Table Of ContentOperations Research and Artificial
Intelligence: The Integration of
Problem-Solvi ng Strateg ies
Operations Research and Artificial
Intelligence: The Integration of
Problem-Salvi ng Strateg ies
edited by
Donald E. Brown
Chelsea C. White, III
University of Virginia
Kluwer Academic Publishers
BostonlDordrechtlLondon
Distributors for North America:
Kluwer Academic Publishers
101 Philip Drive
Assinlppi Park
Norwell, Massachusetts 02061 USA
Distributors for all other countries:
Kluwer Academic Publishers Group
Distribution Centre
Post Office Box 322
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Library of Congress Cataloging-in-Publication Data
Operations research and artificial intelligence: the integration of
problem-solving strategies / edited by Donald E. Brown, Chelsea C.
White, III
p cm.
Includes bibliographical references and Index
ISBN-13: 978-94-010-7488-9 e-ISBN-13: 978-94-009-2203-7
001: 10.1007/978-94-009-2203-7
1. Artificial intelligence. 2. Operations research. 3. Decision
-making. I. Brown, Donald E. II. White, Chelsea C., 1945-
0335.064 1990
006.3-dc20 90-39443
CIP
Copyright © 1990 by Kluwer Academic Publishers
All rights reserved. No part of thiS publication may be reproduced,
stored in a retrieval system or transmitted in any form or by any
means, mechanical, photocopying, recording, or otherwise, without
the prior written permission of the publisher, Kluwer Academic
Publishers, 101 Philip Drive, Assinippi Park, Norwell, Massachusetts
02061
Contents
Contributors vii
Preface ix
Introduction
D.E. Brown and C.C. White, III
I. Search 7
Toward the Modeling, Evaluation and Optimization
of Search Algorithms 11
O. Hansson, G. Holt and A. Mayer
Genetic Algorithms Applications to Set Covering
and Traveling Salesman Problems 29
G.E. Liepins, M.R. Hilliard, J. Richardson and
M. Palmer
Discovering and Refining Algorithms Through
Machine Learning 59
M.R. Hilliard, G.E. Liepins and M. Palmer
II. Uncertainty Management 79
USing Probabilities as Control Knowledge to
Search for Relevant Problem Models in Automated
Reasoning 83
R.K. Bhatnagar and L.N. Kanal
On the Marshalling of Evidence and the Structuring
of Argument 105
D.A. Schum
Hybrid Systems for Failure Diagnosis 141
E. Pate-Cornell and H. Lee
III. Imprecise Reasoning 167
Default Reasoning Through Integer Linear
Programming 171
S.D. Post and C.E. Bell
The Problem of Determining Membership Values in
Fuzzy Sets in Real World Situations 197
E. Triantaphyllou, P.M. Pardalos and S.H. Mann
v
vi CONTENTS
IV. Decision Analysis and Decision Support 215
Applications of Utility Theory in Artificial
Intelligence Research 219
P.H. Farquhar
A Multicriteria Stratification Framework for
Uncertainty and Risk Analysis 237
J. Barlow and F. Glover
Dispute Mediation: A Computer Model 249
K. Sycara
V. Mathematical Programming and AI 275
Eliciting Knowledge Representation Schema for
Linear Programming Formulation 279
M.M. Sklar, RA Pick, G.B. Vesprani and J.R. Evans
A Knowledge Base for Integer Programming-A
Meta-OR Approach 317
F. Zahedi
VI. Performance Analysis and Complexity Management
of Expert Systems 369
Validator, A Tool for Verifying and Validating
Personal Computer Based Expert Systems 373
M. Jafar and A.T. Bahill
Measuring and Managing Complexity in
Knowledge-Based Systems: A Network and
Mathematical Programming Approach 387
D.E. O'Leary
Pragmatic Information-Seeking Strategies for
Expert Classification Systems 427
LA Cox, Jr.
VII. Applications 449
A Knowledge- and Optimization-Based Approach to
Scheduling in Automated Manufacturing Systems 453
A. Kusiak
An Integrated Management Information System for
Wastewater Treatment Plants 481
W. Lai, P.M. Berthouex and D. Hindle
About the Authors 497
Index 507
Contributors
Professor A. Terry Bahill, Systems and Industrial Engineering,
University of AIizona, Tucson, AZ 85721
Professor Colin E. Bell, College of Business Administration,
Department of Management Sciences, The University of Iowa,
Iowa City, IA 52242
Professor P.M. Berthouex, Department of Civil & Environmental
Engineering, University of Wisconsin-Madison, Madison, WI
53706
Mr. Tony Cox, US WEST Advanced Technologies, 6200 South
Quebec Street, Englewood, CO 80111
Professor Peter H. Farquhar, Graduate School of Industrial
Administration, Carnegie-Mellon University, Pittsburgh, PA
15213-3890
Professor Fred Glover, Graduate School of Business, University of
Colorado, Boulder, CO 80309-0419
Dr. Michael R. Hilliard, Research Associate, Martin Marietta
Energy Systems, Inc., P.O. Box 2008, Oak Ridge, TN 37831-
6366
Professor Laveen N. Kanal, Department of Computer Science, The
University of Maryland, College Park, MD 20742
Professor Andrew Kusiak, Industrial & Management Engineering,
The University of Iowa, Iowa City, IA 52242
Dr. Gunar Liepins, Oak Ridge National Laboratory, P.O. Box 2008,
Oak Ridge, TN 37831
Professor Andrew Mayer, Computer Science Division, University of
California at Berkeley, Berkeley, CA 94720
Professor Daniel E. O'Leary, Graduate School of Business,
University of Southern California, Los Angeles, CA 90089-1421
Professor Panos M. Pardalos, Department of Computer Science, The
Pennsylvania State University, University Park, PA 16802
Professor M.E. Pate-Cornell, Industrial Engineering & Engineering
Management, Stanford University, Stanford, CA 94305
Professor David A. Schum, Operations Research & Applied
Statistics, George Mason University, Fairfax, VA 22030
Professor Margaret M. Sklar, Department of Management,
Marketing, and CIS, School of Business, Northern Michigan
University, Marquette, MI 49855
Professor Katia Sycara, The Robotics Institute, Carnegie-MeHon
University, Pittsburgh, PA 15213-3890
Professor Fatemeh Zahedi, Management Sciences Department,
University of Massachusetts - Boston, Harbor Campus, Boston,
MA 02125-3393
vii
Preface
The purpose of this book is to introduce and explain research at the
boundary between two fields that view problem solving from
different perspectives. Researchers in operations research and
artificial intelligence have traditionally remained separate in their
activities. Recently, there has been an explosion of work at the
border of the two fields, as members of both communities seek to
leverage their activities and resolve problems that remain
intractable to pure operations research or artificial intelligence
techniques. This book presents representative results from this
current flurry of activity and provides insights into promising
directions for continued exploration.
