Methodology, Implementation and Applications of Decision Support Systems

323 Pages·1991·28.8 MB·English

by  Andrzej Lewandowski

INTERNATIONAL CENTRE FOR MECHANICAL SCIENCES
COURSES AND LECfURES -No. 320
METHODOLOGY, IMPLEMENTATION
AND APPLICATIONS OF DECISION
SUPPORT SYSTEMS
EDI1EDBY
A. LEW ANDOWSKI
WA Y NE STA 1E UNIVERSITY
P. SERAFINI
UNIVERSITY OF UDINE
M. G. SPERANZA
UNIVERSITY OF BRESCIA
SPRINGER-VERLAG WIEN GMBH
Le spese di stampa di questo volume sono in parte coperte da
contributi del Consiglio Nazionale delle Ricerche.
This volume contains 37 i11ustrations.
This work is subject to copyright.
All rights are reserved,
whether the whole or part of the material is concemed
specifically those of translation, reprinting, re-use of iJiustrations,
broadcasting, reproduction by photocopying machine
or similar means, and storage in data banks.
© 1991 by Springer-Verlag Wien
Originally published by Springer Verlag Wien-New York in 1991
ISBN 978-3-211-82297-5 ISBN 978-3-7091-2606-6 (eBook)
DOI 10.1007/978-3-7091-2606-6
PREFACE
In 1988 the idea arose within the group of people working in the Deci
sion Science Program at the International Institute for Applied System Analysis
(IIASA}, Laxenburg, Austria, to held an international school to review the current
state of the art in the subject of Decision Support Systems, with special emphasis
to management applications. The presence of researcliers from the University of
Udine among these people and the aim of involving an Italian institution in this
inititative, led naturally to the choice of the International Center for Mechanical
Seiences (CISM), Udine, Italy, as a host institution.
After a preparatory phase the school was held in the week September 17-21,
1990 at CISM as ajoint activity between IIASA and CISM. Also the Department of
Mathematics and Computer Science of the University of Udine was partly involved
in the organization.
The topics of the ,,chool were intended to be divided into general methodolog
ical issues and practical applications to various fields, like business, environment,
transportation and production, with the goal, whenever possible, to show also the
actual implementation during special software sessions.
Due to the broad scope of the subject a full week of lectures was planned by
involving as lecturers internationally distinguished experts in the area. The school
arose great interest and was successfull in all respects.
The present volume collects the proceedings of the school, although not all
contributions could be prepared for publication. However, most of the lectures are
reproduced here giving a faithful representation of the Ievel and scope of the School.
For ease of presentation the papers have been arranged in the book according to first
author's alphabetical order.
The editors wish to express their deep gratitude first of all to CISM and
IIASA. Without their organizational support the school could not even be planned.
We are also deeply grateful to Unesco for its generotts contribution which made
possible the attendance to the sch.ool for many re,qearchers from developing coun
tries. Also we want to thank all the CISM'.q sta.ff for its continuous and preciotLS
support which let all organization run smoothly. We also express our gratitude to
all participants in the school whose friendly attitude established a nice and fruitful
atmosphere.
A ndrzej Lewandowski
Paolo Serafini
Maria Grazia Speranza
CONTENTS
Page
Preface
Intelligent decision support systems
by H.W. Gottinger, H.-P. Weimann ............................................................... 1
A decision support system for the management of Corno lake:
structure, implementation and performance
by G. Guariso, S. Rinaldi, R. Soncini-Sessa ................................................... 2 9
A decision generator shell in prolog
by K.M. van Hee, W.P M. Nuijten ............................................................. .4 7
A multi objective decision support system for public planning
by R. Janssen, M. van Herwijnen ................................................................ 6 7
Two decision support systems for continuous and discrete multiple
criteria decision making: VIG and VIMDA
by P. Korhonen ..................................................................................... 8 5
Decision support systems and multiple-criteria optimization
by A. Lewandowski .............................................................................. 1 0 5
Interactive multi-objective programming and its applications
by H. Nakayama .................................................................................. 1 7 5
A hierarchical approach to periodic scheduling of large scale traffic
light systems
by C. Pascolo, P. Serajini, W. Ukovich ....................................................... 1 9 9
A multi-stage decomposition approach for a resource constrained
project scheduling problern
by P. Serafini, M.G. Speranza .................................................................. 2 11
Concepts of the reference point class of methods of interactive multiple
objective programming
by R.E. Steuer, LR. Gardiner .................................................................. 245
Aspiration-ted decision support systems: theory and methodology
by A.P. Wierzbicki ............................................................................... 2 7 1
DSS MIDA: Lessons from experience in development and application
in the chemical industry
by M. Zebrowski .................................................................................. 3 0 3
INTELLIGENT DECISION SUPPORT SYSTEMS
H.W. Gottinger
University of Maastricht, Maastricht, The Netherlands
and
Fraunhofer Institute for Technological Forecasting, Euskirchen,
Germany
H.-P. Weimann
Industrieanlagen-Betriebsgesellschaft, Ottobrunn, Germany
Abstract
This paper explores the basic ingredients of intelligent decision support systems in
partial centrast to approaches followed by expert systems.
