Long-term staffing based on qualification profiles

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.