Markov Renewal Approach to an Opportunistic Replacement Model in Continuous Time

摘要

Most multi-component replacement models are formulated in discrete time and require dynamic programming techniques to obtain the optimal replacement policy. Some models however, admit an optimal policy that is characterized by only a few constants. In the paper the authors consider a continuous time replacement model, introduced by Buzitat (1962), in which the simple structure of the replacement rule is exploited, using embedding techniques and Markov renewal theory.