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Discrete-Event Simulation and System Dynamics for Management Decision Making [Kõva köide]

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Discrete-Event Simulation and System Dynamics for Management Decision MakingSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781118349021.html
Märksõnad:
"Explores the integration of discrete-event simulation (DES) and system dynamics (SD), providing comparisons of each methodology"--

"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.

Preface xv
List of contributors xvii
1 Introduction 1(9)
Sally Brailsford
Leonid Churilov
Brian Dangerfield
1.1 How this book came about
1(1)
1.2 The editors
2(1)
1.3 Navigating the book
3(6)
References
9(1)
2 Discrete-event simulation: A primer 10(16)
Stewart Robinson
2.1 Introduction
10(1)
2.2 An example of a discrete-event simulation: Modelling a hospital theatres process
11(1)
2.3 The technical perspective: How DES works
12(9)
2.3.1 Time handling in DES
14(1)
2.3.2 Random sampling in DES
15(6)
2.4 The philosophical perspective: The DES worldview
21(2)
2.5 Software for DES
23(1)
2.6 Conclusion
24(1)
References
24(2)
3 Systems thinking and system dynamics: A primer 26(26)
Brian Dangerfield
3.1 Introduction
26(2)
3.2 Systems thinking
28(6)
3.2.1 'Behaviour over time' graphs
28(1)
3.2.2 Archetypes
29(1)
3.2.3 Principles of influence (or causal loop) diagrams
30(2)
3.2.4 From diagrams to behaviour
32(2)
3.3 System dynamics
34(6)
3.3.1 Principles of stock—flow diagramming
34(1)
3.3.2 Model purpose and model conceptualisation
35(1)
3.3.3 Adding auxiliaries, parameters and information links to the spinal stock—flow structure
36(1)
3.3.4 Equation writing and dimensional checking
37(3)
3.4 Some further important issues in SD modelling
40(9)
3.4.1 Use of soft variables
40(2)
3.4.2 Co-flows
42(1)
3.4.3 Delays and smoothing functions
43(3)
3.4.4 Model validation
46(2)
3.4.5 Optimisation of SD models
48(1)
3.4.6 The role of data in SD models
49(1)
3.5 Further reading
49(1)
References
50(2)
4 Combining problem structuring methods with simulation: The philosophical and practical challenges 52(24)
Kathy Kotiadis
John Mingers
4.1 Introduction
52(1)
4.2 What are problem structuring methods?
53(1)
4.3 Multiparadigm multimethodology in management science
54(6)
4.3.1 Paradigm incommensurability
55(2)
4.3.2 Cultural difficulties
57(1)
4.3.3 Cognitive difficulties
58(1)
4.3.4 Practical problems
59(1)
4.4 Relevant projects and case studies
60(2)
4.5 The case study: Evaluating intermediate care
62(6)
4.5.1 The problem situation
62(2)
4.5.2 Soft systems methodology
64(2)
4.5.3 Discrete-event simulation modelling
66(1)
4.5.4 Multimethodology
67(1)
4.6 Discussion
68(4)
4.6.1 The multiparadigm multimethodology position and strategy
68(2)
4.6.2 The cultural difficulties
70(1)
4.6.3 The cognitive difficulties
70(2)
4.7 Conclusions
72(1)
Acknowledgements
72(1)
References
72(4)
5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective 76(29)
Kristian Rotaru
Leonid Churilov
Andrew Flitman
5.1 Introduction
76(4)
5.2 Ontological and epistemological assumptions of CR
80(2)
5.2.1 The stratified CR ontology
80(1)
5.2.2 The abductive mode of reasoning
81(1)
5.3 Process system modelling with SD and DES through the prism of CR scientific positioning
82(8)
5.3.1 Lifecycle perspective on SD and DES methods
84(6)
5.4 Process system modelling with SD and DES: Trends in and implications for MS
90(7)
5.5 Summary and conclusions
97(2)
References
99(6)
6 Theoretical comparison of discrete-event simulation and system dynamics 105(20)
Sally Brailsford
6.1 Introduction
105(1)
6.2 System dynamics
106(2)
6.3 Discrete-event simulation
108(2)
6.4 Summary: The basic differences
110(2)
6.5 Example: Modelling emergency care in Nottingham
112(8)
6.5.1 Background
112(1)
6.5.2 The ECOD project
113(1)
6.5.3 Choice of modelling approach
114(1)
6.5.4 Quantitative phase
114(2)
6.5.5 Model validation
116(1)
6.5.6 Scenario testing and model results
116(2)
6.5.7 The ED model
118(1)
6.5.8 Discussion
119(1)
