{"product_id":"discrete-event-simulation-and-system-dynamics-for-management-decision-making-isbn-9781118349021","title":"Discrete-Event Simulation and System Dynamics for Management Decision Making","description":"\u003cp\u003eIn 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.        \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface xv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of contributors xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSally Brailsford, Leonid Churilov and Brian Danger\u003c\/i\u003e\u003ci\u003efi\u003c\/i\u003e\u003ci\u003eeld\u003cbr\u003e \u003cbr\u003e\u003c\/i\u003e1.1 How this book came about 1\u003c\/p\u003e \u003cp\u003e1.2 The editors 2\u003c\/p\u003e \u003cp\u003e1.3 Navigating the book 3\u003c\/p\u003e \u003cp\u003eReferences 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Discrete-event simulation: A primer 10\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eStewart Robinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 10\u003c\/p\u003e \u003cp\u003e2.2 An example of a discrete-event simulation: Modelling a hospital theatres process 11\u003c\/p\u003e \u003cp\u003e2.3 The technical perspective: How DES works 12\u003c\/p\u003e \u003cp\u003e2.3.1 Time handling in DES 14\u003c\/p\u003e \u003cp\u003e2.3.2 Random sampling in DES 15\u003c\/p\u003e \u003cp\u003e2.4 The philosophical perspective: The DES worldview 21\u003c\/p\u003e \u003cp\u003e2.5 Software for DES 23\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 24\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Systems thinking and system dynamics: A primer 26\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBrian Danger\u003c\/i\u003e\u003ci\u003efi\u003c\/i\u003e\u003ci\u003eeld\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 26\u003c\/p\u003e \u003cp\u003e3.2 Systems thinking 28\u003c\/p\u003e \u003cp\u003e3.2.1 ‘Behaviour over time’ graphs 28\u003c\/p\u003e \u003cp\u003e3.2.2 Archetypes 29\u003c\/p\u003e \u003cp\u003e3.2.3 Principles of influence (or causal loop) diagrams 30\u003c\/p\u003e \u003cp\u003e3.2.4 From diagrams to behaviour 32\u003c\/p\u003e \u003cp\u003e3.3 System dynamics 34\u003c\/p\u003e \u003cp\u003e3.3.1 Principles of stock–flow diagramming 34\u003c\/p\u003e \u003cp\u003e3.3.2 Model purpose and model conceptualisation 35\u003c\/p\u003e \u003cp\u003e3.3.3 Adding auxiliaries, parameters and information links to the spinal stock–flow structure 36\u003c\/p\u003e \u003cp\u003e3.3.4 Equation writing and dimensional checking 37\u003c\/p\u003e \u003cp\u003e3.4 Some further important issues in SD modelling 40\u003c\/p\u003e \u003cp\u003e3.4.1 Use of soft variables 40\u003c\/p\u003e \u003cp\u003e3.4.2 Co-flows 42\u003c\/p\u003e \u003cp\u003e3.4.3 Delays and smoothing functions 43\u003c\/p\u003e \u003cp\u003e3.4.4 Model validation 46\u003c\/p\u003e \u003cp\u003e3.4.5 Optimisation of SD models 48\u003c\/p\u003e \u003cp\u003e3.4.6 The role of data in SD models 49\u003c\/p\u003e \u003cp\u003e3.5 Further reading 49\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Combining problem structuring methods with simulation: The philosophical and practical challenges 52\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKathy Kotiadis and John Mingers\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 52\u003c\/p\u003e \u003cp\u003e4.2 What are problem structuring methods? 