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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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TITLE: Joint Optimization of Sampling and Control for Failing Systems Under Partial Observations
SPEAKER: Michael J. Kim
ABSTRACT:
Stochastic control problems that arise in condition-based maintenance typically assume that information used for decision-making is obtained according to a pre-determined sampling schedule. In many real applications however, there is a high sampling cost associated with collecting such data. It is therefore of equal importance to determine when information should be collected as it is to decide how this information should be utilized for maintenance decision-making. This type of joint optimization has been a long-standing problem in the operations research and maintenance optimization literature, and very few results regarding the structure of the optimal sampling and maintenance policy have been published. In this talk, we formulate and analyze the joint optimization of sampling and maintenance decision-making in the partially observable Markov decision process (POMDP) framework. We prove the optimality of a policy that is characterized by three critical thresholds, which have practical interpretation and give new insight into the value of condition-based maintenance programs in life-cycle asset management.
SHORT BIO: Michael Kim is currently an NSERC Postdoctoral Fellow at the University of California, Berkeley in the Department of Industrial Engineering and Operations Research. He is also a visiting Senior Fellow in the Department of Decision Sciences at the National University of Singapore Business School. He received his B.A.Sc. in industrial engineering, M.Sc. in mathematics, and Ph.D. in industrial engineering, from the University of Toronto. His research interests are in stochastic operations research and applied statistics with applications in manufacturing, healthcare decision making, and business analytics. For his recent contributions to these areas, Mike was awarded the first place prize in the 2012 Canadian Operational Research Society (CORS) paper competition, and was the sole Canadian recipient of the 2012 Dimitris N. Chorafas Foundation Award. He has also conducted research collaborations with the Ministry of Transportation of Ontario, and Syncrude, one of the world’s largest producers of synthetics crude oil.
