In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

The analysis and design of engineering and industrial systems has come to rely heavily on the use of optimization techniques. The theory developed over the last 40 years, coupled with an increasing number of powerful computational procedures, has made it possible to routinely solve problems arising in such diverse fields as aircraft design, material flow, curve fitting, capital expansion, and oil refining just to name a few. Mathematical programming plays a central role in each of these areas and can be considered the primary tool for systems optimization. Limits have been placed on the types of problems that can be solved, though, by the difficulty of handling functions that are not everywhere differentiable. To deal with real applications, it is often necessary to be able to optimize functions that while continuous are not differentiable in the classical sense. As the title of the book indicates, our chief concern is with (i) nondifferentiable mathematical programs, and (ii) two-level optimization problems. In the first half of the book, we study basic theory for general smooth and nonsmooth functions of many variables. After providing some background, we extend traditional (differentiable) nonlinear programming to the nondifferentiable case. The term used for the resultant problem is nondifferentiable mathematical programming. The major focus is on the derivation of optimality conditions for general nondifferentiable nonlinear programs. We introduce the concept of the generalized gradient and derive Kuhn-Tucker-type optimality conditions for the corresponding formulations.

Operations Research Management Science approaches have helped people for the last 40 years to understand the complex functioning of the systems based upon natural resources, as well as to manage natural resources in the most efficient manner. The areas usually viewed within the natural resources field are: agriculture, fisheries, forestry, and mining and water resources. All of these areas share the common problem of optimally allocating scarcity over a period of time. The scale of time or length of the planning horizon differs from one area to another. We have almost a continuous renewal in the case of the fisheries, periodic cycles in the case of agriculture and forestry and enormous periods of time much beyond the human perception in the case of mining resources. But in all the cases, the critical issue is to obtain an efficient use of the resource along its planned time horizon.

Another element of connection among the different natural resources is due to the interaction between the use of the resource and the environmental impact caused by its extraction or harvest. This type of interaction implies additional complexities in the underlying decision-making process, making the use of OR/MS tools especially relevant.

HANDBOOK OF OPERATIONS RESEARCH IN NATURAL RESOURCES will be the first systematic handbook treatment of quantitative modeling natural resource problems, their allocated efficient use, and societal and economic impact. AndrA(c)s Weintraub is the very top person in Natural Resource research. Moreover, he has an international reputation in OR and a former president of the International Federation of Operational Research Societies (IFORS). He has selectedco-editors who are at the top of the sub-fields in natural resources: agriculture, fisheries, forestry, and mining. The book will cover these areas in terms with contributions from researchers on modeling natural research problems, quantifying data, developing algorithms, and discussing the benefits of research implementations. The handbook will include tutorial contributions when necessary. Throughout the book, technological advances and algorithmic developments that have been driven by natural resource problems will be called out and discussed.

Franz Ferschl is seventy. According to his birth certificate it is true, but it is unbelievable. Two of the three editors remembers very well the Golden Age of Operations Research at Bonn when Franz Ferschl worked together with Wilhelm Krelle, Martin Beckmann and Horst Albach. The importance of this fruitful cooperation is reflected by the fact that half of the contributors to this book were strongly influenced by Franz Ferschl and his colleagues at the University of Bonn. Clearly, Franz Ferschl left his traces at all the other places of his professional activities, in Vienna and Munich. This is demonstrated by the present volume as well. Born in 1929 in the Upper-Austrian Miihlviertel, his scientific education brought him to Vienna where he studied mathematics. In his early years he was attracted by Statistics and Operations Research. During his employment at the Osterreichische Bundeskammer fUr Gewerbliche Wirtschaft in Vienna he prepared his famous book on queueing theory and stochastic processes in economics. This work has been achieved during his scarce time left by his duties at the Bundeskammer, mostly between 6 a.m. and midnight. All those troubles were, however, soon rewarded by the chair of statistics at Bonn University. As a real Austrian, the amenities of the Rhineland could not prevent him from returning to Vienna, where he took the chair of statistics.

