ENGINEERING TECHNOLOGY FOR STRATEGY AND SECURITY

The module aims to illustrate how design under uncertainty can help in the modelling and design of energy systems. The first part of the module will cover the necessary fundamentals of statistics. Subsequently, different uncertainty quantification methods will be presented, starting from sampling methods such as Monte Carlo and continuing with various approximate methods. An overview of robust design will be provided, focusing on the application of uncertainty quantification methods in optimization problems. In the second part of the course, advanced techniques for Data Driven monitoring will be presented. Both methods will be applied to different case studies.

The course provides the fundamentals for robust design of engineering systems and related procedures for monitoring and diagnostics, through deterministic and stochastic approaches. Practical examples are derived from the fields of mechanical engineering and energy systems.

The first part on robust design requires two reports describing analyses performed by the students with the aid of programming codes shared with them during the class.
The second part on monitoring and diagnostics requires an oral exam.
Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link Comitato di Ateneo per l’inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | Università di Genova

Learning outcomes are assessed through the evaluation of written reports (for the robust design part) and the oral exam (for the monitoring and diagnostics part). Each learning outcome will be verified through specific questions and discussion of case studies, with particular attention to clarity of exposition, use of technical terminology, and the ability to apply the methods learned to real problems.

Spiegel, M.R., Schiller, L.J.(1999), Statistics, McGraw Hill, NYC
Montgomery, D.C. (2000), Design and Analysis of Experiments, John Wiley & Sons, New York
Ralph C. Smith (2013), Uncertainty Quantification: Theory, Implementation, and Applications
T.J. Sullivan (2015), Introduction to Uncertainty Quantification”, Springer
Ghanem, Higdon, Owhadi, (2017), Handbook of Uncertainty Quantification, Springer
Souza de Cursi, Sampaio, (2015), Uncertainty Quantification and Stochastic Modeling with Matlab, ISTE Press – Elsevier
L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström, and S. Wold (2001), Design of Experiments
Marler, R. T., and Arora, J. S., 2004 “Survey of Multi-Objective Optimization Methods for Engineering” Structural and Multidisciplinary Optimization, 26(6), pp.269.295
Myers, R. H., and Montgomery, D. C., 2002, “Response Surface Methodology: Process and Product Optimization Using Designed Experiments” John Wiley & Sons Inc, USA.
Law, A.l., Kelton, W. D., 1991, “Simulation Modeling and Analysis”, Mc Graw Hill
Mäkelä, M., 2017, “Experimental Design and Response Surface Methodology in Energy Applications: A Tutorial Review” Energy Conversion and Management, 151(May), pp. 630–640.
Kleijnen, J. P. C., 2005, “An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis” Eur. J. Oper. Res., 164(2), pp. 287–300.
Tagushi, G., Yokoyama, Y., and Wu, Y., 1993, “Taguchi Methods: Design of Experiments”
K. Kim, M.R. von Spakovsky, M. Wang, D.J. Nelson, 2012. “Dynamic optimization under uncertainty of the synthesis/design and operation/control of a proton exchange membrane fuel cell system, Journal of Power Sources, 205, pp. 252–263
Navarro, M., Witteveen, J., Blom, J., 2014. “Polynomial chaos expansion for general multivariate distributions with correlated variables”
Seshadri, P., Narayan, A., Mahadevan, S., 2016. “Effectively Subsampled Quadratures For Least Squares Polynomial Approximations”. ArXiv e-prints
Giunta, A.A., Eldred, M.S., Castro, J.P., 2004. “Uncertainty quantification using response surface approximations”, 9th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability
M. Cavazzuti, Optimization Methods: From Theory to Design, DOI: 10.1007/978-3-642-31187-1_3, Springer-Verlag Berlin Heidelberg, 2013.

Applied Reinforcement Learning and Strategic Programs aims at introducing students to the theory and practice of Reinforcement Learning algorithms and strategic management and optimization. At the end of the module the Student will be able to critically analyze and use state of the art algorithms and be proactive within an organization dealing with international programs and to use main methodologies for project management.

Strategic programs: Adopting a multidisciplinary approach, the course deals with the management of complex projects, offering participants tools and methodologies for project management analysis and implementation.
Autonomous agents: The course will provide an introduction to the methods for designing and implementing autonomous agents through Reinforcement Learning (RL) techniques.

Applied Reinforcement Learning and Strategic Programs aims at introducing students to the theory and practice of Reinforcement Learning algorithms and strategic management and optimization. At the end of the module the Student will be able to critically analyze and use state of the art algorithms and be proactive within an organization dealing with international programs and to use main methodologies for project management.