6.6 The $64 000 question: Which to choose?
120(3)
6.7 Conclusion
123(1)
References
123(2)
7 Models as interfaces 125(15)
Steffen Bayer
Tim Bolt
Sally Brailsford
Maria Kapsali
7.1 Introduction: Models at the interfaces or models as interfaces
125(1)
7.2 The social roles of simulation
126(3)
7.3 The modelling process
129(2)
7.4 The modelling approach
131(3)
7.5 Two case studies of modelling projects
134(3)
7.6 Summary and conclusions
137(1)
References
138(2)
8 An empirical study comparing model development in discrete-event simulation and system dynamics 140(25)
Antuela Tako
Stewart Robinson
8.1 Introduction
140(2)
8.2 Existing work comparing DES and SD modelling
142(4)
8.2.1 DES and SD model development process
143(3)
8.2.2 Summary
146(1)
8.3 The study
146(5)
8.3.1 The case study
146(1)
8.3.2 Verbal protocol analysis
147(2)
8.3.3 The VPA sessions
149(1)
8.3.4 The subjects
149(1)
8.3.5 The coding process
150(1)
8.4 Study results
151(7)
8.4.1 Attention paid to modelling topics
152(2)
8.4.2 The sequence of modelling stages
154(1)
8.4.3 Pattern of iterations among topics
155(3)
8.5 Observations from the DES and SD expert modellers' behaviour
158(2)
8.6 Conclusions
160(2)
Acknowledgements
162(1)
References
162(3)
9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation 165(34)
John Morecroft
Stewart Robinson
9.1 Introduction
165(1)
9.2 Existing comparisons of SD and DES
166(3)
9.3 Research focus
169(1)
9.4 Erratic fisheries — chance, destiny and limited foresight
170(3)
9.5 Structure and behaviour in fisheries: A comparison of SD and DES models
173(19)
9.5.1 Alternative models of a natural fishery
174(4)
9.5.2 Alternative models of a simple harvested fishery
178(6)
9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing
184(8)
9.6 Summary of findings
192(1)
9.7 Limitations of the study
193(1)
9.8 SD or DES?
194(2)
Acknowledgements
196(1)
References
196(3)
10 DES view on simulation modelling: SIMUL8 199(16)
Mark Elder
10.1 Introduction
199(1)
10.2 How software fits into the project
200(2)
10.3 Building a DES
202(6)
10.4 Getting the right results from a DES
208(4)
10.4.1 Verification and validation
210(1)
10.4.2 Replications
211(1)
10.5 What happens after the results?
212(1)
10.6 What else does DES software do and why?
212(1)