This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas. It showcases emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users' both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. Chapters highlight ways to approach such technical challenges in service science and are based on submissions from the 2020 INFORMS International Conference on Service Science.

This book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz's portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio's decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset. The book explains PSO in detail and demonstrates how to implement Markowitz's portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.

Although chaos theory refers to the existence of between seemingly random events, it has been gaining the attention of science, technology and managements fields. The shift from traditional procedures to the dynamics of chaos and complexity theory has resulted in a new element of complexity thinking allowing for a greater capability for analysing and understanding key business processes. Chaos and Complexity Theory for Management: Nonlinear Dynamics explores the chaos and complexity theory and its relationship with the understanding of the natural chaos in the business environment. Utilising these theories would aid in comprehending the development of businesses as a complex adaptive system.

This open access book presents a new generation multi-criteria, multi-stake holder, decision aide, called an "institutional compass". Based on hard data, the compass tells us what quality-direction we are heading in as an institution, region, system or organisation. The quality is not chosen from the usual scalar qualities of: good, neutral and bad. Instead, it is a quality chosen between: harmony, discipline and excitement. None is good in and of itself. We need some of each. The compass marks a new generation in four respects. 1. The representation of the data is intuitive and simple to understand, and therefore can be used to communicate and justify policy decisions. 2. Any data can be included, i.e., none is excluded. This makes the compass tailored to particular situations, voices and contexts. 3. The data includes different time horizons and different types of value: monetary, use, social, sentimental, religious, intrinsic, existential... 4. The process of compass construction can be made inclusive at several junctions. An institutional compass can be extended to evaluate products, add normativity to a systems analysis, reflect world-views such as that of ecological economists or function as an accounting system to manage scarce resources. There are four parts to the book. The first part introduces the general ideas behind the compass. In the second part, the author presents the method for constructing the compass. This includes data collection, data analysis and a mathematical formula to aggregate the data into a single holistic reading. In the third part, the author extends the methodology: to incorporate it into systems science, adding a normative and quality-direction dimension, to use it as a non-linear accounting method and more thoroughly to reflect the philosophy of ecological economists to give a real measure of sustainability. In the fourth part, we see three case studies: one for the World Health Organisation, a second is the use of the compass to label products in a shop and the third is as a regional compass for Hauts-de-France. The book ends with philosophical conclusions. Throughout the book, we see tight arguments, refreshing ideas and a thorough treatment of objectivity in decision making.

Services requiring parts has become a $1.5 trillion business annually worldwide, creating a tremendous incentive to manage the logistics of these parts efficiently by making planning and operational decisions in a rational and rigorous manner. This book provides a broad overview of modeling approaches and solution methodologies for addressing service parts inventory problems found in high-powered technology and aerospace applications. The focus in this work is on the management of high cost, low demand rate service parts found in multi-echelon settings.

This unique book, with its breadth of topics and mathematical treatment, begins by first demonstrating the optimality of an order-up-to policy [or (s-1, s)] in certain environments. This policy is used in the real world and studied throughout the text. The fundamental mathematical building blocks for modeling and solving applications of stochastic process and optimization techniques to service parts management problems are summarized extensively. A wide range of exact and approximate mathematical models of multi-echelon systems is developed and used in practice to estimate future inventory investment and part repair requirements.

The text may be used in a variety of courses for first-year graduate students or senior undergraduates, as well as for practitioners, requiring only a background in stochastic processes and optimization. It will serve as an excellent reference for key mathematical concepts and a guide to modeling a variety of multi-echelon service parts planning and operational problems.

