Discrete-Event Simulation and System Dynamics for Management Decision Making (eBook)
In recent years, there has been a growing debate, particularly in the UK and Europe, over the merits of using discrete-event simulation (DES) and system dynamics (SD); there are now instances where both methodologies were employed on the same problem. This book details each method, comparing each in terms of both theory and their application to various problem situations. It also provides a seamless treatment of various topics--theory, philosophy, detailed mechanics, practical implementation--providing a systematic treatment of the methodologies of DES and SD, which previously have been treated separately.
In recent years, there has been a growing debate, particularly in the UK and Europe, over the merits of using discrete-event simulation (DES) and system dynamics (SD); there are now instances where both methodologies were employed on the same problem. This book details each method, comparing each in terms of both theory and their application to various problem situations. It also provides a seamless treatment of various topics--theory, philosophy, detailed mechanics, practical implementation--providing a systematic treatment of the methodologies of DES and SD, which previously have been treated separately.
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
Discrete-Event Simulation and System Dynamics for Management Decision Making 1
Contents 9
Preface 17
List of contributors 19
1 Introduction 21
1.1 How this book came about 21
1.2 The editors 22
1.3 Navigating the book 23
References 29
2 Discrete-event simulation: A primer 30
2.1 Introduction 30
2.2 An example of a discrete-event simulation: Modelling a hospital theatres process 31
2.3 The technical perspective: How DES works 32
2.3.1 Time handling in DES 34
2.3.2 Random sampling in DES 35
2.4 The philosophical perspective: The DES worldview 41
2.5 Software for DES 43
2.6 Conclusion 44
References 44
3 Systems thinking and system dynamics: A primer 46
3.1 Introduction 46
3.2 Systems thinking 48
3.2.1 ‘Behaviour over time’ graphs 48
3.2.2 Archetypes 49
3.2.3 Principles of influence (or causal loop) diagrams 50
3.2.4 From diagrams to behaviour 52
3.3 System dynamics 54
3.3.1 Principles of stock–.ow diagramming 54
3.3.2 Model purpose and model conceptualisation 55
3.3.3 Adding auxiliaries, parameters and information links to the spinal stock–flow structure 56
3.3.4 Equation writing and dimensional checking 57
3.4 Some further important issues in SD modelling 60
3.4.1 Use of soft variables 60
3.4.2 Co-flows 62
3.4.3 Delays and smoothing functions 63
3.4.4 Model validation 66
3.4.5 Optimisation of SD models 68
3.4.6 The role of data in SD models 69
3.5 Further reading 69
References 70
4 Combining problem structuring methods with simulation: The philosophical and practical challenges 72
4.1 Introduction 72
4.2 What are problem structuring methods? 73
4.3 Multiparadigm multimethodology in management science 74
4.3.1 Paradigm incommensurability 75
4.3.2 Cultural difficulties 77
4.3.3 Cognitive difficulties 78
4.3.4 Practical problems 79
4.4 Relevant projects and case studies 80
4.5 The case study: Evaluating intermediate care 82
4.5.1 The problem situation 82
4.5.2 Soft systems methodology 84
4.5.3 Discrete-event simulation modelling 86
4.5.4 Multimethodology 87
4.6 Discussion 88
4.6.1 The multiparadigm multimethodology position and strategy 88
4.6.2 The cultural difficulties 90
4.6.3 The cognitive difficulties 90
4.7 Conclusions 92
Acknowledgements 92
References 92
5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective 96
5.1 Introduction 96
5.2 Ontological and epistemological assumptions of CR 100
5.2.1 The stratified CR ontology 100
5.2.2 The abductive mode of reasoning 101
5.3 Process system modelling with SD and DES through the prism of CR scientific positioning 102
5.3.1 Lifecycle perspective on SD and DES methods 104
5.4 Process system modelling with SD and DES: Trends in and implications for MS 110
5.5 Summary and conclusions 117
References 119
6 Theoretical comparison of discrete-event simulation and system dynamics 125
6.1 Introduction 125
6.2 System dynamics 126
6.3 Discrete-event simulation 128
6.4 Summary: The basic differences 130
6.5 Example: Modelling emergency care in Nottingham 132
6.5.1 Background 132
6.5.2 The ECOD project 133
6.5.3 Choice of modelling approach 134
6.5.4 Quantitative phase 134
6.5.5 Model validation 136
6.5.6 Scenario testing and model results 136
6.5.7 The ED model 138
6.5.8 Discussion 139
6.6 The $64 000 question: Which to choose? 140
6.7 Conclusion 143
References 143
7 Models as interfaces 145
7.1 Introduction: Models at the interfaces or models as interfaces 145
7.2 The social roles of simulation 146
7.3 The modelling process 149
7.4 The modelling approach 151
7.5 Two case studies of modelling projects 154
7.6 Summary and conclusions 157
References 158
8 An empirical study comparing model development in discrete-event simulation and system dynamics 160
