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“Simulation-based Case Studies in Logistics” presents an intensive learning course on the application of simulation as a decision support tool to tackle complex logistic problems. The book describes and illustrates different approaches to developing simulation models at the right abstraction level to be used efficiently by engineers when dealing with strategic, tactical or operational decisions in logistic systems. 11 simulation-based case studies in logistics and supply chain management are discussed, based on the results of applied research, covering application areas such as production logistics, warehousing, transportation, material flow management, and hospital logistics. “Simulation-based Case Studies in Logistics” is an essential text for postgraduate engineering students and researchers working in the area of logistics modeling and simulation.
The focus of this volume is comprised of the fundamentals, models, and information technologies (IT) methods and tools for disaster prediction and mitigation. A more detailed list of topics includes mathematical and computational modeling of processes leading to or producing disasters, modeling of disaster effects, IT means for disaster mitigation, including data mining tools, knowledge-based and expert systems for use in disaster circumstances, GIS-based systems for disaster prevention and mitigation and equipment for disaster-prone areas. A specific type or class of disasters (natural or human-made), however will not be part of the main focus of this work. Instead, this book was conceived to offer a comprehensive, integrative view on disasters, seeking to determine what various disasters have in common. Because disaster resilience and mitigation involve humans, societies and cultures, not only technologies and economic models, special attention was paid in this volume to gain a comprehensive view on these issues, as a foundation of the IT tool design.
The main scope of this publication is to promote collaborations among research groups in the community and to interchange ideas, allowing researchers to get a quick overview of the state of the art. This volume looks at topics including robotics and computer vision and multiagent systems.
This book constitutes the refereed proceedings of the 11th International Conference on System Analysis and Modeling, SAM 2019, held in Munich, Germany, in September 2019. The 12 full papers and 2 work in progress papers presented together with one keynote talk were carefully reviewed and selected from 28 submissions. The papers discuss the most recent innovations, trends, and experiences in modeling and analysis of complex systems using ITU-T's Specification and Description Language (SDL-2010) and Message Sequence Chart (MSC) notations, as well as related system design languages — including UML, ASN.1, TTCN, SysML, and the User Requirements Notation (URN). SAM 2019’s theme was “Languages, Methods, and Tools for Industry 4.0.”
Ejemplos prácticos para el modelado, la simulación y el análisis de procesos de fabricación.
Esta obra presenta una colección de 18 casos que han sido creados como resultado del proyecto de innovación docente “Transversalidad en el diseño curricular del Máster Universitario en Ingeniería de Organización”. El proyecto de innovación docente contó con el apoyo del Instituto de Ciencias de la Educación de la Universitat Politècnica de Catalunya y tuvo como finalidad aumentar la interconexión de los contenidos impartidos en el citado Máster. La colección de casos incluidos en esta obra se utiliza como material docente transversal. Hemos indicado en cada caso las asignaturas para las que fueron creados. Para información complementaria sobre las propuestas de resolución de los casos o sus notas de enseñanza, encontrará los datos de contacto de los autores al final del libro.
This book presents for the first time a methodology that combines the power of a modelling formalism such as colored petri nets with the flexibility of a discrete event program such as SIMIO. Industrial practitioners have seen the growth of simulation as a methodology for tacking problems in which variability is the common denominator. Practically all industrial systems, from manufacturing to aviation are considered stochastic systems. Different modelling techniques have been developed as well as mathematical techniques for formalizing the cause-effect relationships in industrial and complex systems. The methodology in this book illustrates how complexity in modelling can be tackled by the u...
Artificial Intelligence (AI) is a scientific field of longstanding tradition, with origins in the early years of computer science. Today AI has reached a level of maturity that allows us to build highly sophisticated systems which perform very different tasks. Nevertheless, its evolution has opened up a number of new problems, ranging from specific algorithms to system integration, which remain elusive and assure a long life for this research field. Research progress in this area is today an international challenge that must be supported by world-class meetings and organizations, but in spite of this fact, there is also an objective need for meetings and organizations that support and disseminate research at other levels. This book focuses on new and original research on Artificial Intelligence.