Robust Optimization for Emergency Logistics Planning: Risk Mitigation in Humanitarian Relief Supply Chains Aharon Ben-Tal Faculty of Industrial Engineering and Management, MINERVA Optimization Center, Technion - Israel Institute of Technology Technion City, Haifa 32000, Israel, abental@ie.technion.ac.il Byung Do Chung, Supreet Reddy Mandala, Tao Yao The Harold & Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802 {buc139@psu.edu, sxm529@psu.edu,taoyao@psu.edu} This paper proposes a methodology to generate a robust logistics plan that can mitigate demand uncertainty in humanitarian relief supply chains. More specifically, we apply robust optimization (RO) for dynamically assigning emergency response and evacuation traffic flow problems with time dependent demand uncertainty. This paper studies a Cell Transmission Model (CTM) based system optimum dynamic traffic assignment model. We adopt a min-max criterion and apply an extension of the RO method adjusted to dynamic opti- mization problems, an Affinely Adjustable Robust Counterpart (AARC) approach. Simulation experiments show that the AARC solution provides excellent results when compared to deterministic solution and sam- pling based stochastic programming solution. General insights of RO and transportation that may have wider applicability in humanitarian relief supply chains are provided. Key words : Robust optimization; Dynamic traffic assignment; Demand uncertainty; Emergency logistics 1. Introduction Over the past three decades, the number of reported disasters have risen threefold. Roughly, 5 billion people have been affected by disasters with an estimated damages of about 1.28 trillion dollars (Guha-Sapir et al. 2004). Although most of these disasters could not have been avoided, significant improvements in death counts and reported property losses could have been made by efficient distribution of supplies. The supplies here could mean personnel, medicine and food which are critical in emergency situations. The supply chains involved in providing emergency services in 1