Online Resource Allocation with an Endogenous Markov State: Fewer LP Solves Earn More
The research optimizes resource allocation where system states change based on previous actions. It compares frequent versus infrequent solving strategies for Linear Programming problems under known and unknown request patterns. The resulting U-shaped policy manages learning and inventory correction, achieving better performance than frequent re-solving in difficult scenarios.