Designing Response Supply Chain Against Bioattacks
Name
sns.pdf
Description
Accepted version
Size
1.75 MB
Format
Adobe PDF
Checksum (MD5)
4f60367fdfe1af7ba69887c6ee5d45f3
Author(s)
Simchi-Levi, David
Trichakis, Nikolaos
Zhang, Yun
Date Issued
August 2019
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Simchi-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 Authors
Version
Author's final manuscript
Abstract
We 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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/OPRE.2019.1862