Risk management has become an essential issue in supply chain management, from the modeling of the decision maker's risk preference, and the studies on uncertain elements such as demand, supply, price, lead time, etc., to the consideration of more practical background including cash flow constraints, inventory financing and delayed cash payment. In this new volume, the authors provide a framework to study the interaction of various factors related to risk and their influence on supply chain management. The scope of areas covered includes operations management, decision analysis, and business administration. This book focuses on several key issues of risk management in supply chains. Specifically, an analysis framework is presented for studying the supplier selection problem and identifying the optimal sourcing strategy in a one-retailer two-suppliers supply chain with random yields. The optimal sourcing strategy of a retailer and the pricing strategies of two suppliers under an environment of supply disruption are investigated. Besides, the authors study the dynamic inventory control problems with cash flow constraints, financing decisions as well as delayed cash payment. In addition, originating from the annual international iron ore price negotiation, the authors model the bargaining process to deal with the risk of wholesale price in the game analysis context. Within the three perspectives of risk management in supply chains, the modeling of decision maker's risk preference has been extensively studied and many results have been obtained to guide the practice. However, the analysis on the other two kinds of topics is still in its infancy, and needs more efforts from academia. It is thus the ambition and innovation for this book to contribute on risk management in supply chains in the following ways: (1) characterizing the explicit sourcing strategy (i.e., single sourcing or dual sourcing) to deal with supply disruption risk; (2) introducing the concepts of financial risk measurement by incorporating cash flow constraints, inventory financing and delayed cash payment into inventory management models; and (3) providing insights for the iron ore price negotiation to help steel manufacturers handle the risk of price increase.

This fully updated third edition is devoted to the analysis of various Stochastic Dominance (SD) decision rules. It discusses the pros and cons of each of the alternate SD rules, the application of these rules to various research areas like statistics, agriculture, medicine, measuring income inequality and the poverty level in various countries, and of course, to investment decision-making under uncertainty. The book features changes and additions to the various chapters, and also includes two completely new chapters. One deals with asymptotic SD and the relation between FSD and the maximum geometric mean (MGM) rule (or the maximum growth portfolio). The other new chapter discusses bivariate SD rules where the individual's utility is determined not only by his own wealth, but also by his standing relative to his peer group. Stochastic Dominance: Investment Decision Making under Uncertainty, 3rd Ed. covers the following basic issues: the SD approach, asymptotic SD rules, the mean-variance (MV) approach, as well as the non-expected utility approach. The non-expected utility approach focuses on Regret Theory (RT) and mainly on prospect theory (PT) and its modified version, cumulative prospect theory (CPT) which assumes S-shape preferences. In addition to these issues the book suggests a new stochastic dominance rule called the Markowitz stochastic dominance (MSD) rule corresponding to all reverse-S-shape preferences. It also discusses the concept of the multivariate expected utility and analyzed in more detail the bivariate expected utility case. From the reviews of the second edition: "This book is an economics book about stochastic dominance. ... is certainly a valuable reference for graduate students interested in decision making under uncertainty. It investigates and compares different approaches and presents many examples. Moreover, empirical studies and experimental results play an important role in this book, which makes it interesting to read." (Nicole Bauerle, Mathematical Reviews, Issue 2007 d)

The contributions in this volume cover a broad range of topics including maximum cliques, graph coloring, data mining, brain networks, Steiner forest, logistic and supply chain networks. Network algorithms and their applications to market graphs, manufacturing problems, internet networks and social networks are highlighted. The "Fourth International Conference in Network Analysis," held at the Higher School of Economics, Nizhny Novgorod in May 2014, initiated joint research between scientists, engineers and researchers from academia, industry and government; the major results of conference participants have been reviewed and collected in this Work. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis.

