Machine Learning Based Genetic Algorithm to Design Job Rotation Schedules Ensuring Homogeneity in Industry 4.0
Keywords:
Industry 4.0, job rotation, machine learning, genetic algorithms, workforce management, homogeneity, skill development, optimization.Abstract
The advent of Industry 4.0 has ushered in an era of dynamic workforce management, necessitating innovative approaches to optimize human resource utilization while maintaining a harmonious work environment. Job rotation, a practice aimed at diversifying employees' tasks, has emerged as a crucial strategy to enhance skill development and mitigate workplace monotony. This paper presents a novel framework leveraging machine learning-based genetic algorithms to design job rotation schedules that ensure homogeneity across various dimensions within the workforce. The methodology begins with the identification of key parameters, including skill sets, experience levels, and ergonomic considerations, to construct a comprehensive representation of the workforce landscape. Subsequently, a genetic algorithm, guided by machine learning models, iteratively generates and refines job rotation schedules to minimize disparities in workload distribution, skill utilization, and fatigue levels among employees. Through iterative optimization, the proposed framework strives to achieve a balance between organizational objectives, such as productivity enhancement and employee satisfaction, while adhering to operational constraints. The efficacy of the approach is demonstrated through simulation studies and real-world case analyses, highlighting its potential to facilitate agile workforce management in the context of Industry 4.0. Overall, the integration of machine learning and genetic algorithms offers a promising avenue for designing job rotation schedules that promote workforce homogeneity, resilience, and adaptability in the dynamic landscape of modern industries.
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