Designing Cyclic Job Rotations to Reduce the Exposure to Ergonomics Risk Factors

Job rotation is an administrative solution to prevent work-related musculoskeletal disorders that has become widespread. However, job rotation schedules development is a complex problem. This is due to the multi-factorial character of the disorders and to the productive and organizational constraints of the real working environments. To avoid these problems, this work presents an evolutionary algorithm to generate rotation schedules in which a set of workers rotate cyclically over a small number of jobs while reducing the potential for injury. The algorithm is able to generate rotation schedules that optimize multiple ergonomics criteria by clustering the tasks into rotation groups, selecting the workers for each group, and determining the sequence of rotation of the workers to minimize the effects of fatigue. The algorithm reduces prolonged exposure to risks related to musculoskeletal injuries and simplifies the assignment of workers to different tasks in each rotation. The presented procedure can be an effective tool for the design of job-rotation schedules that prevent work-related musculoskeletal disorders while simplifying scheduled changeovers at each rotation and facilitating job monitoring.

Hybrid analysis pipelines in the REANA reproducible analysis platform

We introduce the feasibility of running hybrid analysis pipelines in the REANA reproducible analysis platform. The REANA platform allows researchers to specify declarative computational workflow steps describing the analysis process and to execute analysis workload on remote containerised compute clouds. We have designed an abstract job controller component permitting to execute different parts of the analysis workflow on different compute backends, such as HTCondor, Kubernetes and SLURM. We have prototyped the designed solution including the job execution, job monitoring, and input/output file staging mechanism between the various compute backends. We have tested the prototype using several particle physics model analyses. The present work introduces support for hybrid analysis workflows in the REANA reproducible analysis platform and paves the way towards studying underlying performance advantages and challenges associated with hybrid analysis patterns in complex particle physics data analyses.