Optimizing Resource Allocation in the Flexible Job Shop Problem: Assessing the Impact of Rest Breaks on Task Strenuousness Reduction

The integration of collaborative robots (cobots) in
production workshops aims to enhance productivity while minimizing
physical strain for human operators. However, physical
strain is often treated merely as a constraint rather than as
an objective to address. To effectively model the production
process and incorporate human factors, it is crucial to employ
an appropriate index that accounts for precise ergonomic rules.
In this paper, we propose a method to organize induced waiting
times in the context of Flexible Job Shop Scheduling (FJSP)
with the primary goal of evaluating the effects of rest breaks
on fatigue generated by physical efforts in various operations.
Our method introduces a specific index, based on ergonomic
standards, to assess the difficulty level of operations for both
humans and robots. The index incorporates normalized strain
values, ensuring its generalizability across diverse industries.
Moreover, the method considers a wide range of work scenarios,
encompassing both individual and collaborative settings, making
it applicable to different industrial contexts. Furthermore, we
analyze the accumulated fatigue experienced by human agents
over several scenarios, both before and after the optimization
process. By doing so, we not only gain insights into the production
process but also facilitate the optimization of human-robot
collaboration. Our findings indicate the influence of rest break
duration on the recovery of human workers and offer strategies
to reduce strenuousness during production planning involving
cobots. This work contributes to creating a more efficient and
ergonomic working environment for all parties involved, promoting
human well-being and presence in the industrial workshop.
By emphasizing the significance of considering physical strain
and rest breaks as essential factors in the 5.0 factory, we pave
the way for improved human-robot interaction and performance
in manufacturing settings.