53\u003c\/p\u003e \u003cp\u003e4.3 Multiparadigm multimethodology in management science 54\u003c\/p\u003e \u003cp\u003e4.3.1 Paradigm incommensurability 55\u003c\/p\u003e \u003cp\u003e4.3.2 Cultural difficulties 57\u003c\/p\u003e \u003cp\u003e4.3.3 Cognitive difficulties 58\u003c\/p\u003e \u003cp\u003e4.3.4 Practical problems 59\u003c\/p\u003e \u003cp\u003e4.4 Relevant projects and case studies 60\u003c\/p\u003e \u003cp\u003e4.5 The case study: Evaluating intermediate care 62\u003c\/p\u003e \u003cp\u003e4.5.1 The problem situation 62\u003c\/p\u003e \u003cp\u003e4.5.2 Soft systems methodology 64\u003c\/p\u003e \u003cp\u003e4.5.3 Discrete-event simulation modelling 66\u003c\/p\u003e \u003cp\u003e4.5.4 Multimethodology 67\u003c\/p\u003e \u003cp\u003e4.6 Discussion 68\u003c\/p\u003e \u003cp\u003e4.6.1 The multiparadigm multimethodology position and strategy 68\u003c\/p\u003e \u003cp\u003e4.6.2 The cultural difficulties 70\u003c\/p\u003e \u003cp\u003e4.6.3 The cognitive difficulties 70\u003c\/p\u003e \u003cp\u003e4.7 Conclusions 72\u003c\/p\u003e \u003cp\u003eAcknowledgements 72\u003c\/p\u003e \u003cp\u003eReferences 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective 76\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKristian Rotaru, Leonid Churilov and Andrew Flitman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 76\u003c\/p\u003e \u003cp\u003e5.2 Ontological and epistemological assumptions of CR 80\u003c\/p\u003e \u003cp\u003e5.2.1 The stratified CR ontology 80\u003c\/p\u003e \u003cp\u003e5.2.2 The abductive mode of reasoning 81\u003c\/p\u003e \u003cp\u003e5.3 Process system modelling with SD and DES through the prism of CR scientific positioning 82\u003c\/p\u003e \u003cp\u003e5.3.1 Lifecycle perspective on SD and DES methods 84\u003c\/p\u003e \u003cp\u003e5.4 Process system modelling with SD and DES: Trends in and implications for MS 90\u003c\/p\u003e \u003cp\u003e5.5 Summary and conclusions 97\u003c\/p\u003e \u003cp\u003eReferences 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Theoretical comparison of discrete-event simulation and system dynamics 105\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSally Brailsford\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 105\u003c\/p\u003e \u003cp\u003e6.2 System dynamics 106\u003c\/p\u003e \u003cp\u003e6.3 Discrete-event simulation 108\u003c\/p\u003e \u003cp\u003e6.4 Summary: The basic differences 110\u003c\/p\u003e \u003cp\u003e6.5 Example: Modelling emergency care in Nottingham 112\u003c\/p\u003e \u003cp\u003e6.5.1 Background 112\u003c\/p\u003e \u003cp\u003e6.5.2 The ECOD project 113\u003c\/p\u003e \u003cp\u003e6.5.3 Choice of modelling approach 114\u003c\/p\u003e \u003cp\u003e6.5.4 Quantitative phase 114\u003c\/p\u003e \u003cp\u003e6.5.5 Model validation 116\u003c\/p\u003e \u003cp\u003e6.5.6 Scenario testing and model results 116\u003c\/p\u003e \u003cp\u003e6.5.7 The ED model 118\u003c\/p\u003e \u003cp\u003e6.5.8 Discussion 119\u003c\/p\u003e \u003cp\u003e6.6 The $64 000 question: Which to choose? 120\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 123\u003c\/p\u003e \u003cp\u003eReferences 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Models as interfaces 125\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSteffen Bayer, Tim Bolt, Sally Brailsford and Maria Kapsali\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction: Models at the interfaces or models as interfaces 125\u003c\/p\u003e \u003cp\u003e7.2 The social roles of simulation 126\u003c\/p\u003e \u003cp\u003e7.3 The modelling process 129\u003c\/p\u003e \u003cp\u003e7.4 The modelling approach 