Evangelos Grigoroudis – författare

Presents recent advances in both models and systems for intelligent decision making.

Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems.

The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering.

Multicriteria Decision Aid and Artificial Intelligence:

Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.

Presents recent advances in both models and systems for intelligent decision making.

Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems.

The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handling of massive data sets, the modelling of ill-structured information, the construction of advanced decision models, and the development of efficient computational optimization algorithms for problem solving. This book covers a rich set of topics, including intelligent decision support technologies, data mining models for decision making, evidential reasoning, evolutionary multiobjective optimization, fuzzy modelling, as well as applications in management and engineering.

Multicriteria Decision Aid and Artificial Intelligence:

Covers all of the recent advances in intelligent decision making. Includes a presentation of hybrid models and algorithms for preference modelling and optimisation problems. Provides illustrations of new intelligent technologies and architectures for decision making in static and distributed environments. Explores the general topics on preference modelling and learning, along with the coverage of the main techniques and methodologies and applications. Is written by experts in the field. This book provides an excellent reference tool for the increasing number of researchers and practitioners interested in the integration of MCDA and AI for the development of effective hybrid decision support methodologies and systems. Academics and post-graduate students in the fields of operational research, artificial intelligence and management science or decision analysis will also find this book beneficial.

The customer orientation philosophy of modern business organizations and the implementation of the main principles of continuous improvement, justifies the importance of evaluating and analyzing cust omer satisfaction. In fact, customer satisfaction isconsidere d today as a baseline standard of performance and a possi ble standardo f excellence forany business organization. Extensive research has defined several alternative approaches, which examine the customer satisfaction evaluation prob lem from very different perspectives. These approaches include simple quantitative tools, statistical and data analysis techniques, consumer behavioral models, etc. and adopt the following main prin ciples: * The data of the problem are based on th e customers' judgments and are directly collected from them. * This is a multivariate evaluation problem given that customer's overall satisfac tion depends on a setof variables representing product/service characteristic dimensions. * Usually, an additive formula is used in order to aggregate partial evaluations in ano verall satisfaction measure.Many of the aforementioned approaches don ot consider the qualitative form of customers' judgments, although this information constitutes the main satisfaction input data. Furthermore, insev eral cases , the measurements are not sufficient enough to analyze in detail customer sa tisfaction because models' results are mainly focused on a simple descriptive analysis.

The customer orientation philosophy of modern business organizations and the implementation of the main principles of continuous improvement, justifies the importance of evaluating and analyzing cust omer satisfaction. In fact, customer satisfaction isconsidere d today as a baseline standard of performance and a possi ble standardo f excellence forany business organization. Extensive research has defined several alternative approaches, which examine the customer satisfaction evaluation prob lem from very different perspectives. These approaches include simple quantitative tools, statistical and data analysis techniques, consumer behavioral models, etc. and adopt the following main prin ciples: * The data of the problem are based on th e customers' judgments and are directly collected from them. * This is a multivariate evaluation problem given that customer's overall satisfac tion depends on a setof variables representing product/service characteristic dimensions. * Usually, an additive formula is used in order to aggregate partial evaluations in ano verall satisfaction measure.Many of the aforementioned approaches don ot consider the qualitative form of customers' judgments, although this information constitutes the main satisfaction input data. Furthermore, insev eral cases , the measurements are not sufficient enough to analyze in detail customer sa tisfaction because models' results are mainly focused on a simple descriptive analysis.

The Handbook of Research on Artificial Intelligence, Innovation and Entrepreneurship focuses on theories, policies, practices, and politics of technology innovation and entrepreneurship based on Artificial Intelligence (AI). It examines when, where, how, and why AI triggers, catalyzes, and accelerates the development, exploration, exploitation, and invention feeding into entrepreneurial actions that result in innovation success.Individual chapters explore the factors that shape and drive innovation and entrepreneurship, including modalities (such as the Internet of Things (IoT)), challenges (such as privacy and safety concerns), and opportunities (such as augmenting the efficacy frontier of technological solutions enabled by AI).This Handbook provides comprehensive coverage of AI, technology, and innovation and entrepreneurship for academics, policy makers, practitioners, and students.

The Handbook of Research on Artificial Intelligence, Innovation and Entrepreneurship focuses on theories, policies, practices, and politics of technology innovation and entrepreneurship based on Artificial Intelligence (AI). It examines when, where, how, and why AI triggers, catalyzes, and accelerates the development, exploration, exploitation, and invention feeding into entrepreneurial actions that result in innovation success.

Individual chapters explore the factors that shape and drive innovation and entrepreneurship, including modalities (such as the Internet of Things (IoT)), challenges (such as privacy and safety concerns), and opportunities (such as augmenting the efficacy frontier of technological solutions enabled by AI).

This Handbook provides comprehensive coverage of AI, technology, and innovation and entrepreneurship for academics, policy makers, practitioners, and students.

Digital transformation continues to accelerate change in all aspects of modern life. This book examines when, where, how, and why artificial intelligence and digital change can boost innovation and transform the economy, society and democracy. It provides a holistic approach to the promotion of the knowledge economy, knowledge society and knowledge democracy.The book is developed based on the Cyber-D4 nexus, which is a conceptual framework of Cyber-Defense, Cyber-Development, Cyber-Democracy, and Cyber-Diplomacy, and it adopts a Quadruple/Quintuple Innovation Helix (Q2IH) approach. This nexus ties new national and industrial cyber strategies, including business strategies for smart cities and the Internet of Things, with the local, national, regional, and global security and economic objectives.Academics, policy makers, practitioners, researchers and students in combined fields of science, technology, innovation and entrepreneurship, digital transformation, artificial intelligence and the future of industry will appreciate the lens through which the chapter authors explore both the minutiae and expansive influence of digital transformation.