Strategic programs: Adopting a multidisciplinary approach, the course deals with the management of complex projects, offering participants tools and methodologies for project management analysis and implementation.
Autonomous agents: The course will provide an introduction to the methods for designing and implementing autonomous agents through Reinforcement Learning (RL) techniques.

The teaching unit aims at introducing the learner to fundamental methods and tools on shallow machine learning algorithms/models. The goal is for students to become able to tackle real-world problems, using state of the art supervised and unsupervised learning techniques.

The course aims at introducing the learner to the theory and applications of machine learning.

The student will propose and preliminairly agree with the teacher(s) a dataset onto which to prepare a Jupyter notebook (similar to those seen at lecture) for critically implementing a ML task (e.g., classfication, regression, clustering). This also includes: pre-processing, training of various models, hyperparameter tuning, overall testing.
During the exam the student will present this work. Then, the teachers will ask questions potentially on all the topics of the course.

Verification of the knowledge acquired and the ability to apply it in contexts other than those presented in class will be assessed through questions in the interview or written examination.
The evaluation will also take into account the student's participation during the course.

A. Geron, Hands-On Machine Learning With Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, O’ Reilly
A. Geron, Hands-On Machine Learning With Scikit-Learn and Pytorch: Concepts, Tools, and Techniques to Build Intelligent Systems, O’ Reilly
I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, The MIT Press
Lecture notes and other material suggested by the lecturer during the course

The module covers the elements of programming for videogames. Students will learn the basics of both 2D and 3D programming along with the techniques used by the newest CAD tools for videogame design. After an introduction about the various types of simulation, a basic game engine for attaching the components of 2D games programming is presented. The basic of the 3D graphics and 3D videogame through the usage of a CAD graphics tool and an AAA game engine is also provided. One fifth of the credit is obtained through a final project.

Modeling and simulation are fundamental tools for design, training and learning in general. Video games are a particular type of simulation, where additional rules and strategies can be introduced to improve performance in the above-mentioned application areas. In addition, video games require great computing performance, normally achieved through the joint use of optimized hardware / processor architectures and high-level software development tools. The teaching therefore addresses the development of simulations through state-of-the-art 2D and 3D game development platforms

Exam consists in developing of an application project addressing interactive simulation, to be agreed with the teacher and oral discussion. In the commitment required for the project, specific needs for working students will be taken into account.

During the oral exam the student, on the basis of the developed project, will have to show understanding of the topics covered in the course, discuss the design choices, with the aid of the computer analyze the results obtained.

Lecture notes (from AulaWeb)
Books (as references):
B. Tristem,‎ M. Geig. Unity Game Development in 24 Hours. Sams Teach Yourself
J. Hocking. Unity in Action: Multiplatform Game Development in C# with Unity 5. Manning
J. Gregory. Game Engine Architecture CRC Press, 3rd ed.

The subject aims to provide fundamental concepts of Economics, Monopoly & Oligopoly Models, Basic Consumer Theory as well as Game Theory applied to these fields. Price setting strategies will be illustrated to highlight the importance of economic analysis to understand realistic business situations.

This subject addresses central aspects of Industrial Economics and Economic Policy. Its first part focuses on the theoretical, i.e., mathematical and conceptual, models that are used to classify markets in terms of their structural features. Price setting strategies will be illustrated in the second part, which will be followed by relevant topics in competition policy, with reference to EU antitrust cases.

Written exam with open-ended questions, where the student must illustrate the main concepts and implications of a theory, and with numerical exercises.
The exam calendar is available on EasyAcademy.

Learning assessment is carried out through a written exam, which evaluates the ability to solve mathematical models, apply game theory concepts, recognize market strategies in real cases, and communicate effectively using technical terminology.
The evaluation criteria include: accuracy of answers, clarity of exposition, appropriate use of terminology, critical analysis skills, and independent judgement.

Belleflamme, P. and Peitz, M. (2015). Industrial Organization. Markets and Strategies. Cambridge University Press, Cambridge, UK.
Carlton, D. and Perloff, J. 2005. Modern Industrial Organization, Pearson
Davis, P. and Garces, E. (2010). Quantitative Techniques for Competition and Antitrust Analysis. Princeton University Press, Princeton, NJ USA.
Motta, M. (2004). Competition Policy. Theory and Practice. Cambridge University Press, Cambridge, UK.
Pepall, Richards, Norma, Industrial Organization: Contemporary Theory and Empirical Applications. fifth edition, Blackwell Publishing, 2013.
Pepall, Richards, Norman, Calzolari, Organizzazione Industriale, McGraw Hill, III edizione, 2017.

The unit provides students with a basic understanding of business economics and accounting principles, focusing on the role of firms in competitive and globalized markets, the interpretation of accounting and financial information, and the analysis of economic performance and business sustainability. Particular attention is devoted to managerial decision-making processes, financial reporting, cost and profitability analysis, and the use of accounting information to support strategic and operational decisions within organizations.

