Discrete-Event Simulation and System Dynamics for Management Decision Making.

By: Brailsford, SallyContributor(s): Churilov, Leonid | Dangerfield, BrianSeries: Wiley Series in Operations Research and Management Science SerPublisher: New York : John Wiley & Sons, Incorporated, 2014Copyright date: ©2013Edition: 1st edDescription: 1 online resource (362 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781118762752Subject(s): Discrete-time systems - Simulation methodsGenre/Form: Electronic books. Additional physical formats: Print version:: Discrete-Event Simulation and System Dynamics for Management Decision MakingDDC classification: 658.4033 LOC classification: T57.62.D495 2014ebOnline resources: Click to View
Contents:
Discrete-Event Simulation and System Dynamics for Management Decision Making -- Contents -- Preface -- List of contributors -- 1 Introduction -- 1.1 How this book came about -- 1.2 The editors -- 1.3 Navigating the book -- References -- 2 Discrete-event simulation: A primer -- 2.1 Introduction -- 2.2 An example of a discrete-event simulation: Modelling a hospital theatres process -- 2.3 The technical perspective: How DES works -- 2.3.1 Time handling in DES -- 2.3.2 Random sampling in DES -- 2.4 The philosophical perspective: The DES worldview -- 2.5 Software for DES -- 2.6 Conclusion -- References -- 3 Systems thinking and system dynamics: A primer -- 3.1 Introduction -- 3.2 Systems thinking -- 3.2.1 'Behaviour over time' graphs -- 3.2.2 Archetypes -- 3.2.3 Principles of influence (or causal loop) diagrams -- 3.2.4 From diagrams to behaviour -- 3.3 System dynamics -- 3.3.1 Principles of stock-.ow diagramming -- 3.3.2 Model purpose and model conceptualisation -- 3.3.3 Adding auxiliaries, parameters and information links to the spinal stock-flow structure -- 3.3.4 Equation writing and dimensional checking -- 3.4 Some further important issues in SD modelling -- 3.4.1 Use of soft variables -- 3.4.2 Co-flows -- 3.4.3 Delays and smoothing functions -- 3.4.4 Model validation -- 3.4.5 Optimisation of SD models -- 3.4.6 The role of data in SD models -- 3.5 Further reading -- References -- 4 Combining problem structuring methods with simulation: The philosophical and practical challenges -- 4.1 Introduction -- 4.2 What are problem structuring methods? -- 4.3 Multiparadigm multimethodology in management science -- 4.3.1 Paradigm incommensurability -- 4.3.2 Cultural difficulties -- 4.3.3 Cognitive difficulties -- 4.3.4 Practical problems -- 4.4 Relevant projects and case studies -- 4.5 The case study: Evaluating intermediate care.
4.5.1 The problem situation -- 4.5.2 Soft systems methodology -- 4.5.3 Discrete-event simulation modelling -- 4.5.4 Multimethodology -- 4.6 Discussion -- 4.6.1 The multiparadigm multimethodology position and strategy -- 4.6.2 The cultural difficulties -- 4.6.3 The cognitive difficulties -- 4.7 Conclusions -- Acknowledgements -- References -- 5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective -- 5.1 Introduction -- 5.2 Ontological and epistemological assumptions of CR -- 5.2.1 The stratified CR ontology -- 5.2.2 The abductive mode of reasoning -- 5.3 Process system modelling with SD and DES through the prism of CR scientific positioning -- 5.3.1 Lifecycle perspective on SD and DES methods -- 5.4 Process system modelling with SD and DES: Trends in and implications for MS -- 5.5 Summary and conclusions -- References -- 6 Theoretical comparison of discrete-event simulation and system dynamics -- 6.1 Introduction -- 6.2 System dynamics -- 6.3 Discrete-event simulation -- 6.4 Summary: The basic differences -- 6.5 Example: Modelling emergency care in Nottingham -- 6.5.1 Background -- 6.5.2 The ECOD project -- 6.5.3 Choice of modelling approach -- 6.5.4 Quantitative phase -- 6.5.5 Model validation -- 6.5.6 Scenario testing and model results -- 6.5.7 The ED model -- 6.5.8 Discussion -- 6.6 The 64 000 question: Which to choose? -- 6.7 Conclusion -- References -- 7 Models as interfaces -- 7.1 Introduction: Models at the interfaces or models as interfaces -- 7.2 The social roles of simulation -- 7.3 The modelling process -- 7.4 The modelling approach -- 7.5 Two case studies of modelling projects -- 7.6 Summary and conclusions -- References -- 8 An empirical study comparing model development in discrete-event simulation and system dynamics.
8.1 Introduction -- 8.2 Existing work comparing DES and SD modelling -- 8.2.1 DES and SD model development process -- 8.2.2 Summary -- 8.3 The study -- 8.3.1 The case study -- 8.3.2 Verbal protocol analysis -- 8.3.3 The VPA sessions -- 8.3.4 The subjects -- 8.3.5 The coding process -- 8.4 Study results -- 8.4.1 Attention paid to modelling topics -- 8.4.2 The sequence of modelling stages -- 8.4.3 Pattern of iterations among topics -- 8.5 Observations from the DES and SD expert modellers' behaviour -- 8.6 Conclusions -- Acknowledgements -- References -- 9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation -- 9.1 Introduction -- 9.2 Existing comparisons of SD and DES -- 9.3 Research focus -- 9.4 Erratic fisheries - chance, destiny and limited foresight -- 9.5 Structure and behaviour in fisheries: A comparison of SD and DES models -- 9.5.1 Alternative models of a natural fishery -- 9.5.2 Alternative models of a simple harvested fishery -- 9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing -- 9.6 Summary of findings -- 9.7 Limitations of the study -- 9.8 SD or DES? -- Acknowledgements -- References -- 10 DES view on simulation modelling: SIMUL8 -- 10.1 Introduction -- 10.2 How software fits into the project -- 10.3 Building a DES -- 10.4 Getting the right results from a DES -- 10.4.1 Verification and validation -- 10.4.2 Replications -- 10.5 What happens after the results? -- 10.6 What else does DES software do and why? -- 10.7 What next for DES software? -- References -- 11 Vensim and the development of system dynamics -- 11.1 Introduction -- 11.2 Coping with complexity: The need for system dynamics -- 11.3 Complexity arms race -- 11.4 The move to user-led innovation -- 11.5 Software support -- 11.5.1 Apples and oranges (basic model testing) -- 11.5.2 Confidence.
