ISBN-10:
1118349024
ISBN-13:
9781118349021
Pub. Date:
06/03/2014
Publisher:
Wiley
Discrete-Event Simulation and System Dynamics for Management Decision Making / Edition 1

Discrete-Event Simulation and System Dynamics for Management Decision Making / Edition 1

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Product Details

ISBN-13: 9781118349021
Publisher: Wiley
Publication date: 06/03/2014
Series: Wiley Series in Operations Research and Management Science Series
Pages: 360
Product dimensions: 6.20(w) x 9.10(h) x 0.90(d)

About the Author

Sally Brailsford, School of Management, University of Southampton, UK

Leonid Churilov, Melbourne Brain Centre, Victoria, Australia

Brian Dangerfield, Salford Business School, University of Salford, UK

Table of Contents

Preface xv

List of contributors xvii

1 Introduction Sally Brailsford Leonid Churilov Brian Dangerfield 1

1.1 How this book came about 1

1.2 The editors 2

1.3 Navigating the book 3

References 9

2 Discrete-event simulation: A primer Stewart Robinson 10

2.1 Introduction 10

2.2 An example of a discrete-event simulation: Modelling a hospital theatres process 11

2.3 The technical perspective: How DES works 12

2.3.1 Time handling in DES 14

2.3.2 Random sampling in DES 15

2.4 The philosophical perspective: The DES worldview 21

2.5 Software for DES 23

2.6 Conclusion 24

References 24

3 System thinking and system dynamics: A primer Brian Dangerfield 26

3.1 Introduction 26

3.2 Systems thinking 28

3.2.1 'Behaviour over time' graphs 28

3.2.2 Archetypes 29

3.2.3 Principles of influence (or causal loop) diagrams 30

3.2.4 From diagrams to behaviour 32

3.3 System dynamics 34

3.3.1 Principles of stock-flow diagramming 34

3.3.2 Model purpose and model conceptualisation 35

3.3.3 Adding auxiliaries, parameters and information links to the spinal stock-flow structure 36

3.3.4 Equation writing and dimensional checking 37

3.4 Some further important issues in SD modelling 40

3.4.1 Use of soft variable 40

3.4.2 Co-flows 42

3.4.3 Delays and smoothing functions 43

3.4.4 Model validation 46

3.4.5 Optimisation of SD models 48

3.4.6 The role of data is SD models 49

3.5 Further reading 49

References 50

4 Combining problem structuring methods with simulation: The philosophical and practical challenges Kathy Kotiadis John Mingers 52

4.1 Introduction 52

4.2 What are problem structuring methods? 53

4.3 Multiparadigm multimethodology in management science 54

4.3.1 Paradigm incommensurability 55

4.3.2 Cultural difficulties 57

4.3.3 Cognitive difficulties 58

4.3.4 Practical problems 59

4.4 Relevant projects and case studies 60

4.5 The case study: Evaluating intermediate care 62

4.5.1 The problem situation 62

4.5.2 Soft systems methodology 64

4.5.3 Discrete-event simulation modelling 66

4.5.4 Multimethodology 67

4.6 Discussion 68

4.6.1 The multiparadigm multimethodology position and strategy 68

4.6.2 The cultural difficulties 70

4.6.3 The cognitive difficulties 70

4.7 Conclusions 72

Acknowledgements 72

References 72

5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective Kristian Rotaru Leonid Churilov Andrew Flitman 76

5.1 Introduction 76

5.2 Ontological and epistemological assumptions of CR 80

5.2.1 The stratified CR ontology 80

5.2.2 The abductive mode of reasoning 81

5.3 Process system modelling with SD and DES through the prism of CR scientific positioning 82

5.3.1 Lifecycle perspective on SD and DES methods 84

5.4 Process system modelling with SD and DES: Trends in and implications for MS 90

5.5 Summary and conclusions 97

References 99

5 Theoretical comparison of discrete-event simulation and system dynamics Sally Brailsford 105

6.1 Introduction 105

6.2 System dynamics 106

6.3 Discrete-event simulation 108

6.4 Summary: The basic differences 110

6.5 Example: Modelling emergency care in Nottingham 112

6.5.1 Background 112

6.5.2 The ECOD project 113

6.5.3 Choice of modeling approach 114

6.5.4 Quantitative phase 114

6.5.5 Model validation 116

6.5.6 Scenario testing and model results 116

6.5.7 The ED model 118

6.5.8 Discussion 119

6.6 The $64000 question: Which to choose? 120

6.7 Conclusion 123

References 123

7 Models as interfaces Steffen Bayer Tim Bolt Sally Brailsford Maria Kapsali 125

7.1 Introduction: Models at the interfaces or models as interfaces 125

7.2 The social roles of simulation 126

7.3 The modelling process 129

7.4 The modelling approach 131

7.5 Two case studies of modelling projects 134

7.6 Summary and conclusions 137

References 138

8 An empirical study comparing model development in discrete-event simulation and system dynamics Antuela Tako Stewart Robinson 140