Rule based expert systems for decision support have been successful for weil struc
tured, weil understood decision situations of a taxonomic classification type. But, in
general, A.l. has growing influence in software engineering for ill-structured applica
tion areas by supporting an incremental development process with new program
ming techniques and architectures. As uncertainty is prevalent, information costly
and payoff relevant, and the preferred solution depends on the specific beliefs and
preferences of an individual or group decision maker the resolution methods of de
cision theory embodied in first-order predicate logic form a natural basis for compu
terized intelligent decision support. A unified characterization of knowledge and in
ference for logical, probabilistic, and decision-theoretic reasoning is developed for
intelligent decision support over a wide spectrum of decision Situations.
Key-words
Intelligent Decision Support, Expert Systems, lnfluence Diagrams, Decision Theory,
Knowledge Engineering.
2 H.W. Gottinger, H.-P. Wiemann
1. INTRODUCTION
ln the past few years there has been substantial attention devoted to the use of
artificial intelligence (Al) methods and architectures, most commonly rule based
expert systems, as tools for decision support. An inherent focus of expert system de
velopment is the adequate modeling of human problern solving capabilities. ln its
sequel we observe the construction of several methods of representation like pro
duction rules, semantic networks, frames and scripts as weil as inference mecha
nisms such as logic reasoning, non-monotenie reasoning and default reasoning, e.g.,
in facing problems like inconsistency and knowledge gaps. From the software engi
neering point of view, systems analysis can be done on a higher Ievei of abstraction
(closer to the domain expert) and involving the entire engineering cycle (Patrick
1986).
Especially rule based techniques have proven to be very attractive for a variety
of problems particularly those which have fairly weil structured (though possibly
large) problern spaces, which can be solved through the use of heuristic methods or
rules of thumb, and are currently solved by human experts. ln these domains the
reasoning and explanation capabilities affered by rule based expert systems are very
effective. A rule-based approach tends to breakdown when applied to more difficult
problems or problems that require a normative, prescriptive structure for decision
and inference purposes, in particular, relating to the following Situations:
• there is substantial uncertainty on various Ieveis of decision-making;
• the preferred solution is sensitive to the specific preferences and desires ot one
or several decision makers;
problems of rationality and behavioral coherence are intrinsic concerns of deci
sion systems;
problems of resource-boundedness for the user can be dealt with more adequa
tely (Hansson and Mayer, 1988).
ln established fields such as operations research and management science we
have been developing methods for allocating resources under various conditions ot
time, uncertainty and rationality constraints. Central to these methods is the exist
ence of a~ objective or utility function, as an indicator of the desirability of various
outcomes. We will draw on this body of knowledge,. especially elements related to
the normative use of individual and group decision theory to approach difficult deci
sion problems.
On the other hand, an evolutionary approach to system development is a major
advantage of a production system or rule based program architecture and of expert
system techniques in general. That is, once general decisions have been made re
garding the basic control procedures and the organization of the rule base, the
knowledge base can be incrementally improved by adding, modifying or deleting in-
Intelligent Decision Support Systems 3
dividual production rules. The advantage of rule-based program architecture combi
ned with new programming paradigms such as object-oriented programming and
logic programming facilitates advanced prototyping. ln this light we develop methods
for reasoning about the structure of probabilistic and decision theoretic models in a
rule-based manner based on domain knowledge.
