The world we live in presents plenty of tricky, impactful, and hard-tomake decisions to be taken. Sometimes the available options are ample, at other times they are apparently binary, either way, they often confront us with dilemmas, paradoxes, and even denial of values.In the dawn of the age of intelligence, when robots are gradually taking over most decision-making from humans, this book sheds a bit of light on decision rationale. It delves into the limits of these decision processes (fo...

A proposal for a new way to understand cities and their design not as artifacts but as systems composed of flows and networks.In The New Science of Cities, Michael Batty suggests that to understand cities we must view them not simply as places in space but as systems of networks and flows. To understand space, he argues, we must understand flows, and to understand flows, we must understand networks—the relations between objects that compose the system of t...

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, bac...

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in m...

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, includin...

Drawn from a conference honoring Gerald L. Thompson, the pioneer of operations research, this volume brings together some of the latest writings of major figures in the field. The volume is divided into four parts: the first part reviews the career and significance of Thompson, the second concentrates on linear and nonlinear optimization, the third looks at network and integer programming, and the fourth provides examples of applications-oriented research in manufacturing. This volume will...

Fierce competition in today's global market provides a powerful motivation for developing ever more sophisticated logistics systems. This book, written for the logistics manager and researcher, presents a survey of the modern theory and application of logistics. The goal of the book is to present the state of the art in the science of logistics management.This third edition includes new chapters on the subjects of game theory, the power of process flexibility, supply chain competit...

The supplement I is the scientific foundation for any decision making and problem solving environment. There is a universal coordinate system in which all parameters for a decision or a problem are represented for the purpose of evaluating outcomes, any outcomes. Why qQuantum: because it is Time based.

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, ...

This is a comprehensive and authoritarian book on the critical subject that various industrial, technological, managerial and social sectors are trying to find solution for. The ever evolving technology constantly places a new set of Risk and Safefy challange. The book address the concepts and models required to address this growing need of System Safety.

Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research, including propositional satisfiability, constraint satisfaction, routing, and scheduling. SLS algorithms have also become increasingly popular for solving challenging combinatorial problems in many application areas, such as e-commerce and bioinformatics.Hoos and Stützle offer the first syst...

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model pa...