AI Summary & Key Takeaways
AI InsightThe results show that our approach is promising in terms of real life machine scheduling problems under ergonomic risk constraints.
📌 3 Key Takeaways
Özet
In this study, different job deterioration rates with the position dependent learning rates were included in makespan minimization problem (MMP). Physical workloads and ergonomic design risks that the employee is exposed were considered. The European Assembly Worksheet (EAWS) was selected as a risk evaluation method and employed for determining risk deterioration rate, since it makes possible to assess awkward postures, action forces levels, material handlings and repetitive load of the upper limbs. EAWS risk assessments were made for 10 assembly jobs in a company in the manufacturing sector. It was proved and numerically shown that makespan minimization problem with job dependent risk deterioration and position dependent learning effect can be optimally solved by Smallest Deterioration Rule (SDR), only if common process time is used instead of basic process time. The results show that our approach is promising in terms of real life machine scheduling problems under ergonomic risk constraints. The contribution of this paper to the literature is the modeling musculoskeletal disorder risks with EAWS and calculation of deterioration rates by a hyperbolic tangent function for the first time. Furthermore, it was proved and numerically shown that makespan minimization problem can be optimally solved with SDR. As a future work, parallel machine scheduling or different deterioration functions could be employed for the ergonomic risks evaluations.