{"product_id":"discreteevent-simulation-9781118349021","title":"DiscreteEvent Simulation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\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","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406855250263,"sku":"9781118349021","price":70.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118349021.jpg?v=1730497355","url":"https:\/\/bookcurl.com\/products\/discreteevent-simulation-9781118349021","provider":"Book Curl","version":"1.0","type":"link"}