Elements of Business Economics will provide you with a comprehensive introduction to businesses, their nature, their dynamics and their information. In particular, the course will consider the financial and economic aspects of the business dynamic and how we can record and use financial information to appropriately manage and understand a business. Through the course, you will learn the ways in which financial information are prepared and used to improve the quality of business decision-making. This course is specifically designed for students with little or no prior knowledge of accounting.

Students' acquired capabilities will be tested via a final written exam during the regular exam periods, consisting of multiple-choice questions, open theory questions, and practical questions.

Students' knowledge and skills will be assessed via a final written exam that will verify their understanding and abilities related to the three modules. Each learning outcome will be assessed through specific questions (multiple-choice, open questions, and practical exercises) evaluating: clarity of exposition, correct use of terminology, analytical skills, and application of knowledge.

Students will be provided with various learning materials that will be lodged on AulaWeb. Additional readings (that will be shared by the teacher): Atrill, P., McLaney, E. J.(2011), Accounting for Non-specialists. Prentice Hall - Financial Times http://www.mim.ac.mw/books/Peter%20Atrill's%20Accounting%20&%20Finance%20for%20Non%20Specialists.pdf
Deegan, C. (2019), An Introduction to Accounting: Accountability in Organisations and Society. Cengage Learning.

Elements of Computer Programming” provides foundations of using Computer Languages for implementing basic logic flows, functions, processes, data analysis and management. The module introduces mostly in the practical use of Phyton by writing, executing and testing the code created by the Students.

This practical teaching unit introduces programming using Python for Engineering students. No prior coding experience needed. Students will learn to automate tasks, analyze data, and solve real-world problems through hands-on labs and projects.

Project work on an application example agreed between the teachers and the student.
Students with valid certifications for Specific Learning Disorders (SLDs), disabilities, or other educational needs are invited to contact the teacher and the DITEN contact person for disability to agree on the possible use of specific modalities and supports that will be determined on a case-by-case basis, according to the University regulation for the inclusion and right to study of students with disabilities or specific learning disorders.

The exam aims at verifying through a project the knowledge acquisition and solid understanding of the foundations of pyhton programming for data science and engineering applications.
Evaluation will take place in various steps: project definition talks, design/implementation of the solution, code inspection (incl. documentation), assessment of the results, final discussion of a short document describing the work done.
The lecturer will also take into account the student's participation during the course (attention, questions, answers).
Students with valid certifications for Specific Learning Disorders (SLDs), disabilities, or other educational needs are invited to contact the teacher and the DITEN contact person for disability to agree on the possible use of specific modalities and supports that will be determined on a case-by-case basis, according to the University regulation for the inclusion and right to study of students with disabilities or specific learning disorders.

J. VanderPlas, “Python Data Science Handbook”, O’Reilly
W. McKinney, "Python for Data Analysis", O’Reilly
A. Sweigart, "Automate the Boring Stuff with Python"
Documentation of the used libraries
Lecture notes and other material suggested by the lecturer during the course
Non-attending students may contact the lecturer to agree on the best arrangements

How can we explain foreign policy decisions? The course, which aims to provide students tools to interpret foreign policy, examines concepts and theories of Foreign Policy Analysis (FPA). The course illustrates foreign policy by focusing on the decision-making process, investigating the interaction among actors and their environment, and devoting special attention to the influence of domestic factors. The course combines the analysis of theories with a comparative study of foreign policy through selected case studies (e.g., the US decisions to send troops in Vietnam in the 1960s and to invade Iraq in 2003; the Italian and German decision-making process related to the military mission in Libya in 2011, etc.).

How can we explain foreign policy decisions? The course, which aims to provide students tools to interpret foreign policy, examines concepts and theories of Foreign Policy Analysis (FPA), conceived as an approach to the study of international politics.

Oral exam for the Political Science students
For the Strategos students the exam is written and it is based on the manual (ALL the chapters) Steve Smith, Amelia Hadfield, and Tim Dunne (eds.) (2016)Foreign Policy: Theories, Actors, Cases (Oxford University Press), No addional papers

The exam will assess the acquired knowledge in the field of foreign policy analysis, evaluation the students’ skills in addressing specific security issues. The exam allows also assessing the student’s capability in interpreting different security scenarios and actors.

