Table Of ContentARTIFICIAL INTELLIGENCE IN OPERATIONAL RESEARCH
ARTIFICIAL INTELLIGENCE
IN OPERATIONAL RESEARCH
Edited by
Georgios I. Doukidis
Athens UniversityofEconomics and Business
and
Ray J. Paul
London School ofEconomicsand PoliticalScience
M
MACMILLAN
© Operational Research Society Ltd 1992 (papers so markcd)
© The Authors 1992 (remainder)
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First published 1992 by
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ISBN 978-1-349-12364-3 ISBN 978-1-349-12362-9 (eBook)
DOI 10.1007/978-1-349-12362-9
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Contents
Preface VB
Part I: Practical Experiences 1
G. I. Doukidis and R. J. Paul, OperationalResearch Participationin Artificial
Intelligence: PracticalExperiences 3
G. I. Doukidis and R. J. Paul, A Survey oftheApplication ofArtificial Intelligence
Techniques within the OR Society 9
H. Walters and R. Schtaklef, From OR to Knowledge-based Systems: An Industrial
Experience 23
P. N. Finlay, D. G. Johnson, M. King, C. J. Martin and G. J. Phythian, A Five
Hour Expert System Tutorialfor Management Students 31
P. N. Finlay and M. King, Experiencesin Developing an Expert System for MBA
Admissions 45
J. Grassick, Exporting Know-how 57
Part II: Application Areas 61
G. I. Doukidis and R. J. Paul, Combining Operational Researchand Artificial
Intelligence 63
S. Pierre and H.-H. Hoang, An Artificial IntelligenceApproach to Improving
Computer Communications Network Topologies 71
G. Balestra and A. Tsoukias, Multicriteria Analysis Represented by Artificial
Intelligence Techniques 85
J. C. Thorpe, A. Marr and R. S. Slack, Usingan Expert System to Monitor an
Automatic Stock Control System 97
M. King and L. McAulay, Simple Expert Systems for Computer Assisted Instruction 105
I. B. Turksen, Fuzzy Ordinal Modelsfor AI-OR 115
P. N. Finlay, M. King and A. Burnett, Administering Rule Development in
Rule-based Expert Systems 127
Part III: Production Planning and Scheduling 133
R. J. Paul and G. I. Doukidis, Operation Research Approaches to Artificial
Intelligence in Production Planningand Scheduling 135
K. N. McKay, J: A. Buzacott, N. Charness and F. R. Safayeni, The Scheduler's
Predictive Expertise:An Interdisciplinary Perspective 139
P. Duchessi and R. M. O'Keefe, A Knowledge-based Approach to Production
Planning 151
G. Zhang and S. c.-Y. Lu, An Expert System Framework for Economic Evaluation
ofMachining Operation Planning 165
S. F. Smith, P. S. Ow, J.-Y. Potvin, N. Muscettola and D. C. Matthys, An
Integrated Framework for Generatingand Revising FactorySchedules 179
A. Beurrier, Ph. Demota, P. Levine and J.-Ch. Pomerol, Distribution ofEmpty
Railcars by an Expert System: A CaseStudy with Comparison with OR Approaches 193
J.-Y. Potvin, G. Lapalme and J.-M. Rousseau, Integration ofAIand OR
Techniques for Computer-aided Algorithmic Design in the Vehicle Routing Domain 205
C. D. J. Waters, Expert Systems for VehicleScheduling 215
Part IV: Simulation 227
R. J. Paul and G. I. Doukidis, Artificial Intelligenceand Expert Systems in
Simulation Modelling 229
v
Contents
H. Pierreval and H. Ralambondrainy, A Simulation and Learning Technique for
Generating Knowledge about Manufacturing Systems Behavior 239
T. M. Williams, R. P. Gittins and D. M. Burke, Replenishmentat Sea 253
J. W. Rozenblit, J. Hu, T. G. Kim and B. P. Zeigler, Knowledge-based Design
and Simulation Environment (KBDSE): Foundational Concepts and Implementation 261
F. W. Dewhurst and E. A. Gwinnett, Artificial Intelligenceand Decision Analysis 277
M. C. Angelides and G. I. Doukidis, Is Therea Placein OR for Intelligent
Tutoring Systems? 287
PartV:Methodology 301
G. I. Doukidis and R. J. Paul, Methodological Issuesin Artificial Intelligence and
Operational Research 303
M. A. Rodger and J. S. Edwards, A Problem-driven Approach to Expert System
Development 311
E. Kiountouzis and C. Papatheodorou, DistributedArtificial Intelligence and Soft
Systems: A Comparison 321
J. H. Klein, P. L. Powell and N. A. D. Connell, Organizational Design: An
Approach Incorporating Expert Systems 327
N. A. D. Connell and P. L. Powell, A Comparison ofPotentialApplications of
Expert Systems and Decision Support Systems 339
N. A. D. Connell and P. L. Powell, MeasuringSuccessand Failurein the
Commercial Application ofExpert Systems:'Hard' Measuresfor 'Soft' Systems? 349