This book should be of special interest to researchers in artificial
intelligence and operations research because it exposes a number of
applications and techniques, which have benefited from the
integration of problem solving strategies. Even researchers working
on different applications or with different techniques can benefit from
the descriptions contained here, because they provide insight into
effective methods for combining approaches from the two fields.
Additionally, researchers in both communities will find a wealth of
pointers to challenging new problems and potential opportunities
that exist at the interface between operations research and artificial
intelligence.
In addition to the obvious interest the book should have for
members of the operations research and artificial intelligence
communities, the papers here are also relevant to members of other
research communities and development activities that can benefit
from improvements to fundamental problem solving approaches.
Included in this category are engineers and physical and social
scientists, who require improved decision making techniques or
greater understanding of processes involved in problem solving in
complex domains.
Most of the papers in this book were presented at the Joint National
Meetings of the Operations Research Society of America and The
Institute for Management Science. Over the past three years there
were roughly 400 papers presented at these meetings that
incorporated results from artificial intelligence. Officers and council
members of the Artificial Intelligence Technical Section of the
Operations Research Society of America decided to organize and
ix
x
present significant results from among these papers. It was decided
early in this process, that rather than simply collect papers and bind
them, a formal review process should be instituted. Hence, the
papers collected here represent the results of a two tiered review
process, designed to distill and present the more significant results
from these meetings.
We acknowledge the support received in the preparation of this
work from the members of the Artificial Intelligence Technical
Section of the Operations Research Society of America. The project
was particularly encouraged by the first chairperson of the Technical
Section (at the time it was a Special Interest Group), Frank
Morisano, and received the complete support of his successors,
Jerry May and Gunar Liepens. We also appreciate the support of
the referees, who assisted us in reviewing the submitted papers.
We experienced a very high return rate on review requests for this
volume, which made it much easier to compile the papers. Finally,
we owe special thanks to Annelise Tew, who assisted us
throughout the preparation of this book: maintaining files, calling
referees, calling authors, reviewing formats, and generally ensuring
our plans were well executed.
Donald E. Brown
Chelsea C. White, III
Charlottesville, Virginia
Operations Research and Artificial
Intelligence: The Integration of
Problem-Solvi ng Strateg ies
Introduction
Donald E. Brown and Chelsea C. White, III
Department of Systems Engineering
University of Virginia
Charlottesville, Va. 22901
This book contains papers that demonstrate some of the important
results from integrating problem solving techniques typically
associated with operations research (OR) with those typically
associated with artificial intelligence (AI). The papers presented
here exemplify what we believe is a stage in the natural evolution of
both fields toward more powerful strategies of problem solving.
These strategies will find usefulness for both decision aiding, a goal
of OR, and automatic decision making, which is the pursuit of AI.
Historically, the OR and AI research communities worked in relative
isolation from one another. On the one hand this separation is
remarkable, because both disciplines are deeply concerned with
questions of human problem solving and decision making, both are
highly computer dependent, and both share some common
conceptual frameworks (e.g graphs, probability theory, and
heuristics). On the other hand, the separation is understandable in
that OR has sought optimal methods in decision making through
formal mathematical structures. AI has emphasized goal seeking
and the use of workable, although suboptimal, strategies more
closely associated with human performance. While the fields do
share some common conceptual frameworks, there are many more
that are distinct to each field. For example, AI has a strong
foundation in logic with work that emphasizes automatic theorem
proving, while OR has instead emphasized the mathematics of
optimization and the quantification of preference through utility and
value functions.
These differences aside, the complexity of many, if not most real
world decision problems has exposed the limitations of OR and AI
tools, and has caused the two communities to seek solution
approaches that integrate these tools. Perhaps the most significant
call for integrative approaches to complexity came from Simon
(1987), who stressed the common problem solving foundations of
the fields. From the OR perspective a more formal organizational
statement supporting the general contention that significant
advances in problem solving strategies are attainable through the
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