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MODELLING AND DESIGN OF COMPLEX SYSTEMS

CODE 98222
ACADEMIC YEAR 2022/2023
CREDITS
  • 8 cfu during the 1st year of 10728 ENGINEERING TECHNOLOGY FOR STRATEGY (AND SECURITY)(LM/DS) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-IND/17
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER Annual
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    This course deals with developing Models and Solutions to analyze Complex Systems and it is developed in synergy with experts of different fields.

    The course is divided into two main parts:

    - Modeling and Design of Complex Systems (M&DCS) in Industry

    - Modeling and Design of Complex Systems (M&DCS) in Defense & Homeland Security

    Attendees will have to use simulators, tools and AI engines to address realistic problems cooperating with Experts to learn how to apply theories proposed in lectures

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Foundation on Complex Systems. Transfer of knowledge about Simulation Paradigms and Modeling Methodologies effective for addressing Complex Systems.. Transfer of capabilities to analyze real problems and case studies corresponding to Complex Systems. Acquisition of skills in Conceptual Modeling applied to Complex Problems. Acquisition of Skills in design of Simulation Architectures and Model Development applied to Complex Systems.

    AIMS AND LEARNING OUTCOMES

    Main Achievements include acquiring skills on:

    Methodologies and Techniques to Analyze Complex Systems

    Modeling and Simulation of Complex Systems

    Combining Data Analytics and Simulation

    Combining AI and Simulation

    A Posteriori and A Priori Analysis

    Experimental Analysis and Data Farming

    Decision Support in Complex Systems

    PREREQUISITES

    The Course does not require specific prerequisites, being accessible to University students and including all the elements and references necessary for the Candidates; therefore basics know-how in engineering, mathematics, statistics and computer use could be useful to improve the Candidate learning curve and performance.

    TEACHING METHODS

    Frontal Lectures presenting Theory and practical application of Methodologies related to Modelling and Simulation for Complex Systems. Individual and Team Work Exercises in developing Conceptual Models and verifying, validating, tuning and conducting experiments on Simulators of Complex Systems. Training and Education of the Students in Virtual Experiences within Simulation Labs by using directly the presented methodologies and techniques in realistic problems and case studies using M&S solutions.

    Educational material available linked to the course on Web site on www.itim.unige.it/strategos

    Teachers include top experts in this field such as:

    SYLLABUS/CONTENT

    Introduction to the Complex Systems of the class. Definition of Complex Systems and Emergent Behaviors. Complexity Classes, Attributes, Level of Complexity. 
    Computational Complexity, Kolmogorov complexity, Krohn–Rhodes theory, Network Complexity, Hierarchical Complexity.
    Complex System Subjects: Dynamic Structures and Complex Dynamics, Complexity in Physical and Chemical systems, Biological Systems, Human Systems, Engineering and Artificial Systems. 
    System of System Engineering and Industrial Plants as examples of Complex Systems.
    Conceptual Modelling for Complex Systems. Live, Virtual and Constructive Simulation applied to Complex Systems. 
    Challenges in Verification, Validation and Accreditation of Simulators dealing with Complex Systems. 
    Example of Modeling Complex Systems: Stochastic Systems, Multiparticle Systems, Multibody Systems, Models used in Systems of Systems Engineering. 
    Examples of Simulation for different real case of Complex Systems: Case Studies related to real Industrial, Business and Defense Frameworks
    Strategic Analysis and Decision Making related to Complex Systems, Design and Reengineering of Complex Systems based on Quantitative Modelling & Simulation Techniques for Identification and Analysis of Emergent Behaviors Simulation Paradigms of M&S for Complex Systems; theoretical foundations of interoperable Simulation, distributed simulation, MS2G, MSaaS. 
    Interoperable Simulation and Modelling Solution for Complex Systems. 
    Design of Models and Development of Simulators and Federations of Simulators.
    Human Behavior Modeling and Intelligent Agents reproducing Population and Social Systems. 
    Operational Expertise in using Modeling and Simulation (M&S) and related experimental methodologies and techniques to investigate complex systems and to support related decision making processes.
    Lean Simulation: Concept, Methodologies and Techniques, Modeling and Simulation applied to Early Stage Evaluation of Large Programs. Methodologies and Techniques for applying M&S in SME (Small Medium Size Enterprises).
    Simulation as enabler for Applying Artificial Intelligence and Intelligent Agents in Industrial and Defense Applications: Nested and Combined Simulation to support Decision Making and Planning. 
    Artificial Intelligence Techniques integrated with Simulation for Strategic Decision Making. 
    Strategic Decision Making Based on Simulation in Defense over Multiple Join Domains: M&S of Joint Operations over a Comprehensive Approach and Simulation for Transformation (e.g. Autonomous Multi Domain Systems, Hybrid Warfare, Threat Networks).
    Simulation of Complex Systems in Business and Industrial Plants (e.g. MOSES, CUMANA, LEM, LEXIS, GreenLog, SISOM).
    Simulation of Complex Systems in Defense and Homeland Security(e.g. JESSI, CAPRICORN, IA-CGF, IDRAS).
    Direct Experiences in applying different Simulation Tools, Models, Soft Computing Intelligent Agents and technologies to address specific problems involving Complex Systems (e.g. MISCHIEF, SIMCJOH, T-REX, DT).
    Experimentation and Use of Simulation Environments: e.g. LEM Simulation for Strategic Planning in Automotive Industry, MOSES Simulator for Evaluating Sustainability of new Industrial Plant into a Region. GreenLog Green Logistics Modeling. 
    FRINE Simulation and Artificial Intelligence for Production Planning over the Supply Chain, COMADREJA Company Model for Process Reengineering, CALYPSO Life Cycle Simulation for New Carrier in Ship Building, LEXIS Industrial Plant Reorganization Simulator, CUMANA Simulation for Competitive and Cooperative Education & Training of Industrial Managers.
    MEGACITY Models of Development for Logistics, Safety and Energy, SISOM VR & AR for New Service Models, SECSIM Port Simulation, Logos Simulator for Strategic Planning of Fleet, Marlon SOUCI Simulation for Critical Industrial Infrastructure Protection from Physical and Cyber Threats etc.)

