In this paper, we consider a product manufacturer that seeks to leverage the potential of human learning to develop the capacity of its workforce and to reduce its costs. Unlike much of the literature in this area, we do not assume that the rate at which individuals learn is known with certainty. We present a two-stage stochastic programming model of the related production planning problem that quantifies the impact of worker assignment decisions to produce through an exponential learning curve which we linearize to yield a mixed integer linear program that can be solved efficiently. With this stochastic program, we perform a rigorously designed computational study and statistical analysis to derive tactics and managerial insights for how an organization should plan its production operations about assignment, cross-training and practicing. Results suggest that explicitly recognizing uncertainty in learning rates would reduce costs and that when dealing with assignment decisions, the leading factor to consider is the mean learning rate. On the other hand, when dealing with cross-training and practicing decisions, the learning rate variance is more predictive. We also assess the impact of explicitly considering stochastic forgetting rates in the productivity curve, finding that in the optimal assignment schedule, workers practice more and always specialize.

中文翻译：

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不确定学习率下的劳动力生产计划

在本文中，我们考虑了一家产品制造商，该制造商寻求利用人类学习的潜力来发展其劳动力的能力并降低其成本。与该领域的大部分文献不同，我们不假设个人学习的速度是确定的。我们提出了相关生产计划问题的两阶段随机规划模型，该模型通过指数学习曲线量化工人分配决策的影响，我们将其线性化以产生可以有效解决的混合整数线性程序。通过这个随机程序，我们进行了严格设计的计算研究和统计分析，以得出组织应该如何规划其生产运营有关分配、交叉培训和实践的策略和管理见解。结果表明，明确认识到学习率的不确定性会降低成本，并且在处理分配决策时，要考虑的主要因素是平均学习率。另一方面，在处理交叉训练和练习决策时，学习率方差更具预测性。我们还评估了在生产力曲线中明确考虑随机遗忘率的影响，发现在最佳分配时间表中，工人练习更多并且始终专注。