Numerous clothing industries face highly dynamic environments, and growth in this environment depends upon both external and internal factors. External factors are represented by aggressive competition and volatile product demand. Internally, the industry must face an increasingly shorter life cycle of the product and the need to innovate both product and organizational development. The competitive advantage of the industry lies in its ability to design a value-creating system based on the management of both external and internal relationships. The successful management of these relationships relies not only on successful customer relationship management but also on effective product supply and demand upkeep. Management and Inter/Intra Organizational Relationships in the Textile and Apparel Industry provides emerging research exploring relevant theoretical frameworks and the latest empirical research underlining the complexity of management applications within the textile industry. Featuring coverage on a broad range of topics such as consumer relationships, cultural identity, and organizational culture, this book is ideally designed for researchers, academicians, professionals, and students working in various disciplines including management, industrial organization, organizational behavior, human resource management, decision science, design science, and information and communication. Moreover, the book will provide insights and support executives and managers of the textile and apparel industry concerned with the ethic design, contamination, and the management relationships with workers, customers, suppliers, the community, and organizational development.

This book provides a relatively whole view of data-driven decision-making methods for energy service innovation and energy system optimization. Through personalized energy services provision and energy efficiency improvement, the book can contribute to the green transformation of energy system and the sustainable development of the society. The book gives a new way to achieve smart energy management, based on various data mining and machine learning methods, including fuzzy clustering, shape-based clustering, ensemble clustering, deep learning, and reinforcement learning. The applications of these data-driven methods in improving energy efficiency and supporting energy service innovation are presented. Moreover, this book also investigates the role of blockchain in supporting peer-to-peer (P2P) electricity trading innovation, thus supporting smart energy management. The general scope of this book mainly includes load clustering, load forecasting, price-based demand response, incentive-based demand response, and energy blockchain-based electricity trading. The intended readership of the book includes researchers and engineers in related areas, graduate and undergraduate students in university, and some other general interested audience. The important features of the book are: (1) it introduces various data-driven methods for achieving different smart energy management tasks; (2) it investigates the role of data-driven methods in supporting various energy service innovation; and (3) it explores energy blockchain in P2P electricity trading, and thus supporting smart energy management.

This book presents innovative operations research applications in business, specifically industrial engineering and its sub-disciplines. It investigates new perspectives in operations research and management science with regard to research methods, the research context, and industrial engineering, offering readers a broad range of new approaches to management problems. The book features the latest work of researchers who have worked with Professor Fusun Ulengin or built upon her work in their academic careers. Written in honor of Prof. Ulengin, this book was edited by her former Ph.D. students, who are now experts in operations research, multiple criteria decision making, competitiveness, logistics, and supply chain management. Prof. Ulengin's impact in academia is visible in the range of topics and methodologies featured in this book: Location and transportation problems, competitiveness of nations, food supply chains, debt collection, mathematical modelling, multiple criteria decision making, data envelopment analysis, random forests, and Bayesian networks.

This work prepares current and future managers and consultants focused on health care delivery systems to improve the efficiency of processes that deliver care. This material will help you manage capacity, improve patient flow, estimate process costs, and conduct experiments that lead to process improvement. Essential tools covered include process mapping and measurement, data collection and analysis, and the use of discrete event simulation as a tool for virtual experimentation and improvement. Tools are introduced with no assumption of prior training. Many examples of settings, problems, and solutions are presented, along with a generalized approach to process improvement that is specifically tailored to health care settings. Readings, exercises, and cases suitable for discussion or end-course projects are also provided. Writing is based on a decade of experience teaching at the MBA and MS levels, managing dozens of improvement processes, and a host of our prior scholarly publications, book chapters, and case studies.

This book presents scheduling with a medium- and short-term focus, which makes it possible to capitalize on fleeting market opportunities while simultaneously working to reconcile economic and environmental priorities. It introduces a new mixed-integer approach to hierarchical discrete-time and continuous-time scheduling, combining aspects of production and recycling, forward and reverse logistics as well as emissions trading for multi-stage supply chain networks. Problem-specific variants of relax-and-fix heuristics and genetic algorithms are also proposed. Given its scope, the book provides a range of practical tools and new perspectives for researchers and professionals in the field of supply chain management.

