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

Transcription

1 Discrete-Event Simulation and System Dynamics for Management Decision Making Editors Sally Brailsford Southampton Business School, University of Southampton, UK Leonid Churilov Florey Institute ofneuroscience and Mental Health, Melbourne, Australia RMIT University, Melbourne, Victoria, Australia Brian Dangerfield Salford Business School, University ofsalford, UK

2 Contents Preface List of contributors xv xvii 1 Introduction 1 Sally Brailsford, Leonid Churilov and Brian Dangerfield 1.1 How this book came about The editors Navigating the book 3 References 9 2 Discrete-event Simulation: A primer 10 Stewart Robinson 2.1 Introduction An example of a discrete-event Simulation: Modelling a hospital theatres process The technical perspective: How DES works Time handling in DES Random sampling in DES The philosophical perspective: The DES worldview Software for DES Conclusion 24 References 24 3 Systems thinking and system dynamics: A primer 26 Brian Dangerfield 3.1 Introduction Systems thinking 'Behaviour over time' graphs Archetypes Principles of influence (or causal loop) diagrams From diagrams to behaviour System dynamics Principles of stock-flow diagramming Model purpose and model conceptualisation 35

3 viii CONTENTS Adding auxiliaries, parameters and Information links to the spinal stock-flow structure Equation writing and dimensional checking Some further important issues in SD modelling Use of soft variables Co-flows Delays and smoothing functions Model Validation Optimisation of SD models The role of data in SD models Further reading 49 References 50 4 Combining problem structuring methods with Simulation: The philosophical and practica! challenges 52 Kathy Kotiadis and John Mingers 4.1 Introduction What are problem structuring methods? Multiparadigm multimethodology in management science Paradigm incommensurability Cultural difficulties Cognitive difficulties Practica] problems Relevant projects and case studies The case study: Evaluating intermediate care The problem Situation Soft systems methodology Discrete-event Simulation modelling Multimethodology Discussion The multiparadigm multimethodology position and strategy The cultural difficulties The cognitive difficulties 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 76 Kristian Rotaru, Leonid Churilov and Andrew Flitman 5.1 Introduction Ontological and epistemological assumptions of CR The stratified CR ontology The abductive mode of reasoning 81

4 CONTENTS ix 5.3 Process system modelling with SD and DES through the prism of CR scientific positioning Lifecycle perspective on SD and DES methods Process system modelling with SD and DES: Trends in and implications for MS Summary and conclusions 97 References 99 6 Theoretical comparison of discrete-event Simulation and system dynamics 105 Sally Brailsford 6.1 Introduction System dynamics Discrete-event Simulation Summary: The basic differences Example: Modelling emergency care in Nottingham Background The ECOD project Choice of modelling approach Quantitative phase Model Validation Scenario testing and model results The ED model Discussion The $64000 question: Which to choose? Conclusion 123 References Models as interfaces 125 Steffen Bayer, Tim Bolt, Sally Brailsford and Maria Kapsali 7.1 Introduction: Models at the interfaces or models as interfaces The social roles of Simulation The modelling process The modelling approach Two case studies of modelling projects Summary and conclusions 137 References An empirical study comparing model development in discrete-event Simulation and system dynamics 140 Antuela Tako and Stewart Robinson 8.1 Introduction Existing work comparing DES and SD modelling DES and SD model development process Summary 146

5 x CONTENTS 8.3 The study The case study Verbal protocol analysis The VPA sessions The subjects The coding process Study results Attention paid to modelling topics The sequence of modelling stages Pattern of iterations among topics Observation from the DES and SD expert modellers' behaviour Conclusions 160 Acknowledgements 162 References Explaining puzzling dynamics: A comparison of system dynamics and discrete-event Simulation 165 John Morecroft and Stewart Robinson 9.1 Introduction Existing comparisons of SD and DES Research focus Erratic fisheries - chance, destiny and limited foresight Structure and behaviour in fisheries: A comparison of SD and DES models Alternative models of a natural fishery Alternative models of a simple harvested fishery Alternative models of a harvested fishery with endogenous ship purchasing Summary of findings Limitations of the study SD or DES? 194 Acknowledgements 196 References DES view on Simulation modelling: SIMUL8 199 Mark Eider 10.1 Introduction How Software fits into the project Building a DES Gelting the right results from a DES Verification and Validation Replications What happens after the results? What eise does DES Software do and why? 212

6 CONTENTS xi 10.7 What next for DES Software? 213 References Vensim and the development of system dynamics 215 Lee Jones 11.1 Introduction Coping with complexity: The need for system dynamics Complexity arms race The move to user-led Innovation Software support Apples and oranges (basic model testing) Confidence Helping the practitioner do more The future for SD Software Innovation Communication 245 References Multi-method modeling: AnyLogic 248 Andrei Borshchev 12.1 Architectures The choice of model architecture and methods Technical aspect of combining modeling methods System dynamics > discrete elements Discrete elements > system dynamics Agent based <-+ discrete event Example: Consumer market and supply chain The supply chain model The market model Linking the DE and the SD parts The inventory policy Example: Epidemie and clinic The epidemic model The clinic model and the Integration of methods Example: Product portfolio and Investment policy Assumptions The model architecture The agent product and agent population portfolio The Investment policy Closing the loop and implementing launch of new produets Completing the Investment policy Discussion 278 References 279

7 xii CONTENTS 13 Multiscale modelling for public health management: A practical guide 280 Rosemarie Sadsad and Geoff McDonneII 13.1 Introduction Background Multilevel system theories and methodologies Multiscale Simulation modelling and management Discussion Conclusion 290 References Hybrid modelling case studies 295 Rosemarie Sadsad, Geoff McDonnell, Joe Viana, Shivam M. Desai, Paul Harper and Sally Brailsford 14.1 Introduction A multilevel model of MRSA endemicity and its control in hospitals Introduction Method Results Conclusion Chlamydia composite model Introduction Chlamydia DES model of a GUM department SD model of Chlamydia Why combine the models How the models were combined Experiments with the composite model Conclusions A hybrid model for social care services Operations Introduction Population model Model construction Contact centre model Hybrid model Conclusions and lessons learnt 313 References The ways forward: A personal view of system dynamics and discrete-event Simulation 318 Michael Pidd 15.1 Genesis Computer Simulation in management science The effect of developments in Computing 320

8 CONTENTS xiii 15.4 The importance of process My own comparison of the Simulation approaches Time handling Stochastic and deterministic elements Discrete entities versus continuous variables Linking system dynamics and discrete-event Simulation The importance of intended model use Decision automation Routine decision support System investigation and improvement Providing insights for debate The future? Use of both methods will continue to grow Developments in Computing will continue to have an effect Process really matters 335 References 335 Index 337