Modeling and Processing of Time Interval Data for Data-Driven Decision Support

Meisen, Philipp; Meisen, Tobias; Recchioni, Marco; Schilberg, Daniel; Jeschke, Sabina

doi:10.1007/978-3-319-42620-4_69

Cited by 5 publications

(1 citation statement)

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“…At the tactical level, demand modeling determines the number of employees required on different dates and at different times based on forecasts of the expected workload or the actual tasks to be performed (Ernst et al, 2004). Timebased demand modeling generalizes task-based modeling and specifies demand patterns, which can be considered functions that map required staffing levels to time intervals (Meisen et al, 2016). These patterns may be derived from a task-based model of the demand which specifies all the actions which must be performed along with relevant timing information and additional task-specific metadata (Herbers, 2006).…”

Section: Introductionmentioning

confidence: 99%

Demand smoothing in shift design

2020

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Shift design is an essential step in workforce planning in which staffing requirements must be obtained for a set of shifts which best cover forecasted demand given as a demand pattern. Existing models for this challenging optimization problem perform well when these demand patterns fluctuate around an average without any strong variability in demand. However, when demand is irregular, these models inevitably generate solutions with a significant amount of over-or understaffing or an excessive use of short shifts. The present paper explores a strategy which involves modifying the demand patterns such that the variable workload may be better matched using an acceptable number of shifts. Integer programming is employed to solve the resulting optimization problem. A computational study of the proposed model reveals interactions between different problem parameters which control the scope of demand modification and the type of the selected shifts. Moreover, the potential impact from an economic point-of-view is discussed and the time before profitability of the approach is evaluated. These insights enable operations management to better understand the trade-off between solution quality and different types of flexibility which may be realized in an organization.

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Section: Introductionmentioning

confidence: 99%