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Stochastic optimization for adaptive labor staffing in service systems

Prashanth, LA and Bhatnagar, Shalabh and Desai, Nirmit and Prasad, HL and Dasgupta, Gargi (2011) Stochastic optimization for adaptive labor staffing in service systems. In: ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing, 2011, Heidelberg.

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Official URL: http://dx.doi.org/10.1007/978-3-642-25535-9_33

Abstract

Service systems are labor intensive. Further, the workload tends to vary greatly with time. Adapting the staffing levels to the workloads in such systems is nontrivial due to a large number of parameters and operational variations, but crucial for business objectives such as minimal labor inventory. One of the central challenges is to optimize the staffing while maintaining system steady-state and compliance to aggregate SLA constraints. We formulate this problem as a parametrized constrained Markov process and propose a novel stochastic optimization algorithm for solving it. Our algorithm is a multi-timescale stochastic approximation scheme that incorporates a SPSA based algorithm for ‘primal descent' and couples it with a ‘dual ascent' scheme for the Lagrange multipliers. We validate this optimization scheme on five real-life service systems and compare it with a state-of-the-art optimization tool-kit OptQuest. Being two orders of magnitude faster than OptQuest, our scheme is particularly suitable for adaptive labor staffing. Also, we observe that it guarantees convergence and finds better solutions than OptQuest in many cases.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to Springer-Verlag Berlin.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 09 Mar 2013 10:29
Last Modified: 09 Mar 2013 10:29
URI: http://eprints.iisc.ac.in/id/eprint/46020

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