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dc.contributor.authorSimchi-Levi, David
dc.contributor.authorTrichakis, Nikolaos
dc.contributor.authorZhang, Yun
dc.date.accessioned2020-07-14T02:19:47Z
dc.date.available2020-07-14T02:19:47Z
dc.date.issued2019-08
dc.date.submitted2018-12
dc.identifier.issn1526-5463
dc.identifier.urihttps://hdl.handle.net/1721.1/126166
dc.description.abstractWe study a prescriptive model for end-to-end design of a supply chain for medical countermeasures (MCMs) to defend against bioattacks. We model the defender's MCMs inventory prepositioning and dispensing capacity installation decisions, attacker's move, and defender's adjustable shipment decisions so as to minimize inventory and lifeloss costs subject to population survivability targets. We explicitly account for the strategic interaction between defender's and attacker's actions, assuming information transparency. We consider the affinely adjustable robust counterpart (AARC) to our problem, which enables us to deal with realistic networks comprising millions of nodes. We provide theoretical backing to the AARC performance by proving its optimality under certain conditions. We conduct a high-fidelity case study on the design of an MCMs supply chain with millions of nodes to guard against anthrax attacks in the United States. We calibrate our model using data from a wide variety of sources, including literature and field experiments. We produce policy insights that have been long sought after but elusive until now. ©2019 INFORMS.en_US
dc.language.isoen
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.relation.isversionofhttps://dx.doi.org/10.1287/OPRE.2019.1862en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleDesigning Response Supply Chain Against Bioattacksen_US
dc.typeArticleen_US
dc.identifier.citationSimchi-Levi, David et al., "Designing Response Supply Chain Against Bioattacks." Operations Research 67, 5 (September-October 2019): p. 1246–68 doi. 10.1287/opre.2019.1862 ©2019 Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Managementen_US
dc.relation.journalOperations Researchen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-06-02T16:32:23Z
dspace.date.submission2020-06-02T16:32:26Z
mit.journal.volume67en_US
mit.journal.issue5en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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