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Dynamic routing and admission control in high volume service systems: Asymptotic analysis via multi-scale fluid limits., Queueing Systems

Abstract

Motivated by applications in telephone call centers, we consider a service system model with m customer classes and r server pools. The model is one with doubly stochastic arrivals, which means that the m-vector of instantaneous arrival rates is allowed to vary both temporally and stochastically. Two levels of dynamic control are considered: customers may be either blocked or accepted at the time of their arrival, and then accepted customers of each class must be routed, either immediately upon acceptance or after some period of waiting, to a server pool that is qualified to handle that class. Customers who are made to wait before commencement of their service are liable to defect. The objective is to minimize the expected sum of blocking costs, waiting costs and defection costs over a fixed and finite planning horizon. We consider an asymptotic parameter regime in which (i) the arrival rates, service rates and defection rates are uniformly accelerated by a large factor κ, then (ii) arrival rates are increased by an additional factor g(κ), and the number of servers in each pool is increased by g(κ) as well. This produces a separation of time scales, justifying a pointwise stationary stochastic fluid approximation for our original system model. In the stochastic fluid approximation, optimal admission control and routing decisions are determined by a simple linear program that uses the current arrival rate vector as data. We explain how to implement the fluid model's optimal control policy in our original service system context, and prove that the proposed implementation is asymptotically optimal in the first-order sense.

Type

Article

Author(s)

Achal Bassamboo, J.Michael Harrison, Assaf Zeevi

Date Published

2005

Citations

Bassamboo, Achal, J.Michael Harrison, and Assaf Zeevi. 2005. Dynamic routing and admission control in high volume service systems: Asymptotic analysis via multi-scale fluid limits.. Queueing Systems. 51(3-4): 249-285.

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