Affiliations: [a] CX Team, Samsung SDS, Seoul, South Korea | [b] AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA
| [c] Department of Industrial and Management Engineering, Myongji University, Yongin, South Korea | [d] Department of Computer Science Engineering, Seoul National University, Seoul, South Korea
Correspondence:
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Address for correspondence: Myung Hwan Yun, Ph.D. Department of Industrial Engineering, Seoul National University, 39-312, Daehak-dong, Gwanak-gu, Seoul, South Korea. Tel.: +82 2 880 1403; Fax: +82 2 889 8564; E-mail: mhy@snu.ac.kr.
Abstract: BACKGROUND:For the successful implementation of job rotation, jobs should be scheduled systematically so that physical workload is evenly distributed with the use of various body parts. However, while the potential benefits are widely recognized by research and industry, there is still a need for a more effective and efficient algorithm that considers multiple work-related factors in job rotation scheduling. OBJECTIVE:This study suggests a type of job rotation algorithm that aims to minimize musculoskeletal disorders with the approach of decreasing the overall workload. METHODS:Multiple work characteristics are evaluated as inputs to the proposed algorithm. Important factors, such as physical workload on specific body parts, working height, involvement of heavy lifting, and worker characteristics such as physical disorders, are included in the algorithm. For evaluation of the overall workload in a given workplace, an objective function was defined to aggregate the scores from the individual factors. A case study, where the algorithm was applied at a workplace, is presented with an examination on its applicability and effectiveness. RESULTS:With the application of the suggested algorithm in case study, the value of the final objective function, which is the weighted sum of the workload in various body parts, decreased by 71.7% when compared to a typical sequential assignment and by 84.9% when compared to a single job assignment, which is doing one job all day. CONCLUSIONS:An algorithm was developed using the data from the ergonomic evaluation tool used in the plant and from the known factors related to workload. The algorithm was developed so that it can be efficiently applied with a small amount of required inputs, while covering a wide range of work-related factors. A case study showed that the algorithm was beneficial in determining a job rotation schedule aimed at minimizing workload across body parts.
