Markov decision processes with multidimensional action spaces

We study controlled Markov processes where multiple decisions need to be made for each state. We present conditions on the cost structure and the state transition mechanism of the process under which optimal decisions are restricted to a subset of the decision space. As a result, the numerical computation of the optimal policy may be significantly expedited.