題目：Maintenance Optimization for a Markovian Deteriorating System with Population Heterogeneity
主講人：彭昊講師（Eindhoven University of Technology）
時(shí)間：2014年4月10日 （星期四）下午3:00
地點(diǎn)：主樓418會(huì)議室
主講人簡(jiǎn)介：
Dr. Hao Peng, lecturer, at Department of Industrial Engineering & Innovation Sciences,Operations, Planning, Accounting, and Control group, Eindhoven University of Technology, Netherland. She got her PhD in Industrial Engineering in 2010 from University of Houston, and her B.S. in Industrial Engineering in 2006 from Tsinghua University, Beijing, China. She has published more than 10 papers in international Journals such as IEEE Transactions on Reliability and Quality and Reliability Engineering International. Her Research Interests are on condition-based maintenance, quality and reliability engineering, applied probability and statistics.
(Note：She enrolled a PhD student who graduated from School of Management & Economics, BIT)
內(nèi)容簡(jiǎn)介：
We develop a partially observable Markov decision process (POMDP) model to incorporate population heterogeneity when scheduling replacements for a deteriorating system. The single-component system deteriorates over a finite set of condition states according to a Markov chain. The population of spare components that is available for replacements is composed of multiple component types that cannot be distinguished by their exterior appearance, but deteriorate according to different transition matrices. This situation arises, for example, if new components and repaired components are mixed into the population without proper records of their repair history. We provide a set of conditions for which we characterize the structure of the optimal policy that minimizes total expected discounted operating and replacement cost over an infinite horizon. By a numerical experiment, we benchmark the optimal policy against a heuristic policy that neglects population heterogeneity.
