Prescriptive analytics for a multi-shift staffing problem

DOI10.1016/J.EJOR.2022.06.011OpenAlexW4293072975MaRDI QIDQ2098065

Peter K. Wolf, Pascal M. Notz, Richard Pibernik

Publication date: 17 November 2022

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2022.06.011

zbMATH Keywords

decision support systemsqueuingmachine learningprescriptive analyticsdata-driven OM

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Related Items (1)

Explainable subgradient tree boosting for prescriptive analytics in operations management

Cites Work

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• Workload forecasting for a call center: methodology and a case study
• A simulation-based approach to two-stage stochastic programming with recourse
• The impact of special days in call arrivals forecasting: a neural network approach to modelling special days
• Staffing of time-varying queues using a geometric discrete time modelling approach
• Approximation of the non-stationary \(M(t)/M(t)/c(t)\)-queue using stationary queueing models: The stationary backlog-carryover approach
• Modeling Daily Arrivals to a Telephone Call Center
• Capacity Sizing Under Parameter Uncertainty: Safety Staffing Principles Revisited
• Monte Carlo sampling approach to stochastic programming
• The Big Data Newsvendor: Practical Insights from Machine Learning

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