  We consider the verification of multiple expected reward objectives at once
on Markov decision processes (MDPs). This enables a trade-off analysis among
multiple objectives by obtaining the Pareto front. We focus on strategies that
are easy to employ and implement. That is, strategies that are pure (no
randomization) and have bounded memory. We show that checking whether a point
is achievable by a pure stationary strategy is NP-complete, even for two
objectives, and we provide an MILP encoding to solve the corresponding problem.
The bounded memory case can be reduced to the stationary one by a product
construction. Experimental results using \Storm and Gurobi show the feasibility
of our algorithms.
