This volume contains two types of papers-a selection of contributions from the "Second International Conference in Network Analysis" held in Nizhny Novgorod on May 7-9, 2012, and papers submitted to an "open call for papers" reflecting the activities of LATNA at the Higher School for Economics. This volume contains many new results in modeling and powerful algorithmic solutions applied to problems in * vehicle routing * single machine scheduling * modern financial markets * cell formation in group technology * brain activities of left- and right-handers * speeding up algorithms for the maximum clique problem * analysis and applications of different measures in clustering The broad range of applications that can be described and analyzed by means of a network brings together researchers, practitioners, and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the theory and practice of network analysis. Researchers, students, and engineers from various disciplines will benefit from the state-of-the-art in models, algorithms, technologies, and techniques presented.

This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car . Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

The Analytic Hierarchy Process (AHP) is a prominent and powerful tool for making decisions in situations involving multiple objectives. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, 2nd Edition applies the AHP in order to solve problems focused on the following three themes: economics, the social sciences, and the linking of measurement with human values. For economists, the AHP offers a substantially different approach to dealing with economic problems through ratio scales. Psychologists and political scientists can use the methodology to quantify and derive measurements for intangibles. Meanwhile researchers in the physical and engineering sciences can apply the AHP methods to help resolve the conflicts between hard measurement data and human values. Throughout the book, each of these topics is explored utilizing real life models and examples, relevant to problems in today's society. This new edition has been updated and includes five new chapters that includes discussions of the following: - The eigenvector and why it is necessary - A summary of ongoing research in the Middle East that brings together Israeli and Palestinian scholars to develop concessions from both parties - A look at the Medicare Crisis and how AHP can be used to understand the problems and help develop ideas to solve them.

This book offers a comprehensive and systematic introduction to the latest research on hesitant fuzzy decision-making theory. It includes six parts: the hesitant fuzzy set and its extensions, novel hesitant fuzzy measures, hesitant fuzzy hybrid weighted aggregation operators, hesitant fuzzy multiple-criteria decision-making with incomplete weights, hesitant fuzzy multiple criteria decision-making with complete weights information, and the hesitant fuzzy preference relation based decision-making theory. These methodologies are implemented in various fields such as decision-making, medical diagnosis, cluster analysis, service quality management, e-learning management and environmental management. A valuable resource for engineers, technicians, and researchers in the fields of fuzzy mathematics, operations research, information science, management science and engineering, it can also be used as a textbook for postgraduate and senior undergraduate students.

This book describes five qualitative investment decision-making methods based on the hesitant fuzzy information. They are: (1) the investment decision-making method based on the asymmetric hesitant fuzzy sigmoid preference relations, (2) the investment decision-making method based on the hesitant fuzzy trade-off and portfolio selection, (3) the investment decision-making method based on the hesitant fuzzy preference envelopment analysis, (4) the investment decision-making method based on the hesitant fuzzy peer-evaluation and strategy fusion, and (5) the investment decision-making method based on the EHVaR measurement and tail analysis.

This book analyzes the underlying theoretical principles of multi-objective linear programming problems with multi-choice parameters. It studies transportation problems on the same domain with extension to fuzzy stochastic criteria, and offers insights into sensitivity analysis through symmetric duality and complementarity using non-convex programming. These analytical presentations provide ample scope for researchers to contemplate real-world problems with an innovative vision. The formulation, analysis and solution procedures on inventory control models in the book use both deterministic and fuzzy parameters, and provide novel optimal inventory policies. The book discusses a wide range of optimal operational techniques for policy makers, government and private agencies in the fields of e-governance and agricultural crop insurance, which are crucial for developing countries. The recommendations address the gaps and remedies in various schemes that influence decision-making in the context of the economic development of such countries.

This book provides an introduction to vector optimization with variable ordering structures, i.e., to optimization problems with a vector-valued objective function where the elements in the objective space are compared based on a variable ordering structure: instead of a partial ordering defined by a convex cone, we see a whole family of convex cones, one attached to each element of the objective space. The book starts by presenting several applications that have recently sparked new interest in these optimization problems, and goes on to discuss fundamentals and important results on a wide range of topics. The theory developed includes various optimality notions, linear and nonlinear scalarization functionals, optimality conditions of Fermat and Lagrange type, existence and duality results. The book closes with a collection of numerical approaches for solving these problems in practice.