In this book, the theory, methods and applications of separable optimization are considered. Some general results are presented, techniques of approximating the separable problem by linear programming problem, and dynamic programming are also studied. Convex separable programs subject to inequality/ equality constraint(s) and bounds on variables are also studied and convergent iterative algorithms of polynomial complexity are proposed. As an application, these algorithms are used in the implementation of stochastic quasigradient methods to some separable stochastic programs. The problems of numerical approximation of tabulated functions and numerical solution of overdetermined systems of linear algebraic equations and some systems of nonlinear equations are solved by separable convex unconstrained minimization problems. Some properties of the Knapsack polytope are also studied. This second edition includes a substantial amount of new and revised content. Three new chapters, 15-17, are included. Chapters 15-16 are devoted to the further analysis of the Knapsack problem. Chapter 17 is focused on the analysis of a nonlinear transportation problem. Three new Appendices (E-G) are also added to this edition and present technical details that help round out the coverage. Optimization problems and methods for solving the problems considered are interesting not only from the viewpoint of optimization theory, optimization methods and their applications, but also from the viewpoint of other fields of science, especially the artificial intelligence and machine learning fields within computer science. This book is intended for the researcher, practitioner, or engineer who is interested in the detailed treatment of separable programming and wants to take advantage of the latest theoretical and algorithmic results. It may also be used as a textbook for a special topics course or as a supplementary textbook for graduate courses on nonlinear and convex optimization.

In trying to make a satisfactory decision when imprecise and multicriteria situations are involved, a decision maker has to use a fuzzy multicriteria decision making method. "Fuzzy Multi-Criteria Decision Making" (MCDM) presents fuzzy multiattribute and multiobjective decision-making methodologies by distinguished MCDM researchers.

In summarizing the concepts and results of the most popular fuzzy multicriteria methods, using numerical examples, this work examines all the fuzzy multicriteria methods recently developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, fuzzy multiobjective geometric programming, and more. Each of the 22 chapters includes practical applications along with new developments/results.

This book may be used as a textbook in graduate operations research, industrial engineering, and economics courses. It will also be an excellent resource, providing new suggestions and directions for further research, for computer programmers, mathematicians, and scientists in a variety of disciplines where multicriteria decision making is needed.

This volume is the last (IV) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to 'digital transformation" within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress. The focus in this volume is on the directions of Agriculture 4.0 which incorporates the transition to a new era of action in the Agricultural sector, represented by the evolution of digital technologies in 4 aspects: Big Data, Open Data, Internet of Things (IoT), and Cloud Computing. Under the heading of "Action," 14 Chapters investigate the implementation of cutting-edge technologies on real world applications. It will become apparent to the reader that the penetration of ICT in agriculture can result in several benefits related to the sustainability of the sector and to yield the maximum benefits, successful management is required. The entire discussion highlights the importance of proper education in the adoption of innovative technologies starting with the adaption of educational systems to the new era and moving to the familiarization of farmers to the new technologies. This book covers topics that relate to the digital transformation of farming. It provides examples and case studies of this transformation from around the world, examines the process of diffusion of digital technologies, and assesses the current and future sustainability aspects of digital agriculture. More specifically, it deals with issues such as: Challenges and opportunities from the transition to Agriculture 4.0 Safety and health in agricultural work automation The role of digital farming on regional-spatial planning The enrollment of Social Media in IoT-based agriculture The role of education in digital agriculture Real-life implementation cases of smart agriculture around the world

"Schedule-Based Modeling of Transportation Networks: Theory and Applications" follows the book Schedule-Based Dynamic Transit Modeling, published in this series in 2004, recognizing the critical role that schedules play in transportation systems.

Conceived for the simulation of transit systems, in the last few years the schedule-based approach has been expanded and applied to operational planning of other transportation schedule services besides mass transit, e.g. freight transport. This innovative approach allows forecasting the evolution over time of the on-board loads on the services and their time-varying performance, using credible user behavioral hypotheses. It opens new frontiers in transportation modeling to support network design, timetable setting, and investigation of congestion effects, as well as the assessment of such new technologies, such as users system information (ITS technologies).

