Non-ergonomic working conditions are the leading causes of musculoskeletal disorders that seriously affect human health. REBA is widely used tool due to its convenience and consideration of all body parts. However, it heavily relies on the subjective judgments of the assessor, leading to inconsistencies in results, and lacks sensitivity in detecting small changes in ergonomic risk factors. Therefore, there is a need to improve the REBA method by integrating it with new technologies. While a few studies have proposed integrating ergonomic risk measurement tools with ANNs, there is a research gap in comparing different types of neural networks and membership functions to determine the most effective approach for improving the performance of REBA. Additionally, there is a need to apply these integrations to real-life case studies to demonstrate their effectiveness in practice. This study proposes a comparative neural network and neuro-fuzzy-based REBA method that includes various types of neural networks and membership functions. The proposed method is applied to service employee who have experienced increased workloads due to the Covid-19 pandemic. The results show that the neuro-fuzzy method is more accurate than the REBA and provides greater flexibility in defining which member belongs to which risk level cluster. This study is critical because it addresses research gaps in integrating neural networks and REBA and applies these integrations to a real-life case study. By comparing different types of neural networks and membership functions, the study provides insights into which approaches are most effective for improving the performance of REBA.

One of the main causes of the significant commercial vehicle traffic in the city region is last-mile deliveries. Parcel lockers, which are one of the easiest and most environmentally friendly solutions for last-mile delivery, are one of the most studied subjects recently. The parcel locker ensures consumer privacy while being quick and efficient. Its full-time service can effectively address the issue of student and office worker pickup. In this paper, the location of a parcel locker intended to be established in the most convenient location in Beşiktaş district of İstanbul, Turkey has been determined. This problem can be solved using a multi-criteria decision-making (MCDM) structure due to the availability of numerous aspects that must be considered while choosing the optimum location. Additionally, the benefit of fuzzy logic is employed to translate expert opinions into mathematical expressions and incorporate them into decision-making processes. To choose the ideal location for the parcel locker, a novel model integrating the Bayesian Best Worst Method (B-BWM) and Pythagorean fuzzy Weighted Aggregated Sum Product Assessment (PF-WASPAS) approaches is proposed for the first time in the literature. Additionally, a sensitivity analysis is conducted to evaluate the model’s robustness. As a consequence, the suggested model effectively identifies the best location for a parcel locker in Istanbul.