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.