This is the Proceedings of the Eighth International Conference on Management Science and Engineering Management (ICMSEM) held from July 25 to 27, 2014 at Universidade Nova de Lisboa, Lisbon, Portugal and organized by International Society of Management Science and Engineering Management (ISMSEM), Sichuan University (Chengdu, China) and Universidade Nova de Lisboa (Lisbon, Portugal). The goals of the conference are to foster international research collaborations in Management Science and Engineering Management as well as to provide a forum to present current findings. A total number of 138 papers from 14 countries are selected for the proceedings by the conference scientific committee through rigorous referee review. The selected papers in the first volume are focused on Intelligent System and Management Science covering areas of Intelligent Systems, Decision Support Systems, Manufacturing and Supply Chain Management.

With advancing information technology, businesses must adapt to more efficient structures that utilize the latest in robotics and machine learning capabilities in order to create optimal human-robot cooperation. However, there are vital rising concerns regarding the possible consequences of deploying artificial intelligence, sophisticated robotic technologies, automated vehicles, self-managing supply modes, and blockchain economies on business performance and culture, including how to sustain a supportive business culture and to what extent a strategic fit between human-robot collaboration in a business ecosystem can be created. The Handbook of Research on Strategic Fit and Design in Business Ecosystems is a collection of innovative research that builds a futuristic view of evolving business ecosystems and a deeper understanding of business transformation processes in the new digital business era. Featuring research on topics such as cultural hybridization, Industry 4.0, and cybersecurity, this book is ideally designed for entrepreneurs, executives, managers, corporate strategists, economists, IT specialists, IT consultants, engineers, students, researchers, and academicians seeking to improve their understanding of future competitive business practices with the adoption of robotic and information technologies.

Putting forward a unified presentation of the features and possible applications of probabilistic preferences composition, and serving as a methodology for decisions employing multiple criteria, this book maximizes reader insights into the evaluation in probabilistic terms and the development of composition approaches that do not depend on assigning weights to the criteria. With key applications in important areas of management such as failure modes, effects analysis and productivity analysis - together with explanations about the application of the concepts involved -this book makes available numerical examples of probabilistic transformation development and probabilistic composition. Useful not only as a reference source for researchers, but also in teaching classes of graduate courses in Production Engineering and Management Science, the key themes of the book will be of especial interest to researchers in the field of Operational Research.

This guide is designed for systems researchers - emerging and seasoned - searching for holistic approaches of inquiry into complexity, which the Systems Sciences provide. The authors share insight into the foundations of research that are not only systematic in terms of rigor, but systemic in perspective, analysis, design, development, implementation, reporting, and evaluation. This guide also explores researcher competencies necessary to conduct sound systems research. Researchers using this guide will gain understanding of what distinguishes systems research from other types of research and why it is important in research today.

Risk and reward are always foremost in the determination of investment decisions and business transactions. Advances in the area of Information Communication Technologies (ICT) have enabled the development of new business paradigms. Such paradigms involve transactions taking place between loosely connected parties, often totally or partially unknown to one another. One important concept required to ensure such transactions are successful is transactional risk. The importance of doing this has been demonstrated in recent financial crisis. This book is unique in simultaneously taking into account the likelihood of an event occurring and its financial impact and provides an integrated discussion of the process of transactional risk identification, assessment, evaluation, management and recording in these emerging domains. It provides a detailed and clear exposition of the importance of transactional risk before detailing for its assessment and evaluation. The scope of the book is theoretical and practical and as such it will have a broad market both within academia and industry. Specifically this book should be of primary interest to researchers, graduate students and practitioners in the area of developing business intelligence techniques and their application in various real world applications.

This book examines in detail the planning and modelling of local infrastructure like energy systems, including the complexities resulting from various uncertainties. Readers will discover the individual steps involved in infrastructure planning in cities and territories, as well as the primary requirements and supporting quality factors. Further topics covered concern the field of uncertainty and its synergies with infrastructure planning. Theories, methodological backgrounds and concrete case studies will not only help readers to understand the proposed methodologies for modelling and uncertainty analysis, but will also show them how these approaches are implemented in practice.

