 Analytics for labor planning in systems with load-dependent service timesDmitry Smirnov and Arnd Huchzermeier European Journal of Operational Research, 2020, vol. 287, issue 2, 668-681 Abstract: This paper presents a generalized framework for labor planning in systems with load-dependent service times. Our approach integrates customer arrival forecasting, service time estimation, and staffing into an end to end process. More specifically, (i) we propose a hybrid model to forecast customer arrivals, that combines a traditional time-series technique with a state-of-the-art machine learning algorithm, thereby allowing to incorporate a rich set of predictors; (ii) we develop a methodology to estimate the distribution of load-dependent service times from past transactions data; and (iii) we present a stochastic programming formulation to determine staffing levels under quality-of-service constraints. Finally, we develop a heuristic solution algorithm that utilizes an embedded discrete event simulation to evaluate system performance. Keywords: Service operations; Labor planning; Load-dependent service time; Analytics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:287:y:2020:i:2:p:668-681 DOI: 10.1016/j.ejor.2020.04.036 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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