  We consider the problem of remanufacturing planning in the presence of
statistical estimation errors. Determining the optimal remanufacturing timing,
first and foremost, requires modeling of the state transitions of a system. The
estimation of these probabilities, however, often suffers from data inadequacy
and is far from accurate, resulting in serious degradation in performance. To
mitigate the impacts of the uncertainty in transition probabilities, we develop
a novel data-driven modeling framework for remanufacturing planning in which
decision makers can remain robust with respect to statistical estimation
errors. We model the remanufacturing planning problem as a robust Markov
decision process, and construct ambiguity sets that contain the true transition
probability distributions with high confidence. We further establish structural
properties of optimal robust policies and insights for remanufacturing
planning. A computational study on the NASA turbofan engine shows that our
data-driven decision framework consistently yields better worst-case
performances and higher reliability of the performance guarantee
