Multiple criteria decision-making research has developed rapidly and has become a main area of research for dealing with complex decision problems which require the consideration of multiple objectives or criteria. Over the past twenty years, numerous multiple criterion decision methods have been developed which are able to solve such problems. However, the selection of an appropriate method to solve a particular decision problem is today's problem for a decision support researcher and decision-maker. Intelligent Strategies for Meta Multiple Criteria Decision-Making deals centrally with the problem of the numerous MCDM methods that can be applied to a decision problem. The book refers to this as a meta decision problem', and it is this problem that the book analyzes. The author provides two strategies to help the decision-makers select and design an appropriate approach to a complex decision problem. Either of these strategies can be designed into a decision support system itself. One strategy is to use machine learning to design an MCDM method. This is accomplished by applying intelligent techniques, namely neural networks as a structure for approximating functions and evolutionary algorithms as universal learning methods. The other strategy is based on solving the meta decision problem interactively by selecting or designing a method suitable to the specific problem, for example, the constructing of a method from building blocks. This strategy leads to a concept of MCDM networks. Examples of this approach for a decision support system explain the possibilities of applying the elaborated techniques and their mutual interplay. The techniques outlined in the book can be used by researchers, students, and industry practitioners to better model and select appropriate methods for solving complex, multi-objective decision problems.

This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving them. The first two chapters provide general introductions to logistics and supply chain management on the one hand, and to computational intelligence on the other hand. The subsequent chapters cover specific fields in logistics and supply chain management, work out the most relevant problems found in those fields, and discuss approaches for solving them. Chapter 3 discusses problems in the field of production and inventory management. Chapter 4 considers planning activities on a finer level of granularity which is usually denoted as scheduling. In chapter 5 problems in transportation planning such as different types of vehicle routing problems are considered. While chapters 3 to 5 rather discuss planning problems which appear on an operative level, chapter 6 discusses the strategic problem of designing a supply chain or network. The final chapter provides an overview of academic and commercial software and information systems for the discussed applications. There appears to be a gap between general textbooks on logistics and supply chain management and more specialized literature dealing with methods for computational intelligence, operations research, etc., for solving the complex operational problems in these fields. For readers, it is often difficult to proceed from introductory texts on logistics and supply chain management to the sophisticated literature which deals with the usage of advanced methods. This book fills this gap by providing state-of-the-art descriptions of the corresponding problems and suitable methods for solving them.

At a practical level, mathematical programming under multiple objectives has emerged as a powerful tool to assist in the process of searching for decisions which best satisfy a multitude of conflicting objectives, and there are a number of distinct methodologies for multicriteria decision-making problems that exist. These methodologies can be categorized in a variety of ways, such as form of model (e.g. linear, non-linear, stochastic), characteristics of the decision space (e.g. finite or infinite), or solution process (e.g. prior specification of preferences or interactive). Scientists from a variety of disciplines (mathematics, economics and psychology) have contributed to the development of the field of Multicriteria Decision Making (MCDM) (or Multicriteria Decision Analysis (MCDA), Multiattribute Decision Making (MADM), Multiobjective Decision Making (MODM), etc.) over the past 30 years, helping to establish MCDM as an important part of management science. MCDM has become a central component of studies in management science, economics and industrial engineering in many universities worldwide. Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory and Applications aims to bring together state-of-the-art' reviews and the most recent advances by leading experts on the fundamental theories, methodologies and applications of MCDM. This is aimed at graduate students and researchers in mathematics, economics, management and engineering, as well as at practicing management scientists who wish to better understand the principles of this new and fast developing field.

The work presented in this book is based on empirical study undertaken as a case study to understand the challenges faced in massively open online course (MOOC) based learning and experimentation to understand the challenges for presenting theoretical and practical courses. The book proposes a flexible online platform. This solution provides flexibility in distance learning processes including course enrollment, learning, evaluation, and outcome of degrees. The proposed system not only gives students freedom to choose their courses in accordance with their needs but also use earned credit towards online degrees of any university of their choice.

