Benzene is a toxic airborne contaminant and a recognized cancer-causing agent that presents substantial health hazards even at minimal concentrations. The precise prediction of benzene concentrations is crucial for reducing exposure, guiding public health strategies, and ensuring adherence to environmental regulations. Because of benzene's high volatility and prevalence in metropolitan and industrial areas, its atmospheric levels can vary swiftly influenced by factors like vehicular exhaust, weather patterns, and manufacturing processes. Predictive models, especially those driven by machine learning algorithms and real-time data streams, serve as effective instruments for estimating benzene concentrations with notable precision. This research emphasizes the use of recurrent neural networks (RNNs) for this objective, acknowledging that careful selection and calibration of model hyperparameters are critical for optimal performance. Accordingly, this paper introduces a customized version of the elk herd optimization algorithm, employed to fine-tune RNNs and improve their overall efficiency. The proposed system was tested using real-world air quality datasets and demonstrated promising results for predicting benzene levels in the atmosphere.
Transit time in the transportation and logistics sector is typically governed either by contractual agreements between the customer and the service provider or by relevant regulatory frameworks, including national laws and directives. In the context of postal services, where shipment volumes frequently reach millions of items per day, individual contractual definitions of transit time are impractical. Consequently, transit time expectations are commonly established through regulatory standards. These standards, as observed in numerous European Union (EU) countries and Serbia—the focus of the present case study—define expected delivery timelines at an aggregate level, without assigning specific transit time to individual postal items. Under this conventional model, senders are often unaware of the exact delivery schedule but are provided with general delivery expectations. An alternative approach was introduced and evaluated in this study, in which the transit time is explicitly selected by the sender for each shipment, offering predefined options such as D+1 (next-day delivery) and D+3 (three-day delivery). The impact of this individualized approach on operational efficiency and process organization within sorting facilities was examined through its implementation in a national postal company in Serbia. A comparative analysis between the traditional aggregate-based model and the proposed individualized model was conducted to assess variations in process management, throughput efficiency, and compliance with quality standards. The findings suggest that the new approach enhances the predictability of sorting operations, improves resource allocation, and facilitates more flexible workflow planning, thereby contributing to higher overall service quality and customer satisfaction. Furthermore, it was observed that aligning operational processes with explicitly defined transit time commitments can lead to more efficient industrial process management in logistics and postal centers.
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