I. M. Neale, Modelling Expertisefor KBS Development 357
vi
Preface
This book came about as a result of the growing interest in the subject of AI among OR
practitioners. We have surveyed the application of Artificial Intelligence techniques within the
ORSociety, the results ofwhich aregiven inthe first paperin this book. From the interestshown
in the survey, we undertook, at the request of the editor of the Journal of the Operational
Research Society, to produce a special issue of the journal on this subject. We canvassed for
papers on a very wide basis, putting advertisements in a number of newsletters and writing to a
number of contacts in the area. To our pleasure and surprise we received a large number of
letters ofintent, and again to oursurprise,the majorityofthese letters of intent actually became
submitted papers. The success of the canvas for papers was such that even with fairly rigorous
refereeing, the numberofpapersacceptedand ofinterestwas greaterthan that in a normalsingle
issue ofthe journal. Therefore the paperswere split into two special issues published in May and
June of 1990.
It is increasingly difficult to keep in touch with dynamic new subjects when the material is
spread in diverse publications, so we decided to produce this state-of-the-art review volume of
over30 paperson the applicationsof artificial intelligence andexpertsystemsinthe broadfield of
operational research. The volume is compiled from selected papers published in the Journal of
the Operational Research Society and additional contemporary papers which have all been
refereed to the usual high standards of thejournal. We have divided the material into five parts,
with linking chapters introducing each part. The five parts consist of practical experiences of
operational researchers in applying AI; the popular application areas in which research is being
undertaken; production planning and scheduling; simulation; and some methodology papers.
This book, then, shows the diversity of applications of AI in OR, both in theory, in practice, and
in methodological implications. It demonstrates that the AI and OR communities could usefully
work together, balancing better solution theories with pragmatic problem solving.
Georgios I Doukidis
Ray J Paul
vii
PART I: PRACTICAL EXPERIENCES
1
Copyright ©The authors 1992
Operational Research Participation in Artificial
Intelligence: Practical Experiences
GEORGIOS I. DOUKIDIS1 and RAY J. PAUL2
(DepartmentofInformatics, Athens UniversityofEconomicsand Business
2Department ofInformation Systems,London SchoolofEconomics, Houghton Street, London
INTRODUCTION
What is not clear from the many publications on the subject of AI and OR is just how much
activityisactually takingplaceat apracticallevel,andwhatthe natureofthisactivityiswithinthe
OR community. This part of the book covers surveys, and practitioner and academic activity.
SURVEYS
Since 1985, three surveys have tried to quantify this activity using OR Society members in the
UK (see Table 1). Carter' undertook his general membership survey in 1985, sending question
naires to all members ofthe OR Society. He achieved a response rate of42%.The purposes of
thesurveywere to establishthestructureofthe ORSocietymembership, the educational level of
members, their functions, skills and experience, the use ofcomputers, and the modelling areas
used. The last ofthese incorporated someevidence ofthe AI involvement. Carterreported that
29% of members were involved in the production of decision support systems and 9% of
members were actually involved in the production of AI.
The second surveywas undertaken by the London SchoolofEconomicsin 1988and the results
are presented by Doukidisand Paulin the next paperin this part ofthe book. Theirsurveytried
to quantify the application of AI techniques among OR society members. The questionnaires
were sent to approximately one third of the published membership of the OR Society. Of the
sample of 1038, 265 questionnaires were returned, giving a response rate of nearly 26%.
The purpose ofthe survey was to establish how far ideas from AI have infiltrated the work of
members of the Society, at what level, their practical significance, and the prospects for future
use. It is suggested that the type of application area and the tools employed by surveyed users
might indicate areas for fruitful investigation by non-users of AI techniques.