Counting belongs to the most elementary and frequent mental activities of human beings. Its results are a basis for coming to a decision in a lot of situations and dimensions of our life. This book presents a novel approach to the advanced and sophisticated case, called intelligent counting, in which the objects of counting are imprecisely, fuzzily specified. Formally, this collapses to counting in fuzzy sets, interval-valued fuzzy sets or I-fuzzy sets (Atanassov's intuitionistic fuzzy sets). The monograph is the first one showing and emphasizing that the presented methods of intelligent counting are human-consistent: are reflections and formalizations of real, human counting procedures performed under imprecision and, possibly, incompleteness of information. Other applications of intelligent counting in various areas of intelligent systems and decision support will be discussed, too. The whole presentation is self-contained, systematic, and equipped with many examples, figures and tables. Computer and information scientists, researchers, engineers and practitioners, applied mathematicians, and postgraduate students interested in information imprecision are the target readers.

This book offers a thorough and systematic introduction to the modified failure mode and effect analysis (FMEA) models based on uncertainty theories (e.g. fuzzy logic, intuitionistic fuzzy sets, D numbers and 2-tuple linguistic variables) and various multi-criteria decision making (MCDM) approaches such as distance-based MCDM, compromise ranking MCDM and hybrid MCDM, etc. As such, it provides essential FMEA methods and practical examples that can be considered in applying FMEA to enhance the reliability and safety of products and services. The book offers a valuable guide for practitioners and researchers working in the fields of quality management, decision making, information science, management science, engineering, etc. It can also be used as a textbook for postgraduate and senior undergraduate students.

This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.

Scheduling theory has received a growing interest since its origins in the second half of the 20th century. Developed initially for the study of scheduling problems with a single objective, the theory has been recently extended to problems involving multiple criteria. However, this extension has still left a gap between the classical multi-criteria approaches and some real-life problems in which not all jobs contribute to the evaluation of each criterion.

In this book, we close this gap by presenting and developing multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. Several scenarios are introduced, depending on the definition and the intersection structure of the job subsets. Complexity results, approximation schemes, heuristics and exact algorithms are discussed for single-machine and parallel-machine scheduling environments. Definitions and algorithms are illustrated with the help of examples and figures.

The International Civil Aviation Organization's (ICAO) decision to require aviation organizations to adopt Safety Management Systems poses a major problem especially for small and medium sized aviation companies. The complexity of regulations overstrains the aviation stakeholders who seek to fully advantage from them but have no clear guidance. The aim of the book is to show the implementation of such a new system with pragmatic effort in order to gain a gradation for smaller operators. This approach should illustrate the leeway in order to adapt the processes and to show the interfaces between Corporate Risk Management and Safety Management. The book shows how to build a system with reasonable effort, appropriate to the size and complexity of the specific operator. It also gives inputs on the key aspects and how to effectively operate such a system with the various interfaces. Furthermore, the book highlights the importance of Corporate Risk Management independent of Safety Management Systems based on ICAO.

This book provides several inventory models for making the right decision in inventory management under different environments. Basically, the optimal ordering policies are determined for situations with and without shortages in production-inventory systems. The chapters in the book include various features of inventory modeling i.e., inflation, deterioration, supply chain, learning, credit financing, carbon emission policy, stock-dependent demand, among others. The book is a useful resource for academicians, researchers, students, practitioners, and managers who can be benefited with the policies provided in the chapters of the book.

Very little has been published on optimization of pharmaceutical portfolios. Moreover, most of published literature is coming from the commercial side, where probability of technical success (PoS) is treated as fixed, and not as a consequence of development strategy or design. In this book there is a strong focus on impact of study design on PoS and ultimately on the value of portfolio. Design options that are discussed in different chapters are dose-selection strategies, adaptive design and enrichment. Some development strategies that are discussed are indication sequencing, optimal number of programs and optimal decision criteria. This book includes chapters written by authors with very broad backgrounds including financial, clinical, statistical, decision sciences, commercial and regulatory. Many authors have long held executive positions and have been involved with decision making at a product or at a portfolio level. As such, it is expected that this book will attract a very broad audience, including decision makers in pharmaceutical R&D, commercial and financial departments. The intended audience also includes portfolio planners and managers, statisticians, decision scientists and clinicians. Early chapters describe approaches to portfolio optimization from big Pharma and Venture Capital standpoints. They have stronger focus on finances and processes. Later chapters present selected statistical and decision analysis methods for optimizing drug development programs and portfolios. Some methodological chapters are technical; however, with a few exceptions they require a relatively basic knowledge of statistics by a reader.