Summarizing, our attempt is to integrate conventional Al, logic-based approa
ches to problern solving with techniques for probabilistic analysis and decision
making under uncertainty from Operations research and management science to
develop methods for improving the quality of decision making.
ln this view, an intelligent decision support system (IDSS) is an interactive tool
for decision making for well-structured (or well-structurable) decision and planning
situations that uses expert system techniques as weil as specific decision models to
make it a model-based expert system (integration of information systems and deci
sion models for decision support). The decision model imposes a normative profile
on the IDSS serving for problern structuring and knowledge representation.
2. COMPUTER-AIDEO DECISION MAKING
Advances in artificial intelligence, coupled with analytic techniques developed in
the fields of systems analysis and operations research, can provide a means of si
gnificantly improving the quality of decision making by individuals and organizations.
Traditional approaches to computer assisted decision making include decision
support systems (DSS). The typical DSS provides means to sort, select, and trans
form information in the data base. Another recent development has been the use of
artificial intelligent techniques, most commonly rule-based expert systems, as tools
for decision support.
The efficacy of a rule based approach to decision support depends on the nature
of the problern being solved. The classification-recommendation approach to deci
sion making has significant limitations for particular types of domains. There are
several incompatible taxonomies for the categorization of expert system pro!:>lem
areas. The most common scheme (Ciancey, 1986) divides expert systems applica
tion areas into analysis problems (e.g. debugging, diagnosis and interpretation) and
synthesis problems (e.g. configuration, planning and scheduling). Some problems
cannot be classified that way because they comprise subtasks with many inde
pendent or semi-dependent sources of knowledge, interacting to find a common SO
lution. The best example for such class of problems is speech recognition, but also
in decision support we have problern areas such as planning/scheduling in military
command and control. ln those areas the "expert systems" approach supports the
incremental improvement of the heuristic problem-solving process by adequate mo
deling of a rule- or frame-based representation embedded in an appropriate archi
tecture, e.g. the blackboard architecture (Nii, 1986). Especially for the analysis pro-
4 H.W. Gottinger, H.-P. Wiemann
blems, the heuristic classification based an an recommendation approach to deci
sion making has significant limitations for particular domains.
Perhaps the biggest drawback of the "expert systems" approach isthat most im
plementations do not have a generat representation for the preferences or beliefs of
the decision maker. This Iack of a high Ievei map to describe what the decision maker
desires and believes has several ramifications. Another problern isthat traditional Al
systems are not weil equipped to handle small differences in Outcomes on a variety
of attributes which may affect decision making. For example, most planning systems
are based on developing a plan which can be proven to achieve a specific goal. The
plan is either successful or unsuccessful (i.e. it can be proven constructively that
there exists a successful plan or not), but most planning algorithms cannot evalua
te trade-offs among factors such as the speed of achieving a goal versus cost and
safety considerations. This contrasts with real decision Situations, where alternative
possible plans meet a variety of objectives to various degrees. ln specific applica
tion areas combining analysis and synthesis problems (e.g. command and control)
it might be advantageaus to merge expert system techniques with decision proce
dures. ln the planning and scheduling task for air traffic control or for the planning
task of military operations for example we set up utility functions for the resource al
location process while we devise production rules and specific inference engines
basedontemporal reasoning (Allen, 1984) for scheduling activities. Finally, most si
gnificant decisions involve an element of uncertainty: that is, the decision maker Iacks
information about some aspects of his problem. Rule-based systemsoparate deter
ministically in their own reasoning, however, they can be engineered tobe effective
in a particular uncertain domain (the best example is the Mycin approach with un
certainty factors). ln relatively weil understood, static domains, one can design
systems to Iook for the most likely cause of a fault before those that are less likely,
and then recommend the repair strategy most likely tobe effective. Therefore expli
cit treatment is not always necessary for a system which has to deal with uncertain
ty. However, in many cases uncertainty is encountered at a deeper Ievei. Uncertain
ty arises because the Situation is new or has not been previously considered.
Representations of uncertainty must be based an the information and beliefs of
a particular decision maker, and cannot be delegated to an "expert". Finally, if there
is an int~raction between uncertainty, an individual's attitude toward risk, and the
preferred course of action, then explicit consideration of uncertainty is needed.
3. INTEGRATION OF DECISION THEORY
Westart out from recent efforts to design computer systems for decision support
based on decision theory (Holtzman, 1985; Shachter, 1986). Moregeneral systems
can be established by starting from group decision theory (team theory) for distribu
ted decision making (Gottinger, 1989).