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Smart Decisions in Complex Systems
ISBN-13: 9781786301109 / Angielski / Twarda / 2017 / 376 str.
Smart Decisions in Complex Systems
ISBN-13: 9781786301109 / Angielski / Twarda / 2017 / 376 str.
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Faced with ever-increasing complexity on a daily basis, the decision-makers of today are struggling to find the appropriate models, methods and tools to face the issues arising in complex systems across all levels of global operations. Having, in the past, resorted to outdated approaches which limit problem-solving to linear world views, we must now capitalize on complexities in order to succeed and progress in our society. This book provides a guide to harnessing the wealth inherent to complex systems. It organizes the transition to complex decision-making in all business spheres while providing many examples in various application domains. The authors offer fresh developments for understanding and mastering the global "uberization" of the economy, the post-modern management of computer-assisted production and the rise of cognitive robotics science applications.
Faced with ever–increasing complexity on a daily basis, the decision–makers of today are struggling to find the appropriate models, methods and tools to face the issues arising in complex systems across all levels of global operations.
Wydanie ilustrowane
Contents
Preface xiii
Acknowledgments xvii
List of Acronyms xix
Introduction xxv
Part 1. 1
Chapter 1. The Foundations of Complexity 3
1.1. Complexities and simplexities: paradigms and perspectives 3
1.1.1. Positioning the problem 4
1.1.2. Reminders, basics and neologisms 5
1.1.3. What are the analytical steps in a complex system? 16
1.1.4. Organization and management principles in complex systems 31
1.1.5. Action and decision processes in self–organized systems 35
1.1.6. Notions of centralization and decentralization 36
1.2. What is the prerequisite for the handling of a complex system? 43
1.3. Applications: industrial complex systems 45
1.3.1. Distributed workshop management system 45
1.3.2. Analysis and diagnosis of a complex system 47
1.3.3. Some recommendations and comments to conclude 48
1.4. Time to conclude 50
1.4.1. Summary 50
1.4.2. Lessons and perspectives 51
Part 2. 53
Chapter 2. Evidencing Field Complexity 55
2.1. Introduction 55
2.2. Qualitative study of deterministic chaos in a dynamic simple system 58
2.2.1. Description of a few simple cases 58
2.2.2. Initial conditions related to the emergence of chaos 59
2.2.3. Modeling and mathematical analysis of chaos 62
2.2.4. Application at the level of a simple cell 63
2.3. Test for the presence of deterministic chaos in a simple dynamic system 68
2.3.1. Characterization of the systems studied 69
2.3.2. A general question: is there deterministic chaos? 70
2.4. Properties of chaos in complex systems 77
2.4.1. Study of an elementary cell 77
2.4.2. Complex cellular systems 81
2.5. Effects of fractal chaos in Complexity theory 83
2.5.1. Organized complexity 83
2.5.2. Innovative complexity 84
2.5.3. Random complexity 85
2.5.4. Principles of implementation 87
2.6. Self–organization: relations and the role of chaos 87
2.6.1. Introduction 87
2.6.2. How to combine self–organization and chaos 88
2.6.3. Critical self–organized systems 89
2.6.4. Networked systems and co–operative systems 90
2.6.5. The three states of a dynamic complex system 93
2.6.6. Towards a typology of behavioral complexity 94
2.7. Applications: introduction of new concepts in systems 95
2.7.1. Questions on the management of complex industrial systems 95
2.7.2. Implementation of the concepts of chaos and self–organization 96
2.8. Conclusions 98
Chapter 3. The New Complex Operational Context 101
3.1. The five phases of economy how everything
accelerates at the same time 101
3.2. The expected impact on just about everything 105
Chapter 4. Taking Up Complexity 109
4.1. Taking into account complex models 109
4.1.1. A brief overview of the approach called complexity 109
4.1.2. Another (bio–inspired) vision of the world: universality 112
4.1.3. How to address complexity in this universal world? 115
4.1.4. The usefulness of this book 116
4.2. Economy and management of risks 117
4.2.1. Important challenges to raise 117
