A symbolic genetic programming approach for identifying models of learning-by-doing
Introduction
With the ongoing developments in industrial technologies, organizations are putting greater effort into enhancing manufacturing workers’ productivity. In order to achieve such improvements, measuring and utilizing workers’ learning and forgetting behavior has become an important area of study. Correspondingly, learning and forgetting models have been investigated for some time. The learning curve has been used as a management tool for much of the twentieth century, and is seen as an effective method for estimating unit cost and labor trends. Restle and Greeno (1970) suggested that learning is a replacement process in which incorrect responses tend to be replaced by correct ones. A similar theory was also described by Vigil and Sarper (1994), who presented the view that the learning curve is a model of the continual reduction in unit cost or labor that occurs with increasing cumulative production. As a measurement and mathematical description of workers’ performance in repetitive tasks, there are multiple dependent variables in Learning Curve (LC) models, which include: time to produce a single unit, number of units produced per time interval, costs to produce a single unit, and percentage of non-conforming units, as described by Jaber (2016).
Several studies have compared learning models in the literature to one another. Yet, continued interest in developing and comparing models suggests that the question remains unsettled regarding whether there are as yet-undiscovered models that may be useful. Research on the nature of the structure and mathematical form of the human learning curve is broad and includes work from the areas of psychology, mathematics, and many fields of engineering. We employ a novel symbolic regression approach using a multi-genic Genetic Programming (GP) algorithm to secondary field data from a range of manual tasks. A multi-genic approach closely follows the evolutionary inspiration, whereby genetic crossovers provide a useful mechanism to break-away from local solutions, and explore the solution space more broadly, and yet be informed by the fitness of the individual genes. This study contributes to learning curve theory by finding independently discovered models previously suggested from theoretical bases. We also contribute to practice, by presenting an effective and efficient process by which future researchers and practitioners can obtain best-fit models for other learning scenarios.
Section snippets
Review of prior univariate LC models
There have been several surveys, reviews and aggregations of learning curves in the literature. For example, Yelle (1979) surveyed many univariate models of experiential learning and provided a comprehensive review of the extant models. Since then, others including Badiru (1992), Nembhard and Uzumeri, 2000a, Anzanello and Fogliatto, 2011, Srour et al., 2015, Jaber, 2016 have broadened and further examined the set of models available for researchers and practitioners. Thus, in the current study
Methodology
We employ a GP algorithm based symbolic-regression approach to generate potentially useful models and to evaluate the fitness of these models using a dataset from the literature. We consider two independent cohorts in our data, one with learning episodes associated with novice learners (training data), and the other with learning episodes associated with learners having some prior experience (evaluation data). We remark that since we are investigating models of learning behavior, the novices
Results and discussion
The highest ranked model forms for fitting cumulative work, x, to production rate, y and cumulative working time, t to y are summarized in Tables 3 and 4, respectively, along with a summary of the efficiency and stability for each of the high scoring models generated from MSR. The parametric results for each of the models presented below form multivariate normal distributions, and verify that the single cohort population forms a single cluster and distribution.
The models in Table 3, Table 4 are
Conclusions
In this paper, we address the question of what univariate learning curve functional form or forms are most efficient and stable for measuring human learning responses. We employ a symbolic regression approach using GP to generate and investigate new potential forms developed empirically and naturally from field data. We remark that over the past century, numerous learning curve forms were suggested and added to the literature. Several studies have further compared these models to one another.
Acknowledgements
The authors would like to thank The Leonhard Center at Penn State University for partial funding of this research. We also thank Haochen Xie and Chenmu Wang for their input in the early stages of this project.
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