Products related to Optimization:
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Optimization of Logistics : Theory and Practice
This book comes out from the materials I used to refer while doing my research on the optimization issues in logistics.I brought together some of these materials to form a guidance material on the fundamentals of the optimization concepts along with my own studies on the application of optimization methods. This book consists of two parts and six chapters.The first part of the book, which consists of three chapters, is about introduction to optimization with typical base problems and algorithms for solving problems.The second part of this book consists of three my own researches on the application of optimization methods. Each chapter in this book is independent of each other.I hope you will find this book useful, informative, beneficial and appropriate for your needs.
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Modeling and Optimization in Green Logistics
This book presents recent work that analyzes general issues of green logistics and smart cities.The contributed chapters consider operating models with important ecological, economic, and social objectives. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.
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Optimization of Logistics and Supply Chain Systems : Theory and Practice
This book is the collection of my own studies in the optimization of logistics and supply chain systems, targeted to a broad readership. In this book, first few chapters of the optimization concepts come out of the materials I used to refer while doing my research. I brought together some of these materials to form a guidance material on the fundamentals of the optimization concepts along with my own studies in the application of optimization methods.For a macro level logistics and supply chain systems optimization perspective, I added several chapters that are the results of my studies, which use the Global Trade Analysis Project (GTAP and GDyn) simulations. The static and dynamic computable general equilibrium (CGE) concept is also the essential part of the optimization study of logistics and supply chain systems. Therefore, the fundamentals of the static and dynamic (CGE) models are introduced. As the demand for logistics depends mostly on the volume of trade and trade patterns, international trade affects the transport and logistics, as it might generate a higher or lower demand for transport and logistics services, which are calculated by various approaches of optimization studies.This book consists of six parts and twenty chapters. The first part of the book, which includes three chapters, is about introduction to optimization with typical base problems and algorithms for solving these problems. The second part of this book includes five my own researches in the application of optimization methods. The third part of the book shortly introduces you to the general concepts of the computable general equilibrium models (CGE) and presents you the fundamentals of a CGE model. In each chapter of the fourth part, short articles that include five simulations based on various scenarios are presented. The fifth part of the book briefly introduces you to the basic concepts of the computable general equilibrium models (CGE) and then, presents you the fundamentals of dynamic general equilibrium models. In each chapter of the sixth part, two short articles that simulate various scenarios are presented.All the chapters in this book are independent of each other. I hope you will find this book informative, beneficial and appropriate for your needs.
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Bayesian Optimization
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond.This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way.This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations.The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies.Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
Price: 44.99 £ | Shipping*: 0.00 £
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What is price optimization?
Price optimization is the process of using data and analytics to determine the most effective pricing strategy for a product or service. It involves analyzing factors such as market demand, competitor pricing, and customer behavior to set prices that maximize revenue and profit. By continuously adjusting prices based on these insights, businesses can find the optimal balance between attracting customers and maximizing profitability. Price optimization helps businesses stay competitive in the market and adapt to changing conditions to achieve their financial goals.
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What is linear optimization?
Linear optimization, also known as linear programming, is a mathematical method for determining the best outcome in a given mathematical model for a given set of requirements. It involves maximizing or minimizing a linear objective function, subject to a set of linear equality and inequality constraints. Linear optimization is widely used in various fields such as economics, engineering, and business to optimize resource allocation, production planning, and decision-making processes. It provides a systematic and efficient approach to solving complex problems with multiple variables and constraints.
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What is upstream optimization?
Upstream optimization refers to the process of identifying and addressing issues at the beginning of a production or supply chain to improve overall efficiency and effectiveness. By focusing on optimizing processes, materials, and resources at the upstream stage, companies can prevent problems from occurring downstream, leading to cost savings, improved quality, and increased productivity. This proactive approach helps organizations streamline their operations and enhance their competitive advantage in the market.
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Is map optimization harmful?
No, map optimization is not harmful. In fact, it is beneficial for improving the user experience, reducing load times, and increasing website performance. Properly optimizing maps can help websites load faster and provide a better overall experience for users.
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Numerical Optimization
Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization.It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout.There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience.It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business.It also serves as a handbook for researchers and practitioners in the field.The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.
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Numerical Optimization
Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization.It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout.There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience.It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business.It also serves as a handbook for researchers and practitioners in the field.The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition.
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Robust Optimization
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology.Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject.Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution.The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems.The theory is supported by numerous examples and computational illustrations.An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
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Convex Optimization
Convex optimization problems arise frequently in many different fields.This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency.The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems.Duality and approximation techniques are then covered, as are statistical estimation techniques.Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods.The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them.It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Price: 84.99 £ | Shipping*: 0.00 £
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What is mathematical optimization?
Mathematical optimization is the process of finding the best solution to a problem from a set of possible solutions. It involves maximizing or minimizing a certain objective function while satisfying a set of constraints. This can be applied to a wide range of fields, including engineering, economics, and computer science, to help make better decisions and improve efficiency. Optimization problems can be solved using various mathematical techniques such as linear programming, nonlinear programming, and integer programming.
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What is optimization 3?
Optimization 3 refers to the process of maximizing or improving the efficiency and effectiveness of a system, process, or strategy. It involves identifying areas for improvement, setting specific goals, and implementing changes to achieve the best possible outcome. Optimization 3 often involves using data analysis, mathematical modeling, and advanced algorithms to make informed decisions and drive continuous improvement. This approach is commonly used in various fields such as engineering, business, and technology to enhance performance and achieve better results.
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What does self-optimization mean?
Self-optimization refers to the process of improving oneself in various aspects such as physical, mental, emotional, and spiritual well-being. It involves setting personal goals, identifying areas for improvement, and taking deliberate actions to enhance one's overall quality of life. Self-optimization also involves self-reflection, self-awareness, and continuous learning to achieve personal growth and fulfillment. It is a proactive approach to self-improvement and self-care, aiming to maximize one's potential and live a more balanced and fulfilling life.
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Can you explain linear optimization?
Linear optimization, also known as linear programming, is a mathematical method for determining the best outcome in a given mathematical model for a given set of requirements. It involves maximizing or minimizing a linear objective function, subject to a set of linear equality and inequality constraints. The objective function represents the quantity to be optimized, while the constraints represent the limitations or restrictions on the decision variables. Linear optimization is widely used in various fields such as economics, engineering, and business to make efficient use of resources and to optimize decision-making processes.
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