In the Administration building at Linkopi ] ng University we have one of Oscar Reutersvard' ] s "Impossible Figures" in three dimensions. I call it "Perspectives of Science." When viewed from a speci c point in space there is order and structure in the 3-dimensional gure. When viewed from other points there is disorder and no structure. If a speci c scienti c paradigm is used, there is order and structure; otherwise there is disorder and no structure. My perspective in Transportation Science has focused on understanding the mathematical structure and the logic underlying the choice probability models in common use. My book with N. F. Stewart on the Gravity model (Erlander and Stewart 1990), was written in this perspective. The present book stems from the same desire to understand underlying assumptions and structure. It investigateshow far a new way of de ning Cost-Minimizing Behavior can take us.Itturnsoutthatall commonlyusedchoiceprobabilitydistributionsoflogittype- log linear probability functions - follow from cost-minimizing behavior de ned in the new way. In addition some new nested models appear."

This textbook presents a coherent and robust structure for integrated risk management in the context of operations and finance. It explains how the operations-finance interface jointly optimizes material and financial flows under intricate risk exposures. The book covers financial flexibility, operational hedging, enterprise risk management (ERM), supply chain risk management (SCRM), integrated risk management (IRM), supply chain finance (SCF), and financial management of supply chain strategies. Both qualitative and quantitative approaches - including conceptualization, theory building, analytical modeling, and empirical research - are used to assess the value creation by integrating operations and finance. "This book provides a comprehensive description of the interactions between finance and operations and of how managers can best make decisions in recognition of these effects." John R. Birge, University of Chicago"Supply chain finance is an emerging area where innovations can unlock great values to complement the advances in information and physical flows of supply chain." Hau L. Lee, Stanford University"This book provides an excellent overview of supply chain finance and its most recent advances." Jan A. Van Mieghem, Northwestern University"This book is indispensable for advanced students as well as practitioners when looking for a pedagogical sound and scientific rigorous approach to Supply Chain Finance." Ralf W. Seifert, IMD/EPFL"The book advances our knowledge on the interface between operations and finance and provides managerial guidelines for effective risk management in the supply chain." Xiande Zhao, CEIBS

This book introduces readers to advanced data science techniques for signal mining in connection with agriculture. It shows how to apply heuristic modeling to improve farm-level efficiency, and how to use sensors and data intelligence to provide closed-loop feedback, while also providing recommendation techniques that yield actionable insights. The book also proposes certain macroeconomic pricing models, which data-mine macroeconomic signals and the influence of global economic trends on small-farm sustainability to provide actionable insights to farmers, helping them avoid financial disasters due to recurrent economic crises. The book is intended to equip current and future software engineering teams and operations research experts with the skills and tools they need in order to fully utilize advanced data science, artificial intelligence, heuristics, and economic models to develop software capabilities that help to achieve sustained food security for future generations.

This volume is the third (III) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to 'digital transformation" within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress. The focus of this book (III) is on the transformation of collected information into valuable decisions and aims to shed light on how best to use digital technologies to reduce cost, inputs, and time, toward becoming more efficient and transparent. Fourteen chapters are grouped into 3 Sections. The first section of is dedicated to decisions in the value chain of agricultural products. The next section, titled Primary Production, elaborates on decision making for the improvement of processes taking place with the farm under the implementation of ICT. The last section is devoted to the development of innovative decision applications that also consider the protection of the environment, recognizing its importance in the preservation and considerate use of resources, as well as the mitigation of adverse impacts that are related to agricultural production. Planning and modeling the assessment of agricultural practices can provide farmers with valuable information prior to the execution of any task. This book provides a valuable reference for them as well as for those directly involved with decision making in planning and assessment of agricultural production. Specific advances covered in the volume: Modelling and Simulation of ICT-based agricultural systems Farm Management Information Systems (FMIS) Planning for unmanned aerial systems Agri-robotics awareness and planning Smart livestock farming Sustainable strategic planning in agri-production Food business information systems