In this paper, we present a replanning algorithm for a decision-theoretic hierarchical planner, and illustrate the experimental methodology we designed to investigate its performance. The methodology relies on an agentbased framework, in which plan failures emerge from the interplay of the agent and the environment. Given this framework, the replanning algorithm is compared with planning from scratch by executing experiments in different domains. The empirical evaluation shows the superiority of replanning with respect from planning from scratch. However, the observation of significant differences in the data collected across planning domains confirm the importance of empirical evaluation in practical systems.