A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options
A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options
复制标题
用于多轮班、多分析师、劳动力优化的两阶段随机计划，具有多种待命选项
DOI：
10.1007/s10951-017-0554-9
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发表时间：
2017
影响因子：
2
通讯作者：
L. Servi
中科院分区：
文献类型：
--
作者：
Douglas S. Altner;Anthony C. Rojas;L. Servi
在网络安全优化问题的激励下，本文研究了针对未知的需求和多个待命的人员配备选择的优化人员，每天有三个班次，三种分析师类型以及几种人员配备和调度限制。我们将此问题模拟为两个阶段的随机程序，并使用基于柱的启发式方法来解决它。我们的计算研究表明，该方法只需要3分钟即可在150多个测试用例中99％的最佳下限的6％以内产生解决方案。
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.