Guide to Computational Modelling for Decision Processes Theory, Algorithms, Techniques and Applications /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Berry, Stuart (Επιμελητής έκδοσης), Lowndes, Val (Επιμελητής έκδοσης), Trovati, Marcello (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Simulation Foundations, Methods and Applications,
• Θέματα: Computer science.; Operations research.; Decision making.; Algorithms.; Mathematical statistics.; Computer simulation.; Computer Science.; Simulation and Modeling.; Algorithm Analysis and Problem Complexity.; Operation Research/Decision Theory.; Mathematics of Algorithmic Complexity.; Probability and Statistics in Computer Science.

 This interdisciplinary reference and guide provides an introduction to modelling 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 modelling methods and tools, including heuristic and mathematical programming-based models, and queuing 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 queuing theory Reviews examples incorporating system dynamics modelling, cellular automata and agent-based simulations, and the use of big data Contains appendices covering queuing theory, function optimization techniques, Boolean and fuzzy logic, and transport modelling Describes simulation for the evaluation of production planning and control methods, and a model for matching services with users in opportunistic network environments Researchers, practitioners and students in computer science, engineering and business studies will find this work to be an invaluable and in-depth introduction to the use of simulation techniques in the analysis of large and complex problems, in addition to providing an exhaustive description of the theoretical framework and applications being developed to address such problems. . 

 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.