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ENHANCING D IS C R E TE EVENT MODELLING B Y IN T E R FA C IN G EXPERT SYSTEMS AND SIM U LA TIO N MODELS

B y

D aniel Goodman B .S c . (LS E )

Thesis subm itted in p a rtia l fu lfilm e n t of th e requirem ents fo r th e degree of Doctor of Philosophy at

th e London School of Economics and P olitical Science, U n iv e rs ity o f London.

UMI Number: U616004

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A BSTRA C T

T h is thesis in vestig ates th e rep resentation of operational decision m akers w ith in sim ulation m odelling.

A rtific ia l In tellig en ce concepts, such as e x p e rt systems focus on th e problem of re p re s e n tin g , in h ig h -le v e l code, complex re a l-w o rld decision m aking problem s.

The au th o r th e re fo re proposes th a t th e use of e x p e rt system technology may p rovid e an im proved means of rep resen tin g operational decision tasks and th a t as a consequence, a p rio ri possibilities may e x is t in th e co ntext of model experim entation based on a lte rn a tiv e operational policies.

The thesis fu rth e r in vestig ates th e n atu re of operational decision m aking and th e poten tial need to re p resen t w ith in a model, in ter-depend en cies between decision m akers.

ACKNOWLEDGEMENTS

Many thanks to D avid B aim er, my s u p e rv is o r, fo r his in valuable guidance in producing th is th e s is .

CONTENTS

CHAPTER ONE: IN TR O D U C TIO N Rgp

1 .1 The thesis proposition. 1

1 .2 The research s tra te g y . 2

1 .3 Research background. 4

1 .4 S tru c tu re of th e th esis. 6

CHAPTER TWO: RESEARCH CO N TEXT

2 .1 In tro d u c tio n . 8

2 .2 Sim ulation m odelling. 9

2 .2 .1 What is Sim ulation Modelling? 10

2 .2 .2 What is th e purpose of sim ulation modelling? 11 2 .2 .3 Sim ulation model development and exp erim en tation . 13 2 .2 .4 Lim itations of th e sim ulation m odelling approach. 15 2 .2 .5 The relationsh ip between sim ulation and 19

Decision S upport System s.

2 .3 A rtific ia l in tellig en c e. 21

2 .3 .1 What are E xp ert systems and how do th e y work? 23

Knowledge rep resentation : 24

In feren ce & control s trateg ies: 27

2 .4 A I and sim ulation m odelling - m utual s u p p o rt. 34 2 .4 .1 E x p e rt system s and sim ulation - Is th e re a difference? 34 2 .4 .2 Sim ulation and e x p e rt system s - Com plem entary techniques. 37 2 .5 Wider aspects of A I sup p ort of sim ulation m odelling. 40

2 .5 .1 Sim ulation program g e n erato rs. 41

2 .5 .2 Model v e rific a tio n and v a lid a tio n . 43

2 .5 .3 In te llig e n t fro n t-e n d s . 45

2 .5 .4 A I languages & tools in sim ulation. 47 U sing ES shells in developing sim ulation models: 50 LIS P based system and Object O rien ted Program m ing: 52

PROLOG based system s: 56

2 .5 .5 In te rfa c in g e x p e rt system s and sim ulation models. 59

2 .6 Conclusion. 63

CHAPTER TH R E E : REQUIREM ENTS OF A D EC ISIO N O R IEN TED S IM U LA TIO N EN VIR O N M EN T.

3 .1 In tro d u c tio n . 65

3 .2 Decision m aking. 67

3 .3 Decision m aking w ith in sim ulation. 69

3 .4 R epresenting decision m aking usin g e x p e rt system s. 71 3 .5 L in kin g sim ulation and e x p e rt system s - A suggested approach. 73 3 .5 .1 In te g ra tin g e x p e rt system and sim ulation m ethodologies. 77 3 .5 .2 Facilities th a t should be p ro vid ed b y th e e x p e rt system . 85 3 .5 .3 F acilities th a t should be p ro vid ed b y th e sim ulation component89

CHAPTER FOUR: ESSIM - AN ENVIRONMENT FOR SIMULATION

4 .1 In tro d u c tio n . 94

4 .2 Research stages. 96

4 .2 .1 Sim ulation of a Job-Shop. 96

4 .2 .2 The developm ent of ESSIM . 101

4 .3 O verall system design. 103

4 .4 Design of th e e x p e rt system component. 106

4 .4 .1 The know ledge-base. 106

4 .4 .2 M odelling "Cooperative Decision Making" 113

4 .4 .3 The know ledge-base p a rt-co m p ile r. 115

4 .4 .4 The in feren ce engine. 123

4 .5 Design of th e sim ulation component. 128

4 .6 Design of th e communications in te rfa c e . 134

4 . 6 . 1 . T h e C l-g e n e ra to r. 137

4 .7 The m an-machine in te rfa c e . 140

4 .7 .1 ESSIM lib ra ry of lo w -le v e l ro u tin es. 142

4 .7 .2 The graphics display module. 142

4 .7 .3 The m an-machine fro n t-e n d module. 144

4 .7 .4 D esigner. 144

4 .8 The code lin k e r. 148

CHAPTER FIVE: VALIDATION OF ESSIM USING A CONTAINER PORT MODEL

5 .1 In tro d u c tio n . 152

5 .2 Design of th e container p o rt. 157

5 .3 S tru c tu re of th e sim ulation model. 163

5 .4 S tru c tu re of th e e x p e rt system knowledge base. 172

5 .5 Design of th e man-machine in te rfa c e . 182

5 .6 Model v a lid atio n . 187

5 .7 Model experim entation. 189

5 .7 .1 Experim enting w ith ru le param eters. 190

5 .7 .2 Experim enting w ith variab le values w ith in ru le s . 192 5 .7 .3 Experim enting w ith ru le s tru c tu re s . 193

5 .8 The a lte rn a tiv e P ort models. 201

5 .8 .1 Experim enting w ith th e models. 204

5 .9 Concluding thoughts on th e ESSIM modules. 212

5 .9 .1 O bservations on ESSIM’s sim ulation module. 212 5 .9 .2 O bservations on ESSIM's e x p e rt system . 215 5 .9 .3 B enefits and lim itations of the user in te rfa c e . 219

CHAPTER S IX : CONCLUSION

6 .1 Review of th e thesis proposition. 227

6 .2 The research ra tio n a le . 227

6 .3 Review of th e research s tra te g y . 228

6 .4 Conclusions from the model experim entation. 231

6 .5 Summary of th e research achievem ents. 235

6 .5 .1 P rin cip al achievem ents. 235

6 .5 .2 S u b sid iary achievem ents. 237

6 .5 .2 .1 New approaches to e x p e rt system design. 237 6 . 5 . 2 . 2 Im provem ents to the th re e -p h a s e ro u tin e s. 238 6 . 5 . 2 . 3 A dditional softw are developm ents. 238 6 . 5 . 2 . 4 P erip h eral b enefits of th e ESSIM approach. 239

APPENDICES:

A Job-Shop production scheduling using ESSIM . 243

A . l In tro d u c tio n . 243

A .2 O verall system design. 244

A .3 The sim ulation model. 246

A .4 The u ser in te rfa c e . 247

A .5 The e x p e rt system and in te rfa c e s to th e sim ulation model. 249

B The p o rt model know ledge-base u n d e r ESSIM . 252

C A ddition of a ru le -s e t to th e know ledge-base. 266 D Coding th e p o rt model know ledge-base in Pascal. 272 E D esigner - An in te ra c tiv e approach to m an/m achine in te rfa c e 290

Developm ent.

