TY  - BOOK
AU  - Massotte,Pierre
AU  - Corsi,Patrick
TI  - Smart Decisions in Complex Systems
SN  - 9781119368731
AV  - Q295.M377 2017 
U1  - 003 
PY  - 2017///
CY  - Newark
PB  - John Wiley & Sons, Incorporated
KW  - System theory
KW  - Electronic books
N1  - Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- List of Acronyms -- Introduction: A World Swept by Complexity -- PART 1 -- 1. The Foundations of Complexity -- 1.1. Complexities and simplexities: paradigms and perspectives -- 1.1.1. Positioning the problem -- 1.1.2. Reminders, basics and neologisms -- 1.1.3. What are the analytical steps in a complex system? -- 1.1.4. Organization and management principles in complex systems -- 1.1.5. Action and decision processes in self-organized systems -- 1.1.6. Notions of centralization and decentralization -- 1.2. What is the prerequisite for the handling of a complex system? -- 1.3. Applications: industrial complex systems -- 1.3.1. Distributed workshop management system -- 1.3.2. Analysis and diagnosis of a complex system -- 1.3.3. Some recommendations and comments to conclude -- 1.4. Time to conclude -- 1.4.1. Summary -- 1.4.2. Lessons and perspectives -- PART 2 -- 2. Evidencing Field Complexity -- 2.1. Introduction -- 2.2. Qualitative study of deterministic chaos in a dynamic simple system -- 2.2.1. Description of a few simple cases -- 2.2.2. Initial conditions related to the emergence of chaos -- 2.2.3. Modeling and mathematical analysis of chaos -- 2.2.4. Application at the level of a simple cell -- 2.3. Test for the presence of deterministic chaos in a simple dynamic system -- 2.3.1. Characterization of the systems studied -- 2.3.2. A general question: is there deterministic chaos? -- 2.4. Properties of chaos in complex systems -- 2.4.1. Study of an elementary cell -- 2.4.2. Complex cellular systems -- 2.5. Effects of fractal chaos in "Complexity" theory -- 2.5.1. Organized complexity -- 2.5.2. Innovative complexity -- 2.5.3. Random complexity -- 2.5.4. Principles of implementation -- 2.6. Self-organization: relations and the role of chaos; 2.6.1. Introduction -- 2.6.2. How to combine self-organization and chaos -- 2.6.3. Critical self-organized systems -- 2.6.4. Networked systems and co-operative systems -- 2.6.5. The three states of a dynamic complex system -- 2.6.6. Towards a typology of behavioral complexity -- 2.7. Applications: introduction of new concepts in systems -- 2.7.1. Questions on the management of complex industrial systems -- 2.7.2. Implementation of the concepts of chaos and self-organization -- 2.8. Conclusions -- 3. The New "Complex" Operational Context -- 3.1. The five phases of economy - how everything accelerates at the same time -- 3.2. The expected impact on just about everything -- 4. Taking Up Complexity -- 4.1. Taking into account complex models -- 4.1.1. A brief overview of the approach called "complexity" -- 4.1.2. Another (bio-inspired) vision of the world: universality -- 4.1.3. How to address complexity in this universal world? -- 4.1.4. The usefulness of this book -- 4.2. Economy and management of risks -- 4.2.1. Important challenges to raise -- 4.2.2. Adapted vocabulary that it is useful to adopt -- 4.2.3. What do we mean by dynamic pricing? -- PART 3 -- 5. Tackling Complexity with a Methodology -- 5.1. Any methodology must first enrich the systemic interrelationships -- 5.1.1. The innovation economy: the dynamic management of innovation -- 5.1.2. A basic mechanism of efficient innovation -- 5.1.3. The benefits of such a shift mechanism -- 5.2. Towards a transdisciplinary co-economy -- 6. Management and Control of Complex Systems -- 6.1. Introduction -- 6.2. Complex systems: the alternatives -- 6.2.1. Notions of sociability in agent communities -- 6.2.2. The evolutionary principles of complex systems -- 6.3. Control principles of production systems -- 6.3.1. Introduction -- 6.3.2. Control: by scheduling or by configuration?; 6.3.3. The tools used in monitoring and control -- 6.4. PABADIS: an example of decentralized control -- 6.4.1. Introduction -- 6.4.2. Context and objectives of the PABADIS project -- 6.4.3. Conceptual overview of PABADIS -- 6.4.4. Principle of adopted convergence: the inverse solution -- 6.4.5. Implementation -- 6.5. Generalization of the concepts and mechanisms -- 6.5.1. Introduction -- 6.5.2. Allocation of resources: the agents in complex production systems -- 6.5.3. Allocation of resources: the negotiation protocols -- 6.5.4. Optimization of the resource allocation process -- 6.6. A basic mechanism of control - the auction -- 6.6.1. Introduction -- 6.6.2. The mechanism of the auction -- 6.6.3. Comparative review of the types of auctions -- 6.6.4. Findings on the interest of the auction mechanism -- 6.7. The control of self-organized systems -- 6.7.1. Introduction -- 6.7.2. The types and mechanisms of self-organization -- 6.7.3. Towards a dynamic integrated model: Cellular Automata (CA) -- 6.7.4. Self-organization: forms and configurations obtained -- 6.7.5. Conclusion and implementation of the ACCA concept, a major model -- 7. Platforms for Taking up Complexity -- 7.1. The VFDCS: a platform for implementation -- 7.1.1. Controlling the phenomena of self-organization -- 7.1.2. Methodology for implementation and the validation of concepts -- 7.2. The application of VFDCS: the auction market -- 7.2.1. The concept of the "Container" in the auction market -- 7.2.2. Feedbacks and results -- 7.2.3. Discussion -- 7.3. The application of VFDCS: the virtual supply chain -- 7.3.1. Introduction -- 7.3.2. Architecture of the virtual supply chain -- 7.3.3. Results and comments -- 7.3.4. Conclusion -- 7.3.5. Enhancement of the multi-agent platform -- 7.4. General method for the control of systems -- 7.4.1. Introduction -- 7.4.2. Reminders and definitions; 7.4.3. Analytical approach to consistency -- 7.4.4. Methods for the analysis and monitoring of performances -- 7.4.5. Critical analysis of the convergence of configurations -- 7.5. Conclusions and prospects -- 7.5.1. Synthesis -- 7.5.2. Discussion -- 7.5.3. Comparison of approaches, tools and applications -- 7.5.4. Results -- PART 4 -- Introduction to Part 4: Overviweing Trends to Complex Fututes -- 8. Applying Intrinsic Complexity: The Uberization of the Economy -- 8.1. Preamble -- 8.2. The context: new opportunities and new consumption needs -- 8.3. The domains that are studied in this chapter -- 8.4. Concepts, definitions and remainders -- 8.4.1. Uberization -- 8.4.2. Digitalization of the economy -- 8.4.3. Collaborative consumption (CC) -- 8.4.4. Model generalization: the sharing economy -- 8.4.5. Participatory financing -- 8.5. The business model and key elements -- 8.5.1. Practicing networks -- 8.5.2. Positive and negative impacts of network applications -- 8.5.3. The problem of producer-consumers and consumer-producers -- 8.5.4. Underlying mechanisms: some differences with the usual economic systems -- 8.5.5. A form of social hypocrisy? -- 8.5.6. Generalization: the management rules for P2P -- 8.6. The problem of property and resource allocation -- 8.6.1. The growing role of platforms -- 8.6.2. The prisoner's dilemma -- 8.6.3. Games theory: an introduction -- 8.6.4. Nonlinear models in game theory -- 8.7. The uberization approach in context -- 8.7.1. Simplexification -- 8.7.2. Increasing complexity: the influence of cognitive approaches -- 8.8. Generalization: the complexity of allocation problems -- 8.9. Conclusion -- 9. Computer-assisted Production Management -- 9.1. Introduction and reminders -- 9.2. Intercommunication networks -- 9.2.1. Notions of complexity in networks [LET 11] -- 9.2.2. A few concepts of parallelism; 9.2.3. Elements of parallelism and associated architectures -- 9.2.4. Transposition into industrial or social applications -- 9.3. Commmunicatioon network topologies -- 9.3.1. Some characteristics of different network topologies -- 9.3.2. Construction of a hypercube -- 9.3.3. Notions of symmetry: cutting a hypercube -- 9.3.4. The shortest path between two processors -- 9.4. A few important properties -- 9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility -- 9.5.1. General approach: planning and scheduling -- 9.5.2. Illustration in management systems -- 9.5.3. Problems and remarks [MAS 14] -- 9.5.4. Improvements in planning and scheduling -- 9.5.5. Improvements in configuration/reconfiguration [MAS 99a, MAS 99b] -- 9.5.6. Global improvements through simulation -- 9.5.7. Inverse modeling and simulation [MAS 06] -- 9.6. New concepts for managing complex systems -- 9.6. New concepts for managing complex systems -- 9.6.1. Traditional approach -- 9.6.2. Recent improvements in the management of systems [MAS 14] -- 9.7. The change of conduct -- 9.8. Improvements in manufacturing: process balancing -- 9.9. Conclusion: main action principles in complex environments -- 10. Complexity and Cognitive Robotics -- 10.1. Introduction -- 10.2. The new industrial revolution -- 10.3. The factory of the future: trend or revolution? -- 10.4. Inputs for the factory of the future and their impact on the industry's professions -- 10.5. Conditions for success -- 10.6. The data sciences -- 10.6.1. Introduction to the characteristics of "Big Data" -- 10.6.2. The problem of Big Data -- 10.6.3. A new profession: the data scientist -- 10.6.4. Some ask, how will this be possible? -- 10.6.5. The field of large numbers -- 10.7. A few technologies in data sciences; 10.7.1. The steps of reasoning based on the experience of the inductive approach and on the verification of hypotheses
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