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

摘要

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 durations 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.