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

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计划，同时认识到人类学习的近期和长期影响的见解。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使用成为一个挑战。这项工作通过将经验水平和学习之间的非线性关系编码为可以在优化之外计算的参数来克服这一挑战。在减轻了学习曲线的挑战之后，工作将开发出更现实的优化模型，这些模型将认识到操作方面的考虑、训练和交叉训练活动的潜力、学习参数的不确定性以及将要执行的未来任务。劳动力规划模型的现实性的提高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，以获得学习如何影响劳动力规划和策略的见解，并利用这些见解获得最大利益。如果成功，这项研究将产生工具，组织可以使用，以提高他们的竞争力在短期内，并定位他们的员工，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，以及理解招聘和培训/再培训之间的权衡。此外，开发的求解方法，特别是与线性化非线性函数有关的方法，也适用于其他情况。该项目的多学科性质如果成功，将在业务管理/研究和组织心理学杂志上发表文章，可以弥补这些学科之间的差距。

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.