8.1 Introduction 160
8.2 Existing work comparing DES and SD modelling 162
8.2.1 DES and SD model development process 163
8.2.2 Summary 166
8.3 The study 166
8.3.1 The case study 166
8.3.2 Verbal protocol analysis 167
8.3.3 The VPA sessions 169
8.3.4 The subjects 169
8.3.5 The coding process 170
8.4 Study results 171
8.4.1 Attention paid to modelling topics 172
8.4.2 The sequence of modelling stages 174
8.4.3 Pattern of iterations among topics 175
8.5 Observations from the DES and SD expert modellers’ behaviour 178
8.6 Conclusions 180
Acknowledgements 182
References 182
9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation 185
9.1 Introduction 185
9.2 Existing comparisons of SD and DES 186
9.3 Research focus 189
9.4 Erratic fisheries – chance, destiny and limited foresight 190
9.5 Structure and behaviour in fisheries: A comparison of SD and DES models 193
9.5.1 Alternative models of a natural fishery 194
9.5.2 Alternative models of a simple harvested fishery 198
9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing 204
9.6 Summary of findings 212
9.7 Limitations of the study 213
9.8 SD or DES? 214
Acknowledgements 216
References 216
10 DES view on simulation modelling: SIMUL8 219
10.1 Introduction 219
10.2 How software fits into the project 220
10.3 Building a DES 222
10.4 Getting the right results from a DES 228
10.4.1 Verification and validation 230
10.4.2 Replications 231
10.5 What happens after the results? 232
10.6 What else does DES software do and why? 232
10.7 What next for DES software? 233
References 234
11 Vensim and the development of system dynamics 235
11.1 Introduction 235
11.2 Coping with complexity: The need for system dynamics 236
11.3 Complexity arms race 239
11.4 The move to user-led innovation 241
11.5 Software support 242
11.5.1 Apples and oranges (basic model testing) 243
11.5.2 Confidence 244
11.5.3 Helping the practitioner do more 257
11.6 The future for SD software 265
11.6.1 Innovation 265
11.6.2 Communication 265
References 267
12 Multi-method modeling: AnyLogic 268
12.1 Architectures 269
12.1.1 The choice of model architecture and methods 271
12.2 Technical aspect of combining modeling methods 272
12.2.1 System dynamics ? discrete elements 272
12.2.2 Discrete elements ? system dynamics 273
12.2.3 Agent based ? discrete event 275
12.3 Example: Consumer market and supply chain 277
12.3.1 The supply chain model 277
12.3.2 The market model 278
12.3.3 Linking the DE and the SD parts 279
12.3.4 The inventory policy 280
12.4 Example: Epidemic and clinic 282
12.4.1 The epidemic model 282
12.4.2 The clinic model and the integration of methods 284
12.5 Example: Product portfolio and investment policy 287
12.5.1 Assumptions 288
12.5.2 The model architecture 290
12.5.3 The agent product and agent population portfolio 291
12.5.4 The investment policy 294
12.5.5 Closing the loop and implementing launch of new products 295
12.5.6 Completing the investment policy 297
12.6 Discussion 298
References 299
13 Multiscale modelling for public health management: A practical guide 300
13.1 Introduction 300
13.2 Background 301
13.3 Multilevel system theories and methodologies 301
13.4 Multiscale simulation modelling and management 303
13.5 Discussion 309
13.6 Conclusion 310
References 310
14 Hybrid modelling case studies 315
14.1 Introduction 315
14.2 A multilevel model of MRSA endemicity and its control in hospitals 316
14.2.1 Introduction 316
14.2.2 Method 316
14.2.3 Results 317
14.2.4 Conclusion 322
14.3 Chlamydia composite model 322
14.3.1 Introduction 322
14.3.2 Chlamydia 322
14.3.3 DES model of a GUM department 323
14.3.4 SD model of chlamydia 324
14.3.5 Why combine the models 324
14.3.6 How the models were combined 325
14.3.7 Experiments with the composite model 325
14.3.8 Conclusions 327
14.4 A hybrid model for social care services operations 328
14.4.1 Introduction 328
14.4.2 Population model 328
14.4.3 Model construction 329
14.4.4 Contact centre model 330
14.4.5 Hybrid model 331
14.4.6 Conclusions and lessons learnt 333
References 336
15 The ways forward: A personal view of system dynamics and discrete-event simulation 338
15.1 Genesis 338
15.2 Computer simulation in management science 339
15.3 The effect of developments in computing 340
15.4 The importance of process 344
15.5 My own comparison of the simulation approaches 344
15.5.1 Time handling 344
15.5.2 Stochastic and deterministic elements 346
15.5.3 Discrete entities versus continuous variables 347
15.6 Linking system dynamics and discrete-event simulation 348
15.7 The importance of intended model use 349
15.7.1 Decision automation 350
15.7.2 Routine decision support 351
15.7.3 System investigation and improvement 351
15.7.4 Providing insights for debate 352
15.8 The future? 353
15.8.1 Use of both methods will continue to grow 353
15.8.2 Developments in computing will continue to have an effect 354
15.8.3 Process really matters 355
References 355
Index 357
1
Introduction
