The Effect of Job Similarity on Forgetting in Multi-Task Production

Hoedt, Steven; Claeys, Arno; Schamp, Matthias; Ginste, Lauren Van De; Aghezzaf, El‐Houssaine; Cottyn, Johannes

doi:10.1016/j.promfg.2020.01.390

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…Assembling one model can cause a learning or forgetting effect on others. The influence of job similarity on the forgetting effect was already shown in previous work [23]. Based on the work plan of an operator, his/her competence level could be estimated for all tasks in the near future.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 73%

“…As the forgetting slope is still fixed, theT 1 is calculated so it intercepted the learning curve at the point a break starts. Different studies concluded that the forgetting rate depends on the learning rate [23]. Although there is not a full consensus on this topic, most of the studies indicate that a higher learning rate results in a higher forgetting rate [24].…”

Section: Learn-forget Curve Model (Lfcm)mentioning

confidence: 99%

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Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

et al. 2020

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Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 73%

Section: Learn-forget Curve Model (Lfcm)mentioning

confidence: 99%

Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

et al. 2020

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“…The most recent articles used in this research that addressed the forgetting models applied to production were Bailey and Mcintyre (2003); Jaber (2006); Lindeke (2010); Hoedt et al (2019) and Ferreira et al (2019). This analysis included the verification of objectives, research problems, theoretical approaches, types and research methodologies, and conclusions, ending with the critical evaluation by the researchers.…”

Section: Research Methodology and Proceduresmentioning

confidence: 99%

Section: Research Methodology and Proceduresmentioning

confidence: 99%

“…This model was adapted by Hoedt et al, (2019), using the LFCM model and adjusting for a mixed production series, where there was no real interruption of the assembly. In this case, the phenomenon of forgetting is not only caused by the interruption of learning, but also by the interruption during the execution of other work.…”

Section: Multivariate Production Modelmentioning

confidence: 99%

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Forgetting curve models: A systematic review aimed at consolidating the main models and outlining possibilities for future research in production

Ferreira,

Valmorbida,

Sato

et al. 2023

Expert Systems

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This research surveys current knowledge about forgetting curves and their application in production, aiming to identify the main characteristics and tendencies and research gaps on this topic. Faced with the need to improve tools that allow production planners to predict programmed batches with greater precision, it was found that there are still gaps to be filled that allow the application of learning and forgetting techniques in the production process. To compose the scope of this research, a systematization of the existing literature was carried out, using the keywords ‘forgetting curves’, ‘total forgetting’, ‘learning and forgetting curve’ and ‘forgetting effects’, in the databases of Science Direct, Scielo, Scopus, Web of Science and Google Academics, which allowed classifying and organizing the developed models into 3 groups: Deterministic models, Statistical models and Functional models. This systematic process consisted of selecting databases, filtering publications, reviewing information, and analysing models, providing a detailed analysis on a topic that, despite being promising, is poorly explored in the industry, demonstrating and indicating gaps in research and application. To be filled.

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