Collaborative Research: Solving Large-Scale, High-Fidelity Workforce Planning Models That Recognize the Potential of Human Learning

协作研究：解决认识到人类学习潜力的大规模、高保真劳动力规划模型

• 批准号: 1266064
• 负责人: Barrett Thomas
• 金额: $ 21.87万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1266064&HistoricalAwards=false
• 关键词: Collaborative; Research; Solving; Large; Scale

The objective of this award is the development of optimization models, solution methods for those models, and insights that will enable organizations to perform workforce planning while recognizing the near and long-term effects of human learning. These optimization models will be based on descriptive, quantitative models of human learning. Developed by the organizational psychology community, the (nonlinear) shape of these learning models makes them a challenge to use in optimization. This work overcomes this challenge by encoding the nonlinear relationship between experience level and learning into a parameter that can be computed outside of the optimization. Having mitigated the challenge of the learning curves, the work will develop more realistic optimization models that recognize operational considerations, the potential of training and cross-training activities, and uncertainty in learning parameters and future tasks to be performed. The improved realism of our workforce planning models will bring new computational challenges, and the research will develop new solution methods. Computational experiments and analysis will be performed to derive insights into how learning impacts workforce planning and strategies for using these insights for maximum benefit.If successful, this research will produce tools that organizations can use to enhance their competitiveness in the near term and to position their workforce to take advantage of future opportunities. These tools will help organizations make decisions involving hiring and training activities, as well as understand the tradeoffs between hiring and training/re-training. Further, developed solution methods, particularly those related to linearizing nonlinear functions, are applicable in other contexts. The multi-disciplinary nature of the project, which if successful, will yield publications in both operations management/research and organizational psychology journals could bridge the gap between those disciplines.

该奖项的目标是开发优化模型，这些模型的解决方案方法，以及使组织能够执行劳动力规划的见解，同时认识到人类学习的近期和长期影响。 这些优化模型将基于人类学习的描述性定量模型。 由组织心理学社区开发的这些学习模型的（非线性）形状使它们在优化中的使用成为一个挑战。这项工作克服了这一挑战，通过编码的经验水平和学习之间的非线性关系到一个参数，可以计算的优化之外。 在减轻了学习曲线的挑战之后，这项工作将开发更现实的优化模型，这些模型将认识到操作考虑因素、培训和交叉培训活动的潜力以及学习参数和未来任务的不确定性。 我们的劳动力规划模型的改进现实主义将带来新的计算挑战，研究将开发新的解决方法。 将进行计算实验和分析，以深入了解学习如何影响劳动力规划以及利用这些见解实现最大效益的策略。如果成功，这项研究将产生工具，组织可以使用这些工具在短期内提高竞争力，并定位其劳动力，以利用未来的机会。 这些工具将帮助组织做出涉及招聘和培训活动的决策，并了解招聘和培训/再培训之间的权衡。 此外，开发的解决方案的方法，特别是那些有关线性化非线性函数，适用于其他情况下。该项目的多学科性质，如果成功的话，将产生在业务管理/研究和组织心理学期刊上的出版物，可以弥合这些学科之间的差距。