E .l In tro d u c tio n . 290

E .2 Using D esign er. 290

E .3 M odifying D esigner file s . 295

F O b ject-o riented sim ulation. 298

F . 1 In tro d u c tio n . 298

F .2 What is object o riented program m ing. 299

F .3 Examples of languages based on th e object o rien ted a p p ro a c h .300 F .4 A p p lyin g th e object o rien ted approach to model developm ent. 325

G ESSIM o u tp u t displays fo r th e P o rt model. 330

H R eferences. 334

CHAPTER ONE IN T R O D U C T IO N

1 -1 TH E TH ESIS PRO PO SITIO N

T h is thesis w ill in vestig ate possible approaches in using A rtific ia l In te llig e n c e techniques in im proving th e rep resen tatio n o f operational decision m akers w ith in sim ulation models.

The thesis proposition is th a t e x p e rt systems techniques may provid e an im proved means of re p resen tin g w ith in th e model, operational policies which in th e re a l-w o rld d ictate the course of e v e n ts . Such operational policies may re q u ire th e involvem ent of m ultiple decision m akers and may p o te n tia lly in vo lve th e rep resentation of some form of h ierarch ical management s tru c tu re .

The b elief th a t e x p e rt system technology may have a role to p lay w ith in conventional sim ulation m odelling is a consequence of th e fa c t th a t much of A rtific ia l In tellig en ce research is focused on p ro vid in g tools fo r th e resolution of complex re a l-w o rld decision m aking ta s k s .

term "Model A d ap tab ility" w ill be used to describe th is b e n e fit). A second d e riv a tiv e b en efit is th a t adding model d e ta il in th e co n text of operational decision m aking, may ultim ately re s u lt in a model which is a b e tte r rep resentation of th e re a l-w o rld problem . The te rm , "Model Accuracy" w ill be used in th is thesis to describe th is b e n e fit.

O bservations sim ilar to the above have a lre ad y been made b y a num ber of authors including Fishman [ 1973 ] . T h ey assert th a t conventional sim ulation languages are not well suited to th e rep resen tatio n of decision ta s k s . S everal authors have id e n tifie d th e po ten tial of A rtific ia l In te llig e n c e ( A I ) approaches to overcoming these d iffic u ltie s . T h e p o ss ib ility of in te g ra tin g a model of operational decision-m aking in th e form of an e x p e rt system and a conventional sim ulation model has been envisaged b y O 'K eefe and Roach[1 9 8 7 ]. Flitm an and H u rrio n [1987] then provid e th e fir s t p ra c tic al in s ig h t in to th e p o ten tial of lin k in g an A rtific ia l In tellig en c e tool w ith a sim ulation model b y b u ild in g a system based on two inter-com m unicating m icro-com puters. The research presented in th is thesis follows on and bu ild s upon Flitm an's [1986] pioneering work by concentrating on two k e y problem s: (1 ) The rep resen tatio n of operational policies which are re fle c te d b y th e re a l-w o rld operational s ta ff and th e ir cooperative actions. (2 ) The need to create a "practical" m odelling environm ent in which th e lin k between sim ulation model and e x p e rt system is almost seamless.

1 .2 THE RESEARCH STR A TEG Y

co vering both sim ulation modelling and a rtific ia l in te llig e n c e . The emphasis of th e lite ra tu re stu d y is in id e n tify in g ap p ro p riate s ta te -o f-th e -a rt technology w hich could be applied in creatin g a sim ulation environm ent in co rp o ratin g a rtific ia l in tellig en ce techniques. O f p a rtic u la r in te re s t are th e various A rtific ia l In te llig e n c e approaches to th e representation of "Knowledge" and th e in feren ce of conclusions from th is know ledge. These can b roadly be divided in to A I languages ( e . g . Lisp & P rolog) and A I Tools ( e . g . E x p e rt Systems and Object O rien ted environ m ents). A nother im portant aspect of th e lite ra tu re s tu d y , is to le a rn w hat o th er research ers have achieved o r proposed in th e co ntext of com bining a rtific ia l in tellig en ce and sim ulation modelling techniques. F in a lly , much of th is thesis is concerned w ith th e representation of decision making a c tiv itie s and th e in te r-re la tio n s h ip between decision m akers d u rin g th e process of enacting operational policies. C onsequently, background research was necessary in to th e n a tu re of decision m aking and th e im plications of h ierarch ical management s tru c tu re s .

The approach adopted in th is th e s is , was to b u ild upon e a rlie r w ork u n d ertaken b y Flitm an [1986] and to in vestig ate th ro u g h th e developm ent of a num ber of p roto type system s, th e im plications of in te g ra tin g an e x p e rt system model of operational decision m aking and a conventional sim ulation model.

id e n tifie d in th e fir s t version and fo rm s th e basis of a generic sim ulation m odelling environm ent w ith in which th e p ra c tic a l m odelling experience could be ob tained.

A research s tra te g y based on th e development of prototypes is only e ffe c tiv e i f one is able to define a means of com paring th e value of th e new sim ulation technique w ith a more conventional m odelling approach. Th e research th e re fo re includes th e development of th re e sim ulation models of a contain er p o rt. One using th e proposed approach,, ano th er using conventional sim ulation techniq ues, and a th ird using Pascal functions to re p lic a te some of th e ch aracteristics of an e x p e rt system . A num ber of experim ents a re th en devised which seek to assess th e fu n c tio n a lity of th e th re e models against th e id e n tifie d po tential benefits o f th e proposed m odelling environm ent.

1 .3 RESEARCH BACKGROUND

The m otivation fo r th e proposed research o rig in a lly arose from th e au th o r's involvem ent in th e Com puter A ided Sim ulation M odelling ( C . A . S . M ) group a t the London School of Economics which brou g h t to g e th e r a num ber of research studies in sim ulation techniq ues.

th e consequences of d iffe re n t jobbing and batch production s tru c tu re s in a ty p ic a l job-shop environm ent. I . N . T . were to p ro vid e th e necessary e x p ertise in production en gineering w h ilst in th e fin a l stage of th e re se a rch , two m anufacturing concerns, NATEC L T D A and DANCOR S . A . , w ere to p ro vid e th e p ra c tic al co n text. The com puter systems used a t th e proposed sites w ere to be in te g ra te d w ith th e models in p ro v id in g a decision sup p ort system which was to aid management to schedule and contro l prod u ctio n.

The rationale in p ro vid in g such a decision sup p ort tool was based on th e fa c t th a t in a ty p ic a l batch m anufacturing environm ent, more th an 90% of tim e is spent id le in queues aw aiting processing. C onsequently, i t was fe lt th a t th e re was considerable room fo r im provem ents in p ro d u c tiv ity b y ratio n alisin g th e m aterial flow s.

