Iman Rahimi – författare

Multi-Objective Combinatorial Optimization Problems and Solution Methods discusses the results of a recent multi-objective combinatorial optimization achievement that considered metaheuristic, mathematical programming, heuristic, hyper heuristic and hybrid approaches. In other words, the book presents various multi-objective combinatorial optimization issues that may benefit from different methods in theory and practice. Combinatorial optimization problems appear in a wide range of applications in operations research, engineering, biological sciences and computer science, hence many optimization approaches have been developed that link the discrete universe to the continuous universe through geometric, analytic and algebraic techniques.

This book covers this important topic as computational optimization has become increasingly popular as design optimization and its applications in engineering and industry have become ever more important due to more stringent design requirements in modern engineering practice.

Presents a collection of the most up-to-date research, providing a complete overview of multi-objective combinatorial optimization problems and applications Introduces new approaches to handle different engineering and science problems, providing the field with a collection of related research not already covered in the primary literature Demonstrates the efficiency and power of the various algorithms, problems and solutions, including numerous examples that illustrate concepts and algorithms

In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations.From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.

In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations.From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.

Smart Cities: A System of Systems Perspective explores System of Systems Engineering (SoSE) in smart cities. Sections address how analytics and innovative technologies can transform urban environments, equipping readers with the knowledge to optimize city operations, enhance public services, and promote sustainable urban growth. The book begins with the foundational concepts of SoSE and key principles of smart cities, exploring emergent behavior and interoperability. It then examines the integration of SoSE within urban infrastructure, highlighting the roles of IoT, AI, and machine learning. Subsequent sections focus on core technologies and analytical tools, including data analytics, predictive modeling, and decision support systems.Readers will also discover innovative solutions to pressing urban challenges, such as sustainability, cybersecurity, and governance, alongside detailed case studies showcasing successful smart city initiatives from around the globe. This book is invaluable for academics and researchers in urban studies, engineering, analytics, IoT, and AI, as well as data scientists and analytics professionals working with urban datasets. Graduate and postgraduate students in engineering, analytics, computer science, or urban studies will also find it a crucial resource.Covers both theoretical foundations and practical methodologiesUses real-world examples to make the concepts accessible and actionableProvides innovative strategies and tools to address the challenges faced by urban planners and system engineers, such as interoperability between systems, scalability, sustainability, and emergent behavior managementIncludes detailed, practical case studies of successful smart city projects worldwide, showcasing best practices, challenges, and lessons learned and offering valuable insights for replicating and adapting smart city solutionsExplores cutting-edge technologies such as AI, IoT, 5G, and renewable energy integration, providing readers with an understanding of how these innovations are transforming urban systems and highlighting future trends and opportunities in smart city developmentOffers decision-making and policy support frameworks, optimization techniques, and governance models to help readers navigate the complexities of multi-stakeholder collaboration

Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problemsThis book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches.Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book: Provides a representative sampling of real-world problems currently being tackled by practitionersExamines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligenceConsists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling ProblemsEvolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.