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This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into five parts: Technology Policy Planning, Strategic Technology Planning, Technology Assessment, Application Extensions, and Methodology Extensions. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics research and development, education, government planning, agriculture, energy, technology transfer, semiconductor manufacturing, and has influenced policy locally, nationally, and internationally. Moreover, his students developed advanced tools and software applications to further improve and enhance the robustness of the HDM approach. Dr. Kocaoglu has made many contributions to the field of Engineering and Technology Management. During his tenure at Portland State University, he founded the Engineering and Technology Management program, where he served as Program Director and later, Department Chair. He also started the Portland International Conference on Management of Engineering and Technology (PICMET), which organizes an annual conference in international locations such as Korea, Turkey, South Africa, Thailand, and Japan. His teaching has won awards and resulted in a strong sense of student loyalty among his students even decades later. Through his academic work and research, Dr. Kocaoglu has strongly supported researchers of engineering management and has provided tremendous service to the field. This volume recognizes and celebrates Dr. Kocaoglu’s profound contributions to the field, and will serve as a resource for generations of researchers, practitioners and students.
One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problems, and to construct a solution of the original problem from the solutions of these simpler prob lems. Development of such approaches for large complex systems has been identified as a particularly fruitful area by the Committee on the Next Decade in Operations Research (1988) [42] as well as by the Panel on Future Directions in Control Theory (1988) [65]. Most manufacturing firms are complex systems characterized by sev eral decision subsystems, such as finance, personnel, marketing, and op erations. They may have several plants and warehouses and a wide variety of machines and equipment devoted to producing a large number of different products. Moreover, they are subject to deterministic as well as stochastic discrete events, such as purchasing new equipment, hiring and layoff of personnel, and machine setups, failures, and repairs.
This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.
Handbook
Distributed decision making (DDM) has become of increasing importance in quantitative decision analysis. In applications like supply chain management, service operations, or managerial accounting, DDM has led to a paradigm shift. The book provides a unified approach to such seemingly diverse fields as multi-level stochastic programming, hierarchical production planning, principal agent theory, negotiations or contract theory. Different settings like multi-level one-person decision problems, multi-person antagonistic planning, and leadership situations are covered. Numerous examples and real-life planning cases illustrate the concepts. The new edition has been considerably expanded by additional chapters on supply chain management, service operations and multi-agent systems.
This book examines the Analytical Hierarchy Process (AHP) method, its varied uses, as well as its limitations for solving real-world scenarios. While the simplicity of the method compels users to find shortcuts to a real-world problem, it also leads to obtaining wrong results that do not represent reality. By alerting practitioners about the core necessities of a new scenario, this book helps solve this problem, as well as contribute to the field of Multicriteria Decision Making Method (MDCM). The authors use a demonstrative, rather than a theoretical approach, and examine 30 subjects that displays the shortcomings and drawbacks of the AHP. Each one is examined in-depth, discussed, debated and reasoned, using examples, some of them numeric. The book highlights the rationality and common sense of the subjects, and in most cases, validates the criticism by showing through numerical examples, the impossibility of the AHP method to address, let alone solve real-world projects. At the conclusion of each subject, a table is built comparing the similarities and differences between the opinions of the authors and other experts, along with the respective pros and cons.
This volume explores emerging models, methods and tools in the management of research and development (R&D) in the knowledge era, with a particular focus on the challenges of the emerging technologies. The contributions are organized in five parts. Part I, Managing Emerging Technologies, provides methods and tools to understand the challenges created by the emergence of new technologies. Part II, Technology and Engineering Management Tools and Policies, explores different technology and engineering tools, including topics such as product concept development, design, selection and adoption, using technology roadmaps and bibliometrics. Part III, Technological Innovation and Entrepreneurship, explores R&D, knowledge transfer and entrepreneurial education. Part IV, Commercialization of Technological Innovations, explores the development and application of the technology transfer process which allows managers to succeed in commercializing the outcomes of R&D projects. Part V, Managing the Engineering Enterprise, explores the effect economic decision-making, leadership styles, change management and quality management have on an organization’s ability to plan and execute initiatives and projects. Research and Development has always played a critical role in the engineering and technology focused industries. In an era of big data and smart applications, knowledge has become a key enabler for R&D. Managing R&D in the knowledge era requires use of key tools and methods. However, emerging technologies pose many challenges and cause uncertainties or discontinuities, which make the task of managing R&D even more difficult. This book will examine these challenges and provide tools and methods to overcome them. Exploring such industries as automotive, healthcare, business intelligence, energy and home appliances, this book is a valuable resource for academics, scholars, professionals and leaders in innovation, R&D, technology, and engineering management.
Foresters and natural resource managers must balance conflicting objectives when developing land-management plans. Conflicts may encompass economic, environmental, social, cultural, technical, and aesthetic objectives. Selecting the best combination of management uses from numerous objectives is difficult and challenging. Multi-Criteria Decision Models (MCDM) provide a systematic means for comparing tradeoffs and selecting alternatives that best satisfy the decisionmakergass objectives. In recent years, the use of MCDM in forestry and natural resources management has generated a substantial body of literature. This annotated bibliography includes 124 important references ranging from theoretical studies to real-world applications of MCDM.
Design Models for Hierarchical Organizations: Computation, Information, and Decentralization provides state-of-the-art research on organizational design models, and in particular on mathematical models. Each chapter views the organization as an information processing entity. Thus, mathematical models are used to examine information flow and decision procedures, which in turn, form the basis for evaluating organization designs. Each chapters stands alone as a contribution to organization design and the modeling approach to design. Moreover, the chapters fit together and that totality gives us a good understanding of where we are with this approach to organizational design issues and where we should focus our research efforts in the future.
Technologies such as renewable energy alternatives including wind, solar and biomass, storage technologies and electric engines are creating a different landscape for the electricity industry. Using sources and ideas from technologies such as renewable energy alternatives, Research and Technology Management in the Electricity Industry explores a different landscape for this industry and applies it to the electric industry supported by real industry cases. Divided into three sections, Research and Technology Management in the Electricity Industry introduces a range of methods and tools including technology assessment, forecasting, roadmapping, research and development portfolio management and technology transfer. These tools are the applied to emerging technologies in this industry with case studies including data from various organizations including Bonneville Power Administration and Energy Trust of Oregon, from sectors including lighting and wind energy. The final section considers innovation through these technologies. A product result of a collaboration between Bonneville Power Administration and Portland State University, Research and Technology Management in the Electricity Industry is a comprehensive collection of methods, tools, examples and pathways for future innovation in the electricity industry.