1 .4 STR U C TU R E OF THE TH E S IS

C h ap ter two consists of a review of th e two areas of research ap p ro p riate to th is th e s is , sim ulation m odelling and A rtific ia l In te llig e n c e . T h e ch ap ter begins w ith an analysis of sim ulation m odelling covering its purpose, lim itations and a p p lic a b ility w ith in decision sup p o rt system s. A sim ilar approach is taken in in ve s tig atin g th e area of A rtific ia l In tellig en c e though p a rtic u la r emphasis is placed on know ledge rep res e n ta tio n , a topic p a rtic u la rly p e rtin e n t to th is th e s is . A stu d y of recen t papers follow s, o u tlin in g w hat are c u rre n tly considered to be "advanced" systems in th e area of co -o p erative systems in vo lvin g both sim ulation and A I.

C h ap ter th re e focuses on th e ch aracteristics o f decision m aking and seeks to id e n tify th e possible ways of rep resen tin g these w ith in sim ulation m odelling and A rtific ia l In te llig e n c e . The possible ways of com bining sim ulation and A rtific ia l In te llig e n c e knowledge representation s a re id e n tifie d and th e b en efits and lim itations c ritic a lly com pared. A choice is u ltim ately made as to th e approach to be selected fo r th e purpose of bu ild in g a p ro to typ e system .

C h apter fiv e outlines th e developm ent of th e un-m anned contain er p o rt model alread y re fe rre d to in section 1 .3 . T h e im plem entation of th is model form s th e basis of th e valid ation o f th e proposed methodology based on th e lin k between sim ulation system and e x p e rt system . T h e s tru c tu re of th e sim ulation system component o f th e p o rt model is exp lain ed followed b y th e e x p e rt system know ledge-base com ponent. The process of experim enting w ith th e p o rt model is assessed w ith resp ect to th e in tro d u c tio n of m odifications to know ledge base and sim ulation system code. P a rtic u la r emphasis is placed d u rin g th is assessment process on th e im pact o f th e e x p e rt system approach on m odelling aspects in clu ding model "Accuracy" and "A d a p ta b ility ". The value of th e new modelling process as encompassed in ESSIM is then compared w ith more conventional approaches th ro u g h th e im plem entation of th e same p o rt model code using e xistin g m odelling tools. The b en efits and lim itations of th e ESSIM approach to m odelling are th en sum m arised.

CHAPTER TWO RESEARCH C O N TEXT

2 .1 IN T R O D U C T IO N

The research presented in th is thesis covers two d is tin c t areas of know ledge, sim ulation and a rtific ia l in tellig en c e. The background lite ra tu re stu d y presented and discussed in th is ch ap ter th e re fo re commences w ith a review of th e n a tu re , goals and lim itations of each of these technologies.

The lite ra tu re stu d y revealed th a t th e re existed some degree of overlap between sim ulation m odelling and e x p e rt systems approaches. A num ber of published papers were also found which argu ed th is case. C h ap ter two th e re fo re continues to in vestig ate th e sim ilarities between sim ulation modelling and e x p e rt systems and explores th e w ork of o th e r research ers who have attem pted to c a rry out sim ulation modelling using a rtific ia l in tellig en ce languages and tools.

techniques and methods used in b rin g in g to g e th e r sim ulation and A I. The chapter th e re fo re concludes w ith a re v ie w of research studies which have in vestig ated th e in te rfa c in g of sim ulation models and A I languages and tools.

2 .2 SIM U LA TIO N MODELLING

th e g e n e ra to r. Also u n d er th e um brella of th e CASM p ro je c t, was w ork un d ertaken b y D oukidis (See Paul and D o u k id is [19 8 6 ]) on autom ating th e process of model form ulation using a N atu ra l Language U n derstan ding System

(N L U S ). The Pascal sim ulation ro utin es used in th e CASM projects a re w ell documented and tested and are consequently of p o ten tial b e n e fit to th e research in th is th esis. These ro utin es form p a rt of th e Extended Lancaster Sim ulation Environm ent (eLS E ) and are w ell described b y Chew [1986].

The term s 'sim ulation' and 'm odelling' have a w idespread and v a ried usage. C onsequently, th e ir meaning in th e context of th e thesis re q u ire s some c la rific a tio n .

2 .2 .1 What is Sim ulation Modelling?

Each form o f modelling has its s tren g th s and weaknesses. Mathem atical o r an alytic models a re pow erful w ith respect to th e le v e l of g e n e ra lity of th e ir associated solution techniques. H ow ever, such an advantage leads to th e converse disadvantage of making it d iffic u lt to make model behavio ur match th a t of th e re a l w o rld . In th e case of sim ulation models, w here th e analogy between th e model rep resen tatio n and th e re a l w orld are th a t much g re a te r, th e re is a s in g u lar lack of g e n e ra lity , power and elegance as compared to th e compact mathematical solution tech n iq u e. On th e o th e r h and , considerable b en e fit is to be gained by g re a te r fa ith fu ln ess to d e ta il in th a t in vestig atio n b y experim entation is made possible b y allowing analogical relationsh ips w ith th e re a l world to be m aintained.

2 .2 .2 What is th e purpose of sim ulation modelling?

A sim ulation model is sim ply a statem ent of th e way in which th e variou s components of a re a l w orld system in te ra c t to produce a behavioural p a tte rn .

The im plem entation of th e model on a com puter perm its tim e scales to be reduced to a manageable le v e l and hence perm its th e program code to be used as a basis fo r experim entation. P idd[1992] and M cA rth u r e t al[1986] id e n tify a num ber of reasons fo r ju s tify in g th e cost in tim e and e ffo rt of developing a model:

D an ger: D ire c t experim entation on a re a l w orld process can be dangerous. E xperim enting, fo r exam ple, w ith th e op erating characteristics of a nuclear pow er station o r a irc ra ft may be unw ise.

Tim e: S pecifying th e logic of a model and im plem enting i t as program code can take an in o rd in ate amount of tim e. On th e o th e r h an d , once im plem ented, th e model can be used to ru n innum erable experim ents on a time scale d ra s tic ally reduced from th a t of re a l tim e, ( e . g . , economic systems could not possibly be experim ented on d ire c tly because o f th e time f a c t o r . )

In e v ita b ility : Some re a l w orld system s, such as th e solar system cannot be m anipulated d ire c tly .

Cost: Sim ulation models are ty p ic a lly expensive to develop given th a t skilled analysts and program m ers are re q u ire d o ver a s ig n ific a n t period of tim e. N evertheless a rash decision implemented as an op erating policy on the re al w orld system can tu rn out to be more c o s tly .

a f f e c t i n g s y s te m p e r f o r m a n c e . O p t im is in g p r o c e d u r e s c a n b e u s e d t o f i n e - t u n e

s y s te m p e r f o r m a n c e . A n i n v e s t ig a t io n c a n b e m a d e i n t o e s t a b lis h in g t h e

f u n c t io n a l r e la t io n s h ip s t h a t e x i s t b e tw e e n o n e o r m o re p a r a m e te r s i n t h e

s y s te m . F i n a l l y , a m o d e l e n a b le s t r a n s i e n t b e h a v io u r s u c h a s q u e u e b u i l d u p s ,

b o t t le n e c k s , a n d u t i l i s a t i o n le v e ls t o b e i d e n t i f i e d .