11.5.3 Helping the practitioner do more -- 11.6 The future for SD software -- 11.6.1 Innovation -- 11.6.2 Communication -- References -- 12 Multi-method modeling: AnyLogic -- 12.1 Architectures -- 12.1.1 The choice of model architecture and methods -- 12.2 Technical aspect of combining modeling methods -- 12.2.1 System dynamics → discrete elements -- 12.2.2 Discrete elements → system dynamics -- 12.2.3 Agent based ↔ discrete event -- 12.3 Example: Consumer market and supply chain -- 12.3.1 The supply chain model -- 12.3.2 The market model -- 12.3.3 Linking the DE and the SD parts -- 12.3.4 The inventory policy -- 12.4 Example: Epidemic and clinic -- 12.4.1 The epidemic model -- 12.4.2 The clinic model and the integration of methods -- 12.5 Example: Product portfolio and investment policy -- 12.5.1 Assumptions -- 12.5.2 The model architecture -- 12.5.3 The agent product and agent population portfolio -- 12.5.4 The investment policy -- 12.5.5 Closing the loop and implementing launch of new products -- 12.5.6 Completing the investment policy -- 12.6 Discussion -- References -- 13 Multiscale modelling for public health management: A practical guide -- 13.1 Introduction -- 13.2 Background -- 13.3 Multilevel system theories and methodologies -- 13.4 Multiscale simulation modelling and management -- 13.5 Discussion -- 13.6 Conclusion -- References -- 14 Hybrid modelling case studies -- 14.1 Introduction -- 14.2 A multilevel model of MRSA endemicity and its control in hospitals -- 14.2.1 Introduction -- 14.2.2 Method -- 14.2.3 Results -- 14.2.4 Conclusion -- 14.3 Chlamydia composite model -- 14.3.1 Introduction -- 14.3.2 Chlamydia -- 14.3.3 DES model of a GUM department -- 14.3.4 SD model of chlamydia -- 14.3.5 Why combine the models -- 14.3.6 How the models were combined -- 14.3.7 Experiments with the composite model -- 14.3.8 Conclusions.
14.4 A hybrid model for social care services operations -- 14.4.1 Introduction -- 14.4.2 Population model -- 14.4.3 Model construction -- 14.4.4 Contact centre model -- 14.4.5 Hybrid model -- 14.4.6 Conclusions and lessons learnt -- References -- 15 The ways forward: A personal view of system dynamics and discrete-event simulation -- 15.1 Genesis -- 15.2 Computer simulation in management science -- 15.3 The effect of developments in computing -- 15.4 The importance of process -- 15.5 My own comparison of the simulation approaches -- 15.5.1 Time handling -- 15.5.2 Stochastic and deterministic elements -- 15.5.3 Discrete entities versus continuous variables -- 15.6 Linking system dynamics and discrete-event simulation -- 15.7 The importance of intended model use -- 15.7.1 Decision automation -- 15.7.2 Routine decision support -- 15.7.3 System investigation and improvement -- 15.7.4 Providing insights for debate -- 15.8 The future? -- 15.8.1 Use of both methods will continue to grow -- 15.8.2 Developments in computing will continue to have an effect -- 15.8.3 Process really matters -- References -- Index.
Summary: 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..
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Discrete-Event Simulation and System Dynamics for Management Decision Making -- Contents -- Preface -- List of contributors -- 1 Introduction -- 1.1 How this book came about -- 1.2 The editors -- 1.3 Navigating the book -- References -- 2 Discrete-event simulation: A primer -- 2.1 Introduction -- 2.2 An example of a discrete-event simulation: Modelling a hospital theatres process -- 2.3 The technical perspective: How DES works -- 2.3.1 Time handling in DES -- 2.3.2 Random sampling in DES -- 2.4 The philosophical perspective: The DES worldview -- 2.5 Software for DES -- 2.6 Conclusion -- References -- 3 Systems thinking and system dynamics: A primer -- 3.1 Introduction -- 3.2 Systems thinking -- 3.2.1 'Behaviour over time' graphs -- 3.2.2 Archetypes -- 3.2.3 Principles of influence (or causal loop) diagrams -- 3.2.4 From diagrams to behaviour -- 3.3 System dynamics -- 3.3.1 Principles of stock-.ow diagramming -- 3.3.2 Model purpose and model conceptualisation -- 3.3.3 Adding auxiliaries, parameters and information links to the spinal stock-flow structure -- 3.3.4 Equation writing and dimensional checking -- 3.4 Some further important issues in SD modelling -- 3.4.1 Use of soft variables -- 3.4.2 Co-flows -- 3.4.3 Delays and smoothing functions -- 3.4.4 Model validation -- 3.4.5 Optimisation of SD models -- 3.4.6 The role of data in SD models -- 3.5 Further reading -- References -- 4 Combining problem structuring methods with simulation: The philosophical and practical challenges -- 4.1 Introduction -- 4.2 What are problem structuring methods? -- 4.3 Multiparadigm multimethodology in management science -- 4.3.1 Paradigm incommensurability -- 4.3.2 Cultural difficulties -- 4.3.3 Cognitive difficulties -- 4.3.4 Practical problems -- 4.4 Relevant projects and case studies -- 4.5 The case study: Evaluating intermediate care.

