  Consumption Markov Decision Processes (CMDPs) are probabilistic
decision-making models of resource-constrained systems. In a CMDP, the
controller possesses a certain amount of a critical resource, such as electric
power. Each action of the controller can consume some amount of the resource.
Resource replenishment is only possible in special reload states, in which the
resource level can be reloaded up to the full capacity of the system. The task
of the controller is to prevent resource exhaustion, i.e. ensure that the
available amount of the resource stays non-negative, while ensuring an
additional linear-time property. We study the complexity of strategy synthesis
in consumption MDPs with almost-sure B\"uchi objectives. We show that the
problem can be solved in polynomial time. We implement our algorithm and show
that it can efficiently solve CMDPs modelling real-world scenarios.