2 . 2 . 3 S im u la tio n m o d e l d e v e lo p m e n t a n d e x p e r im e n t a t io n .

1. Definition of the problem based on an analysis of the actual or proposed real-world system.

\

2. Assessment of the feasibility of the simulation, drawing on relevant experience in solution techniques.

I

3. Identification of objectives and critical system components.

\

4. Formulation of a conceptual model followed by its representation as a communicative model.

i

5. Creation of a programmed model.

1

6. Design of experiments leading to the validation of the model and the

presentation of model results. Return to previous stages in developing modified versions of the model.

I

7. Transfer of the model conclusions to the real world application.

I n b r o a d t e r m s , t h e d e v e lo p m e n t o f a s im u la t io n m o d e l in v o lv e s t h e

im p le m e n ta tio n o f t h e m o d e l o f t h e r e a l w o r ld s y s te m u s in g e i t h e r a g e n e r a l

p u r p o s e h ig h le v e l la n g u a g e o r s im u la t io n s p e c if ic p r o g r a m m in g la n g u a g e ,

fo llo w e d b y a n in v e s t ig a t io n o f t h e m o d e l t h r o u g h e x p e r im e n t a t io n .

PROBLEM

DEFINITION

PHASES

DECISION SUPPORT

PHASES

DECISION MAKERS

i 1

INTEGRATED DECISION SUPPORT

COMMUNICATED PROBLEM

PROBLEM FORMULATION

FORMULATED PROBLEM

INVESTIGATION OF SOLUTION TECHNIQUES, r

PROPOSED SOLUTION TECHNIQUE

(REQUIRING MODELLING)

SYSTEM INVESTIGATION

PRESENTATION OF MODEL RESULTS

MODEL RESULTS

3

SYSTEM AND OBJECTIVE DEFINITION

REDEFINITION

CONCEPTUAL MODEL

MODEL 1

REPRESENTATION

COMMUNICATIVE MODEL(S)

M O D E L

DEVELOPM ENT

PHASES

FIGURE 2

MODEL LIFE CYCLE

EXPERIMENTATION PROGRAMMING

z

A proposed sim ulation model life cycle is defined by Nance [1981] & B alci[1986] and is illu s tra te d in fig u re 2 . The basic stages are lis te d in fig u re 1.

As w ith conventional program development life cycles, th e process must be trea te d as ite ra tiv e , p a rtic u la rly in the model validation stage, where th rou g h display of o u tp u t, e rro rs or omissions in th e logic of the model ty p ic a lly become a p p a re n t. F u rth erm o re, experim entation re q u ire s a re -a n alys is of the logic of the model and im plem entation of such changes thro u g h m odification of th e program code.

2 .2 .4 Lim itations of th e sim ulation m odelling approach.

la s t re s o rt - to be used if a ll else fa ils '. In d e e d , many see the p rim ary co n trib u tion of sim ulation to decision supp ort as being lim ited to areas of high ris k stra te g ic decision making in which physical dang er o r capital investm ents are major fa c to rs .

Sim ulation m odellers face a num ber of o th e r lim itations th a t cannot easily be overcome and these are ty p ic a lly acknowledged as shortcom ings which are offset b y the benefits th a t th e model occasions (See Koskossidis & D avies[1987] and Fishman[1 9 7 3 ]). Some such lim itations can be classified as follow s: (T h e fir s t two have also been discussed in chapter one)

situatio n can be ju s t as much of a problem . The gains achieved by an o v e rly detailed model may be to ta lly outweighed b y th e developm ent overheads in c u rre d and the d iffic u ltie s th a t ensue in m odifying th e model lo g ic. C onsequently, a care fu l balance is re q u ire d between th e le v e l of d etail and th e investm ent necessary in achieving th e degree of represen tatio n al accuracy.

M ain tain ab ility: Sim ulation modelling is p a rtia lly a cyclical process re q u irin g th e m odeller to switch between experim enting w ith th e model and m odifying th e code in testin g a lte rn a tiv e s . The need to repeated ly a lte r th e model imposes in to lerab le burdens on th e s tru c tu re and m ain tain ab ility of the code.

Ease of use: As pointed out e a rlie r, experim entation necessitates m odification which means th a t th e analyst and program m er have to be invo lved throug hout th e duration of the model life -c y c le .

Speed: Even a t the best of tim es, long sim ulation ru n s are time consuming. C onsequently, repeat ru n s necessary in in ve s tig atin g a range of a lte rn a tiv e param eter settings can be a problem . In some cases, sim ulations ru n slower than real time p o ten tially erad icating any gain in developing and using the model. (See M cA rth u r et a l.[1 9 8 6 ])

In te rp re ta tio n : Sim ulation models ty p ic a lly produce a mass of d ata. I f th e m odelling exercise is to be of any v a lu e , the data has to be c o rre c tly in te rp re te d which is an e rro r prone and time consuming process. M c A rth u r e t a l.[1 9 8 6 ] give m ilitary sim ulations as an exam ple, em phasising th e d iffic u ltie s in isolating th e c ritic a l behavioural p ro p ertie s from ’hundreds of pages of num erical o u tp u t'.

2 .2 .5 Th e relatio n sh ip between sim ulation and Decision S up port System s.

Decision Support Systems (D SS) are fle x ib le com puter based systems th a t help th e decision m aker u tilis e available resources in reaching a specific decision in an u n stru ctu red environm ent such as management and operational control o r s tra te g ic plan n in g . As stressed by G ray and B o ro v its [1 9 8 6 ], th e role o f a DSS should not be m isunderstood. The in ten tio n is to p ro vid e supp ort ra th e r than generate specific solutions which th e u ser accepts as a fin a l decision.

when experim en ting w ith th e model, needs to be guided ra th e r than le ft to th e slow process of 'tr ia l and e r ro r'. T h is problem is fu rth e r compounded by th e fa c t th a t th e analysis of th e ou tp ut of a stochastic sim ulation req u ires a deal of s ta tis tic al e x p e rtis e and cannot sensibly be le ft to a busy manager faced w ith an u rg e n t decision. Even if such s ta tis tic a l analysis could be reduced to a simple ro u tin e and th e whole embedded w ith in an optim ising algorithm , th e execution of th e m ultiple replications of each of th e a lte rn a tiv e decision scenarios re q u ire d by such a process may pose in to lerab le com putational burdens fo r an o n -lin e decision sup p ort system .

