Prescriptive analytics for a multi-shift staffing problem
type: Item
instance of: scholarly article
title: Prescriptive analytics for a multi-shift staffing problem
author: Pascal M. Notz
author: Peter Wolf
author: Richard Pibernik
published in: European Journal of Operational Research
publication date: 2022-11-17T00:00:00Z
Mathematics Subject Classification ID: 90Bxx
zbMATH DE Number: 7619291
zbMATH Keywords: decision support systems
zbMATH Keywords: prescriptive analytics
zbMATH Keywords: machine learning
zbMATH Keywords: data-driven OM
zbMATH Keywords: queuing
MaRDI profile type: Publication
full work available at URL: j.ejor.2022.06.011
OpenAlex ID: W4293072975
cites work: Workload forecasting for a call center: methodology and a case study
cites work: Modeling Daily Arrivals to a Telephone Call Center
cites work: The Big Data Newsvendor: Practical Insights from Machine Learning
cites work: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days
cites work: Capacity Sizing Under Parameter Uncertainty: Safety Staffing Principles Revisited
cites work: Staffing of time-varying queues using a geometric discrete time modelling approach
cites work: A simulation-based approach to two-stage stochastic programming with recourse
cites work: Monte Carlo sampling approach to stochastic programming
cites work: Approximation of the non-stationary \(M(t)/M(t)/c(t)\)-queue using stationary queueing models: The stationary backlog-carryover approach
cites work: Q4261789
DOI: 10.1016/J.EJOR.2022.06.011
description: scientific article; zbMATH DE number 7619291