10.7 What next for DES software?
213(1)
References
214(1)
11 Vensim and the development of system dynamics 215(33)
Lee Jones
11.1 Introduction
215(1)
11.2 Coping with complexity: The need for system dynamics
216(3)
11.3 Complexity arms race
219(2)
11.4 The move to user-led innovation
221(1)
11.5 Software support
222(23)
11.5.1 Apples and oranges (basic model testing)
223(1)
11.5.2 Confidence
224(13)
11.5.3 Helping the practitioner do more
237(8)
11.6 The future for SD software
245(2)
11.6.1 Innovation
245(1)
11.6.2 Communication
245(2)
References
247(1)
12 Multi-method modeling: AnyLogic 248(32)
Andrei Borshchev
12.1 Architectures
249(3)
12.1.1 The choice of model architecture and methods
251(1)
12.2 Technical aspect of combining modeling methods
252(5)
12.2.1 System dynamics -> discrete elements
252(1)
12.2.2 Discrete elements -> system dynamics
253(2)
12.2.3 Agent based <-> discrete event
255(2)
12.3 Example: Consumer market and supply chain
257(5)
12.3.1 The supply chain model
257(1)
12.3.2 The market model
258(1)
12.3.3 Linking the DE and the SD parts
259(1)
12.3.4 The inventory policy
260(2)
12.4 Example: Epidemic and clinic
262(5)
12.4.1 The epidemic model
262(2)
12.4.2 The clinic model and the integration of methods
264(3)
12.5 Example: Product portfolio and investment policy
267(11)
12.5.1 Assumptions
268(2)
12.5.2 The model architecture
270(1)
12.5.3 The agent product and agent population portfolio
271(3)
12.5.4 The investment policy
274(1)
12.5.5 Closing the loop and implementing launch of new products
275(2)
12.5.6 Completing the investment policy
277(1)
12.6 Discussion
278(1)
References
279(1)
13 Multiscale modelling for public health management: A practical guide 280(15)
Rosemarie Sadsad
Geoff McDonnell
13.1 Introduction
280(1)
13.2 Background
281(1)
13.3 Multilevel system theories and methodologies
281(2)
13.4 Multiscale simulation modelling and management
283(6)
13.5 Discussion
289(1)
13.6 Conclusion
290(1)
References
290(5)
14 Hybrid modelling case studies 295(23)
Rosemarie Sadsad
Geoff McDonnell
Joe Viana
Shivam M. Desai
Paul Harper
Sally Brailsford
14.1 Introduction
295(1)
14.2 A multilevel model of MRSA endemicity and its control in hospitals
296(6)
14.2.1 Introduction
296(1)
14.2.2 Method
296(1)
14.2.3 Results
297(5)
14.2.4 Conclusion
302(1)
14.3 Chlamydia composite model
302(6)
14.3.1 Introduction
302(1)
14.3.2 Chlamydia
302(1)
14.3.3 DES model of a GUM department
303(1)
14.3.4 SD model of chlamydia
304(1)
14.3.5 Why combine the models
304(1)
14.3.6 How the models were combined
305(1)
14.3.7 Experiments with the composite model
305(2)
14.3.8 Conclusions
307(1)
14.4 A hybrid model for social care services operations
308(8)
14.4.1 Introduction
308(1)
14.4.2 Population model
308(1)
14.4.3 Model construction
309(1)
14.4.4 Contact centre model
310(1)
14.4.5 Hybrid model
311(2)
14.4.6 Conclusions and lessons learnt
313(3)
References
316(2)
15 The ways forward: A personal view of system dynamics and discrete-event simulation 318(19)
Michael Pidd
15.1 Genesis
318(1)
15.2 Computer simulation in management science
319(1)
15.3 The effect of developments in computing
320(4)
15.4 The importance of process
324(1)
15.5 My own comparison of the simulation approaches
324(4)
15.5.1 Time handling
324(2)
15.5.2 Stochastic and deterministic elements
326(1)
15.5.3 Discrete entities versus continuous variables
327(1)
15.6 Linking system dynamics and discrete-event simulation
328(1)
15.7 The importance of intended model use
329(4)
15.7.1 Decision automation
330(1)
15.7.2 Routine decision support
331(1)
15.7.3 System investigation and improvement
331(1)
15.7.4 Providing insights for debate
332(1)
15.8 The future?
333(2)
15.8.1 Use of both methods will continue to grow
333(1)
15.8.2 Developments in computing will continue to have an effect
334(1)
15.8.3 Process really matters
335(1)
References
335(2)
Index 337
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