T akin g an a lte rn a tiv e view p o in t, sim ulation m odelling can be seen to make sign ifican t co ntrib u tio n s to decision supp ort system s. For exam ple, The actual process of developing a sim ulation model may occasion w ith in the user an enhanced appreciation of the operation of the system m odelled. This m ay, in its e lf prove usefu l in supporting decision making o r may co n trib u te in d ire c tly to the process of creatin g a DSS. A nother p o ssib ility is th a t th e model produced could be used in a form al series of experim ents which p o ten tially culm inate in a ru le o r set of ru le s th a t are then used as p a rt of th e DSS. For in stan ce, a regression model could be fitte d to the sim ulation ou tp u t which then adequately summarises th e e ffec ts of changes in c e rta in in p u t param eters. The changes to the in p u t param eters could rep resen t a lte rn a tiv e decisions on th e operation and management of th e re al world system and consequently, th e regression equation could be in corp orated in th e DSS which th en re q u ire s no fu rth e r reference back to the sim ulation model. (See Nathan and Sokol[1 9 8 6 ])

developed b y Basset and K o ch h a r[1 98 5 ], provides data analysis and re p o rt generation routines b u t does not p ro vid e th e user w ith any degree of fle x ib ility and the problems of using sim ulation fo r decision support hig h lig h ted in th e previous paragraphs remain un resolved . O th er w rite rs such as Moser[1986] take a sim ilar approach bu t re ly on a ru le-b ased e x p e rt system fo r th e in te rp re ta tio n of th e sim ulation o u tp u t. T h is e x p e rt system is developed in p arallel w ith th e sim ulation and embodies th e knowledge of both sim ulation analyst and domain e x p e rts used in th e in te rp re ta tio n of o u tp u t. A no th er system fo rm erly known as KBS and now named Sim ulation C ra ft takes a fa r more am bitious approach (M cRoberts e t a l.[1 9 8 6 ], R eddy e t a l.[1 9 8 6 ]/ R ed d y [1 98 7 ], and S athi et a l.[1 9 8 6 ]). I t is proposed th a t Sim ulation C ra ft be capable of id e n tify in g a p p ro p riate sets of scenarios, autom atically gen eratin g a num ber of experim ents such th a t th e stated 'goal1 be attained and producing a re p o rt exp lain in g th e scenario selected . Such a system o ffe rs a fu n c tio n a lity w h ich , if fu lly re a lis e d , reserves fo r sim ulation a place w ith in th e realm of o n -lin e DSS.

The n e x t section w ill provid e an overview to th e area of A I and in p a rtic u la r, e x p e rt system s, p rio r to in ve s tig atin g th e research th a t has been un dertaken in combining ch aracteristics of sim ulation and A I.

2 .3 A R T IF IC IA L IN T E L LIG E N C E .

1956, a conference at Dartm outh college on symbolic com putation paved th e way fo r th e development of p ractical applications. H ow ever, it was not u n til th e

1970's th a t th e concept of A I was to fin d acceptance outside research environm ents. U n fo rtu n a te ly , in te re s t dw indled because th e A I applications were too slow coupled w ith high development costs and small p ractical re tu rn s

(Harmon and K in g [1 9 8 5 ]). I t was not u n til th e 1980's th a t A I was fin a lly to gain acceptance, and not so much because of any s ig n ific a n t th e o retical advances, b u t because developments in chip technology led to th e in tro d u ctio n of a new generation of su b stan tially more pow erful com puters a t re la tiv e ly low er costs.

A I is concerned w ith how humans 'a c q u ire , o rg a n ize , and use know ledge' (Shannon et a l.[1 9 8 5 ]). The constitu en t areas of A I are not c le a rly defined b u t broadly fa ll into th re e classes. N atu ral language processing, rob o tics, and knowledge based system s.

N atu ral Language Processing (N LP ) is p rim a rily concerned w ith the development of computer applications th a t can read documents, speak, and recognise spoken words (speech re c o g n itio n ). The in te re s t in NLP is spearheaded by a need to provid e a more pow erful means of communication between man and com puter, coupled w ith th e commercial a v a ila b ility in recent years o f, te x t scanners, speech synthesisers and speech recognition equipm ent. (See W inograd[1972] fo r a more detailed coverage of N L P ).

wide usage of such technologies in in d u s try , p rim a rily in th e con text of Autom ated Guided Vehicles (A G V s ), image recognition (e .g fin g e rp rin t id e n tific a tio n ), and machine guidance (e .g . w elding and c u ttin g in th e car m anufacturing in d u s try ). The scope fo r th e use of robotics is su b stantial as businesses cu t overheads in s triv in g to remain com petitive. (See P r a tt[1978] and Brooks e t a l.[1 9 7 9 ] fo r a more detailed discussion of th e to p ic ) .

Knowledge based systems include E xp ert Systems and N eural N etw orks, a new area of research m ainly dedicated to machine le a rn in g . E x p e rt Systems (E S s) are concerned w ith the automation of mental tasks th a t are norm ally un d ertaken by an e x p e rt in a specific application a re a . E x p e rt systems d iffe r s ig n ific a n tly from o th er A I applications, namely NLP and ro b o tics, in th a t the u n d erlyin g goal is not th a t of gaining an in s ig h t in to how human e x p e rts reach a given conclusion, bu t ra th e r, th a t of devising methods by which such conclusions may e ffe c tiv e ly be duplicated

(Shannon e t a l. [1 9 8 5 ]). The research presented in th is thesis is p rim a rily concerned w ith th e contributions th a t e x p e rt systems can make to sim ulation, and consequently, th e follow ing sections w ill focus exclu sively on ES th eo ries.

2 .3 .1 What a re E x p e rt systems and how do th e y work?

both in term s of th e development process and a rc h ite c tu re of th e implemented end p ro d u c t. The procedural approach used in conventional high level languages is abandoned in fa v o u r of an a rch ite ctu re which is ty p ic a lly based on the use of th re e d istin ct modules th a t rep resen t th e knowledge of th e system . The th re e components are:

A database (o r e q u ivalen t) fo r th e storage of data corresponding to th e 'd e clarative knowledge' to be used by th e ES, and ru n -tim e data rep resen tin g th e c u rre n t status of th e system . ( declarative know ledge is d a ta , specified before the s ta rt of the in feren ce p ro cess).

A know ledge-base which encapsulates the facts and ru les th a t embody th e e x p e rt's domain know ledge.

An in feren ce engine th a t consists of deductive strateg ies th a t defin e th e problem solving approach to be used. The in feren ce engine analyses available facts and ru les and attem pts to draw conclusions which get added to the database or are used to m odify c u rre n t database e n trie s . The in ference engine is fu rth e r responsible fo r in stig a tin g o rd e r in th e p a tte rn of in q u iry .

form s of represen tatio n in clu d in g program code, ru le s , conditional p ro b ab ilitie s , and firs t-o rd e r logic which are used almost ex clu s iv e ly in rep resen tin g domain specific know ledge in th e know ledge-base.

Semantic netw orks are one of th e oldest and most general representation schemes fo r declarative know ledge (Harm on and K in g [1 9 8 5 ]). O bjects to be represented are symbolised b y nodes and relationships denoted by arcs th a t lin k th e objects to g e th e r. The advantage of such a rep resen tatio n is th e clear image th a t can be obtained as to relationsh ips between objects th ro u g h grap h ical rep resen tatio n compared to lin es of code in a classical program . Semantic netw orks are fle x ib le inasmuch as new nodes and arcs can be added as needed and have th e b e n e fit of perm ittin g objects to in h e rit the a ttrib u te values of o th er objects thro u g h the creation of addition al a rcs .

Frames are a form of rep resen tatio n fo r objects which contains slots fo r the storage of facts about th e o b ject. The slots may contain values or p o in te rs. A p o in ter may point to anoth er fram e o r a lte rn a tiv e ly to a procedure o r set of ru le s th a t re tu rn a v a lu e . C onsequently, fram es are capable of both procedural and d e c larative rep resen tatio n al form s. As w ith semantic n etw o rks, fram es can in h e rit th e a ttrib u te values of o th er objects. (See A lty and Coom bs[1984])

diagnosis e x p e rt system (see Buchanan and S h o rtliffe [1 9 8 4 ]) .

