Dr. Weihong “Grace” Guo (郭韦宏)

Associate Professor, Department of Industrial and Systems Engineering, Rutgers University

Biography:

Dr. Weihong “Grace” Guo is an Associate Professor in the Department of Industrial and Systems Engineering at Rutgers University. She earned her B.S. degree in Industrial Engineering from Tsinghua University, China, in 2010 and her Ph.D. in Industrial & Operations Engineering from the University of Michigan, Ann Arbor, in 2015. Her research focuses on developing novel methodologies for extracting and analyzing massive and complex data to facilitate effective monitoring of operational quality, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. She has collaborated with a domestic logistics/supply chain company, a university- affiliated heal the system and worldwide manufacturers of automobiles and personal care products. She received the Barbara M. Fossum Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers in 2019. She also received several best paper awards/finalists. She is a member of INFORMS, IISE, ASME, SME, IEEE, and Tau Beta Pi.

Session Title: A simulation-optimization framework for food supply chain network design to ensure food accessibility under uncertainty

Abstract:

There are a myriad of factors currently affecting the global food supply chain, including factors that are a consequence of responses to COVID and factors that reflect the evolving natural disasters and geopolitical events. How to ensure food security, especially accessibility of nutrition, while the food supply chain suffers from disruptive forces is an emerging and critical issue. A food accessibility evaluation index is proposed in this work to quantify how nutrition needs are met. The proposed index is then embedded in a multi-objective optimization problem to determine the optimal supply chain design to maximize food accessibility and minimize cost. Considering uncertainty in demand and supply, the multi-objective optimization problem is solved in a two-stage simulation-optimization framework where Green Field Analysis is used to determine supply chain configuration and then Monte Carlo simulation is used to determine supply chain operations by solving a stochastic programming problem.