T21:12:03","date_gmt":"2021-06-04T21:12:03","guid":{"rendered":"https:\/\/sites.rutgers.edu\/weiwei-chen\/?p=656"},"modified":"2024-08-23T18:54:06","modified_gmt":"2024-08-23T18:54:06","slug":"call-for-papers-iise-transactions","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/weiwei-chen\/call-for-papers-iise-transactions\/","title":{"rendered":"Call for Papers: IISE Transactions"},"content":{"rendered":"

Call for Papers: IISE Transactions<\/em> (Focused Issue on Scheduling and Logistics)
\nSpecial Issue: “Modeling and Optimization of Supply Chain Resilience to Pandemics and Long-Term Crises”
\nSubmission portal: https:\/\/think.taylorandfrancis.com\/special_issues\/supply-chain-resilience-pandemics\/<\/a><\/p>\n

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 \u201cnew normal\u201d 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 \u201cold normal\u201d 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 \u201cold normal\u201d (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.<\/p>\n

The supply chain crisis context can be described with the following characteristics:<\/p>\n