Competences-based performance model of multi-skilled workers with learning and forgetting |
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| Affiliation: | 1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea;2. Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea;1. Research Center of Computational Perception and Cognition, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;2. Information and Computer Engineering College, Northeast Forestry University, Harbin 150001, PR China;1. Intelligent Data Analytics Research Program Dept. Aselsan Research Center, Ankara, Turkey;2. Department of Electrical Engineering, Stanford University, CA, USA;3. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey;4. National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey |
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| Abstract: | The relationship between performance and experience is non-linear, thus planning models that seek to manage workforce development through task assignment are difficult to solve. This gets even more complicated when taking into account multi-skilled workers that are capable of performing a variety of tasks. In this paper we develop a competences-based analytical model of the performance of multi-skilled workers undertaking repetitive tasks, taking into account learning and forgetting. A learning curve can be used to estimate improvement when repeating the same operation. Inverse phenomenon is forgetting, which can occur due to interruption in the production process. The Performance Evaluation Algorithm (PEA) was developed for two cases: fixed shift duration and fixed production output. The aim was to build a tool that better describes the capabilities of workers to perform repetitive tasks by binding together hierarchical competences modeled as a weighted digraph together with a learning and forgetting curve model (LFCM) to express individual learning rates. |
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