Stochastic Linear Programming
Todaymanyeconomists, engineers and mathematicians are familiar with linear programming and are able to apply it. This is owing to the following facts: during the last 25 years efficient methods have been developed; at the same time sufficient computer cap
- PDF / 7,232,591 Bytes
- 103 Pages / 482 x 692 pts Page_size
- 53 Downloads / 243 Views
Herausgegeben von Edited by M. Beckmann, MUnchenfProvidence R. Henn, Karlsruhe A. Jaeger, Bochum W. Krelle, Bonn H. P. KUnzi, ZUrich K. Wenke, ZUrich Ph. Wolfe, New York Geschiiftsfuhrende Herausgeber Managing Editors W. Krelle H. P. KUnzi
Peter Kall
Stochastic Linear Programming
Springer-Verlag Berlin Heidelberg New York 1976
Peter Kall Institute for Operations Research and Mathematical Methods in Economics, University of Zurich
AMS Subject Classifications (1970): 28A20, 60E05, 90-02, 9OC05, 90C15, 9OC20, 90C25 ISBN-13: 978-3-642-66254-6 DOl: 10.1007/978-3-642-66252-2
e-ISBN-13: 978-3-642-66252-2
Library of Congress Cataloging in Publication Data Kall, Peter. Stochastic linear programming. «()konometrie und Unternehmesforschung; 21). Bibliography: p. Includes index. 1. Linear programming. 2. Stochastic processes. I. Title. II. Series.
HB143.K35
519.7'2.
75·30602.
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifical1y those of translation, reprinting. fe-use of illustrations, broadcasting, reproduction by photocopying machine or similar means, and storage in data banks. Under § 54 of the German Copyright Law where copies 3rc made for other than private use a fee is payable to the publisher, the amount of fee to be determined by agreement with the publisher.
© by Springer. Verlag Berlin Heidelberg 1976. Softcover reprint of the hardcover 1st edition 1976
Preface Todaymanyeconomists, engineers and mathematicians are familiar with linear programming and are able to apply it. This is owing to the following facts: during the last 25 years efficient methods have been developed; at the same time sufficient computer capacity became available; finally, in many different fields, linear programs have turned out to be appropriate models for solving practical problems. However, to apply the theory and the methods of linear programming, it is required that the data determining a linear program be fixed known numbers. This condition is not fulfilled in many practical situations, e. g. when the data are demands, technological coefficients, available capacities, cost rates and so on. It may happen that such data are random variables. In this case, it seems to be common practice to replace these random variables by their mean values and solve the resulting linear program. By 1960 various authors had already recognized that this approach is unsound: between 1955 and 1960 there were such papers as "Linear Programming under Uncertainty", "Stochastic Linear Programming with Applications to Agricultural Economics", "Chance Constrained Programming", "Inequalities for Stochastic Linear Programming Problems" and "An Approach to Linear Programming under Uncertainty". The aim of this book is to give some insight into this challenging field which has to be understood as a special subject of planning under uncertainty. A complete collection of results obtained so far did not seem entirely appropriate, and my preference led me to choose those topics and res
Data Loading...