Predicate calculus is a simple language fo r th e d efin itio n of objects and facts (p red ic a te s) re la tin g to these objects. The form at of statem ents in predicate calculus consist of a fa c t followed b y one o r more object names between parentheses. For exam ple, "Is-A ssem bly Machine (M ach_A)" is equivalen t to the statem ent th a t "machine A is a machine fo r th e assembly process". Such an assertion can e ith e r be TRUE o r FALSE. P redicate calculus has th e advantage of being fa ir ly English lik e and y e t has a simple and lim ited s y n ta x . (See A lty and C oom bs[1984])

Program code is often used in conjunction w ith o th er knowledge representation s tru c tu re s in defin ing th e domain specific know ledge. A procedure may be called when a given set o f conditions are s a tis fie d . A number of e x p e rt system shells provide fa c ilitie s fo r in te rfa c in g to conventional high le v e l languages though data sh aring is often impossible or awkw ard to use.

M Y C IN ).

F irs t-o rd e r lo g ic, o r more s p e c ific a lly , H orn clauses can be used in defin ing knowledge in e ith e r a d eclarative o r procedural sense (See Futo[.1985], B u llers & S c h u ltz [ 1986], C leary e t a l.[1 9 8 5 ] and A d e ls b e rg e r[1 9 8 4 ]) . For exam ple, B A I , . . ,A n . can be in te rp re te d as a logical statem ent th a t says th a t B is tru e if A I to An are tru e . A lte rn a tiv e ly , in th e procedural sense, th e statem ent can be in te rp re te d as being th a t th e problem of evalu atin g B is reduced to th e sub-problem of evaluating A I to A n . A more detailed evaluation of firs t-o rd e r predicate logic is reserved fo r a la te r section in a discussion of the fa c ilitie s provid ed by Prolog.

2 .In fe re n c e & co ntrol s tra teg ies: The in feren ce engine is th e p a rt of th e e x p e rt system th a t embodies the strate g ies th a t are used to draw inferences from th e facts and ru les declared in th e database and know ledge-base, and th a t controls th e reasoning process. T he in feren ce engine also acts as an in te rfa c e between the e n d -u se r and the stored know ledge, e ffe c tiv e ly conducting a consultation w h ilst draw ing on th e know ledge to provid e solutions.

usin g ano th er ru le . As mentioned in th e section on ru le s , pro b ab ilities can also be associated w ith statem ents th a t re fle c t the u n c e rta in ty of th e v a lid ity of given in form atio n.

A t th e control le v e l, th e in feren ce engine must organise the steps taken in solving a problem . The in ference engine is also responsible fo r th e follow ing tas ks :

1. Selecting a position from which the reasoning process can begin. 2 . Resolving co nflicts in logic between ru le s .

3. Choosing, a ru le from a set of ru les th a t can a ll be evaluated. 4 . In te rru p tin g th e in ference process in o rd e r to obtain missing

inform ation from th e o p erato r.

The two most common control strateg ies are fo rw ard chaining and backward ch ain in g , the use of which w ill depend on th e problem domain. F u rth erm o re, fo rw ard and backw ard chaining can e ith e r be c a rrie d out using a d e p th -firs t or b re a d th -firs t searching s tra te g y .

desired state is reached o r u n til no rem aining prem ises can be s a tis fie d . The d iffic u lty w ith th e fo rw ard chaining s tra te g y is th a t a t each step in th e cycle, a choice has to be made between a num ber of ru les th a t have premises th a t are s a tis fie d . As th e num ber of such ru le s increases, a noticeable deterio ration in perform ance is fe lt consequent to th e increased com plexity of th e selection process.

A backw ard chaining o r g o al-d irected s tra te g y is used in circum stances where the desired end goal is know n. The goal is evaluated b y searching fo r a ru le (o r ru le s ) th a t has an action th a t satisfies the prem ise of th e goal. Th is ru le is then defined as a sub-goal and th e process repeated u n til th e premise of th e o rig in a l goal is satisfied or u n til no more sub-goals can be id e n tifie d . I f the search s tra te g y is 'irrevo cab le' and th e goal is u n reso lved, th e in ference engine can proceed no fu rth e r (see Shannon e t a l.[1 9 8 5 ]). A lte rn a tiv e paths through the solution space can only be attem pted by re-comm encing the inference process. I f th e search s tra te g y is 'te n ta tiv e ', th e in ference engine can backtrack to an e a rlie r su b -g o al, select a new ru le , and again endeavour to fin d a solution.

In D e p th -firs t searchin g, p rio rity is given to producing sub-g oals. Hence, a lte rn a tiv e paths th ro u g h the solution space are only considered once a p a rtic u la r path reaches a d ead -en d . I f th e e x p e rt system in terro g a te s the operator fo r in p u t, th e feelin g given is one of a search which resu lts in questions of e v e r-g re a te r d e ta il.

t h a t c o u ld le a d t o t h e s o lu t io n a r e in v e s t ig a t e d s im u lt a n e o u s ly . T h e e f f i c i e n c y

o f b r e a d t h - f i r s t s e a r c h in g is d e p e n d e n t o n h o w q u i c k l y a r u l e p re m is e c a n b e

f o u n d t h a t s a t is f ie s t h e g o a l. B r e a d t h - f i r s t s e a r c h in g t e n d s t o b e u n p o p u la r i n

s y s te m s t h a t r e q u i r e s u b s t a n t ia l u s e r in t e r a c t io n c o n s e q u e n t t o t h e o p e r a t o r

f e e lin g u n e a s y a b o u t h a v in g t o a n s w e r q u e s tio n s t h a t seem t o b e o r d e r e d a t

r a n d o m .

BREADTH-F/RST SEARCH

DEPTH-F/RST

SEARCH

BACKWARD CHAINING

GOALS O ---►

GOALS

BREADTH-F/RST SEARCH

DEP7H-F/RST

SEARCH

FORWARD CHAINING

2 .3 .2 What is th e purpose of an e x p e rt system?

The p rin cip le u n d erlin in g th e e x p e rt system approach, is to enable the rep resentation of th e knowledge of one o r more e x p e rts w ith in a specific domain. For exam ple, in Fox and S m ith[1984] th e p e rtin e n t e x p e rt knowledge is concerned w ith th e scheduling of jobs w ith in a machine shop. T his know ledge-base is searched to p rovid e answers to questions such as which jobs should be given p rio rity if a goal of ensuring th a t contractu al agreem ents on d e liv e ry dates has to be m et.

In many cases, the knowledge represen ted in th e developed system relates to some complicated decision-m aking process b u t cannot be described as an e x p e rt's know ledge. C onsequently, e x p e rt system s can be used in a method akin to conventional program m ing in situ atio ns w here th e in feren ce s tra te g y and increm ental development process of a know ledge-base are deemed advantageous.