4.2.2. Adapted vocabulary that it is useful to adopt 118
4.2.3. What do we mean by dynamic pricing? 119
Part 3. 121
Chapter 5. Tackling Complexity with a Methodology 123
5.1. Any methodology must first enrich the systemic interrelationships 123
5.1.1. The innovation economy: the dynamic management of innovation 124
5.1.2. A basic mechanism of efficient innovation 125
5.1.3. The benefits of such a shift mechanism 126
5.2. Towards a transdisciplinary co–economy 126
Chapter 6. Management and Control of Complex Systems 129
6.1. Introduction 129
6.2. Complex systems: the alternatives 132
6.2.1. Notions of sociability in agent communities 132
6.2.2. The evolutionary principles of complex systems 134
6.3. Control principles of production systems 135
6.3.1. Introduction 135
6.3.2. Control: by scheduling or by configuration? 136
6.3.3. The tools used in monitoring and control 140
6.4. PABADIS: an example of decentralized control 141
6.4.1. Introduction 141
6.4.2. Context and objectives of the PABADIS project 142
6.4.3. Conceptual overview of PABADIS 142
6.4.4. Principle of adopted convergence: the inverse solution 144
6.4.5. Implementation 145
6.5. Generalization of the concepts and mechanisms 146
6.5.1. Introduction 146
6.5.2. Allocation of resources: the agents in complex production systems 147
6.5.3. Allocation of resources: the negotiation protocols 147
6.5.4. Optimization of the resource allocation process 148
6.6. A basic mechanism of control the auction 150
6.6.1. Introduction 150
6.6.2. The mechanism of the auction 151
6.6.3. Comparative review of the types of auctions 153
6.6.4. Findings on the interest of the auction mechanism 155
6.7. The control of self–organized systems 156
6.7.1. Introduction 156
6.7.2. The types and mechanisms of self–organization 157
6.7.3. Towards a dynamic integrated model: Cellular Automata (CA) 160
6.7.4. Self–organization: forms and configurations obtained 165
6.7.5. Conclusion and implementation of the ACCA concept, a major model 167
Chapter 7. Platforms for Taking up Complexity 169
7.1. The VFDCS: a platform for implementation 169
7.1.1. Controlling the phenomena of self–organization 171
7.1.2. Methodology for implementation and the validation of concepts 172
7.2. The application of VFDCS: the auction market 174
7.2.1. The concept of the Container in the auction market 176
7.2.2. Feedbacks and results 176
7.2.3. Discussion 178
7.3. The application of VFDCS: the virtual supply chain 179
7.3.1. Introduction 179
7.3.2. Architecture of the virtual supply chain 181
7.3.3. Results and comments 184
7.3.4. Conclusion 185
7.3.5. Enhancement of the multi–agent platform 186
7.4. General method for the control of systems 186
7.4.1. Introduction 186
7.4.2. Reminders and definitions 187
7.4.3. Analytical approach to consistency 188
7.4.4. Methods for the analysis and monitoring of performances 189
7.4.5. Critical analysis of the convergence of configurations 192
7.5. Conclusions and prospects 194
7.5.1. Synthesis 194
7.5.2. Discussion 195
7.5.3. Comparison of approaches, tools and applications 197
7.5.4. Results 199
Part 4. 201
Introduction to Part 4 203
Chapter 8. Applying Intrinsic Complexity:
The Uberization of the Economy 207
8.1. Preamble 207
8.2. The context: new opportunities and new consumption needs 207
8.3. The domains that are studied in this chapter 208
8.4. Concepts, definitions and remainders 209
8.4.1. Uberization 209
8.4.2. Digitalization of the economy 210
8.4.3. Collaborative consumption (CC) 211
8.4.4. Model generalization: the sharing economy 211
8.4.5. Participatory financing 211
8.5. The business model and key elements 213
8.5.1. Practicing networks 213
8.5.2. Positive and negative impacts of network applications 214
8.5.3. The problem of producer consumers and consumer producers 215
8.5.4. Underlying mechanisms: some differences with the usual economic systems 216
8.5.5. A form of social hypocrisy? 217
8.5.6. Generalization: the management rules for P2P 219
8.6. The problem of property and resource allocation. 220
8.6.1. The growing role of platforms 220
8.6.2. The prisoner s dilemma 223
8.6.3. Games theory: an introduction 224
8.6.4. Nonlinear models in game theory 224
8.7. The uberization approach in context 226
8.7.1. Simplexification. 227
8.7.2. Increasing complexity: the influence of cognitive approaches 227
