Peter Yun Zhang

PhD Candidate, Institute for Data, Systems, and Society, MIT (2013-present)
Advisor: David Simchi-Levi
Google Scholar page
Contact: pyzhang at mit dot edu
Bio. I am a fifth year PhD Candidate in the Institute for Data, Systems, and Society at MIT. My current research interests lie in optimization theory, and its application to designing resilient service and production systems. I have 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. My 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 second place, and Ford 2015 Engineering Excellence Award. Prior to studying at MIT, I obtained Bachelor of Applied Science and Master of Applied Science degrees from the University of Toronto. I am a recipient of the 2013 University of Toronto Gordon Cressy Student Leadership Award.

Research Summary. I define a class of network design problems that are motivated by modern operations research applications, from public health network design, to supply chain risk management, to robust renewable energy system design. I formulate the general decision framework as a dynamic optimization problem on networks. Such multi-stage problems are intractable. I characterize a class of heuristics that are suitable for solving such problems, trading off between tractability and optimality. More precisely, I show that the simplest of such heuristics (so-called affine policies) are optimal for simple networks (trees). Such insight allows me to translate the complexity of the network structure to the complexity of optimal solution heuristics. For application, I focus on the design of an antibiotic supply chain to defend against large-scale bioattacks. 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 defender’s static antibiotic inventory prepositioning decision and dispensing capacity installation decision, attacker’s move, and defender’s adjustable shipment decision, so as to minimize inventory and life loss costs, subject to population survivability targets. We explicitly account for the strategic interaction between defender’s and attacker’s actions, assuming information transparency. I perform a high-fidelity case study on the design of an antibiotic supply chain with millions of nodes to guard against anthrax attacks. I calibrate the model using data from a wide variety of sources, including literature and field experiments, producing produce policy insights that have been long sought after but elusive up until now.