The most common use of e x p e rt systems is as ad viso ry systems in which some form of in te ra c tiv e consultation takes place. Two of th e best known exam ples are M YC IN (S h o rtliffe [1 9 7 6 ] and Buchanan & S h o rtliffe [1 9 8 4 ]) fo r m edical diagnosis and PROSPECTOR (D uda e t a l.[1 9 7 9 ]) fo r th e analysis of geological data. A nother common use fo r e x p e rt systems is fo r tra in in g and educational purposes, e ith e r through m odification of an e x is tin g ES (Buchanan & S h o rtliffe [1 9 8 4 ]) , o r by using an approach th a t 'custom ises1 th e teaching session according to past attainm ent. Research is also being c a rrie d out in to th e use of e x p e rt systems as in te g ra l modules in softw are fo r o n -lin e decision m aking fo r m anufacturing process control (B row n et a l.[1 9 8 5 ]) and as in te llig e n t fro n t-e n d s ( M uetzelfeldt et a l.[1 9 8 5 ]).

2 .3 .3 Lim itations of e x p e rt system s.

tra n s fe rrin g knowledge represented in th is form at to o th e r applications. Production ru les are an ideal rep resen tatio n fo r th e ru les of thum b used by th e e x p e rt, b u t otherw ise necessitate a considerable amount of domain knowledge to be discarded a t th e expense of th e addition of extraneous com putational know ledge. .

The use of production ru les leads to a tendency to expand th e know ledge-base increm entally as ru les are elicited from th e e x p e rt. T h is can lead to an scatterin g of ru les which in e v ita b ly resu lts in a system which is e ith e r incom plete, ambiguous or inco nsistent. Poor perform ance of th e ES resu lts from d iffic u ltie s in m aintaining o rd e r in th e know ledge-base which would otherw ise b en efit the re la tiv e ly sim plistic search and p a tte rn m atching

procedures of the in ference engine ( M u lle r[1 9 8 6 ]).

Maintenance of a know ledge-base is fra u g h t w ith d iffic u ltie s re s u ltin g from an in a b ility to m anually trace throug h the logic of th e system consequent to the scattering of the production ru les and th e lack of an e x p lic it d e fin itio n of the e x p e rt systems inference s tra te g y . Most e x p e rt systems consequently incorporate an 'explanatio n’ fa c ility th a t lis ts th e ru les th a t a re activated at each step in the in ference process.

Most e x p e rt systems are poor a t in co rp o ratin g algorithm ic approaches to supplement th e rule-b ased reasoning and many have no fa c ilitie s fo r execu tin g

procedural code.

o n -lin e data may fo r example have to c a rry out fo rw a rd projections in reaching a decision. The incorporation of tim e in to th e in fere n c e mechanism b lu rs th e d istin ctio n between e x p e rt systems and sim ulation models. ( M ille r[ 1986])

2 .4 A I AND SIM U LA TIO N MODELLING - M UTUAL SUPPO RT.

Previous sections have id e n tifie d the general ch aracteristics of both sim ulation and e x p e rt system s, as w ell as th e shortcom ings and ben efits of each approach. The sim ilarities between sim ulation and e x p e rt systems are now considered w ith emphasis being placed on the p o ssib ility of adapting an e x p e rt system to c a rry out the ro le of a sim ulation model (and v ic e -v e rs a ). The possible ways of in te g ra tin g sim ulation and A I techniques are then considered.

2 .4 .1 E x p e rt systems and sim ulation - Is th e re a difference?

A dvice provid ed by th e ES may then be applied in th e re a l w o rld.

S im ilarities between sim ulation and e x p e rt system s have also been id e n tifie d a t a methodological le v e l. For instance, D oukidis[1987] argues th a t a " th ree-p h ase sim ulation system can be seen as a production system ", his reasoning being th a t th e th re e essential components a re p resen t: Data memory, production model, and in ference engine. In d iscrete even t sim ulation, model execution is effected through a th ree-p h ase e x ecu tive which perform s a tim e-advance in th e A phase, executes a ll c u rre n t tim e-dep en dent events in the B phase and examines and executes w here a p p ro p ria te a ll state-d ep en d en t events in th e C phase ( T o ch er[ 1 9 6 2 ]). The execu tive can be compared to a forw ard chaining inference engine, w hich, at each tim e-advan ce, scans the state-dep en dent C events (th e production ru le s ) in search of routines th a t can be a c tiv ate d . The d efin itio n of th e model lo gic, sep arate from th e executive con trollin g model executio n, gives th re e -p h a s e sim ulation some o f the characteristics of a declarative lang uage. A diagram m atic rep resen tatio n of the th ree-p h ase approach is shown in fig u re 4.

s p e c if ie d u s in g a d e c la r a t iv e e x p e r t s y s te m a p p r o a c h i n w h ic h t h e tim e h a n d lin g

c a p a b ilit y i s d e f in e d i n t e r m s o f p r o d u c t io n r u l e s . A lt h o u g h f e a s ib le , e x p e r t

s y s te m p r o d u c t io n r u le s a r e n o t a n id e a l m e d iu m f o r t h e r e p r e s e n t a t io n o f

s im u la t io n e n t i t i e s a n d a c t i v i t i e s a n d t h e a p p r o a c h i s c o n s e q u e n t ly o f n o

s i g n i f i c a n t b e n e f i t . A n a l t e r n a t i v e a p p r o a c h i s t o a d a p t t h e s t r a t e g y u s e d b y

t h e in f e r e n c e e n g in e t o r e p r e s e n t d is c r e t e a d v a n c e s i n tim e b y m a in t a in in g a

d i a r y o f s c h e d u le d e v e n t s . T h e n e c e s s a r y a lt e r a t io n s a r e s i g n i f i c a n t a n d

p r e v e n t t h e r e a f t e r t h e u s e o f t h e in f e r e n c e e n g in e i n i t s t r a d i t i o n a l r o l e . T h e

a p p r o a c h h a s b e e n i n v e s t ig a t e d b y R o b e r ts o n [1 9 8 6 ] a n d i s d e s c r ib e d i n m o re

d e t a il i n s e c t io n 2 . 5 . 4 .

N O

FINISHED

Y E S

F IN A L IS A T IO N B PHASE

C PHASE I N IT IA L IS A T IO N

A PHASE

2 .4 .2 Sim ulation and e x p e rt systems - Complementary techniq ues.

Many applications have been developed th a t in some way make use of both a sim ulation model and an e x p e rt system . Such applications have evolved from a realisation th a t the stren g th s of sim ulation complement th e weaknesses of e x p e rt systems and v ic e -v e rs a . The potential fo r in tera c tio n between both technologies has been noted by many w rite rs (O 'K eefe e t a l.[1 9 8 6 ], Helman &

B ah u g u n a[1986], Flitm an & H u rrio n [1 9 8 7 ], H ill & R oberts [ 1987 ] , and Shannon e t a l. [1 9 8 5 ]).

E x p e rt systems can also be of b e n e fit to sim ulation m odellers. One of th e shortcom ings of sim ulation mentioned in section 2 .2 .4 is the necessity fo r considerable e x p e rtise in producing th e model and analysing th e generated o u tp u t. A d viso ry e x p e rt systems th a t provide support to the user by em bodying th e know ledge of experienced sim ulation m odellers are c u rre n tly being considered b y several research ers. Doukidis and Paul [1991] describe S IPD ES, a system which helps users to discover th e location of com pilation e rro rs o ccurring w ith in th e ir sim ulation program and proposes possible solutions. S im ila rly, th e experim entation and analysis phases of sim ulation m odelling are being supported by autom atic systems such as th a t embodied in th e 'model execution1 and 'model analysis' modules of Sim ulation C ra ft (S ath i e t a l.[1 9 8 6 ]). The model execution e x p e rt is p rim a rily responsible fo r determ ining the necessary experim ents and the corresponding num ber of ru n s th a t are re q u ire d . The model analysis e x p e rt is claimed to evaluate experim ents, generate a lte rn a tiv e s , and provide explanation fa c ilitie s using s ta tis tic a l ro u tin e s .

