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Computer Science (R0)

Stanislaw Gawiejnowicz

Models and Algorithms of Time-Dependent Scheduling

&lt;p&gt;Part I, Fundamentals.- Fundamentals.- Preliminaries.- Problems and Algorithms.- NP-Complete Problems.- Part II, Scheduling Models.- The Classical Scheduling Theory.- The Modern Scheduling Theory.- The Time-Dependent Scheduling.- Part III, Polynomial Problems.- Polynomial Single Machine Problems.- Polynomial Parallel Machine Problems.- Polynomial Dedicated Machine Problems.- Part IV, NP-Hard Problems.- NP-Hard Single Machine Problems.- NP-Hard Parallel Machine Problems.- NP-Hard Dedicated Machine Problems.- Part V, Algorithms.- Exact Algorithms.- Approximation Algorithms and Schemes.- Greedy Algorithms Based on Signatures.- Heuristic Algorithms.- Local Search and Meta-heuristic Algorithms.- Part VI, Advanced Topics.- Time-Dependent Scheduling Under Precedence Constraints.- Matrix Methods in Time-Dependent Scheduling.- Bi-criteria Time-Dependent Scheduling.- New Topics in Time-Dependent Scheduling.- App. A, Open Time-Dependent Scheduling Problems.- List of Algorithms.- List of Figures.- List of Tables.- Symbol Index.- Subject Index.&lt;br&gt;&lt;/p&gt;

This open access book critically analyses a wide range of consumer law issues generated or exacerbated by the Internet of Things. The Internet of Things challenges many of the dichotomies which have shaped current laws: goods-services, hardware-software, tangible-intangible, online-offline. The book focuses on 4 specific challenges: things-as-service, liability, bricking of physical devices through software control, and the potential for enhanced personalisation, especially for digital assistants. The chapters analyse specific applications such as smart homes, connected cars, and consumer wearables with a view to providing innovative solutions to the new challenges posed by the Internet of Things, and to steer the development of the Internet of Things in a fairer direction. As well as offering an important analysis of the potential for smart consumer laws in the Internet of Things, the book also provides important insights into the growing divergence between Germany's and the UK's consumer laws since Brexit. The ebook editions of this book are available open access under a CC BY 4.0 licence on bloomsburycollections.com. Open access was funded by UKRI (Arts and Humanities Research Council) and the German Research Foundation (DFG).

Lessons from Germany and the United Kingdom

From Smart Technologies to Smart Consumer Law

Business Guides on the Go

A Framework for Data-Driven Decisions

Practical Business Analytics

Series on Grey System

This book delineates how medical researchers and practitioners analyze complex, uncertain, and data-scarce clinical systems by introducing a groundbreaking integration of gray system theory with contemporary medical challenges. Unlike traditional approaches that are often hampered by incomplete datasets and ambiguous relationships, this work harnesses the adaptability of gray system methodologies to reveal hidden patterns, optimize decision-making, and translate sparse information into actionable insights. Tailored for both theorists and practitioners, it effectively bridges the divide between abstract mathematical models and practical medical applications, providing essential tools to navigate the intrinsic "grayness" of biological systems.
At its core, this book pioneers a paradigm shift in medical research by treating uncertainty not as a limitation but as a critical feature to be modeled and exploited. Traditional statistical methods often falter in scenarios with small samples, missing data, or nonlinear interactions (common in clinical settings). Here, gray system theory’s unique focus on "poor information" systems is systematically adapted to address these challenges. New methodologies, such as polar gray relational analysis for disease subtype differentiation, ergodic gray relational analysis for dynatic clinic data and longitudinal patient monitoring, are introduced to resolve previously intractable problems. The integration of gray entropy-weighted clustering into medical management further exemplifies the book’s innovative edge, enabling resource allocation and risk stratification under uncertainty. New conception and test method, generalized grey relational degree and grey relational order test method is also introduced in this book, making its as a critical supplement for grey system theory.
This book caters to:1) medical researchers seeking robust analytical tools for sparse or noisy datasets. 2) clinical practitioners aiming to enhance diagnostic or prognostic precision. 3) Healthcare administrators optimizing resource allocation and policy design. 4) Data scientists exploring novel applications of gray systems in biomedicine. The content balances mathematical rigor with practical clarity, avoiding excessive formalism while maintaining technical depth. It suitable for readers with varying levels of expertise.