Sally Brailsford,1 Leonid Churilov2 and Brian Dangerfield3
1Southampton Business School, University of Southampton, UK
2Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; RMIT University, Melbourne, Victoria, Australia
3Salford Business School, University of Salford, UK
1.1 How this Book Came About
To begin at the end … the final chapter in this book, by Michael Pidd, contains both a backwards and a forwards look at system dynamics and discrete-event simulation. Historically, both modelling approaches originate from around the same time, the late 1950s and early 1960s. However, over the intervening decades they developed into separate scientific and practitioner communities, each with its own learned societies, academic journals and conferences. Discrete-event simulation (DES) has been a core subject on MSc programmes in operational research (OR) or management science (MS) from the 1970s onwards, and is a standard technique in the ‘OR/MS toolkit’. For many operational researchers, ‘simulation’ is synonymous with DES, and indeed the aim of the UK OR Society's own Journal of Simulation (quoting directly from the journal's web site) is to provide ‘a single source of accessible research and practice in the fast developing field of discrete-event simulation’ (http://www.palgrave-journals.com/jos/index.html). However, this is not true of system dynamics (SD): the SD community was, and still is to some extent, distinct from the OR community. While there is obviously some overlap in membership, there are many members of the international SD Society who are not members of their national OR Society (and vice versa). SD was certainly not taught on the MSc in OR at the University of Southampton in the 1980s and 1990s.
In 2000, the Simulation Special Interest Group of the UK OR Society held a joint meeting with the UK Chapter of the SD Society, entitled ‘Never the Twain Shall Meet’. At this meeting David Lane presented a paper (Lane, 2000) in which he discussed the differences between SD and DES and posed the question about whether they were ‘chalk and cheese’ or were actually two sides of the same coin. This meeting led to the foundation of a new OR Society Special Interest Group called ‘SD+’ whose aim was to bring the SD and OR communities together. The ‘+’ in SD+ was broader than just DES: it included many other OR techniques and approaches with which SD could interface. The ‘Never the Twain’ meeting also led to a number of academic papers exploring the similarities and differences between DES and SD, including the well-known study by Robinson and Morecroft which forms Chapter 9 of this book. Indirectly, it led to this book itself!
In some application areas the use of SD has expanded rapidly since 2000, and healthcare – the specialist application field of all three editors of this book – is one such area. However, despite initiatives like SD+, it is still true to say even today that SD is less well known in the mainstream OR community than DES. The number of DES papers at the annual Winter Simulation Conference, the major US conference on simulation, always greatly exceeds the number of SD papers. The main aim of this book is to begin to address this disparity. The book provides an integrated overview of SD and DES, a detailed comparison of the two approaches from a variety of perspectives, and a practical guide to how both may be used, either separately or together.
1.2 The Editors
As editors, we should declare our own personal interests. Having started out in the early 1990s as a dedicated user of DES, Sally Brailsford became interested in SD as a result of the ‘Never the Twain’ meeting, and joined David Lane in co-founding the SD+ group. Subsequently she became a zealous convert, using SD for several modelling studies. Like many other researchers, she was fascinated by the relationship between DES and SD, and in particular in the domain of healthcare (Brailsford and Hilton, 2001), but first used SD in practice in a project to model demand for emergency healthcare in Nottingham, England (Brailsford et al., 2004) which is described in detail in Chapter 6.
Leonid Churilov has a firm belief that real management problems do not come cleanly separated by disciplinary lines and, as a result, can rarely be comprehensively addressed using a single given modelling method. This basic premise is the source of continuous motivation for his keen interest in combining and contrasting different OR/MS techniques for management decision support. His research, in particular, included combining DES and clustering/classification techniques for decision support in hospital emergency departments, the use of both DES and SD for process systems modelling, and the original value-focused process engineering methodology that integrates the approaches from both the decision sciences and business process modelling domains. His work on philosophical underpinnings of both DES and SD worldviews from the critical realist perspective is featured in Chapter 5.
