題目：Closed-Form Approximations for Optimal (r; q) and (S; T) Policies in a Parallel Processing Environment
主講人：Hanqin Zhang 教授 ( Business School, National University of Singapore)
時間：2015年7月27日上午10：00-12：00
地點：主樓6層會議室
主講人簡介：
Hanqin Zhang received his phd degree from Institute of Applied Mathematics, Chinese Academy of Sciences (CAS) in 1991. Currently he is a full professor at Academy of Mathematics and Systems Science, CAS, and NUS Business School, National University of Singapore. He serves/served on the editorial boards of some journals in operations research and optimal control such as European Journal of Operational Research, Mathematical Methods of Operations Research, IEEE Transactions on Automatic Control, etc. His research interests include stochastic optimization, queueing theory, stochastic inventory, and applied probability. His works have been mainly published in the top journals of operations research and operations management areas such as Operations Research, Mathematics of Operations Research, Manufacturing and Service Operations Management, Production and Operations Management, and INFORMS Journal of Computing, his works have been also published in the top journals of optimal control and applied probability such as Automatic, IEEE Transactions on Automatic Control, SIAM Journals, Annals of Applied Probability, and Advances in Applied Probability. He coauthored two books: Inventory and Supply Chain Management with Forecast Updates. Springer-Verlag, New York, 2005, and Average-Cost Control of Stochastic Manufacturing Systems. Springer-Verlag, New York, 2004. He received Academic Excellence Award for One-Hundred Talents Program of CAS, Distinguished Young Investigator Grant (China), and Best Paper Award from IIE Transactions (2011).
內容簡介：
We consider a single-item continuous-review (r; q) inventory system with i.i.d. stochastic leadtimes. Using a stationary marked point process technique and a heavy trac limit, we prove a previous conjecture that inventory position and inventory on-order are asymptotically independent. We also establish closed-form expressions for the optimal policy parameters and system cost in heavy trac limit, the rst of their kind to our knowledge. These expressions sharpen our understanding of the key determinants of the optimal policy and their quantitative and qualitative impacts. For example, the results demonstrate that the well-known square-root relationship between the optimal order quantity and demand rate under a sequential processing environment is replaced by the cube root under a stochastic parallel processing environment. We further extend the study to periodic-review (S; T)systems with constant leadtimes.
（承辦：能源與環境政策研究中心、科研與學術交流中心）
