Guide to Computational Modelling for Decision Processes

Theory, Algorithms, Techniques and Applications

  • Stuart Berry
  • Val Lowndes
  • Marcello Trovati

Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Introduction to Modelling and Model Evaluation

    1. Front Matter
      Pages 1-1
    2. Val Lowndes (Retired), Stuart Berry, Marcello Trovati, Amanda Whitbrook
      Pages 3-53
    3. Val Lowndes, Adrian Bird, Stuart Berry
      Pages 55-73
    4. Val Lowndes, Stuart Berry
      Pages 75-120
    5. Val Lowndes, Stuart Berry
      Pages 121-143
    6. Val Lowndes, Stuart Berry
      Pages 145-171
  3. Case Studies

    1. Front Matter
      Pages 173-173
    2. Val Lowndes, Ovidiu Bagdasar, Stuart Berry
      Pages 175-197
    3. Val Lowndes, Stuart Berry, Chris Parkes, Ovidiu Bagdasar, Nicolae Popovici
      Pages 199-235
    4. Val Lowndes, Stuart Berry
      Pages 251-263
    5. Bruce Wiggins, Stuart Berry, Val Lowndes
      Pages 273-284
    6. Chris Parkes, Stuart Berry, John Stubbs
      Pages 285-297
    7. Kim Smith, Richard Hill, Stuart Berry, Richard Conniss
      Pages 307-331
    8. Marcello Trovati, Andy Baker
      Pages 333-345
  4. Appendices

  5. Back Matter
    Pages 391-396

About this book


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.



Modelling Simulation Complex processes Operational research Problem solving Heuristics

Editors and affiliations

  • Stuart Berry
    • 1
  • Val Lowndes
    • 2
  • Marcello Trovati
    • 3
  1. 1.Department of Computing and Mathematics, College of Engineering and TechnologyUniversity of DerbyDerbyUnited Kingdom
  2. 2.University of DerbyDerbyUnited Kingdom
  3. 3.University of DerbyDerbyUnited Kingdom

Bibliographic information