Brian Dangerfield discovered SD (industrial dynamics as it then was) over 40 years ago after a period in an OR unit in industry. Working at the (then) University of Liverpool School of Business Studies, he was a researcher on a project looking at the role of stocks in the UK economy. Rather than take the obvious econometric route he realised that an understanding of the macro role that stocks played in the workings of the economy would be much enhanced if, instead, macro-economic models were SD simulation models. Variables representing stocks had to be divorced from the flows which changed them. He was impressed with the way (i) SD models were forged at the policy level, (ii) separated out resource flows from the information flows driving changes in those resources and (iii) were also able to embrace relevant ‘soft’ variables which, using another methodology, might be excluded altogether. In sum he concluded that, given his overall knowledge and real-world experience with OR (including traditional simulation techniques), the SD methodology was just about the most promising in the landscape of OR, and his subsequent research career has concentrated on its use and development.
1.3 Navigating the Book
To our knowledge this book is unique – there is no similar coverage in one single volume. Books which cover both methodologies (e.g. Pidd's Computer Simulation in Management Science, 2009) merely split the page coverage – there is no attempt at integration, or a detailed comparison and description of how both approaches may be used in the same project. This book provides a seamless treatment of a variety of topics: theory, philosophy, detailed mechanics and practical implementation, all written by experts in the field. While some chapters are aimed at beginners, others are more advanced; the book also includes three software chapters which are very practical in nature.
The book is structured in seven unequal sections, three of which only contain one chapter. This Introduction forms the first section, and Pidd's concluding chapter the seventh. The second section, ‘Primers’, contains two chapters which provide a basic introduction to DES and SD respectively. These chapters are both written by experts with many years' experience of teaching and using each technique: Chapter 2 on DES (Stewart Robinson) and Chapter 3 on SD (Brian Dangerfield). The authors assume no prior knowledge of either technique, or an academic background in mathematics, statistics or OR. They are aimed at students and practitioners alike. The aim of both primers is to provide sufficient understanding to enable the average reader to get a basic grasp of the topic and be able to appreciate the subsequent chapters. The primers do not attempt to provide the breadth and depth of material offered in a typical MSc course in SD or DES, and they do not contain a great deal of technical detail. References are provided for anyone who does wish to delve deeper into the technicalities!
The third section also consists of a single chapter. By way of contrast with Chapter 2 and 3, the authors of Chapter 4 (Kathy Kotiadis and John Mingers) take a strongly academic stance. This chapter provides a theoretical background for much of the discussion in later chapters about combining different modelling paradigms. This chapter, which was originally published as a research article in the Journal of the Operational Research Society, discusses the combination of problem structuring methods with hard OR methodologies. Kotiadis and Mingers reflect on the barriers to such combinations that can be seen at the philosophical level – paradigm incommensurability – and cognitive level – type of personality and difficulty of switching paradigm. They then examine the combination of soft systems methodology and DES within a healthcare case study. They argue, by way of the practical application, that these problems are not insurmountable and that the result can be seen as the interplay of the soft and hard paradigms. The idea of yin and yang is proposed as a metaphor for this process.
The fourth section, ‘Comparisons’, is by far the largest and represents the heart of the book – its raison d'être. It contains five chapters, all of which...
| Erscheint lt. Verlag | 31.3.2014 |
|---|---|
| Reihe/Serie | Wiley Series in Operations Research and Management Science | Wiley Series in Operations Research and Management Science |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Technik ► Maschinenbau | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | Betriebswirtschaft u. Operationsforschung • Brian Dangerfield • Business & Management • comparing DES and SD • comparing DES and SD methods • DES Management • DES vs SD • Discrete-Event Simulation • Discrete-Event Simulation and System Dynamics for Management Decision Making • Discrete-Event Simulation Management • integrating DES and SD • integrating DES and SD methods • Leonid Churilov • Management Science/Operational Research • Mathematics • Mathematik • Operations Research & Management Science • Qualität, Produktivität u. Zuverlässigkeit • Qualität, Produktivität u. Zuverlässigkeit • Quality, Productivity & Reliability • Sally Brailsford • SD Management • Statistics • Statistik • System Dynamics • system dynamics Management • System Dynamics Simulation • Unternehmensforschung u. Betriebswirtschaft • Wirtschaft u. Management |
| ISBN-13 | 9781118762769 / 9781118762769 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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