Boris Goldengorin – författare

This book focuses on a development of optimal, flexible, and efficient models and algorithms for cell formation in group technology. Its main aim is to provide a reliable tool that can be used by managers and engineers to design manufacturing cells based on their own preferences and constraints imposed by a particular manufacturing system. This tool could potentially lower production costs by minimizing other costs in a number of areas, thereby increasing profit in a manufacturing system. In the volume, the cell formation problem is considered in a systematic and formalized way, and several models are proposed, both heuristic and exact. The models are based on general clustering problems, and are flexible enough to allow for various objectives and constraints. The authors also provide results of numerical experiments involving both artificial data from academic papers in the field and real manufacturing data to certify the appropriateness of the models proposed. The book was intended to suit the broadest possible audience, and thus all algorithmic details are given in a detailed description with multiple numerical examples and informal explanations are provided for the theoretical results. In addition to managers and industrial engineers, this book is intended for academic researchers and students. It will also be attractive to many theoreticians, since it addresses many open problems in computer science and bioinformatics.

This book focuses on a development of optimal, flexible, and efficient models and algorithms for cell formation in group technology. Its main aim is to provide a reliable tool that can be used by managers and engineers to design manufacturing cells based on their own preferences and constraints imposed by a particular manufacturing system.

Clusters, Orders, and Trees: Methods and Applications

The volume is dedicated to Boris Mirkin on the occasion of his 70th birthday. In addition to his startling PhD results in abstract automata theory, Mirkin’s ground breaking contributions in various fields of decision making and data analysis have marked the fourth quarter of the 20th century and beyond. Mirkin has done pioneering work in group choice, clustering, data mining and knowledge discovery aimed at finding and describing non-trivial or hidden structures—first of all, clusters, orderings and hierarchies—in multivariate and/or network data.This volume contains a collection of papers reflecting recent developments rooted in Mirkin’s fundamental contribution to the state-of-the-art in group choice, ordering, clustering, data mining and knowledge discovery. Researchers, students and software engineers will benefit from new knowledge discovery techniques and application directions.

This book presents open optimization problems in graph theory and networks. Each chapter reflects developments in theory and applications based on Gregory Gutin’s fundamental contributions to advanced methods and techniques in combinatorial optimization. Researchers, students, and engineers in computer science, big data, applied mathematics, operations research, algorithm design, artificial intelligence, software engineering, data analysis, industrial and systems engineering will benefit from the state-of-the-art results presented in modern graph theory and its applications to the design of efficient algorithms for optimization problems.  Topics covered in this work include:·         Algorithmic aspects of problems with disjoint cycles in graphs·         Graphs where maximal cliques and stable sets intersect·         The maximum independent set problem with special classes·         A general technique for heuristic algorithms for optimization problems ·         The network design problem with cut constraints·         Algorithms for computing the frustration index of a signed graph·         A heuristic approach for studying the patrol problem on a graph·         Minimum possible sum and product of the proper connection number·         Structural and algorithmic results on branchings in digraphs ·         Improved upper bounds for Korkel--Ghosh benchmark SPLP instances

This book presents the state-of-the-art in the emerging field of data science and includes models for layered security with applications in the protection of sites—such as large gathering places—through high-stake decision-making tasks. Such tasks include cancer diagnostics, self-driving cars, and others where wrong decisions can possibly have catastrophic consequences. Additionally, this book provides readers with automated methods to analyze patterns and models for various types of data, with applications ranging from scientific discovery to business intelligence and analytics.The book primarily includes exploratory data analysis, pattern mining, clustering, and classification supported by real life case studies. The statistical section of this book explores the impact of data mining and modeling on the predictability assessment of time series. Further new notions of mean values based on ideas of multi-criteria optimization are compared with their conventional definitions, leading to new algorithmic approaches to the calculation of the suggested new means. The style of the written chapters and the provision of a broad yet in-depth overview of data mining, integrating novel concepts from machine learning and statistics, make the book accessible to upper level undergraduate and graduate students in data mining courses. Students and professionals specializing in computer and management science, data mining for high-dimensional data, complex graphs and networks will benefit from the cutting-edge ideas and practically motivated case studies in this book.

This book presents the state-of-the-art in the emerging field of data science and includes models for layered security with applications in the protection of sites—such as large gathering places—through high-stake decision-making tasks. Such tasks include cancer diagnostics, self-driving cars, and others where wrong decisions can possibly have catastrophic consequences. Additionally, this book provides readers with automated methods to analyze patterns and models for various types of data, with applications ranging from scientific discovery to business intelligence and analytics.The book primarily includes exploratory data analysis, pattern mining, clustering, and classification supported by real life case studies. The statistical section of this book explores the impact of data mining and modeling on the predictability assessment of time series. Further new notions of mean values based on ideas of multi-criteria optimization are compared with their conventional definitions, leading to new algorithmic approaches to the calculation of the suggested new means. The style of the written chapters and the provision of a broad yet in-depth overview of data mining, integrating novel concepts from machine learning and statistics, make the book accessible to upper level undergraduate and graduate students in data mining courses. Students and professionals specializing in computer and management science, data mining for high-dimensional data, complex graphs and networks will benefit from the cutting-edge ideas and practically motivated case studies in this book.

This book celebrates the remarkable contributions of Panos M. Pardalos, offering a comprehensive collection of 20 rigorously peer-reviewed articles that span the breadth of his research interests. From deterministic and stochastic global optimization to combinatorial optimization, this volume provides insights into solving modern applied problems in planning theory, support vector machines, data mining, supply chain optimization, and more. Readers will encounter a diverse array of topics, including Pareto optimality, game and equilibrium theory, vehicle routing problems, energy systems, and financial markets. Each article stands alone in its scientific, methodological, and practical individuality, making this collection a must-read for both junior university students and seasoned professionals. The contributing authors, many of whom are former students and now esteemed professors, offer unique perspectives and cutting-edge research findings. This book is an invaluable resource for researchers, academics, and practitioners in fields such as operations research, resource management, computer science, and data analysis. Whether you are taking your first steps in optimization or are an advanced professional seeking to deepen your understanding, this volume provides the tools and insights necessary to navigate the complex landscape of applied optimization problems.

This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference “Optimization and Its Applications in Control and Data Science” dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015.This book reflects developments in theory and applications rooted by Professor Polyak’s fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.

This book focuses on recent research in modern optimization and its implications in control and data analysis.