The sample was found to be representative of the membership generally. Just over a fifth
(21.5%) of the sample was involved in AI in some way or other, but expected to double
involvement (45.5%) in the future. Comparing general characteristics with those found in
Carter'ssurvey showsthat thesample isfairly representativeofthe Society in mostofits aspects.
The one significant difference is that the greater management concentration of the respondents
and the lower information systems' orientation might lead to an underestimate of the involve
ment of the OR community in AI. Therefore, the 12.8% involved in the production of AI
applications shows a considerable change over the 9% in 1985.
The third survey was undertaken by Cornford and Doukidis/ in 1989 to investigate the
relationship of OR and computers. The sample frame was British OR society members. A total
of1005 questionnaires was sent out, of which 209 were returned, giving a response rate of about
20%. The analysis of the replies gave very comparable results for distribution in employment
sector and area in organization with those reported by Carter. The main difference is that the
greater information systems orientation might lead to an overestimation of the involvement of
the OR community in AI.
One ofthesignificant results of this surveyisthefact that61% ofthesampledevelopedspecific
computer applications as part oftheir job. Analysis ofthe developers by department shows that
the developers are significant within OR departments where the figure was 84%. Figures on the
numberofapplicationsdeveloped revealed thatabout halfofthe developershaddeveloped more
3
Artificial Intelligence in Operational Research
than 5 applications in the past three years. The number of respondents involved in the
development of AI isofspecial interest. The proportion ofthose who develop Decision Support
Systems was 36% and those who develop some kind of AI was about 20%.
TABLE 1. OR SocietymembersdevelopingAI
Survey Year %
Carter 1985 9
Doukidisand Paul 1988 12.8
Cornfordand Doukidis 1989 20
The paper by Doukidis and Paul presents the findings of the second survey where its main
objective wasto analyze the application ofAI techniques among OR Society members. The first
main conclusion of the survey was that professional and organisational motivation was the main
reason for usingAI,sinceitseemedto relate tosecuringthe position ofthe ORprofession and of
the individual OR worker.
ES were the major AI technique either developed or used.The survey found that 85% of the
applications were ES. More generally, AI meant ES for most respondents to the survey. The
remaining applications ranked in order of popularity were of intelligent interfaces with
computers, intelligent planning systems, program generators and intelligent tutorial systems.
Most systems were used as support for decision making.Typical OR application areas such as
production and transport scheduling and planning ranked only 5th in the list. As one might
expect, most systemswere assessed for use byaclient department; they helped to improve OR's
image. Very few were developed for use withinthe OR department itself.
62% of applications were based on the use of shells on PCs, reflecting the fact that OR
scientists are developing systems in order to help themselves learn. Prolog dominates as the
European AI language, although Lisp is quite popular.
The time taken to implement ES isfelt asa strong constraint in the practical development of
systems. Other strong constraints were the cost, the lack of practical use, technological
limitations, and lack of experts helping in the implementation of ES. It isevident that ES isstill
far from being a 'user-friendly' technology.
Although, the practical benefits appeared to belimited, the vast majorityof respondents had a
positive attitude towards ES in the future. About 40% of UK organizations with OR members
are involved to some extent indeveloping or usingES, and this figure includes21.5% where the
OR community in the UK isdirectly involved.
As we have noted above, wellover three quarters of respondents were not involved in AI at
all. The reasons are mostly the same as given by those who were involved but not doing more.
The broadest interpretation thatcan be givento these results isthat AIlES still has a longwayto
go before it will be widely applicable in OR, and that OR departments, societies etc. should
provide more reliable information on the potential of AIlES.
A TYPICAL OR DEPARTMENTS INVOLVEMENT WITH AI
The involvementinAI inmost OR departmentsstarted inthe mid 1980s.However, the formal
setting up of ES developing groups and delivery of real products started in the second half of
1980.The overall effort devoted to develop AI applications among other activities in OR groups
(interms ofman-powercommitment) isveryvariable,from5% to 70%. There are three types of
OR departments' in terms of commitment, tools used, applications developed etc.
The less experienced teams with low commitment to AI (5% to 10%) are developing AI
applications as the best approach to projects requested byclients. These are mainly developing
ES ofrelatively lowcomplexity;they have availableonlyshellsfor the development and they are
not interested in more complex applications.
The OR departments with more experience and involvement in AI (their man-power
commitment is usually 30% to 50%) are set up in organizations where ideas of AI have been
spread and supported by organizational policies through to demonstration of applications. The
OR departments are developing AI products/demonstrators and are providing technical
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