The work presented in this book is based on empirical study undertaken as a case study to understand the challenges faced in massively open online course (MOOC) based learning and experimentation to understand the challenges for presenting theoretical and practical courses. The book proposes a flexible online platform. This solution provides flexibility in distance learning processes including course enrollment, learning, evaluation, and outcome of degrees. The proposed system not only gives students freedom to choose their courses in accordance with their needs but also use earned credit towards online degrees of any university of their choice.

The proceedings volume presents selected papers from the International Conference on Sustainability in Software Engineering & Business Information Management: Innovation & Applications (SSEBIM 2021) held in Olten, Switzerland from 8-9 October, 2021.  It includes research related to sustainability from both a business and technical point of view. From a business perspective, it not only addresses how to make the business operations more sustainable, but also considers factors such as human values, ethics, environment and responsibility of the businesses. From the technical perspective of software development companies, it focuses on sustainability in software engineering ranging from practices, tools, techniques and methods. The contributions reflect how software engineering teams exhibited pro-activeness in their approaches to lead to sustainable development of the software that is of highest quality and reliability. It is intended for a broad audience, including students, researchers and practitioners who work in software engineering and business information management fields.

This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 58 selected papers from the 20th International Conference on Hybrid Intelligent Systems (HIS 2020) and 20 papers from the 12th World Congress on Nature and Biologically Inspired Computing (NaBIC 2020), which was held online, from December 14 to 16, 2020. A premier conference in the field of artificial intelligence, HIS - NaBIC 2020 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 25 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of science and engineering.

This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving them. The first two chapters provide general introductions to logistics and supply chain management on the one hand, and to computational intelligence on the other hand. The subsequent chapters cover specific fields in logistics and supply chain management, work out the most relevant problems found in those fields, and discuss approaches for solving them. Chapter 3 discusses problems in the field of production and inventory management. Chapter 4 considers planning activities on a finer level of granularity which is usually denoted as scheduling. In chapter 5 problems in transportation planning such as different types of vehicle routing problems are considered. While chapters 3 to 5 rather discuss planning problems which appear on an operative level, chapter 6 discusses the strategic problem of designing a supply chain or network. The final chapter provides an overview of academic and commercial software and information systems for the discussed applications. There appears to be a gap between general textbooks on logistics and supply chain management and more specialized literature dealing with methods for computational intelligence, operations research, etc., for solving the complex operational problems in these fields. For readers, it is often difficult to proceed from introductory texts on logistics and supply chain management to the sophisticated literature which deals with the usage of advanced methods. This book fills this gap by providing state-of-the-art descriptions of the corresponding problems and suitable methods for solving them.

This book contains a selection of refereed and revised papers of Intelligent Informatics Track originally presented at the third International Symposium on Intelligent Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers selected for this Track cover several intelligent informatics and related topics including signal processing, pattern recognition, image processing data mining and their applications.

At a practical level, mathematical programming under multiple objectives has emerged as a powerful tool to assist in the process of searching for decisions which best satisfy a multitude of conflicting objectives, and there are a number of distinct methodologies for multicriteria decision-making problems that exist. These methodologies can be categorized in a variety of ways, such as form of model (e.g. linear, non-linear, stochastic), characteristics of the decision space (e.g. finite or infinite), or solution process (e.g. prior specification of preferences or interactive). Scientists from a variety of disciplines (mathematics, economics and psychology) have contributed to the development of the field of Multicriteria Decision Making (MCDM) (or Multicriteria Decision Analysis (MCDA), Multiattribute Decision Making (MADM), Multiobjective Decision Making (MODM), etc.) over the past 30 years, helping to establish MCDM as an important part of management science. MCDM has become a central component of studies in management science, economics and industrial engineering in many universities worldwide. Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory and Applications aims to bring together `state-of-the-art' reviews and the most recent advances by leading experts on the fundamental theories, methodologies and applications of MCDM. This is aimed at graduate students and researchers in mathematics, economics, management and engineering, as well as at practicing management scientists who wish to better understand the principles of this new and fast developing field.

