Title: A genetic algorithm approach to design job rotation schedules ensuring homogeneity and diversity of exposure in the automotive industry

Biography:

Nafiseh Mollaei holds a Ph.D. in Biomedical Engineering. She is an expert in a wide variety of AI algorithms like natural language processing and machine learning. As a part of her industry program, she has been working at Volkwagen Autoeuopa to predict occupational disorders in order to increase the productivity of the automotive sector. Besides, she has worked several articles in terms of the applicability of AI in this domain, such as subjects of Human_Centered Explanaible AI, Knowledge Discovery Exploratory based on association rules mining, and also Reinforcement Learning in Industry 5.0. Presently, she has holder of post doctoral fellowership in Biosensors in Baylor Collage of Medicine, USA. She is working on Center to Stream Healthcare in Place (C2SHIP).

Abstract

Job rotation is a work organization strategy with increasing popularity, given its benefits for workers and companies, especially those working with manufacturing. This study proposes a formulation to help the team leader in an assembly line of the automotive industry to achieve job rotation schedules based on three major criteria: improve diversity, ensure homogeneity, and thus reduce exposure level. The formulation relied on a genetic algorithm, that took into consideration the biomechanical risk factors (EAWS), workers’ qualifications, and the organizational aspects of the assembly line. Moreover, the job rotation plan formulated by the genetic algorithm formulation was compared with the solution provided by the team leader in a real life-environment. The formulation proved to be a reliable solution to design job rotation plans for increasing diversity, decreasing exposure, and balancing homogeneity within workers, achieving better results in all of the outcomes when compared with the job rotation schedules created by the team leader. Additionally, this solution was less time-consuming for the team leader than a manual implementation. This study provides a much needed solution to the job rotation issue in the manufacturing industry, with the genetic algorithm taking less time and showing better results than the job rotations created by the team leaders.