Two-stage stochastic days-off scheduling of multi-skilled analysts with training options

Douglas S. Altner (), Erica K. Mason () and Les D. Servi ()
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Douglas S. Altner: The MITRE Corporation
Erica K. Mason: ThirdLove
Les D. Servi: The MITRE Corporation

Journal of Combinatorial Optimization, 2019, vol. 38, issue 1, No 6, 129 pages

Abstract: Abstract Motivated by a cybersecurity application, this paper studies a two-stage, stochastic days-off scheduling problem with (1) many types of jobs that require specialized training, (2) many multi-skilled analysts, (3) the ability to shape analyst skill sets through training decisions, and (4) a large number of possible future demand scenarios. We provide an integer linear program for this problem and show it can be solved with a direct feed into Gurobi with as many as 50 employees, 6 job types, and 50 demand scenarios per day without any decomposition techniques. In addition, we develop a matheuristic—that is, an integer-programming-based local search heuristic—for instances that are too large for a straightforward feed into a commercial solver. Computational results show our matheuristic can, on average, produce solutions within 4–7% of an upper bound of the optimal objective value.

Keywords: Days-off scheduling; Multi-skilled workforce; Stochastic integer programming; Matheuristics; Cybersecurity operations; Training (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10878-018-0368-5

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