This book provides a comprehensive treatment of assignment problems from their conceptual beginnings in the 1920s through present-day theoretical, algorithmic, and practical developments. The revised reprint provides details on a recent discovery related to one of Jacobi's results, new material on inverse assignment problems and quadratic assignment problems, and an updated bibliography. The authors have organized the book into 10 self-contained chapters to make it easy for readers to use the specific chapters of interest to them without having to read the book linearly. The topics covered include bipartite matching algorithms, linear assignment problems, quadratic assignment problems, multi-index assignment problems, and many variations of these problems. Exercises in the form of numerical examples provide readers with a method of self-study or students with homework problems, and an associated webpage offers applets that readers can use to execute some of the basic algorithms as well as links to computer codes that are available online. Researchers will benefit from the book's detailed exposition of theory and algorithms related to assignment problems, including the basic linear sum assignment problem and its many variations. Practitioners will learn about practical applications of the methods, the performance of exact and heuristic algorithms, and software options. This book also can serve as a text for advanced courses in discrete mathematics, integer programming, combinatorial optimization, and algorithmic computer science.

Assignment Problems is a useful tool for researchers, practitioners and graduate students. In 10 self-contained chapters, it provides a comprehensive treatment of assignment problems from their conceptual beginnings through present-day theoretical, algorithmic and practical developments. The topics covered include bipartite matching algorithms, linear assignment problems, quadratic assignment problems, multi-index assignment problems and many variations of these. Researchers will benefit from the detailed exposition of theory and algorithms related to assignment problems, including the basic linear sum assignment problem and its variations. Practitioners will learn about practical applications of the methods, the performance of exact and heuristic algorithms, and software options. This book also can serve as a text for advanced courses in areas related to discrete mathematics and combinatorial optimisation. The revised reprint provides details on a recent discovery related to one of Jacobi's results, new material on inverse assignment problems and quadratic assignment problems, and an updated bibliography.

Effective decision-making while trading off the constraints and conflicting multiple objectives under rapid technological developments, massive generation of data, and extreme volatility is of paramount importance to organizations to win over the time-based competition today. While agility is a crucial issue, the firms have been increasingly relying on evidence-based decision-making through intelligent decision support systems driven by computational intelligence and automation to achieve a competitive advantage. The decisions are no longer confined to a specific functional area. Instead, business organizations today find actionable insight for formulating future courses of action by integrating multiple objectives and perspectives. Therefore, multi-objective decision-making plays a critical role in businesses and industries. In this regard, the importance of Operations Research (OR) models and their applications enables the firms to derive optimum solutions subject to various constraints and/or objectives while considering multiple functional areas of the organizations together. Hence, researchers and practitioners have extensively applied OR models to solve various organizational issues related to manufacturing, service, supply chain and logistics management, human resource management, finance, and market analysis, among others. Further, OR models driven by AI have been enabled to provide intelligent decision-support frameworks for achieving sustainable development goals. The present issue provides a unique platform to showcase the contributions of the leading international experts on production systems and business from academia, industry, and government to discuss the issues in intelligent manufacturing, operations management, financial management, supply chain management, and Industry 4.0 in the Artificial Intelligence era. Some of the general (but not specific) scopes of this proceeding entail OR models such as Optimization and Control, Combinatorial Optimization, Queuing Theory, Resource Allocation Models, Linear and Nonlinear Programming Models, Multi-objective and multi-attribute Decision Models, Statistical Quality Control along with AI, Bayesian Data Analysis, Machine Learning and Econometrics and their applications vis-à-vis AI & Data-driven Production Management, Marketing and Retail Management, Financial Management, Human Resource Management, Operations Management, Smart Manufacturing & Industry 4.0, Supply Chain and Logistics Management, Digital Supply Network, Healthcare Administration, Inventory Management, consumer behavior, security analysis, and portfolio management and sustainability. The present issue shall be of interest to the faculty members, students, and scholars of various engineering and social science institutions and universities, along with the practitioners and policymakers of different industries and organizations.

This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.