Digital transformation continues to accelerate change in all aspects of modern life. This book examines when, where, how, and why artificial intelligence and digital change can boost innovation and transform the economy, society and democracy. It provides a holistic approach to the promotion of the knowledge economy, knowledge society and knowledge democracy.

The book is developed based on the Cyber-D4 nexus, which is a conceptual framework of Cyber-Defense, Cyber-Development, Cyber-Democracy, and Cyber-Diplomacy, and it adopts a Quadruple/Quintuple Innovation Helix (Q2IH) approach. This nexus ties new national and industrial cyber strategies, including business strategies for smart cities and the Internet of Things, with the local, national, regional, and global security and economic objectives.

Academics, policy makers, practitioners, researchers and students in combined fields of science, technology, innovation and entrepreneurship, digital transformation, artificial intelligence and the future of industry will appreciate the lens through which the chapter authors explore both the minutiae and expansive influence of digital transformation.

This book presents the main principles of preference disaggregation analysis and covers theoretical advances in preference modelling, group decision making, classification methods, robustness analysis, process mining, and decision support systems. In addition, it highlights several applications of the preference disaggregation analysis in a wide range of areas, such as customer satisfaction analysis, consumer behavior, energy and environmental policy, strategy development, and agricultural marketing.This book was published in honor of Yannis Siskos on the occasion of his retirement from the University of Piraeus, Greece. It offers a unique snapshot of the preference disaggregation philosophy in multiple criteria decision analysis and presents a range of research ideas, many of which were significantly influenced by Professor Siskos work.

Thisbook gathers a selection of refereed papers presented at the 4th InternationalSymposium and 26th National Conference of the Hellenic Operational ResearchSociety. It highlights recent scientific advances in operational research andmanagement science (OR/MS), with a focus on linking OR/MS with other areas ofquantitative methods in a multidisciplinary framework. Topics covered includeareas such as business process modeling, supply chain management, organizationperformance and strategy planning, revenue management, financial applications,production planning, metaheuristics, logistics, inventory systems, and energysystems.

Thisbook gathers a selection of refereed papers presented at the 4th InternationalSymposium and 26th National Conference of the Hellenic Operational ResearchSociety. It highlights recent scientific advances in operational research andmanagement science (OR/MS), with a focus on linking OR/MS with other areas ofquantitative methods in a multidisciplinary framework. Topics covered includeareas such as business process modeling, supply chain management, organizationperformance and strategy planning, revenue management, financial applications,production planning, metaheuristics, logistics, inventory systems, and energysystems.

This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies.Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a “big-data'” era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.

This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies.Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a “big-data''” era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.

This volume presents a state-of-the-art overview of the rapidly evolving field of agribusiness, highlighting the most current issues, concepts, trends and themes in research, practice and policy.  With a particular emphasis on technology, product and process innovation, the authors cover a wide array of topics relating to such issues as research and development, technology transfer and patents and licensing, with particular respect to the roles of academic institutions, private organizations and public agencies in generating and disseminating knowledge.Featuring case studies of innovative initiatives across the industry, this book will appeal to researchers, business leaders, university administrators and policymakers concerned with the multi-faceted implications of this dynamic and controversial sector.

This volume presents a state-of-the-art overview of the rapidly evolving field of agribusiness, highlighting the most current issues, concepts, trends and themes in research, practice and policy.  With a particular emphasis on technology, product and process innovation, the authors cover a wide array of topics relating to such issues as research and development, technology transfer and patents and licensing, with particular respect to the roles of academic institutions, private organizations and public agencies in generating and disseminating knowledge.

Featuring case studies of innovative initiatives across the industry, this book will appeal to researchers, business leaders, university administrators and policymakers concerned with the multi-faceted implications of this dynamic and controversial sector.

Topics covered includeareas such as business process modeling, supply chain management, organizationperformance and strategy planning, revenue management, financial applications,production planning, metaheuristics, logistics, inventory systems, and energysystems.

This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies.Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a “big-data'” era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.

This volume presents a state-of-the-art overview of the rapidly evolving field of agribusiness, highlighting the most current issues, concepts, trends and themes in research, practice and policy.  With a particular emphasis on technology, product and process innovation, the authors cover a wide array of topics relating to such issues as research and development, technology transfer and patents and licensing, with particular respect to the roles of academic institutions, private organizations and public agencies in generating and disseminating knowledge.Featuring case studies of innovative initiatives across the industry, this book will appeal to researchers, business leaders, university administrators and policymakers concerned with the multi-faceted implications of this dynamic and controversial sector.

This book presents the main principles of preference disaggregation analysis and covers theoretical advances in preference modelling, group decision making, classification methods, robustness analysis, process mining, and decision support systems. In addition, it highlights several applications of the preference disaggregation analysis in a wide range of areas, such as customer satisfaction analysis, consumer behavior, energy and environmental policy, strategy development, and agricultural marketing.This book was published in honor of Yannis Siskos on the occasion of his retirement from the University of Piraeus, Greece. It offers a unique snapshot of the preference disaggregation philosophy in multiple criteria decision analysis and presents a range of research ideas, many of which were significantly influenced by Professor Siskos work.

This book presents the main principles of preference disaggregation analysis and covers theoretical advances in preference modelling, group decision making, classification methods, robustness analysis, process mining, and decision support systems. In addition, it highlights several applications of the preference disaggregation analysis in a wide range of areas, such as customer satisfaction analysis, consumer behavior, energy and environmental policy, strategy development, and agricultural marketing. This book was published in honor of Yannis Siskos on the occasion of his retirement from the University of Piraeus, Greece. It offers a unique snapshot of the preference disaggregation philosophy in multiple criteria decision analysis and presents a range of research ideas, many of which were significantly influenced by Professor Siskos work.