Multiple criteria decision-making research has developed rapidly and has become a main area of research for dealing with complex decision problems which require the consideration of multiple objectives or criteria. Over the past twenty years, numerous multiple criterion decision methods have been developed which are able to solve such problems. However, the selection of an appropriate method to solve a particular decision problem is today's problem for a decision support researcher and decision-maker. Intelligent Strategies for Meta Multiple Criteria Decision-Making deals centrally with the problem of the numerous MCDM methods that can be applied to a decision problem. The book refers to this as a `meta decision problem', and it is this problem that the book analyzes. The author provides two strategies to help the decision-makers select and design an appropriate approach to a complex decision problem. Either of these strategies can be designed into a decision support system itself. One strategy is to use machine learning to design an MCDM method. This is accomplished by applying intelligent techniques, namely neural networks as a structure for approximating functions and evolutionary algorithms as universal learning methods. The other strategy is based on solving the meta decision problem interactively by selecting or designing a method suitable to the specific problem, for example, the constructing of a method from building blocks. This strategy leads to a concept of MCDM networks. Examples of this approach for a decision support system explain the possibilities of applying the elaborated techniques and their mutual interplay. The techniques outlined in the book can be used by researchers, students, and industry practitioners to better model and select appropriate methods for solving complex, multi-objective decision problems.

This book constitutes the refereed proceedings of the 6th International Symposium on Advances in Signal Processing and Intelligent Recognition Systems, SIRS 2020, held in Chennai, India, in October 2020. Due to the COVID-19 pandemic the conference was held online. The 22 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 50 submissions. The papers cover wide research fields including information retrieval, human-computer interaction (HCI), information extraction, speech recognition.

Das Buch zeigt komplexe Probleme in den Bereichen Logistik und Supply Chain Management und eroertert fortschrittliche Methoden, insbesondere aus dem Bereich Computational Intelligence (CI), zu deren Loesung. Die ersten beiden Kapitel bieten allgemeine Einfuhrungen in die Logistik, das Lieferkettenmanagement und in die Computational Intelligence. Die folgenden Kapitel behandeln spezifische Bereiche der Logistik und des Supply Chain Managements und diskutieren Loesungsansatze. In Kapitel 3 werden Probleme der Transportplanung, wie z. B. Arten von Vehicle Routing, betrachtet. In Kapitel 4 werden Probleme aus dem Bereich der Produktions- und Lagerverwaltung eroertert. Kapitel 5 befasst sich mit Planungsaktivitaten beim Scheduling. Wahrend in den Kapiteln 3 bis 5 eher Planungsprobleme auf operativer Ebene behandelt werden, geht es in Kapitel 6 um das strategische Problem der Gestaltung einer Lieferkette oder eines Netzwerks. Das letzte Kapitel gibt einen UEberblick uber akademische und kommerzielle Software und Informationssysteme fur die diskutierten Anwendungen. Es scheint eine Lucke zu geben zwischen allgemeinen Lehrbuchern uber Logistik und Supply Chain Management und speziellerer Literatur, die sich mit Methoden der Computational Intelligence, des Operations Research usw. zur Loesung komplexer betrieblicher Probleme in diesen Bereichen befasst. Fur den Leser ist es oft schwierig, von einfuhrenden Texten uber Logistik und Supply Chain Management zu der anspruchsvollen Literatur uber die Anwendung fortgeschrittener Methoden uberzugehen. Dieses Buch fullt diese Lucke, indem es Beschreibungen der entsprechenden Probleme und geeignete Methoden zu ihrer Loesung auf dem neuesten Stand der Technik bereitstellt. Dieses Buch ist eine UEbersetzung einer deutschen Originalausgabe. Die UEbersetzung wurde mit Hilfe von kunstlicher Intelligenz (maschinelle UEbersetzung durch den Dienst DeepL.com) erstellt. Eine anschliessende menschliche UEberarbeitung erfolgte vor allem in Bezug auf den Inhalt, so dass sich das Buch stilistisch anders liest als eine herkoemmliche UEbersetzung.

This book highlights recent research on intelligent systems and nature-inspired computing. It presents 132 selected papers from the 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 34 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.