題目：Optimal Deployment of Emergency Supply Inventory for the Recurring Disaster with a Humanitarian Relief Objective （災(zāi)難中人道救援物品的應(yīng)急庫(kù)存部署研究）
主講人：Fang Liu Assistant professor（Nanyang Technological University）
時(shí)間：2014年10月9日上午10:30
地點(diǎn)：主樓418會(huì)議室
主講人簡(jiǎn)介：
Liu Fang is an assistant professor in the department of Information Technology and Operations Management, Nanyang Business School. She received her doctoral degree in Operations Management from the Fuqua School of Business, Duke University and her Bachelor’s degree from the School of Mathematics, Peking University. She has presented her research in conferences such as INFORMS, MSOM and POMS. Her paper 'Good and bad news about the (S, T) policy' has been accepted in the leading operations journal Manufacturing & Service Operations Management.
內(nèi)容簡(jiǎn)介：
We consider the pre-positioning of the emergency supply inventories (ESI) for the recurring disasters such as foods and hurricanes and investigate its optimal allocation policy with the objective of minimizing the total expected number of casualties. Different from the past literature that only consider static processes of ESI deployment and needs, in this paper, we model them as dynamic processes: the arrival epochs of the ESI at the disaster affected location(s) from the supply network is a dynamic process and the corresponding need for the ESI is a time-dependent stochastic process. Consequently, the objective function (i.e., the expected number of casualties) becomes nonlinear. This adds significant complexity to the classic linear programming (LP) models built for the ESI pre-positioning problems. Despite such complexity, the objective function is shown to have a nice convexity property in the allocation of the ESI and, based on this property we are able to develop two algorithms to readily find the optimal ESI allocation. In the end, we conduct a numerical study of a large-scale humanitarian relief case as stated in Rawls and Turnquist (2009). We compute the optimal pre-positioning decisions for three types of ESIs, namely, water, food and medical supply. One prominent observation is that the optimal ESI pre-positioned at one place not necessarily increases with the total available ESI amount. The comparison with the static model also suggests that it is crucial to consider dynamic ESI deployment and demand processes when the total amount of ESI is large.
