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DTSTAMP:20241105T001441Z
SUMMARY:Esra Büyüktahtakιn Toy\, PhD\, Virginia Tech 
DESCRIPTION: \n\nABSTRACT\n\nBiological systems have highly uncertain behav
 iors to predict. The\ninherent uncertainty in species’ population dynami
 cs and\ncombinatorial resource allocation decisions often result in\nmulti
 -stage stochastic optimization problems. In this talk\, we will\npresent a
 n innovative data-driven multi-stage stochastic mixed-integer\noptimizatio
 n modeling and cutting-plane approach to tackle a\nlarge-scale optimizatio
 n problem under decision-dependent uncertainty.\nWe demonstrate our model 
 and the algorithm on one of the most pressing\nproblems of the USDA Forest
  Service\, the Emerald Ash Borer (EAB)\ninfestation killing millions of as
 h trees in North America. We\nvalidate our operations research approach us
 ing 7-years of unique ash\nhealth data collected by our collaborators from
  the USDA Forest\nService over multiple spatial locations in Ohio. Computa
 tional results\nshow that our cutting plane-based method can substantially
  reduce the\nsolution time for this forest insect infestation problem with
  binary\nand continuous resource allocation decisions. Our findings provid
 e\ncritical insight into a long-debated question among foresters:\ntreatme
 nt versus removal of the ash trees to save as many trees as\npossible. Thi
 s study is a part of the ongoing joint work with\ncollaborators from the U
 .S. Forest Service (Robert Haight\, PhD\;\nKathleen Knight\, PhD\; and Cha
 rlie Flower\, PhD) and former doctoral\nstudents (Eyyub Kibis\, PhD\; Saba
 h Bushaj\, PhD\; and Chen Chen\, PhD) and\na faculty collaborator from NJI
 T\, Wenbo Cai\, PhD.\n 
DTSTART:20230316T140000Z
DTEND:20230316T150000Z
LOCATION:https://drexel.zoom.us/j/82658935184
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