4.5.1 The problem situation -- 4.5.2 Soft systems methodology -- 4.5.3 Discrete-event simulation modelling -- 4.5.4 Multimethodology -- 4.6 Discussion -- 4.6.1 The multiparadigm multimethodology position and strategy -- 4.6.2 The cultural difficulties -- 4.6.3 The cognitive difficulties -- 4.7 Conclusions -- Acknowledgements -- References -- 5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective -- 5.1 Introduction -- 5.2 Ontological and epistemological assumptions of CR -- 5.2.1 The stratified CR ontology -- 5.2.2 The abductive mode of reasoning -- 5.3 Process system modelling with SD and DES through the prism of CR scientific positioning -- 5.3.1 Lifecycle perspective on SD and DES methods -- 5.4 Process system modelling with SD and DES: Trends in and implications for MS -- 5.5 Summary and conclusions -- References -- 6 Theoretical comparison of discrete-event simulation and system dynamics -- 6.1 Introduction -- 6.2 System dynamics -- 6.3 Discrete-event simulation -- 6.4 Summary: The basic differences -- 6.5 Example: Modelling emergency care in Nottingham -- 6.5.1 Background -- 6.5.2 The ECOD project -- 6.5.3 Choice of modelling approach -- 6.5.4 Quantitative phase -- 6.5.5 Model validation -- 6.5.6 Scenario testing and model results -- 6.5.7 The ED model -- 6.5.8 Discussion -- 6.6 The 64 000 question: Which to choose? -- 6.7 Conclusion -- References -- 7 Models as interfaces -- 7.1 Introduction: Models at the interfaces or models as interfaces -- 7.2 The social roles of simulation -- 7.3 The modelling process -- 7.4 The modelling approach -- 7.5 Two case studies of modelling projects -- 7.6 Summary and conclusions -- References -- 8 An empirical study comparing model development in discrete-event simulation and system dynamics.

