Filters

2022 - 20263
Reset filters

Settings

Author: Erkan, T.E.

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Queue Management Systems in Airport Management: Enhancing Passenger Flow and Operational Efficiency
    (Springer Science and Business Media Deutschland GmbH, 2026) Erkan, T.E.; Ozdemir, M.F.
    Queue management systems (QMS) streamline airport operations by determining travel patterns that help manage the passenger flow, reducing wait times, and enhancing operational efficiency. They help optimize queue configurations by real-time data analytics and employing algorithms, working to guide passengers efficiently through the place for check-in, security clearance, and boarding. It leads to a much more pleasant time for the passengers while ensuring better resource utilization and operational planning. This Paper investigates a new queue management system that utilizes advanced technologies like artificial intelligence and real-time data analytics. Such systems enable monitoring of passenger flocking and behavior for adjustability concerning the length of queues and the optimization of service times. The result is an enhanced travel experience and improved airport operational efficiency. The paper takes Singapore Changi Airport as a case study to aid the understanding of how effective management of queues can lead to effective Rose operational efficiency. These findings show that airports can significantly reduce passenger wait times using advanced queue management technologies and enhance the travel experience while facilitating operations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
  • Thumbnail Image
    Book Part
    Citation - Scopus: 1
    Going Green: Adoption of Green Supply Chain Management Practices for Sustainable Development
    (IGI Global, 2025) Erkan, T.E.; Barre, A.S.
    Organizations are assessed for both their economic and sustainable development when it comes to their sustainable performance more so in the dynamic global supply chain management. There has been growing concern over the sustainability of the planet and corporate stakeholders are becoming more aware of the rising environmental concerns. A key strategy for global organizations in supply chain management for environmental awareness is the implementation of a green supply chain (GSC). Going green and the complete adoption of Green Supply chain is the pathway to global environmental solutions and a contributor to the achievement of sustainable development goals. This paper seeks to assess the impact of implementing GSC management practices on sustainable development. The study will reply on secondary data from available literature on the theme of green supply chain management and sustainable development. Data will be analysed using both quantitative and qualitative analysis techniques. © 2025, IGI Global Scientific Publishing. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 10
    Citation - Scopus: 12
    A Study of Load Demand Forecasting Models in Electricity Using Artificial Neural Networks and Fuzzy Logic Model
    (Materials & Energy Research Center-merc, 2022) Al-ani, B. R. K.; Erkan, E. T.; Erkan, T.E.
    Since load time series are very changeable. demand forecasting of the short-term load is challenging based on hourly, daily, weekly, and monthly load forecast demand. As a result, the Turkish Electricity Transmission Company (TEA) load forecasting is proposed in this paper using artificial neural networks (ANN) and fuzzy logic (FL). Load forecasting enables utilities to purchase and generate electricity, load shift, and build infrastructure. A load forecast was classified into three sorts (hourly, weekly and monthly). Over time, forecasting power loads with artificial neural networks and fuzzy logic reveals a massive decrease in ANN and a progressive increase in FL from 24 to 168 hours. As illustrated, fuzzy logic and artificial neural netANorks outperform regression algorithms. This study has the highest growth and means absolute percentage error (MAPE) rates compared to FL and ANN. Although regression has the highest prediction growth rate, it is less precise than FL and ANN due to their lower MAPE percentage. Artificial Neural Networks and Fuzzy Logic are emerging technologies capable of forecasting and mitigating demand volatility. Future research can forecast various Turkish states using the same approach.