This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.

This book provides in-depth guidance on how to use multi-criteria decision analysis methods for risk assessment and risk management. The frontiers of engineering operations management methods for identifying the risks, investigating their roles, analyzing the complex cause-effect relationships, and proposing countermeasures for risk mitigation are presented in this book. There is a total of ten chapters, mainly including the indicators and organizational models for risk assessment, the integrated Bayesian Best-Worst method and classifiable TOPSIS model for risk assessment, new risk prioritization model, fuzzy risk assessment under uncertainties, assessment of COVID-19 transmission risk based on fuzzy inference system, risk assessment and mitigation based on simulation output analysis, energy supply risk analysis, risk assessment and management in cash-in-transit vehicle routing problems, and sustainability risks of resource-exhausted cities. The most significant feature of this book is that it provides various systematic multi-criteria decision analysis methods for risk assessment and management, and illustrates the application of these methods in different fields. This book is beneficial to policymakers, decision-makers, experts, researchers and students related to risk assessment and management.

This publication presents the latest innovations and achievements of academic communities on Decision Support Systems (DSS). These advances include theory systems, computer-aided methods, algorithms, techniques and applications related to supporting decision making. The aim is to develop approaches for applying information systems technology to increase the effectiveness of decision making in situations where the computer system can support and enhance human judgments in the performance of tasks that have elements which cannot be specified in advance. Also it is intended to improve ways of synthesizing and applying relevant work from resource disciplines to practical implementation of systems that enhance decision support capability.The resource disciplines include: information technology, artificial intelligence, cognitive psychology, decision theory, organizational theory, operations research and modeling. Researchers come from the Operational Research area but also from Decision Theory, Multicriteria Decision Making methodologies, Fuzzy sets and modeling tools. Based on the introduction of Information and Communication Technologies in organizations, the decisional process is evolving from a mono actor to a multi actor situation in which cooperation is a way to make the decision.

The Value TRAI is a four part classification which has been developed to enable any business entity, process, project or job to be broken down into its component value elements. This then provides a framework for the identification, prioritisation, evaluation and management of business risks. In addition to the value TRAI this book introduces a series of simple tools which address business risks: both threats and opportunities.The Author, Chris Duggleby, has spent over thirty years managing chemicals businesses around the world working with several internationally respected partners. His recent work focused on the management of Joint Ventures and the design of Management of Change processes for transformation projects. His first-hand knowledge of managing industrial business risks has been used in the design of the tools and processes described in this book.Using a standardised set of easy to apply risk management tools is fundamental to the introduction of an enterprise wide risk management system. This book, supported by the www.bizchangers.com website, describes these tools and how to apply them.

This book includes a collection of articles that present recent developments in the fields of optimization and dynamic game theory, economic dynamics, dynamic theory of the firm, and population dynamics and non standard applications of optimal control theory. The authors of the articles are well respected authorities in their fields and are known for their high quality research in the fields of optimization and economic dynamics.

Top businesses recognise risk management as a core feature of their project management process and approach to the governance of projects. However, a mature risk management process is required in order to realise its benefits; one that takes into account the design and implementation of the process and the skills, experience and culture of the people who use it. To be mature in the way you manage risk you need an accepted framework to assess your risk management maturity, allowing you to benchmark against a recognised standard. A structured pathway for improvement is also needed, not just telling you where you are now, but describing the steps required to reach the next level. The Project Risk Maturity Model detailed here provides such an assessment framework and development pathway. It can be used to benchmark your project risk processes and support the introduction of effective in-house project risk management. Using this model, implementation and improvement of project risk management can be managed effectively to ensure that the expected benefits are achieved in a way that is appropriate to the needs of each organisation. Martin Hopkinson has developed The Project Risk Maturity Model into a robust framework, and this book allows you to access and apply his insights and experience. A key feature is a downloadable resource containing a working copy of the QinetiQ Project Risk Maturity Model (RMM). This will enable you to undertake maturity assessments for as many projects as you choose. The RMM has been proven over a period of 10 years, with at least 250 maturity assessments on projects and programmes with a total value exceeding AGBP60 billion. A case study in the book demonstrates how it has been used to deliver significant and measurable benefits to the performance of major projects.

