Kim2x Tamparong. Rahmat Khan. Jade Davis. Avanger Stevan Grbic. More From mksayshi. MAns GreedhArry. Pablo Moreno. Michael Read. Frank Wilcox, Jr fewilcox. Christopher Baptiste. Dan Castello Branco. Jonathan Azamor.
Ravenloft the Complete Book of Witches and Warlock 1. J Paul Borrero. Robert Tucker. Yoann Peltier. Charles Weber. Ian McDonald. Daniel Clowers. Alessandro Moraes. The final chapters present network optimization and integer programming problems, highlighting various specialized topics including label-correcting algorithms for the shortest path problem, preprocessing and probing in integer programming, lifting of valid inequalities, and branch and cut algorithms. Concepts and approaches are introduced by outlining examples that demonstrate and motivate theoretical concepts.
The accessible presentation of advanced ideas makes core aspects easy to understand and encourages readers to understand how to think about the problem, not just what to think. Thoroughly class-tested to ensure a straightforward, hands-on approach, Deterministic Operations Research is an excellent book for operations research of linear optimization courses at the upper-undergraduate and graduate levels.
It also serves as an insightful reference for individuals working in the fields of mathematics, engineering, computer science, and operations research who use and design algorithms to solve problem in their everyday work. Pricing is shown for items sent to or within the U. Please consult the store to determine exact fees. No warranties are made express or implied about the accuracy, timeliness, merit, or value of the information provided.
Information subject to change without notice. Send in your comments. Bibliographic information on isbn. Words in title. This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.
The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented.
A considerable amount of scientific evidence has been collected leading to the conclusion that urban wastewater components should be designed as one integrated system, in order to protect the receiving waters cost-effectively. Moreover, there is a need to optimize the design and operation of the sewerage network and wastewater treatment plant WwTP considering the dynamic interactions between them and the receiving waters. This book introduces a method called Model Based Design and Control MoDeCo for the optimum design and control of urban wastewater components.
The book presents a detailed description of the integration of modelling tools for the sewer, the wastewater treatment plants and the rivers. The complex modelling structure used for the integrated model challenge previous applications of integrated modelling approaches presented in scientific literature.
The combination of modelling tools and multi-objective evolutionary algorithms demonstrated in this book represent an excellent tool for designers and managers of urban wastewater infrastructure.
This book also presents two alternatives to solve the computing demand of the optimization of integrated systems in practical applications: the use of surrogate modelling tools and the use of cloud computer infrastructure for parallel computing.
Author : Stanislav Uryasev,Panos M. Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks.
Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation.
Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: 1 advances in theory and implementation of stochastic programming algorithms; 2 sensitivity analysis of stochastic systems; 3 stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering.
The book is appropriate as supplementary reading in courses on optimization and financial engineering. Optimization plays a key role in the design, planning and operation of chemical and related processes for several decades.
Techniques for solving optimization problems are of deterministic or stochastic type. Of these, stochastic techniques can solve any type of optimization problems and can be adapted for multiple objectives. Differential evolution DE , proposed about two decades ago, is one of the stochastic techniques.
Its algorithm is simple to understand and use. DE has found many applications in chemical engineering. This unique compendium focuses on DE, its recent developments and applications in chemical engineering. It will cover both single and multi-objective optimization. The book contains a number of chapters from experienced editors, and also several chapters from active researchers in this area.
Stochastic Optimization Algorithms have become essential tools in solving a wide range of difficult and critical optimization problems. Such methods are able to find the optimum solution of a problem with uncertain elements or to algorithmically incorporate uncertainty to solve a deterministic problem. They even succeed in fighting uncertainty with uncertainty. This book discusses theoretical aspects of many such algorithms and covers their application in various scientific fields.
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