Endüstri 4.0 Bağlamında İnsan–makine Etkileşimi ve Çok Kriterli Karar Verme Yaklaşımları

This work comprehensively examines human-machine interaction and multi-criteria decision-making approaches within the context of Industry 4.0. Covering a wide range of industrial engineering topics such as supply chain management, mental workload, neuroergonomics, urban rail system potential, agile manufacturing, ergonomic risk analysis, assembly line improvement in automotive sub-industry, challenges in corporate transformation, anomaly detection in waste collection data, e-waste management, integration of e-VTOL vehicles into supply chain operations, AI-driven network management, and natural language processing-based classification of e-commerce user reviews. The book includes both theoretical frameworks and practical case studies, utilizing multi-criteria decision-making methods to systematically investigate challenges. Measurement methods of mental workload and integration with occupational health and safety play critical roles in the dynamics of Industry 4.0; technological and methodological barriers in neuroergonomics and human factor management are discussed in depth. Antalya’s 4th Stage Light Rail System is analyzed using multi-dimensional performance indicators. Agile manufacturing models are highlighted for their strategic importance in production efficiency, flexibility, and sustainability. The OCRA method is applied for ergonomic risk assessment in assembly lines. Multi-criteria decision making and time study optimize work flows in an automotive assembly line. Five key challenges in corporate transformation are discussed, supported by field experiences. Anomaly detection based on Z-score in waste collection systems is presented accompanied by comparative analysis with a virtual fill-level model. The integration of e-VTOL vehicles into supply chain operations is evaluated through a PESTEL analysis of macro-environmental factors. A security-focused multi-criteria AI decision support system is proposed for network management. Natural language processing-based classification of e-commerce reviews supports product and category management decisions. The life cycle and e-waste management of electric vehicle batteries are examined from a circular economy perspective. A simulation-based process improvement study is conducted in the dyeing department of the textile sector with validation of results. Collectively, these studies provide valuable insights for managing human-machine interaction and decision-making processes in the multifaceted dynamics of Industry 4.0 with an interdisciplinary approach.