Decision-Making Management: A Tutorial and Applications provides practical guidance for researchers seeking to optimizing business-critical decisions employing Logical Decision Trees thus saving time and money. The book focuses on decision-making and resource allocation across and between the manufacturing, product design and logistical functions. It demonstrates key results for each sector with diverse real-world case studies drawn primarily from EU projects. Theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data and lot of data. Binary Decision Diagrams are presented as the operating approach for evaluating large Logical Decision Trees, helping readers identify Boolean equations for quantitative analysis of multifaceted problem sets. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The final objective is to optimize dynamic decisions with original approaches employing useful tools, including Big Data analysis. Extensive annexes provide useful supplementary information for readers to follow methods contained in the book.

This book proposes several commonly used interval-valued solution concepts of interval-valued cooperative games with transferable utility. It thoroughly investigates these solutions, thereby establishing the properties, models, methods, and applications. The first chapter proposes the interval-valued least square solutions and quadratic programming models, methods, and properties. Next, the satisfactory-degree-based non-linear programming models for computing interval-valued cores and corresponding bisection algorithm are explained. Finally, the book explores several simplification methods of interval-valued solutions: the interval-valued equal division and equal surplus division values; the interval-valued Shapley, egalitarian Shapley, and discounted Shapley values; the interval-valued solidarity and generalized solidarity values; and the interval-valued Banzhaf value. This book is designed for individuals from different fields and disciplines, such as decision science, game theory, management science, operations research, fuzzy sets or fuzzy mathematics, applied mathematics, industrial engineering, finance, applied economics, expert system, and social economy as well as artificial intelligence. Moreover, it is suitable for teachers, postgraduates, and researchers from different disciplines: decision analysis, management, operations research, fuzzy mathematics, fuzzy system analysis, applied mathematics, systems engineering, project management, supply chain management, industrial engineering, applied economics, and hydrology and water resources.

Have you ever wondered why you make bad decisions? Or why it's so hard to make a decision in the first place? Through pioneering research into behavioural science, decisions expert Dr Sheheryar Banuri has designed an entirely novel decision-making framework which can be adopted into everyday life to help us better our decision-making skills by understanding and streamlining the process. The result? Simple, effective and efficient techniques to combat indecision. The Decisive Mind will draw on examples from evolutionary psychology, examine our ability (or inability) to prioritise and highlight the scenarios that force decision-making errors, and help us understand our own minds. By unpicking a lifetime's worth of misconceptions about our own decision-making patterns and habits, this book will guide you on your first steps towards optimising your own brain space.

This cutting-edge book presents the theory and practice of the Graph Model for Conflict Resolution (GMCR), which is used for strategically investigating disputes in any field to enable informed decision making. It clearly explains how GMCR can determine what is the best a particular decision maker (DM) can independently achieve in dynamic interaction with others. Moves and counter-moves follow various stability definitions reflecting human behavior under conflict. The book defines a wide range of preference structures to represent a DM's comparisons of states or scenarios: equally preferred, more or less preferred; unknown; degrees of strength of preference; and hybrid. It vividly describes how GMCR can ascertain whether a DM can fare even better by cooperating with others in a coalition. The book portrays how a conflict can evolve from the status quo to a desirable resolution, and provides a universal design for a decision support system to implement the innovative decision technologies using the matrix formulation of GMCR. Further, it illustrates the key ideas using real-world conflicts and supplies problems at the end of each chapter. As such, this highly instructive book benefits teachers, mentors, students and practitioners in any area where conflict arises.

This book focuses on the issues of decision-making with several numerical criteria. It introduces an original general approach to solving multicriteria problems given quantitative information about the preference relation of a decision-maker. It considers the problems with crisp as well as fuzzy preference relations, accepting the four axioms of "reasonable choice". Further, it defines the notion of an information quantum about the preference relation of a decision-maker and studies the reduction of the Pareto set using a finite collection of information quanta, demonstrating that the original approach yields a good approximation for the set of nondominated alternatives in a multicriteria problem. Lastly, it analyzes a possible combination of the axiomatic approach with other well-known methods. Intended for a wide range of professionals involved in solving multicriteria problems, including researchers, design engineers, product engineers, developers and analysts, the book is also a valuable resource for undergraduate and postgraduate students of mathematics, economics, and engineering.