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

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 num...

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Bibliographic Details
Published inJournal of combinatorial optimization Vol. 38; no. 1; pp. 111 - 129
Main Authors Altner, Douglas S., Mason, Erica K., Servi, Les D.
Format Journal Article
LanguageEnglish
Published New York Springer US 15.07.2019
Springer Nature B.V
Subjects
Combinatorics
Convex and Discrete Geometry
Cybersecurity
Decision analysis
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Production scheduling
Scheduling
Theory of Computation
Training
Upper bounds
Training
Cybersecurity operations
Multi-skilled workforce
Stochastic integer programming
Days-off scheduling
Matheuristics
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Summary: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.
ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-018-0368-5