131\u003c\/p\u003e \u003cp\u003e7.5 Two case studies of modelling projects 134\u003c\/p\u003e \u003cp\u003e7.6 Summary and conclusions 137\u003c\/p\u003e \u003cp\u003eReferences 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 An empirical study comparing model development in discrete-event simulation and system dynamics 140\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAntuela Tako and Stewart Robinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 140\u003c\/p\u003e \u003cp\u003e8.2 Existing work comparing DES and SD modelling 142\u003c\/p\u003e \u003cp\u003e8.2.1 DES and SD model development process 143\u003c\/p\u003e \u003cp\u003e8.2.2 Summary 146\u003c\/p\u003e \u003cp\u003e8.3 The study 146\u003c\/p\u003e \u003cp\u003e8.3.1 The case study 146\u003c\/p\u003e \u003cp\u003e8.3.2 Verbal protocol analysis 147\u003c\/p\u003e \u003cp\u003e8.3.3 The VPA sessions 149\u003c\/p\u003e \u003cp\u003e8.3.4 The subjects 149\u003c\/p\u003e \u003cp\u003e8.3.5 The coding process 150\u003c\/p\u003e \u003cp\u003e8.4 Study results 151\u003c\/p\u003e \u003cp\u003e8.4.1 Attention paid to modelling topics 152\u003c\/p\u003e \u003cp\u003e8.4.2 The sequence of modelling stages 154\u003c\/p\u003e \u003cp\u003e8.4.3 Pattern of iterations among topics 155\u003c\/p\u003e \u003cp\u003e8.5 Observations from the DES and SD expert modellers’ behaviour 158\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 160\u003c\/p\u003e \u003cp\u003eAcknowledgements 162\u003c\/p\u003e \u003cp\u003eReferences 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation 165\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJohn Morecroft and Stewart Robinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 165\u003c\/p\u003e \u003cp\u003e9.2 Existing comparisons of SD and DES 166\u003c\/p\u003e \u003cp\u003e9.3 Research focus 169\u003c\/p\u003e \u003cp\u003e9.4 Erratic fisheries – chance, destiny and limited foresight 170\u003c\/p\u003e \u003cp\u003e9.5 Structure and behaviour in fisheries: A comparison of SD and DES models 173\u003c\/p\u003e \u003cp\u003e9.5.1 Alternative models of a natural fishery 174\u003c\/p\u003e \u003cp\u003e9.5.2 Alternative models of a simple harvested fishery 178\u003c\/p\u003e \u003cp\u003e9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing 184\u003c\/p\u003e \u003cp\u003e9.6 Summary of findings 192\u003c\/p\u003e \u003cp\u003e9.7 Limitations of the study 193\u003c\/p\u003e \u003cp\u003e9.8 SD or DES? 194\u003c\/p\u003e \u003cp\u003eAcknowledgements 196\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 DES view on simulation modelling: SIMUL8 199\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMark Elder\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 199\u003c\/p\u003e \u003cp\u003e10.2 How software fits into the project 200\u003c\/p\u003e \u003cp\u003e10.3 Building a DES 202\u003c\/p\u003e \u003cp\u003e10.4 Getting the right results from a DES 208\u003c\/p\u003e \u003cp\u003e10.4.1 Verification and validation 210\u003c\/p\u003e \u003cp\u003e10.4.2 Replications 211\u003c\/p\u003e \u003cp\u003e10.5 What happens after the results? 212\u003c\/p\u003e \u003cp\u003e10.6 What else does DES software do and why? 212\u003c\/p\u003e \u003cp\u003e10.7 What next for DES software? 