Transportation management in today's consumer goods industry can be characterized by a high proportion of outsourced transportation services. Due to rising freight costs consumer goods manufacturers are seeking opportunities to increase the efficiency of their transportation network. This book presents an operational transportation planning problem typical of the consumer goods industry focusing on a network of suppliers, production facilities and warehouses. With respect to the large share of outsourcing in these networks a detailed analysis of freight costs based on contractual agreements is provided. From this analysis a number of opportunities for efficiency gains are identified and consolidated in an operative transportation planning problem that is numerically investigated. Furthermore, an overview of processes and organizational requirements in transportation management is given with special focus on the integration of existing commercial Transportation Management Systems (TMS).

This book presents the proceedings of the Seventh International Conference on Management Science and Engineering Management (ICMSEM2013) held from November 7 to 9, 2013 at Drexel University, Philadelphia, Pennsylvania, USA and organized by the International Society of Management Science and Engineering Management, Sichuan University (Chengdu, China) and Drexel University (Philadelphia, Pennsylvania, USA). The goals of the Conference are to foster international research collaborations in Management Science and Engineering Management as well as to provide a forum to present current research findings. The selected papers cover various areas in management science and engineering management, such as Decision Support Systems, Multi-Objective Decisions, Uncertain Decisions, Computational Mathematics, Information Systems, Logistics and Supply Chain Management, Relationship Management, Scheduling and Control, Data Warehousing and Data Mining, Electronic Commerce, Neural Networks, Stochastic Models and Simulation, Fuzzy Programming, Heuristics Algorithms, Risk Control, Organizational Behavior, Green Supply Chains, and Carbon Credits. The proceedings introduce readers to novel ideas on and different problem-solving methods in Management Science and Engineering Management. We selected excellent papers from all over the world, integrating their expertise and ideas in order to improve research on Management Science and Engineering Management.

Dynamics of Information Systems: Algorithmic Approaches presents recent developments and results found by participants of the Fourth International Conference on the Dynamics of Information Systems, which took place at the University of Florida, Gainesville FL, USA on February 20-22, 2012. The purpose of this conference was to bring together scientists and engineers from industry, government, and universities to exchange knowledge and results in a broad range of topics relevant to the theory and practice of the dynamics of information systems. Dynamics of Information plays an increasingly critical role in our society. The influence of information on social, biological, genetic, and military systems must be better understood to achieve large advances in the capability and understanding of these systems. Applications are widespread and include: detection of terrorist networks, design of highly efficient businesses, computer networks, quantum entanglement, genome modeling, multi-robotic systems, and industrial and manufacturing safety. The book contains state-of-the-art work on theory and practice relevant to the dynamics of information systems. It covers algorithmic approaches to numerical computations with infinite and infinitesimal numbers; presents important problems arising in service-oriented systems, such as dynamic composition and analysis of modern service-oriented information systems and estimation of customer service times on a rail network from GPS data; addresses the complexity of the problems arising in stochastic and distributed systems; and discusses modulating communication for improving multi-agent learning convergence. Network issues-in particular minimum-risk maximum-clique problems, vulnerability of sensor networks, influence diffusion, community detection, and link prediction in social network analysis, as well as a comparative analysis of algorithms for transmission network expansion planning-are described in later chapters.

This volume provides a unique collection of mathematical tools and industrial case studies in digital manufacturing. It addresses various topics, ranging from models of single production technologies, production lines, logistics and workflows to models and optimization strategies for energy consumption in production. The digital factory represents a network of digital models and simulation and 3D visualization methods for the holistic planning, realization, control and ongoing improvement of all factory processes related to a specific product. In the past ten years, all industrialized countries have launched initiatives to realize this vision, sometimes also referred to as Industry 4.0 (in Europe) or Smart Manufacturing (in the United States). Its main goals are * reconfigurable, adaptive and evolving factories capable of small-scale production * high-performance production, combining flexibility, productivity, precision and zero defects * energy and resource efficiency in manufacturing None of these goals can be achieved without a thorough modeling of all aspects of manufacturing together with a multi-scale simulation and optimization of process chains; in other words, without mathematics. To foster collaboration between mathematics and industry in this area the European Consortium for Mathematics in Industry (ECMI) founded a special interest group on Math for the Digital Factory (M4DiFa). This book compiles a selection of review papers from the M4DiFa kick-off meeting held at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, Germany, in May 2014. The workshop aimed at bringing together mathematicians working on modeling, simulation and optimization with researchers and practitioners from the manufacturing industry to develop a holistic mathematical view on digital manufacturing. This book is of interest to practitioners from industry who want to learn about important mathematical concepts, as well as to scientists who want to find out about an exciting new area of application that is of vital importance for today's highly industrialized and high-wage countries.