ISyE Seminar Series: Yongjia Song
"Multi-stage Stochastic Programming Models and Approaches for Hurricane Relief Logistics Planning"
Presentation by Yongjia Song
Associate Professor, Department of Industrial Engineering
Clemson University
3:30 pm - Seminar
4:30 pm - Reception, cookies and coffee
About the seminar:
In this talk, we will discuss multi-stage stochastic programming (MSP) models and solution approaches for humanitarian relief logistics planning in natural disasters such as hurricanes. We consider logistics decision-making such as the relief item prepositioning and contingency modality selection over multiple periods prior to the landfall of an impending hurricane. Using stochastic forecast information about the hurricane’s attributes over time, we propose MSP models which provide optimal adaptive logistics decision policies. We will focus on two such models. The first model is motivated by the challenge of having a random number of stages in the MSP model due to the random landfall time of the hurricane. The second model is motivated by the contingency modality selection in disaster relief logistics planning. We propose an aggregation framework that imposes additional structure to the integer state variables by leveraging the information of the underlying Markov process of the hurricane’s evolution. We present an integrated branch-and-cut algorithm with stochastic dual dynamic programming as an exact solution method to the aggregated MSILP, which can also be used in an approximation form to obtain dual bounds and implementable feasible solutions. Our preliminary numerical results and sensitivity analysis demonstrate the value of MSP for hurricane relief logistics planning, as well as the trade-offs between policy flexibility, solution quality, and computational effort.
Bio:
Dr. Yongjia Song is an associate professor in the Department of Industrial Engineering at Clemson University. Dr. Song received his Ph.D. degree in industrial and systems engineering from University of Wisconsin-Madison in 2013. Dr. Song’s research interests include optimization under uncertainty, integer programming, and applications of optimization in transportation and logistics, networks, and health care. Dr. Song is a recipient of the NSF CAREER award in 2021, and his research has been supported by several federal funding agencies, such as the NSF, DOE, ONR, among others. Dr. Song currently serves on the board of directors for the IISE Operations Research Division. He also serves as an associate editor for Networks and INFORMS Journal on Computing.