Guide to Computational Modelling for Decision Processes: Theory, Algorithms, Techniques and Applications (Simulation Foundations, Methods and Applications)

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. 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.