{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T16:28:50Z","timestamp":1725467330358},"reference-count":48,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Operations Research"],"published-print":{"date-parts":[[2019,9]]},"abstract":" To protect civilians in the event of a bioattack, that is, the intentional release of pathogens against them to cause harm, public authorities maintain stockpiles of antibiotics. Unfortunately, a minute quantity of pathogens is sufficient to infect a large number of people. Furthermore, the treatment time window can be very short, for example, on the order of hours. Therefore, a significant amount of resources goes into maintaining a responsive antibiotics supply chain. In \u201cDesigning Response Supply Chain Against Bioattacks,\u201d Simchi-Levi, Trichakis, and Zhang devise a decision-support framework for end-to-end supply chain design that captures logistical costs, transportation and dispensing times, and health effects of treatment delays. The authors model the problem as two-stage robust optimization on a network, which has been shown in the literature to be NP-hard. Using the so-called affinely adjustable robust counterpart, the authors are able to solve this problem on cases with hundreds of thousands of nodes. Furthermore, they prove that affine policies are optimal under certain conditions. Extensive numerical studies demonstrate the efficacy of affine policies for the general problem. 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