Time：2018-03-22 16:15
Add： Jinguan Building704
Abstract：As the important preparedness actions, appropriate pre-positioning of relief supplies and configuration of supplier are beneficial for mitigating impacts of disasters on population. This paper considers uncertainties in demand and damage of facilities and suppliers, and proposes a two-stage stochastic programming model to produce plans including facility location and inventory, supplier selection, and distribution of relief supplies with the objective of minimizing economic costs and shortage risks of relief supplies. Inspired by the supplier selection literature and humanitarian relief practice, quantity- and lead time- based price discount for procuring relief supplies and supplier-owned physical inventory are integrated for selecting supplier. Based on a real-world example where a surge in demand was incurred by a snowstorm, earthquake, flood and typhoon in China in 2008, a case study is presented to investigate the applicability of the proposed model, and its implications are discussed based on numerical studies. The model can assist relief agencies for managing supplies, especially in analyze and assess the impacts of outsourcing (e.g. agreement terms offered by candidate suppliers).
Bio: Dr. Zhijie (Sasha) Dong received her B.S. degree in Architectural Engineering at Nanjing University in 2011, M.S. degree in Civil Engineering at Columbia University in 2012, and Ph.D. degree in Transportation Systems Engineering at Cornell University in 2015. Before joining Texas State University as an Assistant Professor of Industrial Engineering, she interned at CSX Transportation as an Operations Research Intern, and worked for FedEx Freight as a Senior Operations Research Analyst.
Dr. Dong is leading the UTIL (Urban, Transportation, Informatics and Logistics) lab at Texas State University. Dr. Dong’s primary research interest is the development of mathematical models and solution methods for use in the management of complex systems. She has particular interest in systems that can be represented mathematically as networks, including transportation and logistics systems, civil infrastructure networks and project networks.
