| This research focuses on the problem of capacity management under uncertainty when a firm needs to satisfy demand with time-differentiated customer classes. The differentiation of customer classes may result from different degrees of urgency, the fact that the firm offers time-based strategies to attract customers, and/or customer choice behavior. We study such problems by exploring three Markov-Decision-Process (MDP) formulations that are inspired by real applications. In the first application, we examine the implication of outsourcing on a firm's capacity decisions. In the second application, we consider contract manufacturers who attempt to maximize profits by selling capacity under different contract terms to different buyers. The third application relates to advanced access in primary care clinics, which is a capacity management approach designed to reduce wait and improve patients' access to their designated physicians. All three problems are formulated in the multi-dimensional MDP framework. We analyze the resulting models and characterize the nature of the optimal capacity management policies. When optimal policies are hard to compute/implement, we propose scalable heuristics and obtain bounds. Numerical examples that confirm theoretical results and provide additional insights are reported. |
