A REBA-Based Mathematical Model for Ergonomic Workload Index Minimization and Operator Task Allocation in Textile Manufacturing

The textile industry is considered a high-risk sector due to its labor-intensive nature and the numerous hazards present in the production area. Furthermore, musculoskeletal disorders resulting from repetitive movements, improper posture, and excessive physical workload observed in textile workers are among the most significant occupational health problems. In this study, ergonomic risks in the dyeing, weaving, and sewing departments of an upholstery fabric manufacturing factory were evaluated using the Rapid Entire Body Assessment (REBA) method. Based on the REBA scores, cycle times, and repetition frequencies, an REBA-weighted ergonomic workload index was calculated for each task. Based on the obtained data, separate mathematical optimization models were developed for each department to minimize the maximum individual REBA-weighted ergonomic workload index under different predefined staffing scenarios. For each scenario, the model optimized the allocation of task repetitions among the available operators, and alternative staffing configurations were evaluated based on ergonomic workload distribution and workforce requirements. The results showed that the proposed approach enabled a more balanced distribution of ergonomic workload among operators, particularly in departments characterized by high repetition frequencies. Based on the evaluated scenarios, configurations of six operators for dyeing, three for weaving, and five for sewing were selected as the proposed staffing configurations. These findings indicate that ergonomic improvement in textile production should not rely solely on posture correction but should also consider workload distribution, staffing levels, and organizational improvements. Overall, the proposed approach provides a decision-support framework for evaluating staffing scenarios and optimizing task allocation to improve ergonomic workload balance in textile production environments.