Project Selection Under Uncertainty is the result of a five-year research program on the selection of projects in New Product Development (NPD). Choosing the New Product Development portfolio is of critical importance in today's business environment. The NPD portfolio has considerable strategic effect on the "middle term" success of a business. This book takes a step in developing a theory that addresses the need for quantitative prioritization criteria within the broader strategic context of the R&D portfolios. Its foundation lies in mathematical theory of resource-constrained optimization with the goal to maximize quantitative returns. The book seeks to broaden the portfolio discussion in two ways. First, simplified models - appropriate for the data-poor NPD context - are developed, which attempt to illuminate the structure of the choice problem and robust qualitative rules of thumb, rather than detailed algorithmic decision support. Such robust rules can be applied in the R&D environment of poor data availability. Second, the annual portfolio review is not the only important choice in resource allocation. In addition, the book discusses how ideas might be pre-screened as they emerge, and how projects should be prioritized once they are funded and ongoing.

Models & Methods for Project Selection systematically examines in this book treatment the latest work in the field of project selection modeling. The models presented are drawn from mathematical programming, decision theory, and finance. These models are examined in two categorical streams: the management science stream and the financial model stream. The book describes the assumptions and limitations of each model and provides appropriate solution methodologies. Its organization follows three main themes: *Criteria for Choice: Chapters 1-3 investigate the effect of the choice of optimization criteria on the results of the portfolio optimization problem. *Risk and Uncertainty: Chapters 4-7 deal with uncertainty in the project selection problem. *Non-Linearity and Interdependence: These chapters deal with problems of non-linearity and interdependence as they arise in the project selection problem. Chapters 8, 9 and 10 present solution methodologies, which can be used to solve these most general project selection models.

This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.

This book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times. It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.

Due to the increasing importance of product differentiation and collapsing product life cycles, a growing number of value-adding activities in the industry and service sector are organized in projects. Projects come in many forms, often taking considerable time and consuming a large amount of resources. The management and scheduling of projects represents a challenging task and project performance may have a considerable impact on an organization's competitiveness. This handbook presents state-of-the-art approaches to project management and scheduling. More than sixty contributions written by leading experts in the field provide an authoritative survey of recent developments. The book serves as a comprehensive reference, both, for researchers and project management professionals. The handbook consists of two volumes. Volume 1 is devoted to single-modal and multi-modal project scheduling. Volume 2 presents multi-project problems, project scheduling under uncertainty and vagueness, managerial approaches and a separate part on applications, case studies and information systems.

The book reports on new theories and applications in the field of intelligent systems and computing. It covers computational and artificial intelligence methods, as well as advances in computer vision, current issue in big data and cloud computing, computation linguistics, cyber-physical systems as well as topics in intelligent information management. Written by active researchers, the different chapters are based on contributions presented at the workshop in intelligent systems and computing (ISC), held during CSIT 2016, September 6-9, and jointly organized by the Lviv Polytechnic National University, Ukraine, the Kharkiv National University of Radio Electronics, Ukraine, and the Technical University of Lodz, Poland, under patronage of Ministry of Education and Science of Ukraine. All in all, the book provides academics and professionals with extensive information and a timely snapshot of the field of intelligent systems, and it is expected to foster new discussions and collaborations among different groups.