213\u003c\/p\u003e \u003cp\u003eReferences 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Vensim and the development of system dynamics 215\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLee Jones\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 215\u003c\/p\u003e \u003cp\u003e11.2 Coping with complexity: The need for system dynamics 216\u003c\/p\u003e \u003cp\u003e11.3 Complexity arms race 219\u003c\/p\u003e \u003cp\u003e11.4 The move to user-led innovation 221\u003c\/p\u003e \u003cp\u003e11.5 Software support 222\u003c\/p\u003e \u003cp\u003e11.5.1 Apples and oranges (basic model testing) 223\u003c\/p\u003e \u003cp\u003e11.5.2 Confidence 224\u003c\/p\u003e \u003cp\u003e11.5.3 Helping the practitioner do more 237\u003c\/p\u003e \u003cp\u003e11.6 The future for SD software 245\u003c\/p\u003e \u003cp\u003e11.6.1 Innovation 245\u003c\/p\u003e \u003cp\u003e11.6.2 Communication 245\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multi-method modeling: AnyLogic 248\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAndrei Borshchev\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Architectures 249\u003c\/p\u003e \u003cp\u003e12.1.1 The choice of model architecture and methods 251\u003c\/p\u003e \u003cp\u003e12.2 Technical aspect of combining modeling methods 252\u003c\/p\u003e \u003cp\u003e12.2.1 System dynamics ® discrete elements 252\u003c\/p\u003e \u003cp\u003e12.2.2 Discrete elements ® system dynamics 253\u003c\/p\u003e \u003cp\u003e12.2.3 Agent based « discrete event 255\u003c\/p\u003e \u003cp\u003e12.3 Example: Consumer market and supply chain 257\u003c\/p\u003e \u003cp\u003e12.3.1 The supply chain model 257\u003c\/p\u003e \u003cp\u003e12.3.2 The market model 258\u003c\/p\u003e \u003cp\u003e12.3.3 Linking the DE and the SD parts 259\u003c\/p\u003e \u003cp\u003e12.3.4 The inventory policy 260\u003c\/p\u003e \u003cp\u003e12.4 Example: Epidemic and clinic 262\u003c\/p\u003e \u003cp\u003e12.4.1 The epidemic model 262\u003c\/p\u003e \u003cp\u003e12.4.2 The clinic model and the integration of methods 264\u003c\/p\u003e \u003cp\u003e12.5 Example: Product portfolio and investment policy 267\u003c\/p\u003e \u003cp\u003e12.5.1 Assumptions 268\u003c\/p\u003e \u003cp\u003e12.5.2 The model architecture 270\u003c\/p\u003e \u003cp\u003e12.5.3 The agent product and agent population portfolio 271\u003c\/p\u003e \u003cp\u003e12.5.4 The investment policy 274\u003c\/p\u003e \u003cp\u003e12.5.5 Closing the loop and implementing launch of new products 275\u003c\/p\u003e \u003cp\u003e12.5.6 Completing the investment policy 277\u003c\/p\u003e \u003cp\u003e12.6 Discussion 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multiscale modelling for public health management: A practical guide 280\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRosemarie Sadsad and Geoff McDonnell\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 280\u003c\/p\u003e \u003cp\u003e13.2 Background 281\u003c\/p\u003e \u003cp\u003e13.3 Multilevel system theories and methodologies 281\u003c\/p\u003e \u003cp\u003e13.4 Multiscale simulation modelling and management 283\u003c\/p\u003e \u003cp\u003e13.5 Discussion 289\u003c\/p\u003e \u003cp\u003e13.6 Conclusion 290\u003c\/p\u003e \u003cp\u003eReferences 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Hybrid modelling case studies 295\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRosemarie Sadsad, Geoff McDonnell, Joe Viana, Shivam M. Desai, Paul Harper and Sally Brailsford\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 295\u003c\/p\u003e \u003cp\u003e14.2 A multilevel model of MRSA endemicity and its control in hospitals 296\u003c\/p\u003e \u003cp\u003e14.2.1 Introduction 296\u003c\/p\u003e \u003cp\u003e14.2.2 Method 