Such m utual supp ort a c tiv itie s are c le a rly beneficial and do not necessitate any d ire c t in teractio n between e x p e rt system and sim ulation model o ther than fo r the sharing of data. A no th er area fo r m utual co-operation is in the m arriage of ES and sim ulation techniques in p ro vid in g a sim ulation environm ent th a t perm its th e modelling of in te llig e n t b eh avio ur, the handling of events o ver tim e, and th e rep resen tatio n of algorithm ic components of th e model.

management, can be considered as a p a rtic u la r form of e x p e rtis e . T h is exp e rtise may be represented in th e form of ru les which may be c le a rly -s ta te d in s tru c tio n s , ru les of plausible reason ing, or ru les of thum b (h e u ris tic s ). The knowledge of employees is fu rth e r supplem ented by "facts" which may have been acquired th ro u g h job experience and data which may be p u b licly a v a ila b le .

The basic functions and perform ance of machines in clu d in g du ratio ns of operations and th e basic processing sequences of product are well described b y th e conventional data s tru c tu re s and are well handled in conventional procedural languages usually used in sim ulation. A ny m aterial requirem ents planning functions depending on o rd ers outstanding and c u rre n t cost data can also be well accommodated w ith in a procedural fram ew ork.

V ariou s methods have been considered in com bining th e fu n c tio n a lity of ESs and sim ulation. Some research ers, and often those w ith a strong background in a rtific ia l in tellig en ce have opted fo r using A I languages, usually LISP or PROLOG. In th e U nited S ta te s, LIS P is th e main language used in A I and so th e re is a n a tu ra l in clin atio n tow ards its use in th is c o n te x t. Most LISP based sim ulation environm ents operate according to th e object oriented program m ing paradigm . In Europe and Japan, governm ent sponsored research has given th e PROLOG approach th e leading edge. In some cases, modified versions of standard PROLOG have been used th a t are ta ilo re d to sim ulation. A nother approach, though usually discussed ra th e r than attem pted, is to in terfa c e a sim ulation model w ith an e x p e rt system . The d iffic u ltie s w ith th is approach consist of im plem enting an adequate form of communication between fu n ctio n ally incom patible so ftw are. An approach which has been used in overcoming th is problem is to im plem ent th e sim ulation model and e x p e rt system on separate com puters and achieve data sh aring th ro u g h a generalised communication protocol ( see section 2 .5 .5 ) .

2 .5 WIDER ASPECTS OF A I SUPPORT OF S IM U LA TIO N M O DELLING .

The le v e l of capital investm ent necessary in u n d ertakin g a sim ulation s tu d y is now less of an issue, p a rtic u la rly in th e context of m icrocom puter based system s w here th e presence of easily accessible, colour graphics has promoted th e grow th of windowing environm ents and iconic disp lays. The o v erall e ffe c t has been th a t researchers have focused th e ir in te re s t on developing tools th a t enable the re la tiv e ly inexperienced sim ulation m odeller to define and develop models, devise experim ents and then analyse sim ulation o u tp u t w itho ut th e need to call on th e resources of more experienced p ra c titio n e rs .

2 .5 .1 Sim ulation program generato rs.

R esearchers have invested considerable time and e ffo rt in th e developm ent of sim ulation program generators w ith a view to reducing th e necessary tim e span in the model creation stage of th e sim ulation m odelling life -c y c le (see C lem entson[1982]) . A second consideration has been to attem pt to devise fle x ib le and u s e r-frie n d ly systems th a t guide the inexperienced u ser throug h a model specification process.

The use of graphical depictions as a means of form alising th e behaviour of a system is a long standing approach to m odelling (See C lem entson[1978], M atthew son[1975], Gordon[1 9 8 1 ], and Z e ig le r[1 9 7 6 ]) . Such an approach has th e b en efit of p ro vid in g a simple vehicle fo r discussion between c lie n t and analyst and. perm its the detection of poten tial logic e rro rs . The main lim itation associated w ith graphical depictions is th e d iffic u lty in rep resen tin g complex re al-w o rld systems in which the paths between queues and a c tiv itie s are numerous and often ambiguous. Fu rth erm o re, graphical rep resentations omit all references to decision making in clu d in g conditional branching and batch processing of queue e n titie s . Such problems re s tric t th e value of using graphical model representations as in p u t to program generators as only th e sim plest of modelling tasks can be dealt w ith .

A few researchers in clud ing D oukidis[1987] take a d iffe re n t approach and re ly on a te n ta tiv e method based on techniques d erived from N atu ral Language U nderstanding Systems (N L U S ). Th e clien t and an alyst are expected to go through the consultation session to g e th e r, th e end product being a logic model which can in tu rn be used as in p u t to a program g e n erato r.

F u rth e rm o re , th e model specification is defined in term s of a simple language re fe rre d to as th e Conditional Specification (C S ). A ccording to Nance and O v e rs tre e t, CS s trik e s a balance between 'd e s c rip tiv e g e n erality and an in s tru c tiv e form alism ' which perm its th e analyst to fu rth e r develop and test th e model before g enerating th e source code.

2 .5 .2 Model v e rific a tio n and valid atio n .

Th e c u rre n t tre n d in creatin g development environm ents has n a tu ra lly led to research in to ways of autom ating th e process of model v e rific a tio n and v a lid a tio n .

V e rific a tio n is th e process of debugging th e sim ulation code and checking th a t the model operates as intended (See Koskossidis & D avies[1 9 8 7 ]). The Sim ulation Model Development Environm ent (SMDE) as described in th e previous section includes a model analyzer th a t diagnoses th e model specification created by the model g e n e ra to r. The inten tion is to help id e n tify m istakes, in p a rtic u la r conceptual and d e scrip tive e rro rs , to suggest a lte rn a tiv e model configurations th a t may prove to be more e ffic ie n t, and to provide general guidance d u rin g the modelling e ffo r t. The approach taken in S IM U LA TIO N CRAFT (S ath i et a l.[1 9 8 6 ]) is sim ilar though th is tim e, the embedded model bu ild ing ES is responsible fo r consistency and completeness checks d u rin g the graphical model in p u t process. O th er research projects have also used the e x p e rt system approach to model v e rific a tio n . SIPDES (D o u k id is [1 9 8 7 ]) and TIM (H ill &

The generation of execution e rro rs d u rin g th e model bu ild in g and experim entation processes are a considerable help to model v e rific a tio n and ten d to form th e basis fo r the diagnosis processes in sim ulation support so ftw a re . In c o n tra s t, th e valid ation of a model is a complex process, necessitating from th e analyst considerable s k ill and experience. The form alisation of such knowledge in th e development of an e x p e rt system is ren d ered im practicable by the problem -dependent n a tu re of