E-CARGO and Role-Based Collaboration A model for collaboratively solving complex problems E-CARGO and Role-Based Collaboration offers a unique guide that explains the nature of collaboration, explores an easy-to-follow process of collaboration, and defines a model to solve complex problems in collaboration and complex systems. Written by a noted expert on the topic, the book initiates the study of an effective collaborative system from a novel perspective. The role-based collaboration (RBC) methodology investigates the most important aspects of a variety of collaborative systems including societal-technical systems. The models and algorithms can also be applied across system engineering, production, and management. The RBC methodology provides insights into complex systems through the use of its core model E-CARGO. The E-CARGO model provides the fundamental components, principles, relationships, and structures for specifying the state, process, and evolution of complex systems. This important book: Contains a set of concepts, models, and algorithms for the analysis, design, implementation, maintenance, and assessment of a complex system Presents computational methods that use roles as a primary underlying mechanism to facilitate collaborative activities including role assignment Explores the RBC methodology that concentrates on the aspects that can be handled by individuals to establish a well-formed team Offers an authoritative book written by a noted expert on the topic Written for researchers and practitioners dealing with complex problems in collaboration systems and technologies, E-CARGO and Role-Based Collaboration contains a model to solve real world problems with the help of computer-based systems.

This volume of the SPAR series brings the proceedings of the fourteen edition of the DARS symposium on Distributed Autonomous Robotic Systems, whose proceedings have been published within SPAR since the past edition. This symposium took place in Boulder, CO from October 15th to 17th, 2018. The volume edited by Nikolaus Correll and Mac Schwager contains 36 scientific contributions cutting across planning, control, design, perception, networking, and optimization, all united through the common thread of distributed robotic systems.

Smart Delivery Systems: Solving Complex Vehicle Routing Problems examines both exact and approximate methods for delivering optimal solutions to rich vehicle routing problems, showing both the advantages and disadvantages of each approach. It shows how to apply machine learning and advanced data analysis techniques to improve routing systems, familiarizing readers with the concepts and technologies used in successfully implemented delivery systems. The book explains both the latest theoretical and practical advances in intelligent delivery and scheduling systems and presents practical applications for designing new algorithms for real-life scenarios. - Emphasizes both sequential and parallel algorithms - Uniquely combines methods and algorithms, real-life applications, and parallel computing - Includes recommendations on how to choose between different methods for solving applications - Provides learning aids, end of chapter references, bibliography, worked examples and exercises

This book covers all aspects of robot intelligence from perception at sensor level and reasoning at cognitive level to behavior planning at execution level for each low level segment of the machine. It also presents the technologies for cognitive reasoning, social interaction with humans, behavior generation, ability to cooperate with other robots, ambience awareness, and an artificial genome that can be passed on to other robots. These technologies are to materialize cognitive intelligence, social intelligence, behavioral intelligence, collective intelligence, ambient intelligence and genetic intelligence. The book aims at serving researchers and practitioners with a timely dissemination of the recent progress on robot intelligence technology and its applications, based on a collection of papers presented at the 4th International Conference on Robot Intelligence Technology and Applications (RiTA), held in Bucheon, Korea, December 14 - 16, 2015. For better readability, this edition has the total of 49 articles grouped into 3 chapters: Chapter I: Ambient, Behavioral, Cognitive, Collective, and Social Robot Intelligence, Chapter II: Computational Intelligence and Intelligent Design for Advanced Robotics, Chapter III: Applications of Robot Intelligence Technology .

This book presents expert descriptions of the successful application of operations research in both the private and the public sector, including in logistics, transportation, product design, production planning and scheduling, and areas of social interest. Each chapter is based on fruitful collaboration between researchers and companies, and company representatives are among the co-authors. The book derives from a 2017 call by the Italian Operations Research Society (AIRO) for information from members on their activities in promoting the use of quantitative techniques, and in particular operations research techniques, in society and industry. A booklet based on this call was issued for the annual AIRO conference, but it was felt that some of the content was of such interest that it deserved wider dissemination in more detailed form. This book is the outcome. It equips practitioners with solutions to real-life decision problems, offers researchers examples of the practical application of operations research methods, and provides Master’s and PhD students with suggestions for research development in various fields.

This book constitutes the refereed proceedings of the 14th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2019, held in Kunming, China, in August 2019. The 52 revised full papers and 10 short papers were carefully reviewed and selected from 169 submissions. The papers of this volume are organized in topical sections on: collaborative models, approaches, algorithms, and systems; social computing (online communities, crowdsourcing, recommendation, sentiment analysis, etc.); AI for CSCW and social computing.