A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options
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Motivated by a cybersecurity workforce optimization problem, this paper investigates optimizing staffing and shift scheduling decisions given unknown demand and multiple on-call staffing options at a 24/7 firm with three shifts per day, three analyst types, and several staffing and scheduling constraints. We model this problem as a two-stage stochastic program and solve it with a column-generation-based heuristic. Our computational study shows this method only needs 3 min to produce solutions within 6% of a true lower bound of the optimal for 99% of over 150 test cases.
KeywordsWorkforce optimization Shift scheduling Staffing Scheduling On-call options Stochastic programming Column generation Cybersecurity
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