Seems you have not registered as a member of localhost.saystem.shop!
You may have to register before you can download all our books and magazines, click the sign up button below to create a free account.
Introduction to Stochastic Programming
This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.
Introduction to Stochastic Programming
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal i...
Introduction to Stochastic Programming
This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.
Fleet Management and Logistics
TEODOR GABRIEL CRAINIC, DIRECTOR The Centre for Research on Transportation (C.R.T.) was founded in 1971 by the Universite de Montreal. From 1988 on, it is jointly managed by the Universite de Montreal and its affiliated schools, the Ecole des Hautes Etudes Commerciales and Ecole Poly technique. Professors, students and researchers from many institutions in the Montreal area join forces at the C.R.T. to analyze transportation, logistics and telecommunication systems from a multidisciplinary perspective. The C.R.T. pursues three major, complementary objectives: training of high-level specialists; the advancement of knowledge and technology; the transfer of technology towards industry and the p...
Optimization in the Energy Industry
This book offers a broad, in-depth overview that reflects the requirements, possibilities and limits of mathematical optimization and, especially, stochastic optimization in the energy industry.
Stochastic Optimization
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.
Operations Research and Decision Aid Methodologies in Traffic and Transportation Management
Every one relies on some kind of transportation system nearly every day. Go ing to work, shopping, dropping children at school and many other cultural or social activities imply leaving home, and using some form of transportation, which we expect tobe eflicient and reliable. Of course, efliciency and reliabil ity do not occur by chance, but require careful and often relatively complex planning by transportation system managers, both in the public and private sectors. It has long been recognized that mathematics, and, more specifically, op erations research is an important tool of this planning process. However, the range of skills required to cover both fields, even partially, is very large,...
Innovations in Distribution Logistics
In a globalized economy logistics has become a crucial area for the success of companies. The performance of each company depends on the performance of its suppliers and of its business partners. The customers of each company are spread on a large geographical space. For this reason distribution logistics is the most important and complex part of logistics. An efficient and effective management of distribution logistics is a key issue for the success of a company. There are many different problems to deal with, from facility location to transportation, to inventory management, and, most important, to the integration and optimization of the entire logistics network. Quantitative methods provide relevant tools to support decisions, from strategic to operational, in distribution logistics.