296\u003c\/p\u003e \u003cp\u003e14.2.3 Results 297\u003c\/p\u003e \u003cp\u003e14.2.4 Conclusion 302\u003c\/p\u003e \u003cp\u003e14.3 Chlamydia composite model 302\u003c\/p\u003e \u003cp\u003e14.3.1 Introduction 302\u003c\/p\u003e \u003cp\u003e14.3.2 Chlamydia 302\u003c\/p\u003e \u003cp\u003e14.3.3 DES model of a GUM department 303\u003c\/p\u003e \u003cp\u003e14.3.4 SD model of chlamydia 304\u003c\/p\u003e \u003cp\u003e14.3.5 Why combine the models 304\u003c\/p\u003e \u003cp\u003e14.3.6 How the models were combined 305\u003c\/p\u003e \u003cp\u003e14.3.7 Experiments with the composite model 305\u003c\/p\u003e \u003cp\u003e14.3.8 Conclusions 307\u003c\/p\u003e \u003cp\u003e14.4 A hybrid model for social care services operations 308\u003c\/p\u003e \u003cp\u003e14.4.1 Introduction 308\u003c\/p\u003e \u003cp\u003e14.4.2 Population model 308\u003c\/p\u003e \u003cp\u003e14.4.3 Model construction 309\u003c\/p\u003e \u003cp\u003e14.4.4 Contact centre model 310\u003c\/p\u003e \u003cp\u003e14.4.5 Hybrid model 311\u003c\/p\u003e \u003cp\u003e14.4.6 Conclusions and lessons learnt 313\u003c\/p\u003e \u003cp\u003eReferences 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 The ways forward: A personal view of system dynamics and discrete-event simulation 318\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMichael Pidd\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Genesis 318\u003c\/p\u003e \u003cp\u003e15.2 Computer simulation in management science 319\u003c\/p\u003e \u003cp\u003e15.3 The effect of developments in computing 320\u003c\/p\u003e \u003cp\u003e15.4 The importance of process 324\u003c\/p\u003e \u003cp\u003e15.5 My own comparison of the simulation approaches 324\u003c\/p\u003e \u003cp\u003e15.5.1 Time handling 324\u003c\/p\u003e \u003cp\u003e15.5.2 Stochastic and deterministic elements 326\u003c\/p\u003e \u003cp\u003e15.5.3 Discrete entities versus continuous variables 327\u003c\/p\u003e \u003cp\u003e15.6 Linking system dynamics and discrete-event simulation 328\u003c\/p\u003e \u003cp\u003e15.7 The importance of intended model use 329\u003c\/p\u003e \u003cp\u003e15.7.1 Decision automation 330\u003c\/p\u003e \u003cp\u003e15.7.2 Routine decision support 331\u003c\/p\u003e \u003cp\u003e15.7.3 System investigation and improvement 331\u003c\/p\u003e \u003cp\u003e15.7.4 Providing insights for debate 332\u003c\/p\u003e \u003cp\u003e15.8 The future? 333\u003c\/p\u003e \u003cp\u003e15.8.1 Use of both methods will continue to grow 333\u003c\/p\u003e \u003cp\u003e15.8.2 Developments in computing will continue to have an effect 334\u003c\/p\u003e \u003cp\u003e15.8.3 Process really matters 335\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 337\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cstrong\u003eSally Brailsford\u003c\/strong\u003e, School of Management, University of Southampton, UK \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eLeonid Churilov\u003c\/strong\u003e, Melbourne Brain Centre, Victoria, Australia \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBrian Dangerfield\u003c\/strong\u003e, Salford Business School, University of Salford, UK\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989074526437,"sku":"NP9781118349021","price":117.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118349021.jpg?v=1761782689","url":"https:\/\/k12savings.com\/products\/discrete-event-simulation-and-system-dynamics-for-management-decision-making-isbn-9781118349021","provider":"K12savings","version":"1.0","type":"link"}