8.1 Introduction -- 8.2 Existing work comparing DES and SD modelling -- 8.2.1 DES and SD model development process -- 8.2.2 Summary -- 8.3 The study -- 8.3.1 The case study -- 8.3.2 Verbal protocol analysis -- 8.3.3 The VPA sessions -- 8.3.4 The subjects -- 8.3.5 The coding process -- 8.4 Study results -- 8.4.1 Attention paid to modelling topics -- 8.4.2 The sequence of modelling stages -- 8.4.3 Pattern of iterations among topics -- 8.5 Observations from the DES and SD expert modellers' behaviour -- 8.6 Conclusions -- Acknowledgements -- References -- 9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation -- 9.1 Introduction -- 9.2 Existing comparisons of SD and DES -- 9.3 Research focus -- 9.4 Erratic fisheries - chance, destiny and limited foresight -- 9.5 Structure and behaviour in fisheries: A comparison of SD and DES models -- 9.5.1 Alternative models of a natural fishery -- 9.5.2 Alternative models of a simple harvested fishery -- 9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing -- 9.6 Summary of findings -- 9.7 Limitations of the study -- 9.8 SD or DES? -- Acknowledgements -- References -- 10 DES view on simulation modelling: SIMUL8 -- 10.1 Introduction -- 10.2 How software fits into the project -- 10.3 Building a DES -- 10.4 Getting the right results from a DES -- 10.4.1 Verification and validation -- 10.4.2 Replications -- 10.5 What happens after the results? -- 10.6 What else does DES software do and why? -- 10.7 What next for DES software? -- References -- 11 Vensim and the development of system dynamics -- 11.1 Introduction -- 11.2 Coping with complexity: The need for system dynamics -- 11.3 Complexity arms race -- 11.4 The move to user-led innovation -- 11.5 Software support -- 11.5.1 Apples and oranges (basic model testing) -- 11.5.2 Confidence.

11.5.3 Helping the practitioner do more -- 11.6 The future for SD software -- 11.6.1 Innovation -- 11.6.2 Communication -- References -- 12 Multi-method modeling: AnyLogic -- 12.1 Architectures -- 12.1.1 The choice of model architecture and methods -- 12.2 Technical aspect of combining modeling methods -- 12.2.1 System dynamics → discrete elements -- 12.2.2 Discrete elements → system dynamics -- 12.2.3 Agent based ↔ discrete event -- 12.3 Example: Consumer market and supply chain -- 12.3.1 The supply chain model -- 12.3.2 The market model -- 12.3.3 Linking the DE and the SD parts -- 12.3.4 The inventory policy -- 12.4 Example: Epidemic and clinic -- 12.4.1 The epidemic model -- 12.4.2 The clinic model and the integration of methods -- 12.5 Example: Product portfolio and investment policy -- 12.5.1 Assumptions -- 12.5.2 The model architecture -- 12.5.3 The agent product and agent population portfolio -- 12.5.4 The investment policy -- 12.5.5 Closing the loop and implementing launch of new products -- 12.5.6 Completing the investment policy -- 12.6 Discussion -- References -- 13 Multiscale modelling for public health management: A practical guide -- 13.1 Introduction -- 13.2 Background -- 13.3 Multilevel system theories and methodologies -- 13.4 Multiscale simulation modelling and management -- 13.5 Discussion -- 13.6 Conclusion -- References -- 14 Hybrid modelling case studies -- 14.1 Introduction -- 14.2 A multilevel model of MRSA endemicity and its control in hospitals -- 14.2.1 Introduction -- 14.2.2 Method -- 14.2.3 Results -- 14.2.4 Conclusion -- 14.3 Chlamydia composite model -- 14.3.1 Introduction -- 14.3.2 Chlamydia -- 14.3.3 DES model of a GUM department -- 14.3.4 SD model of chlamydia -- 14.3.5 Why combine the models -- 14.3.6 How the models were combined -- 14.3.7 Experiments with the composite model -- 14.3.8 Conclusions.

14.4 A hybrid model for social care services operations -- 14.4.1 Introduction -- 14.4.2 Population model -- 14.4.3 Model construction -- 14.4.4 Contact centre model -- 14.4.5 Hybrid model -- 14.4.6 Conclusions and lessons learnt -- References -- 15 The ways forward: A personal view of system dynamics and discrete-event simulation -- 15.1 Genesis -- 15.2 Computer simulation in management science -- 15.3 The effect of developments in computing -- 15.4 The importance of process -- 15.5 My own comparison of the simulation approaches -- 15.5.1 Time handling -- 15.5.2 Stochastic and deterministic elements -- 15.5.3 Discrete entities versus continuous variables -- 15.6 Linking system dynamics and discrete-event simulation -- 15.7 The importance of intended model use -- 15.7.1 Decision automation -- 15.7.2 Routine decision support -- 15.7.3 System investigation and improvement -- 15.7.4 Providing insights for debate -- 15.8 The future? -- 15.8.1 Use of both methods will continue to grow -- 15.8.2 Developments in computing will continue to have an effect -- 15.8.3 Process really matters -- References -- Index.

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..

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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