Department of Industrial Engineering

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Browsing Department of Industrial Engineering by browse.metadata.advisor "Bitsch, Prof. Dr. Günter"

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    Development of a human digital twin for human-centric dispatching for an assembly process
    (Stellenbosch : Stellenbosch University, 2023-12) Kneissl, Anja; Jooste, Dr. J. L.; Bitsch, Prof. Dr. Günter; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: Assembly workers experience higher stress levels than other positions. Reducing the stress of assembly workers is important for workers and companies as it has an impact on long-term health, employee commitment to the company and job satisfaction. The research presented in this thesis develops a new approach to strain reduction using an assembly process. Considering workers' preferences increases their autonomy and control over their work, which is expected to reduce strain. This is implemented in worker dispatching for an assembly process. The approach presented in this thesis considers the individual worker and their preferences regarding the type of task in the dispatching procedure. In addition, machine learning is used to predict the worker's strain in a particular task to evaluate the worker with the least expected strain in that task from a group of workers. By incorporating preferences, worker autonomy can be increased. Combined with strain prediction using machine learning, which incorporates individual worker strain based on various factors into the dispatching system, worker strain can be reduced. A human-centric dispatching system that includes a human digital twin as a digital representation of the individual worker is developed. The human digital twin consists of three elements: data storage, strain prediction and strain monitoring. The worker attributes included in the data storage are availability, age, assembly competence, preference regarding the type of task, and worker strain using heart rate as an objective measure. The worker's strain is predicted using task-specific training data from three scooter assembly workstations at the "Werk 150" logistics learning factory at the University of Reutlingen, Germany. The worker attributes included in the training data for strain prediction are task type, age, time of day, preference, and assembly skill. A Random Forest regressor trained on the augmented dataset 1 is used to predict the median heart rate. The mean average error is 5,64 beats per minute and the deviation between predicted and test values is 39,66 %. The developed dispatching procedure considering preferences and including strain prediction is evaluated in Werk 150. The field experiment indicated that using the developed human-centric dispatching system including the worker's human digital twin leads to a decrease in strain. Using the NASA TLX as a subjective strain measurement, the average worker strain decreases by 27 % measured with the NASA RTLX and by 33 % measured with the weighted NASA TLX across all three assembly tasks and subscales. Using heart rate as an objective strain measurement, the strain decreases by 42,27 % when measuring mean heart rate and by 41,08 % for median heart rate. Compared to random task assignment, the developed human-centric dispatching system, therefore, reduces worker strain on average by 35,84 %.In conclusion, considering the preferences of the workers in dispatching and combining this with strain prediction leads to strain reduction. This is a starting point for increased human-centricity in production and a step forward in the implementation of the Industry 5.0 concept.