8.8. Generalization: the complexity of allocation problems 230
8.9. Conclusion 234
Chapter 9. Computer–assisted Production Management 235
9.1. Introduction and reminders 235
9.2. Intercommunication networks 236
9.2.1. Notions of complexity in networks 236
x Smart Decisions in Complex Systems
9.2.2. A few concepts of parallelism 237
9.2.3. Elements of parallelism and associated architectures 237
9.2.4. Transposition into industrial or social applications 239
9.3. Communication network topologies 240
9.3.1. Some characteristics of different network topologies 241
9.3.2. Construction of a hypercube 242
9.3.3. Notions of symmetry: cutting a hypercube 243
9.3.4. The shortest path between two processors 244
9.4. A few important properties 244
9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility 246
9.5.1. General approach: planning and scheduling 247
9.5.2. Illustration in management systems 247
9.5.3. Problems and remarks 250
9.5.4. Improvements in planning and scheduling 251
9.5.5. Improvements in configuration/reconfiguration 252
9.5.6. Global improvements through simulation 253
9.5.7. Inverse modeling and simulation 254
9.6. New concepts for managing complex systems 256
9.6.1. Traditional approach 257
9.6.2. Recent improvements in the management of systems 260
9.7. The change of conduct 264
9.8. Improvements in manufacturing: process balancing 266
9.9. Conclusion: main action principles in complex environments 267
Chapter 10. Complexity and Cognitive Robotics 271
10.1. Introduction 271
10.2. The new industrial revolution 272
10.3. The factory of the future: trend or revolution? 272
10.4. Inputs for the factory of the future and their impact on the industry s professions 275
10.5. Conditions for success 276
10.6. The data sciences 277
10.6.1. Introduction to the characteristics of Big Data 277
10.6.2. The problem of Big Data 277
10.6.3. A new profession: the data scientist 279
10.6.4. Some ask, how will this be possible? 279
10.6.5. The field of large numbers 280
10.7. A few technologies in data sciences 281
10.7.1. The steps of reasoning based on the experience of the inductive approach and on the verification of hypotheses 281
10.7.2. The Lasso method 281
10.7.3. Kernel regression methods 282
10.7.4. The random forests 283
10.7.5. Neural networks 284
10.7.6. Comments on clustering and graph partitioning issues 286
10.7.7. Cognitive informatics cognitivism 286
10.8. Mechanisms of conventional cognitive engineering 288
10.9. The new mechanisms of engineering 289
10.9.1. Transduction 289
10.9.2. Reasoning by constructed analogies 290
10.10. The study of links and relationships in large databases 290
10.10.1. Comment 291
10.11. Application of cognitive robotics: the Watson platform 291
10.11.1. Applications 292
10.12. The impossibilities and unpredictabilities of complexity 293
10.13. Current strategies of digitalization 295
10.13.1. Reference examples and discussion 296
10.13.2. GNOSIS 298
10.13.3. Data is Centric 299
10.14. Conclusion: a maximum risk economy 300
Bibliography 303
Index 327
Pierre MASSOTTE, Pr. HDr.Ing., has long worked for IBM in Quality then Advanced Technologies (AoT), then as scientific director in EMEA Manufacturing, to improve European Manufacturing plants and Development Laboratories competitivity. Lately, he joined "Ecole des Mines d′Alès" as Deputy Director within the Nîmes EMA Laboratory. His research and development topics are related to complexity, self–organization, and issues on business competitiveness and sustainability in global companies. He is the co–author of several books in production systems management. He is now involved, as senior consultant, in various ′inclusive society′ projects.
Patrick CORSI , Dr. Ing. is an international consultant specialized in breakthrough design innovation processes. After an engineering and managerial career in industry with IBM Corp., IBM France, SYSECA/THOMSON–CSF and a successful start–up in artificial intelligence in Paris, he acted as a Project Officer within the European Commission in Brussels, specializing in R&D projects in advanced AI technologies. He is an ex–Associate Professor, a serial business books and eBooks author and a professional speaker, and an Associate Practitioner with Mines ParisTech in a large number of application domains.
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