P. Kall – författare

This vo1ume contains most of the papers (two of the~ as extended abstracts) presented at a meeting on stochastic programming, held at Oberwolfach, January 28 - February 3, 1979. A1though the number of participants had to be small for technical rea- sons, the area covered by the 1ectures during the meeting was rather broad. lt contains fundamental theoretical problems - e.g. continuity in parametric programming, optima1ity conditions and decision rules for stochastic programming problems and convexity statements also nee- ded for chance constrained problems - as well as very important prac- tical problems, as computational methods for various models and appli- cations to water storage problems, dynamic inventory control, asphalt mixing, portfolio selection, and so on. Without any doubt there are still many theoretical and computational problems of this field unso1ved, and some of them can be discovered in this volume.On the other hand, the papers presented here also show, that during the last two decades knowledge - theoretical and computa- tional - on stochastic programming, and practical experience with it, have been developped so far, that neglecting apriori the stochastic nature of parameters for almost every price - very often done in mo- delling a practical decision situation as deterministic optimization problem - can no longer be justified.

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 recog­ nized that this approach is unsound: between 1955 and 1960 there were such papers as "Linear Programming under Uncertainty", "Stochastic Linear Pro­ gramming with Applications to Agricultural Economics", "Chance Constrained Programming", "Inequalities for Stochastic Linear Programming Problems" and "An Approach to Linear Programming under Uncertainty".

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 recog­ nized that this approach is unsound: between 1955 and 1960 there were such papers as "Linear Programming under Uncertainty", "Stochastic Linear Pro­ gramming with Applications to Agricultural Economics", "Chance Constrained Programming", "Inequalities for Stochastic Linear Programming Problems" and "An Approach to Linear Programming under Uncertainty".

Die 6. Jahrestagung der Deutschen Gesellschaft fUr Operations Research fand vom 21. bis 23. September 1977 in den Raumen der Christian-Albrechts-Universitat in Kiel statt. Mit etwa 300 Teilnehmern und Referenten aus Deutschland und mehreren europaischen Landern wurde das groBe Interesse der Fachvertreter aus Wissenschaft, Wirtschaft und offentlicher Verwaltung an den DGOR- Jahrestagungen dokumentiert. Die zunehmende Anzahl von Beitragen zum Tagungsprogramm laBt erken- nen. daB Operations Research insbesondere fUr die Anwendungen in der Praxis starkere Beachtung findet. Diese Entwicklung findet ihren Niederschlag in zahlreichen Arbeiten dieses Tagungsbandes, wobei vor allem das heute erreichte breite Anwendungsspektrum von OR-Verfahren sichtbar wird. Gerade die Kombination des Operations Research mit den Moglichkeiten der EDV eroffnet hier neue Wege. Die VerfUgbarkeit kostengUnstiger und leistungsstarker Computersysteme wird kUnftig dazu beitragen, dem Operations Research neue Impulse zu vermitteln. Besonderer Dank fUr die erfolgreiche Gestaltung der Tagung gilt Herrn Prof. Dr.Reinhart Schmidt, Universitat Kiel, und seinen Mit- arbeitern, denen die Tagungsvorbereitung und ortliche Tagungsorga- nisation Ubertragen war. Unser Dank gilt auch den Referenten und dem Verlag fUr ihre bereitwilligeKooperation bei der Drucklegung dieses Tagungsbandes.

The first Joint Meeting of the Austrian and Swiss Operations Research Societies was held at the University of Technology in Vienna, Austria, from 22-24 September 1980. A selection of the papers then presented are published in this volume. The papers included provide a picture of Operations Research as a discipline comprehending various methods and models. The volume is divided into two parts. The first part contains papers on mathematical programming and optimal control theory. Moreover, two papers on decision and game theory are included as well as two on biomathematical to~ics. Being more heterogeneous, the second part, on stochastic models, includes such topics as queueing theory, econometrics and time series analysis. As a special feature of the symposium, a series of applications of OR models in energy planning were discussed, e.g., load and demand forecasting, power distribution and extension planning, reliability and work-safety, etc. These papers have been incorporated into parts one and two according to the models used. GUSTAV FEICHTINGER PETER !CALL Institut fUr Okonometrie und Institut fUr Operations Research und Operations Research mathematische Methoden der Wirtschafts- Technische UniversitHt wissenschaften Wien UniversitHt ZUrich November 198 I ix Gustav Feichtinger and Peter Kall (eds.), Operations Research in Progress, ix..