Optimal investment in product-flexible manufacturing capacity
Management Science
A queueing model to analyze the value of centralized inventory information
Operations Research
Optimality of routing and servicing in dependent parallel processing systems
Queueing Systems: Theory and Applications
Principles on the benefits of manufacturing process flexibility
Management Science
Self-buffering, self-balancing, self-flushing production lines
Management Science
Investment Strategies for Flexible Resources
Management Science
Heavy traffic resource pooling in parallel-server systems
Queueing Systems: Theory and Applications
Process Flexibility in Supply Chains
Management Science
A Staffing Algorithm for Call Centers with Skill-Based Routing
Manufacturing & Service Operations Management
Operational Flexibility and Financial Hedging: Complements or Substitutes?
Management Science
Convexity in queues with general inputs
IEEE Transactions on Information Theory
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We analytically study optimal capacity and flexible technology selection in parallel queuing systems. We consider N stochastic arrival streams that may wait in N queues before being processed by one of many resources technologies that differ in their flexibility. A resource's ability to process k different arrival types or classes is referred to as level-k flexibility. We determine the capacity portfolio consisting of all resources at all levels of flexibility that minimizes linear capacity and linear holding costs in high-volume systems where the arrival rate λ → ∞. We prove that “a little flexibility is all you need”: the optimal portfolio invests Oλ in specialized resources and only O√λ in flexible resources and these optimal capacity choices bring the system into heavy traffic. Further, considering symmetric systems with type-independent parameters, a novel “folding” methodology allows the specification of the asymptotic queue count process for any capacity portfolio under longest-queue scheduling in closed form that is amenable to optimization. This allows us to sharpen “a little flexibility is all you need”: the asymptotically optimal flexibility configuration for symmetric systems with mild economies of scope invests a lot in specialized resources but only a little in flexible resources and only in level-2 flexibility, but effectively nothing o√λ in level-k 2 flexibility. We characterize “tailored pairing” as the theoretical benchmark configuration that maximizes the value of flexibility when demand and service uncertainty are the main concerns.