Motivated by queues with many servers, we study Brownian steadystate approximations for continuous time Markov chains (CTMCs). Our approximations are based on diffusion models (rather than a diffusion limit) whose steady-state, we prove, approximates that of the Markov chain with notable precision. Strong approximations provide such "limitless" approximations for process dynamics. Our focus here is on steady-state distributions, and the diffusion model that we propose is tractable relative to strong approximations.Within an asymptotic framework, in which a scale parameter n is taken large, a uniform (in the scale parameter) Lyapunov condition imposed on the sequence of diffusion models guarantees that the gap between the steady-state moments of the diffusion and those of the properly centered and scaled CTMCs shrinks at a rate of √ n. Our proofs build on gradient estimates for solutions of the Poisson equations associated with the (sequence of) diffusion models and on elementary martingale arguments. As a by-product of our analysis, we explore connections between Lyapunov functions for the fluid model, the diffusion model and the CTMC.This requirement is intimately linked to our Lyapunov requirement; see Lemma 3.1.

Motivated by telephone call centers, we study large-scale service systems with multiple customer classes and multiple agent pools, each with many agents. To minimize staffing costs subject to service-level constraints, where we delicately balance the service levels (SLs) of the different classes, we propose a family of routing rules called fixed-queue-ratio (FQR) rules. With FQR, a newly available agent next serves the customer from the head of the queue of the class (from among those he is eligible to serve) whose queue length most exceeds a specified proportion of the total queue length. The proportions can be set to achieve desired SL targets. The FQR rule achieves an important state-space collapse (SSC) as the total arrival rate increases, in which the individual queue lengths evolve as fixed proportions of the total queue length. In the current paper we consider a variety of service-level types and exploit SSC to construct asymptotically optimal solutions for the staffing-and-routing problem. The key assumption in the current paper is that the service rates depend only on the agent pool.

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Delay announcements informing customers about anticipated service delays are prevalent in service-oriented systems.How delay announcements can influence customers in service systems is a complex problem which depends on both the dynamics of the underlying queueing system and on the customers' strategic behavior. We examine this problem of information communication by considering a model in which both the firm and the customers act strategically: the firm in choosing its delay announcement while anticipating customer response, and the customers in interpreting these announcements and in making the decision about when to join the system and when to balk. We characterize the equilibrium language that emerges between the service provider and her customers. The analysis of the emerging equilibria provides new and interesting insights into customer-firm information sharing. We show that even though the information provided to customers is non-verifiable, it improves the profits of the firm and the expected utility of the customers. The robustness of the results is illustrated via various extensions of the model. In particular, studying models with incomplete information on the system parameters and multiple customer types allows us also to highlight the role of information provision in managing customer expectations regarding the congestion in the system. Further, the information could be as simple as "High Congestion"/"Low Congestion" announcements, or could be as detailed as the true state of the system.We also show that firms may choose to shade some of the truth by using intentional vagueness to lure customers.*

We consider the optimal control of matching queues with random arrivals. In this model, items arrive to dedicated queues, and wait to be matched with items from other (possibly multiple) queues. A match type corresponds to the set of item classes required for a match. Once a decision has been made to perform a match, the matching itself is instantaneous and the matched items depart from the system. We consider the problem of minimizing finite-horizon cumulative holding costs. The controller must decide which matchings to execute given multiple options. In principle, the controller may choose to wait until some "inventory" of items builds up to facilitate more profitable matches in the future.We introduce a multi-dimensional imbalance process, that at each time t, is given by a linear function of the cumulative arrivals to each of the item classes. A non-zero value of the imbalance at time t means that no control could have matched all the items that arrived by time t. A lower bound based on the imbalance process can be specified, at each time point, by a solution to an optimization problem with linear constraints. While not achievable in general, this lower bound can be asymptotically approached under a dedicated item condition (an analogue of the local traffic condition in bandwidth sharing networks). We devise a myopic discrete-review matching control that asymptotically-as the arrival rates become large-achieves the imbalance-based lower bound.