Guide to Computational Modelling for Decision Processes

Theory, Algorithms, Techniques and Applications

AvStuart Berry,Val Lowndes

Häftad, Engelska, 2018

Del i serien Simulation Foundations, Methods and Applications

574 kr

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Beskrivning

This interdisciplinary reference and guide provides an introduction to modeling methodologies and models which form the starting point for deriving efficient and effective solution techniques, and presents a series of case studies that demonstrate how heuristic and analytical approaches may be used to solve large and complex problems. Topics and features: introduces the key modeling methods and tools, including heuristic and mathematical programming-based models, and queueing theory and simulation techniques; demonstrates the use of heuristic methods to not only solve complex decision-making problems, but also to derive a simpler solution technique; presents case studies on a broad range of applications that make use of techniques from genetic algorithms and fuzzy logic, tabu search, and queueing theory; reviews examples incorporating system dynamics modeling, cellular automata and agent-based simulations, and the use of big data; supplies expanded descriptions and examples in the appendices.

Produktinformation

Utgivningsdatum:2018-05-09
Mått:155 x 235 x 21 mm
Vikt:702 g
Format:Häftad
Språk:Engelska
Serie:Simulation Foundations, Methods and Applications
Antal sidor:396
Förlag:Springer International Publishing AG
ISBN:9783319856544

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Mer om författaren

Dr. Stuart Berry is a lecturer in the Department of Computing and Mathematics at the University of Derby, UK. Dr. Marcello Trovati is a Senior Lecturer at the same institution. He is also a co-editor of the Springer titles Guide to Security Assurance for Cloud Computing and Big-Data Analytics and Cloud Computing. Val Lowndes is a Chartered Mathematician, who formerly worked at the University of Derby.

Innehållsförteckning

Part I: Introduction to Modelling and Model Evaluation.- Model Building.- Introduction to Cellular Automata in Simulation.- Introduction to Mathematical Programming.- Heuristic Techniques in Optimisation.- Introduction to the Use of Queueing Theory and Simulation.- Part II: Case Studies.- Case Studies: Using Heuristics.- Further Use of Heuristic Methods.- Air Traffic Controllers Planning: A Rostering Problem.- Solving Multiple Objective Problems: Modelling Diet Problems.- Fuzzy Scheduling Applied to Small Manufacturing Firms.- The Design and Optimisation of Surround Sound Decoders Using Heuristic Methods.- System Dynamics Case Studies.- Applying Queueing Theory to the Design of a Traffic Light Controller.- Cellular Automata and Agents in Simulations.- Three Big Data Case Studies.- Part III: Appendices.- Appendix A: Queueing Theory.- Appendix B: Function Optimisation Techniques: Genetic Algorithms and Tabu Searches.- Appendix C: What to Simulate to Evaluate Production Planning and Control Methods in Small Manufacturing Firms.- Appendix D: Defining Boolean and Fuzzy Logic Operators.- Appendix E: Assessing the Reinstated Waverley Line.- Appendix F: Matching Services with Users in Opportunistic Network Environments.