    Educational material available by link on web site on www.itim.unige.it/strategos

    RECOMMENDED READING/BIBLIOGRAPHY

    • Banks, J. (1998) "Handbook of Simulation: Principles, Methodology, Advances, Applications and Practice", John Wiley & Sons, ISBN 978-0471134039
    • Bossomaier, T.R. & Green, D.G. (2000) “Complex systems”, Cambridge University Press, UK
    • Bruzzone, A.G. & Massei, M. (2017) "Simulation-Based Military Training", in Guide to Simulation-Based Disciplines, Springer, pp. 315-361, ISBN 978-3319612638 
    • Bruzzone A.G., Giambiasi N., Gambardella L.M., Merkuryev Y.A. (2001) "Harbour, Maritime & Multmodal Logistics Modelling & Simulation 2001", SCS Europe Publishing House, ISBN 90-77039-03-1
    • Bruzzone, A.G., Kerckhoffs (1996) "Simulation in Industry", SCS Europe Publishing House, Vol. I & II, ISBN 1-56555-099-4
    • Clemen R.T, Reilly T. (2001) "Making Hard Decisions", Duxbury, Pacific Grove CA
    • McLeod, J. (1982) “Computer Modeling and Simulation: Principles of Good Practice”, Society for Computer Simulation, San Diego, ISBN 978-9992501733
    • Montgomery, D.C. (2000) "Design and Analysis of Experiments", John Wiley & Sons, New York
    • Stern, C.W., Stalk, G. (1998) "Perspective on Strategy", John Wiley & Sons, Hoboken, NJ
    • Spiegel, M.R., Schiller, L.J.(1999) "Statistics", McGraw Hill, NYC
    • Zeigler, B.P., Praehofer, H. & Kim, T. G. (2000) "Theory of Modeling and Simulation integrating Discrete Event and Continuous Complex Dynamic Systems", Elsevier, Academic Press, ISBN 978-0127784557

    TEACHERS AND EXAM BOARD

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    Multiple Experiences carried out in Virtual Labs where the Students are evaluated on Simulation Exercises and Experiences, based on Individual and in team working by Collaborative and/or Competitive approach, representing Micro Projects devoted to address specific issues within realistic complex problems by using M&S (e.g. MISCHIEF, SIMCJOH, T-REX, DT). Some Homeworks will be provided to the students as reporting on class works and simulations. Final Exam will be carried out by Oral Exam including review of the Simulation Exercises & Experiences and by requiring to demonstrate skills in conceptual modeling and simulation development.

    ASSESSMENT METHODS

    Oral exam with review of Exercises and Experiences carried out in Class as well as discussion over homeworks.

    Exam schedule

    Date Time Location Type Notes
    27/12/2022 10:00 GENOVA Orale
    23/01/2023 10:00 GENOVA Orale
    21/06/2023 10:00 GENOVA Orale
    18/07/2023 10:00 GENOVA Orale
    22/08/2023 10:00 GENOVA Orale
    12/09/2023 10:00 GENOVA Orale

    FURTHER INFORMATION

    Time Zone A: Genoa, Italy (CET), GMT+1 (normally), during daylight saving time GMT+2