Wiley Interscience Series in Systems and Optimization – serie

Traditionally engineers devised communication services without reference to how they should be priced. In today's environment pricing is a very complex subject and in practice depends on many parameters of the actual market - including amount of traffic, architecture of the network, technology, and cost. The challenge is to provide a generic service model which accurately captures aspects such as quality and performance, and can be used to derive optimal pricing strategies. Recent technology advances, combined with the deregulation of the telecommunication market and the proliferation of the internet, have created a highly competitive environment for communication service prividers. Pricing is no longer as simple as picking an appropriate model for a particular contract. There is a real need for a book that explains the provision of new services, the relation between pricing and resource allocation in networks; and the emergence of the internet and how to price it.Pricing Communication Networks provides a framework of mathematical models for pricing these multidimensional contracts, and includes background in network services and contracts, network techonology, basic economics, and pricing strategy. It can be used by economists to fill in the gaps in their knowledge of network services and technology, and for engineers and operational researchers to gain the background in economics required to price communication services effectively.* Provides a broad overview of network services and contracts* Includes a primer on modern network technology and the economic concepts relevant to pricing and competition* Includes discussion of mathematical models of traffic flow to help describe network capability and derive pricing strategies* Includes coverage of specialist topics, such as regulation, multicasting, and auctions* Illustrated throughout by detailed real examples* Suitable for anyone with an understanding of basic calculus and probability Primarily aimed at graduate students, researchers and practitioners from electrical engineering, computer science, economics and operations research Pricing Communication Networks will also appeal to telecomms engineers working in industry.

Provides a clear and systematic introduction to the use of stochastic Petri nets in communications systems engineering and the analysis techniques and algorithms used in performance evaluation.The field of communication systems is full of complex design questions concerning performance and reliability. Since data traffic and errors occur in a random fashion, stochastic models are used for developing and comparing systems. In particular, stochastic Petri nets have become a popular tool for the description and automatic evaluation of such models. The use of non-Markovian models has become important as they allow more flexibility.This book* Provides a clear exposition of the use of stochastic Petri nets in communication systems engineering* Introduces the reader to the analysis techniques and algorithsm used in performance evaluation* Provides an accompanying example to clarify the use of each definition, concept and algorithm* Mathematica routines used for implementing the algorithms are available on the Wiley ftp siteThe text will appeal to researchers, industrial engineers, and graduate students studying communication systems and stochastic modeling. The numerous examples will benefit those working in performance evaluation, reliability, operations research, queueing theory and computer science.The Mathematica routines used for implementing the algorithms are available for downloading on the following Wiley ftp site: ftp://ftp.wiley.co.uk/pub/books/german

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comprehensive coverage of this growing area of researchCarefully introduces each algorithm with examples and in-depth discussionIncludes many applications to real-world problems, including engineering design and schedulingIncludes discussion of advanced topics and future researchCan be used as a course text or for self-studyAccessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithmsThe integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

The concept of a system as an entity in its own right has emergedwith increasing force in the past few decades in, for example, theareas of electrical and control engineering, economics, ecology,urban structures, automaton theory, operational research andindustry. The more definite concept of a large-scale system isimplicit in these applications, but is particularly evident infields such as the study of communication networks, computernetworks and neural networks. The Wiley-Interscience Series inSystems and Optimization has been established to serve the needs ofresearchers in these rapidly developing fields. It is intended forworks concerned with developments in quantitative systems theory,applications of such theory in areas of interest, or associatedmethodology. This is the first book-length treatment of risk-sensitive control,with many new results. The quadratic cost function of the standardLQG (linear/quadratic/Gaussian) treatment is replaced by theexponential of a quadratic, giving the so-called LEQG formulationallowing for a degree of optimism or pessimism on the part of theoptimiser. The author is the first to achieve formulation and proofof risk-sensitive versions of the certainty-equivalence andseparation principles. Further analysis allows one to formulate theoptimization as the extremization of a path integral and tocharacterize the solution in terms of canonical factorization. Itis thus possible to achieve the long-sought goal of an operationalstochastic maximum principle, valid for a higher-order model, andin fact only evident when the models are extended to therisk-sensitive class. Additional results include deduction ofcompact relations between value functions and canonical factors,the exploitation of the equivalence between policy improvement andNewton Raphson methods and the direct relation of LEQG methods tothe H??? and minimum-entropy methods. This book will proveessential reading for all graduate students, researchers andpractitioners who have an interest in control theory includingmathematicians, engineers, economists, physicists andpsychologists. 1990 Stochastic Programming Peter Kall, Universityof Zurich, Switzerland and Stein W. Wallace, University ofTrondheim, Norway Stochastic Programming is the first textbook toprovide a thorough and self-contained introduction to the subject.Carefully written to cover all necessary background material fromboth linear and non-linear programming, as well as probabilitytheory, the book draws together the methods and techniquespreviously described in disparate sources. After introducing theterms and modelling issues when randomness is introduced in adeterministic mathematical programming model, the authors coverdecision trees and dynamic programming, recourse problems,probabilistic constraints, preprocessing and network problems.Exercises are provided at the end of each chapter. Throughout, theemphasis is on the appropriate use of the techniques, rather thanon the underlying mathematical proofs and theories, making the bookideal for researchers and students in mathematical programming andoperations research who wish to develop their skills in stochasticprogramming. 1994

The concept of a system as an entity in its own right has emerged with increasing force in the past few decades in, for example, the areas of electrical and control engineering, economics, ecology, urban structures, automaton theory, operational research and industry. The more definite concept of a large-scale system is implicit in these applications, but is particularly evident in fields such as the study of communication networks, computer networks and neural networks. The Wiley-Interscience Series in Systems and Optimization has been established to serve the needs of researchers in these rapidly developing fields. It is intended for works concerned with developments in quantitative systems theory, applications of such theory in areas of interest, or associated methodology. Of related interest Stochastic Programming Peter Kall, University of Zurich, Switzerland and Stein W. Wallace, University of Trondheim, Norway Stochastic Programming is the first textbook to provide a thorough and self-contained introduction to the subject. Carefully written to cover all necessary background material from both linear and non-linear programming, as well as probability theory, the book draws together the methods and techniques previously described in disparate sources. After introducing the terms and modelling issues when randomness is introduced in a deterministic mathematical programming model, the authors cover decision trees and dynamic programming, recourse problems, probabilistic constraints, preprocessing and network problems. Exercises are provided at the end of each chapter. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories, making the book ideal for researchers and students in mathematical programming and operations research who wish to develop their skills in stochastic programming.

Decision Theory An Introduction to Dynamic Programming and Sequential Decisions John Bather University of Sussex, UK Mathematical induction, and its use in solving optimization problems, is a topic of great interest with many applications. It enables us to study multistage decision problems by proceeding backwards in time, using a method called dynamic programming. All the techniques needed to solve the various problems are explained, and the author's fluent style will leave the reader with an avid interest in the subject.* Tailored to the needs of students of optimization and decision theory* Written in a lucid style with numerous examples and applications* Coverage of deterministic models: maximizing utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity* Coverage of stochastic models: stochastic dynamic programming, optimal stopping problems and other special topics* Coverage of advanced topics: Markov decision processes, minimizing expected costs, policy improvements and problems with unknown statistical parameters* Contains exercises at the end of each chapter, with hints in an appendixAimed primarily at students of mathematics and statistics, the lucid text will also appeal to engineering and science students and those working in the areas of optimization and operations research.