Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

Noah Gans (), Haipeng Shen (), Yong-Pin Zhou (), Nikolay Korolev (), Alan McCord () and Herbert Ristock ()
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Noah Gans: Operations, Information and Decisions Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Haipeng Shen: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599; and Innovation and Information Management, School of Business, University of Hong Kong, Pok Fu Lam, Hong Kong
Yong-Pin Zhou: Department of Information Systems and Operations Management, The Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195
Nikolay Korolev: Genesys Telecommunications Laboratories, Daly City, California 94014
Alan McCord: Genesys Telecommunications Laboratories, Daly City, California 94014
Herbert Ristock: Genesys Telecommunications Laboratories, Daly City, California 94014

Manufacturing & Service Operations Management, 2015, vol. 17, issue 4, 571-588

Abstract: We develop and test an integrated forecasting and stochastic programming approach to workforce management in call centers. We first demonstrate that parametric forecasts, discretized using Gaussian quadrature, can be used to drive stochastic programs whose results are stable with relatively small numbers of scenarios. We then extend our approach to include forecast updates and two-stage stochastic programs with recourse and provide a general modeling framework for which recent, related models are special cases. In our formulations, the inclusion of multiple arrival-rate scenarios allows call centers to meet long-run average quality-of-service targets, and the use of recourse actions helps them to lower long-run average costs. Experiments with two large sets of call-center data highlight the complementary nature of these elements.

Keywords: call-center management; production planning and scheduling; service operations; distributional forecast updating; stochastic programming with recourse (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:17:y:2015:i:4:p:571-588

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