Medical Applications of Grey System Models

This book provides a clear and structured guide to the international certifications and standards that organizations adopt to govern and improve their operations, products, and processes, while responding to regulatory, market, and societal demands for accountability and transparency.The book is organized into chapters dedicated to the most widely adopted standards: ISO 9001 (quality management), ISO 14001 and EMAS (environmental management), ISO 45001 (occupational health and safety), SA8000 (social responsibility), and ISO/IEC 27001 (information security). It also covers emerging governance frameworks such as B Corp certification and the United Nations Global Compact. Two concluding chapters address the practical journey of adopting a standard and the strategies for integrating multiple management systems into a coherent whole. Each chapter combines theoretical foundations with real-world case studies, helping readers understand how organizations translate these standards into practice.Offering a comparative and interdisciplinary perspective that is both rigorous and accessible, this book will be of great interest to scholars, students, managers, auditors and consultants. It helps readers not only understand each certification individually, but also see how standards interact with one another and with broader organizational strategies. Moreover, the book fills a gap between fragmented academic literature and practitioner oriented manuals by providing a balanced mix of conceptual grounding, case-informed examples, and operational tools.Matteo Podrecca
 is an Assistant Professor in the Department of Economics and Management “Marco Fanno”, University of Padova, Italy.
 Marco Sartor
 is a Full Professor in the Polytechnic Department of Engineering and Architecture, University of Udine, Italy.

This book provides a clear and structured guide to the international certifications and standards that organizations adopt to govern and improve their operations, products, and processes, while responding to regulatory, market, and societal demands for accountability and transparency.The book is organized into chapters dedicated to the most widely adopted standards: ISO 9001 (quality management), ISO 14001 and EMAS (environmental management), ISO 45001 (occupational health and safety), SA8000 (social responsibility), and ISO/IEC 27001 (information security). It also covers emerging governance frameworks such as B Corp certification and the United Nations Global Compact. Two concluding chapters address the practical journey of adopting a standard and the strategies for integrating multiple management systems into a coherent whole. Each chapter combines theoretical foundations with real-world case studies, helping readers understand how organizations translate these standards into practice.Offering a comparative and interdisciplinary perspective that is both rigorous and accessible, this book will be of great interest to scholars, students, managers, auditors and consultants. It helps readers not only understand each certification individually, but also see how standards interact with one another and with broader organizational strategies. Moreover, the book fills a gap between fragmented academic literature and practitioner oriented manuals by providing a balanced mix of conceptual grounding, case-informed examples, and operational tools.

Theory and Practice

Quality Management Standards

International Series in Operations Research & Management Science

This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.

Valid Causal Inference from Observational Data

This textbook is a comprehensive resource designed to bridge the gap between theoretical concepts and practical applications in the field of decision-making. This book aims to equip readers with the knowledge and tools necessary to develop and implement effective models using Excel, a widely accessible and powerful tool. From streamlining processes and optimizing marketing efforts to resource allocation and trend forecasting, this guide demonstrates how modelling can support critical organizational goals.This book demystifies the modelling process, guiding readers through each step - from understanding core principles to implementing decision-making strategies in Excel. Designed to address real-world challenges in diverse fields such as finance, logistics, healthcare, and manufacturing, this resource combines clear explanations, practical examples, and actionable insights to help readers make effective, data-driven decisions.Whether you are a beginner or an experienced user, this book provides practical guidance across multiple disciplines, including business, mathematics, education, computer science, engineering, and psychology. Academics, researchers, students, and professionals will gain the tools needed to leverage Excel modelling to enhance operational efficiency, drive sustainable success, and confidently navigate today’s complex environment.

Modelling for Decision Making: A Step-by-Step Guide Using Excel

Synthesis Lectures on Operations Research and Applications

From Centralized to Decentralized Operations

The Science of Flexibility in Mobility

The Fight for the Future of Work

We Are Not Machines

Foundations for Decision-Focused Regularized Learning

Operational Data Analytics

PERSONAL KAIZEN

Progress in IS

A Powerful Theory for Understanding, Modeling and Simulating Collective Phenomena

The Combinatory Systems Theory

From Conceptual Algorithms to Real-World Applications

Methods of Nonsmooth Optimization in Stochastic Programming

Models and Algorithms of Time-Dependent Scheduling

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