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Scheduling is a resource allocation problem which exists in virtually every type of organization. Scheduling problems have produced roughly 40 years of research primarily within the OR community. This community has traditionally emphasized mathematical modeling techniques which seek exact solutions to well formulated optimization problems. While this approach produced important results, many contemporary scheduling problems are particularly difficult. Hence, over the last ten years operations researchers interested in scheduling have turned increasingly to more computer intensive and heuristic approaches. At roughly the same time, researchers in AI began to focus their methods on industrial and management science applications. The result of this confluence of fields has been a period of remarkable growth and excitement in scheduling research. Intelligent Scheduling Systems captures the results of a new wave of research at the forefront of scheduling research, of interest to researchers and practitioners alike. Presented are an array of the latest contemporary tools -- math modeling to tabu search to genetic algorithms -- that can assist in operational scheduling and solve difficult scheduling problems. The book presents the most recent research results from both operations research (OR) and artificial intelligence (AI) focusing their efforts on real scheduling problems.
This book focuses on the design of Robotic Flexible Assembly Cell (RFAC) with multi-robots. Its main contribution consists of a new effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems occur. The developed strategy, which is based on a combination of advanced solution approaches such as simulation, fuzzy logic, system modeling and the Taguchi optimization method, fills an important knowledge gap in the current literature and paves the way for future research towards the goal of employing flexible assembly systems as effectively as possible despite the complexity of their scheduling.
Scheduling complex processes, such as chemical manufacturing or space shuttle launches, is a focus of substantial effort throughout industry and government. In the past 20 years, the fields of operations research and operations management have tackled scheduling problems with considerable success. Recently, the artificial intelligence community has turned its attention to this class of problems, resulting in a fresh corpus of research and application that extends previous results. This book, comprising original contributions from experts in the field, highlights these new advances. These chapters present complete systems, stressing their unique characteristics, rather than presenting simple research results. Applications-oriented chapters are also included to inform researchers of state-of-the-art methodologies. Researchers and practitioners in industry and government will find this book valuable. It will also serve as an ideal text for a graduate course in knowledge-based scheduling.
The first comparative examination of planning paradigms This text begins with the principle that the ability to anticipateand plan is an essential feature of intelligent systems, whetherhuman or machine. It further assumes that better planning resultsin greater achievements. With these principles as a foundation,Planning in Intelligent Systems provides readers with the toolsneeded to better understand the process of planning and to becomebetter planners themselves. The text is divided into two parts: * Part One, "Theoretical," discusses the predominant schools ofthought in planning: psychology and cognitive science,organizational science, computer science, mathematics, artificialintelligence, and systems theory. In particular, the book examinescommonalities and differences among the goals, methods, andtechniques of these various approaches to planning. The result is abetter understanding of the process of planning through thecross-fertilization of ideas. Each chapter contains a shortintroduction that sets forth the interrelationships of that chapterto the main ideas featured in the other chapters. * Part Two, "Practical," features six chapters that center on acase study of The Netherlands Railways. Readers learn to applytheory to a real-world situation and discoverhow expanding theirrepertoire of planning methods can help solve seemingly intractableproblems. All chapters have been contributed by leading experts in thevarious schools of planning and carefully edited to ensure aconsistent high standard throughout. This book is designed to not only expand the range of planningtools used, but also to enable readers to use them moreeffectively. It challenges readers to look at new approaches andlearn from new schools of thought. Planning in Intelligent Systemsdelivers effective planning approaches for researchers, professors,students, and practitioners in artificial intelligence, computerscience, cognitive psychology, and mathematics, as well as industryplanners and managers.
The Intelligent Techniques for Planning presents a number of modern approaches to the area of automated planning. These approaches combine methods from classical planning such as the construction of graphs and the use of domain-independent heuristics with techniques from other areas of artificial intelligence. This book discuses, in detail, a number of state-of-the-art planning systems that utilize constraint satisfaction techniques in order to deal with time and resources, machine learning in order to utilize experience drawn from past runs, methods from knowledge systems for more expressive representation of knowledge and ideas from other areas such as Intelligent Agents. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments on similar approaches done in the past.
This volume encompasses state-of-the-art developments in AI-based reactive scheduling for real-time operation management in manufacturing shop floors. It is a collection of papers from the Second International Workshop of the IFIP Working Group 5.7 which brought together researchers from management information systems and knowledge engineering to expand the focus on applying new knowledge-based techniques.
This volume encompasses state-of-the-art developments in AI-based reactive scheduling for real-time operation management in manufacturing shop floors. It is a collection of papers from the Second International Workshop of the IFIP Working Group 5.7 which brought together researchers from management information systems and knowledge engineering to expand the focus on applying new knowledge-based techniques.
This book discusses automated computing systems which are mostly powered by intelligent technologies like artificial intelligence, machine learning, image recognition, speech processing, cloud computing, etc., to perform complex automated tasks which are not possible by traditional computing systems. The chapters are extended version of research works presented at first Ph.D. Research Symposium in various advanced technologies used in the field of computer science. This book provides an opportunity for the researchers to get ideas regarding the ongoing works that help them in formulating problems of their interest. The academicians can also be benefited to know about the current research trends that smooth the way to guide their students to carry out research work in the proper direction. The industry people will be also facilitated to know about the current advances in research work and materialize the research work into industrial applications.
This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.