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Online Resource Allocation with an Endogenous Markov State: Fewer LP Solves Earn More
We study finite-horizon online resource allocation with i.i.d. requests and an endogenous Markov state on a finite state space: each action affects the transition of the state that governs future rewards and resource consumption. In this problem, a transient fluid LP benchmark upper bounds the expec
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