Seminar | January 22 | 3:30-5 p.m. | 306 Soda Hall
Peter Zhang, Massachusetts Institute of Technology
Abstract: Bioattacks, i.e., the intentional release of pathogens or biotoxins against humans to cause serious illness and death, pose a significant threat to public health and safety due to the availability of pathogens worldwide, scale of impact, and short treatment time window. I model the defenders static antibiotic inventory prepositioning decision and dispensing capacity installation decision, attackers move, and defenders adjustable shipment decision, so as to minimize inventory and life loss costs, subject to population survivability targets. I explicitly account for the strategic interaction between defenders and attackers actions, assuming information transparency. I perform a high-fidelity case study on the design of an antibiotic supply chain with hundreds of thousands of nodes to guard against anthrax attacks. I calibrate the model using data from a wide variety of sources, including literature and field experiments, and produce policy insights that have been long sought after but elusive up until now. More generally, via this work I define a class of network design problems that are motivated by modern operations applications, from public health network design to supply chain risk management. Such multi-stage problems are intractable. But as illustrated by the biodefense supply chain model, I characterize a class of heuristics that are suitable for solving such problems, trading off tractability and optimality. More specifically, I show that the simplest of such heuristics (affine policies) are optimal for simple networks (trees).
Bio: Peter Zhang is a PhD Candidate in the Institute for Data, Systems, and Society at MIT. His current research interests lie in optimization theory, and combining learning and optimization in operations problems. He has developed quantitative decision support solutions in both private and public sectors. The former resulted in the implementation of a supply chain resiliency decision support tool in Fortune top 50 automotive and aerospace companies, and the latter included simulation testing on a million-node national biodefense network to elicit public health policy recommendations. His aforementioned PhD works have been recognized in the academic community and industry via several awards, including the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice, POMS Supply Chain Management Student Paper Competition 2 nd place, and Ford Engineering Excellence Award. Prior to studying at MIT, Peter obtained Bachelor of Applied Science in Engineering Science and Master of Applied Science degrees from the University of Toronto. He was a recipient of the University of Toronto Gordon Cressy Student Leadership Award.