8.1 Introduction 140

8.2 Existing work comparing DES and SD modelling 142

8.2.1 DES and SD model development process 143

8.2.2 Summary 146

8.3 The study 146

8.3.1 The case study 146

8.3.2 Verbal protocol analysis 147

8.3.3 The VPA sessions 149

8.3.4 The subjects 149

8.3.5 The coding process 150

8.4 Study results 151

8.4.1 Attention paid to modelling topics 152

8.4.2 The sequence of modelling stages 154

8.4.3 Pattern of iterations among topics 155

8.5 Observations from the DES and SD expert modellers' behaviour 158

8.6 Conclusions 160

Acknowledgements 162

References 162

9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation John Morecroft Stewart Robinson 165

9.1 Introduction 165

9.2 Existing comparison of SD and DES 166

9.3 Research focus 169

9.4 Erratic fisheries - chance, destiny and limited foresight 170

9.5 Structure and behaviour in fisheries: A comparison of SD and DES models 173

9.5.1 Alternative models of a natural fishery 174

9.5.2 Alternative models of a simple harvested fishery 178

9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing 184

9.6 Summary of findings 192

9.7 Limitations of the study 193

9.8 SD or DES? 194

Acknowledgements 196

References 196

10 DES view on simulation modelling: SIMUL8 Mark Elder 199

10.1 Introduction 199

10.2 How software fits into the projects 200

10.3 Building a DES 202

10.4 Getting the right results from a DES 208

10.4.1 Verification and validation 210

10.4.2 Replications 211

10.5 What happens after the results 212

10.6 What else does DES software do and why? 212

10.7 What next for DES software? 213

References 214

11 Vensim and the development of system dynamics Lee Jones 215

11.1 Introduction 215

11.2 Coping with complexity: The need for system dynamics 216

11.3 Complexity arms race 219

11.4 The move to user-led innovation 221

11.5 Software support 222

11.5.1 Apples and oranges (basic model testing) 223

11.5.2 Confidence 224

11.5.3 Helping the practitioner do more 237

11.6 The future for SD software 245

11.6.1 Innovation 245

11.6.2 Communication 245

References 247

12 Multi-method modeling: AnyLogic Andrei Borshchev 248

12.1 Architectures 249

12.1.1 The choice of model architecture and methods 251

12.2 Technical aspect of combining modeling methods 252

12.2.1 System dynamics → discrete elements 252

12.2.2 Discrete elements → system dynamics 253

12.2.3 Agent based → discrete event 255

12.3 Example: Consumer market and supply chain 257

12.3.1 The supply chain model 257

12.3.2 The market model 258

12.3.3 Linking the DE and the SD parts 259

12.3.4 The inventory policy 260

12.4 Example: Epidemic and clinic 262

12.4.1 The epidemic model 262

12.4.2 The clinic model and the integration of methods 264

12.5 Example: Product portfolio and investment policy 267

12.5.1 Assumptions 268

12.5.2 The model architecture 270

12.5.3 The agent product and agent population portfolio 271

12.5.4 The investment policy 274

12.5.5 Closing the loop and implementing launch of new products 275

12.5.6 Completing the investment policy 277

12.6 Discussion 278

References 279

13 Multiscale modelling for public health management: A practical guide Rosemarie Sadsad Geoff McDonnell 280

13.1 Introduction 280

13.2 Background 281

13.3 Multilevel system theories and methodologies 281

13.4 Multiscale simulation modelling and management 283

13.5 Discussion 289

13.6 Conclusion 290

References 290

14 Hybrid modelling case studies Rosemarie Sadsad Geoff McDonnell Joe Viana Shivam M. Desai Paul Harper Sally Brailsford 295

14.1 Introduction 295

14.2 A multilevel model of MRSA endemicity and its control in hospitals 296

14.2.1 Introduction 296

14.2.2 Method 296

14.2.3 Results 297

14.2.4 Conclusion 302

14.3 Chlamydia composite model 302

14.3.1 Introduction 302

14.3.2 Chlamydia 302

14.3.3 DES model of a GUM department 303

14.3.4 SD model of chlamydia 304

14.3.5 Why combine the models 304

14.3.6 How the models were combined 305

14.3.7 Experiments with the composite model 305

14.3.8 Conclusions 307

14.4 A hybrid model for social care services operations 308

14.4.1 Introduction 308

14.4.2 Population model 308

14.4.3 Model construction 309

14.4.4 Contact centre model 310

14.4.5 Hybrid model 311

14.4.6 Conclusions and lessons learnt 313

References 316

15 The ways forward: A personal view of system dynamics and discrete-event simulation Michael Pidd 318

15.1 Genesis 318

15.2 Computer simulation in management science 319

15.3 The effect of development in computing 320

15.4 The importance of process 324

15.5 My own comparison of the simulation approaches 324

15.5.1 Time handling 324

15.5.2 Stochastic and deterministic elements 326

15.5.3 Discrete entities versus continuous variables 327

15.6 Linking system dynamics and discrete-event simulation 328

15.7 The importance of intended model use 329

15.7.1 Decision automation 330

15.7.2 Routine decision support 331

15.7.3 System investigation and improvement 331

15.7.4 Providing insights for debate 332

15.8 The future? 333

15.8.1 Use of both methods will continue to grow 333

15.8.2 Developments in computing will continue to have an effect 334

15.8.3 Process really matters 335

References 335

Index 337

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