This book constitutes the proceedings of the 4th International Conference on Decision Support Systems, ICDSST 2018, held in Heraklion, Greece, in May 2018. The main topic of this year’s conference was “Sustainable Data-Driven and Evidence Based Decision Support”. The 15 papers presented in this volume were carefully reviewed and selected from 71 submissions. They were organized in topical sections named: decision support systems for a sustainable society; decision support systems serving the public; decision support systems in management and organization; and advances in decision support systems’ technologies and methods. The EWG-DSS series of International Conference on Decision Support System Technology (ICDSST), starting with ICDSST 2015 in Belgrade, were planned to consolidate the tradition of annual events organized by the EWG-DSS in offering a platform for European and international DSS communities, comprising the academic and industrial sectors, to present state-of-the-art DSS research and developments, to discuss current challenges that surround decision-making processes, to exchange ideas about realistic and innovative solutions, and to co-develop potential business opportunities.

This book constitutes the proceedings of the 8th International Conference on Decision Support Systems Technologies, ICDSST 2022, held during May 23-25, 2022. The EWG-DSS series of International Conference on Decision Support System Technology (ICDSST) is planned to consolidate the tradition of annual events organized by the EWG-DSS in offering a platform for European and international DSS communities, comprising the academic and industrial sectors, to present state-of-the-art DSS research and developments, to discuss current challenges that surround decision-making processes, to exchange ideas about realistic and innovative solutions, and to co-develop potential business opportunities. The main aim of this year’s conference is to investigate the role DSS and related technologies can play in mitigating the impact of pandemics and post-crisis recovery. The 15 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections as follows: decision support addressing modern industry; decision support addressing business and societal needs, and multiple criteria approaches.

Decision Support Systems for Sustainable Computing investigates recent technological advances in decision support systems models designed to solve real world applications. The book provides a broad overview of digital technology transformation as applied to the circular economy which is seeking to drive improvements in scientific research, communication, logistics, automation, production, and the improved sustainability of these processes and products. The book explores applications of decision support for sustainable development across supply chain management, business intelligence, agriculture, aviation, communications, and finance. - Provides a broad overview of emerging trends and technologies in decision support systems applications - Investigates recent trends and core concepts in digital technology transformation as applied to the circular economy and sustainable development - Analyzes the application of decision support systems models across a range of case studies and processes which rely on multi-criteria decision-making and have been designed specifically to improve overall sustainability

This book recapitulates the major developments in Decision Support Systems (DSS) over the last 30 years in order to evaluate the research areas of decision making and in which direction the field should proceed. As it attempts to find a consensus about the next steps for the future of DSS research, the book also enforces the trends and new technologies currently in use. The book examines topics such as decision analysis for enterprise systems and non-hierarchical networks, integrated solutions for decision support and knowledge management in distributed environments, decision support system evaluation and analysis through social networks, and e-learning and its application to real environments. It clearly presents the evidence to support their cases and attempts to promote an extensive and objective discussion. In addition, the book also reflects on approaches to dead-end ideas and failures in DSS to better understand the lessons learned. The contributions for this book have been written by thought leaders and influential researchers from the EURO Working Group of Decision Support Systems (EWG-DSS).

Data-driven decision making is crucial for ensuring the long-term sustainability of businesses and economic growth. While rapid technological advancements have enabled the collection and analysis of data on an unprecedented scale, businesses face challenges in adopting evidence-based decision making. Data-Driven Intelligent Business Sustainability is a comprehensive guide that examines the challenges and opportunities presented by data-driven decision making. It covers new technologies like blockchain, IoT, and AI, explores their potential for sustainable business success, and provides guidance on managing cybersecurity threats. The book also includes case studies and examples of successful implementations of data-driven decision making, making it a practical resource for those seeking to upskill or reskill in this field. Targeted at computer science and engineering professionals, researchers, and students, the book provides valuable insights into the role of data-driven decision making in business sustainability, helping businesses achieve long-term success.

This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications. Industry 4.0, the Fourth Industrial Revolution, combines traditional manufacturing with automation and data exchange. Machine vision is used in the industry for reliable product inspections, quality control, and data capture solutions. It combines different technologies to provide important information from the acquisition and analysis of images for robot-based inspection and guidance. Features Presents a comprehensive guide on how to use machine vision for Industry 4.0 applications, such as analysis of images for automated inspections, object detection, object tracking, and more Includes case studies of Robotics Internet of Things with its current and future applications in healthcare, agriculture, and transportation Highlights the inclusion of impaired people in the industry, for example, an intelligent assistant that helps deaf-mute individuals to transmit instructions and warnings in a manufacturing process Examines the significant technological advancements in machine vision for Industrial Internet of Things and explores the commercial benefits using real-world applications from healthcare to transportation Discusses a conceptual framework of machine vision for various industrial applications The book addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and postgraduate students, researchers, and anyone interested in the trends, development, and opportunities for machine vision for Industry 4.0 applications.

This book presents the application of system analysis techniques with case studies to help readers learn how the techniques can be applied, how the problems are solved, and which sustainable management strategies can be reached.

This book constitutes the refereed proceedings of the Second International Conference on Decision Support Systems Technology, ICDSST 2016, held in Plymouth, UK, May 23-25. The theme of the event was “Decision Support Systems Addressing Sustainability & Societal Challenges”, organized by the EURO (Association of European Operational Research Societies) working group of Decision Support Systems (EWG-DSS). The 15 full papers presented in this book were selected out of 51 submissions after being carefully reviewed by internationally experts from the ICDSST 2016 Program Committee and external invited reviewers. The selected papers are representative of current and relevant research activities in various areas of decision support systems, such as sustainability and societal challenges; risk management and project portfolio management; business intelligence and knowledge management; and technologies to improve system usability.