Article ID: | iaor20173710 |
Volume: | 48 |
Issue: | 4 |
Start Page Number: | 691 |
End Page Number: | 722 |
Publication Date: | Aug 2017 |
Journal: | Decision Sciences |
Authors: | Campbell Gerard M |
Keywords: | scheduling, combinatorial optimization, decision, service, forecasting: applications, demand, timetabling, simulation |
This article develops a framework for staffing in a service environment when multiple opportunities exist for prescheduling overtime prior to the start of a shift. Demand forecasts improve as the shift approaches, while the availability of workers to be scheduled for overtime decreases. First, a single‐shift model is developed and used in computational studies to evaluate the benefits of time‐staged overtime staffing, which include slightly lower costs and significant reductions in unscheduled overtime and outside agents. A multishift model is then developed to consider constraints on consecutive hours worked and minimum rest intervals between shifts. A multishift computational study shows how the benefits of time‐staged overtime staffing depend on problem characteristics when interactions between shifts are considered. The article discusses how single‐shift and multishift models relate to each other and alternative ways the models may be used in practice, including decentralized open shift management and centralized overtime scheduling.