The present research proposes a novel dynamic patching policy (DPP) to minimize the total system cost. Specifically, we formulate the patching problem as a Markov Decision Process (MDP) problem and derive the optimal policies under both finite and infinite time horizons.

In this paper, we propose an effective threshold-based policy to help retailers make well-informed decisions, by formulating the MFC inventory decision problem as a Markov decision process.

This study present an analytical model to identify the optimal live selling strategy for maximizing brand profits.

This study addresses the patching decision problem by proposing a novel deep reinforcement learning-based approach.