Long-term staffing based on qualification profiles

Andreas Drexl () and Martin Mundschenk ()

Mathematical Methods of Operations Research, 2008, vol. 68, issue 1, 47 pages

Abstract: Manpower still is one of the most expensive resources, in spite of increasing automation. While employee scheduling and rostering has been the topic of extensive research over the past decades, usually it is assumed that the demand for staff is either given or can be obtained without difficulty. In this research we provide an integer programming model for long-term staffing decisions which fits to the needs of manufacturing-to-order companies. The model is based on qualification profiles, the number of which grows exponentially in terms of the number of processes considered. In order to compute tight lower bounds we provide a column generation technique. The subproblem is a shortest path problem in a network where the arcs have multiple weights. Upper bounds, that is, feasible solutions are calculated by means of local search. We present computational results for randomly generated instances and empirical results for examples from practice. The results show that substantial cost savings can be achieved. Copyright Springer-Verlag 2008

Keywords: Work force; Planning/staffing; Qualification profile; Manufacturing-to-order; Column generation; Local search; Computational/empirical evaluation (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00186-007-0192-7 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:mathme:v:68:y:2008:i:1:p:21-47

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/00186

DOI: 10.1007/s00186-007-0192-7

Access Statistics for this article

Mathematical Methods of Operations Research is currently edited by Oliver Stein

More articles in Mathematical Methods of Operations Research from Springer, Gesellschaft für Operations Research (GOR), Nederlands Genootschap voor Besliskunde (NGB)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().