Modelling and simulation of team effectiveness emerged from member-task interaction
Proceedings of the 40th Conference on Winter Simulation
Information Systems Research
A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects
Information Systems Research
Optimal employee retention when inferring unknown learning curves
Proceedings of the Winter Simulation Conference
Manual assembly line operator scheduling using hierarchical preference aggregation
Proceedings of the Winter Simulation Conference
Computers and Industrial Engineering
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The authors investigate through several simulations how patterns of learning and forgetting affect the operating performance of an assembly line. A unique aspect of this study is that a distribution of learning/forgetting behavior based on an empirical population of workers is used rather than assuming the same learning pattern for all employees. The paper demonstrates that modeling only central tendency and not the variations across workers tends to systematicallyunderestimate overall productivity. The data used to estimate the parameters for the distribution of learning curves were collected from an assembly line that produces car radios.Analysis of the models fit to a population of workers reveals that higher levels of previous experience are positively correlated with higher steady-state productivity levels and negatively correlated with the learning rate. To further motivate the study, a conceptual model with several factors hypothesized to influence assembly line productivity is presented. Among key factors included in the model are the rate of worker learning, the size of the worker pool, task tenure, and the magnitude of worker forgetting. In controlled computer simulation experiments, each of these factors was found to be statistically significant, as were a number of the two-way interaction terms.