Investigating contextual queueing systems with logit-based arrival processes, aiming to capture customer heterogeneity and context-dependent arrival behaviors.
Conducted an extensive literature review on queueing control, contextual bandits, and logit demand models to identify theoretical gaps and practical challenges.
Formulated the research problem as learning and optimizing resource allocation under contextual arrivals, and outlined an initial algorithmic framework for sequential decision-making.
