Dragan Pamucar – författare

OPTIMIZATION of INDUSTRIAL SYSTEMS Including the latest industrial solution-based practical applications, this is the most comprehensive and up-to-date study of the optimization of industrial systems for engineers, scientists, students, and other professionals. In order to deal with societal challenges, novel technologies play an important role. For the advancement of technology, it is essential to share innovative ideas and thoughts on a common platform where researchers across the globe meet together and revitalize their knowledge and skills to tackle the challenges that the world faces. The high complexity of the issues related to societal interdisciplinary research is the key to future revolutions. From research funders to journal editors, policymakers to think tanks, all seem to agree that the future of research lies outside disciplinary boundaries. In such prevailing conditions, various working scenarios, conditions, and strategies need to be optimized. Optimization is a multidisciplinary term, and its essence can be inculcated in any domain of business, research, and other associated working dynamics. Globalization provides all-around development, and this development is impossible without technological contributions. This volume’s mission is at the core of industrial engineering. All the manuscripts appended in this volume were double-blind peer-reviewed by committee members and the review team, promising high-quality research. This book provides deep insights to its readers about the current scenarios and future advancements of industrial engineering.

This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.

This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.

This proceedings book constitutes the refereed proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics (AISI 2023), which took place in Port Said University, Port Said, Egypt, during September 20–22, 2023, Egypt, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics and intelligent systems, technologies, and applications.

This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.

This book comprises select proceedings of the 1st International Conference on Computational Intelligence for Engineering and Management Applications (CIEMA - 2022).

This book comprises select proceedings of the 1st International Conference on Computational Intelligence for Engineering and Management Applications (CIEMA - 2022).

This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations.

This book covers the applications of computational intelligence techniques in business systems and advocates how these techniques are useful in modern business operations.