Compulsory texts
Selected chapters (1-6, 8-11, 13-14, 16, 21) from: Steve Smith, Amelia Hadfield, and Tim Dunne (eds.) (2016)Foreign Policy: Theories, Actors, Cases (Oxford University Press),

The course is split in two modules. The Organization module aims to provide the fundamental conceptual and practical tools for the organizational analysis and design, both from an internal and an external (e.g. consultancy) perspective. The module will focus on the logic of organizational design such as structural dimensions and organizational configurations, and on the following topics regarding human behaviour inside an organization: personality, perception and decision-making, motivations and incentives, Leadership & Group behavior, and organizational change. The Strategic business management module aims at helping students to understand and interpret the companies’ behaviours and strategies with reference to their surrounding environment. The specific focus is on competitive/collaborative relationships among companies and their stakeholders. Some modelling paradigms are presented, along with case studies. - main goals of a business - companies as complex social systems - creating shared value and Porter's Value Chain - mission, vision and strategy - the company and the environment (general and competitive environment; Porter's five forces analysis) - internal environment of companies (resources, competences) - business and corporate strategies - competitive strategies (cost leadership, differentiation, focus) and the competitive advantage - innovation and exploration vs exploitation strategies - business functions - business processes - modelling and simulating an enterprise (overview of different paradigms) - agent based models for strategic management simulation and modelling - examples and case studies.

Please refer to the English section
Fare riferimento alla sezione in lingua inglese

Please refer to the English section
Fare riferimento alla sezione in lingua inglese

Please refer to the English section
Fare riferimento alla sezione in lingua inglese

Please refer to the English section
Fare riferimento alla sezione in lingua inglese

The educational objective of the teaching unit is to provide students with basic knowledge of partial differential equations, the main solution techniques, and their application to real-world problems.

These classes provide an overview of the most common partial differential equations (PDEs) and related solution techniques, with particular focus on second order equations. The role of the teaching unit within the curriculum is to provide tools for the analysis of mathematical models in various applications.

The exam consists of a written test, possibly complemented by an oral test at the teacher's choice. Minimal score is 18/30.

Learning assessment is carried out through a written test, during which the ability to classify equations, solve exercises, and apply the learned techniques will be evaluated. Assessment criteria include correctness of solutions, clarity of exposition, andappropriate use of terminology.

A.N.Tichonov, A.A.Samarskij: Equazioni della Fisica matematica, Problemi della fisica matematica, Mosca,1982;
R. Courant, D. Hilbert, Methods of Mathematical Phisics vol I e II, Interscience, NY, 1973;
R. Bracewell, The Fourier Transform and Its Applications, New York: McGraw-Hill, 1999;
P. V. O’ Neil, Advanced engineering mathematica, Brooks Cole, 2003;
H. Goldstein, Meccanica Classica, Zanichelli, Bologna, 1985;
V. I. Smirnov. Corso di Matematica superiore, Vol. 3. MIR (1978).

Foundation on Complex Systems. Transfer of knowledge about Strategic Engineering, Simulation Paradigms and Modeling Methodologies, Data Analytics & AI 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 Strategic Engineering Solutions, Simulation Architectures and Model Development applied to Complex Systems.

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

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.

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

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

The module provides students the basics of operations research, which are most relevant to the strategic and operational planning of enterprises. The module aims to develop optimization models and provide mathematical programming methods, both exact and heuristic, for decision-makers. Students are also provided with the necessary knowledge to understand the structure of an optimization algorithm and to implement it with Python. Emphasis is given to logistics and transportation problems. Students will evaluate the acquiring of their skills by examining, developing and analyzing case studies in a computer classroom using the spreadsheet optimization tool of Excel and ad-hoc software environments. By the end of the module, students will have the skills necessary to identify the methodological approach needed to address a decision-making problem and the ability to apply that method to determine the good solutions.

Operational research is a discipline included in decision science and management science; therefore, in addition to the basic notions of this subject, the course provides professional skills related to Problem Solving and Decision Making relevant to address strategic choices. The course provides students the most relevant operations research and decision support methods, to strategic decisions. Among the main techniques, Linear programming, Integer Linear Programming, heuristic and meta-heuristic algorithms are presented.

The examination includes a written test to be taken in the classroom.
The test will contain short answer questions, multiple choice questions and exercises.

In addition to the written examination, the following are scheduled during the course:
Online test during lessons,
Project discussion in class.

The following books and articles are suggested.
Hillier, Lieberman, “Introduction to Operations Research”, McGraw Hill, 2016.
J.F.McCloskey, “OR FORUM- The Beginnings of Operations Research: 1934-1941”, Operations Research, V.35, N.1, 1987, 143-152.
J.F.McCloskey, “OR FORUM- British Operational Research in World War II””, Operations Research, V.35, N.3, 1987, 453-469.
A. L. Jaimes, S. Zapotecas-Martinez, C.A. Coello, “An introduction to multiobjective optimization techniques”, in Optimization in Polymer Processing, A. Gaspar-Cunha, J.A. Covas (Editors), Nova Science Publishers, 29-57 (2009)
M. Ehrgott, “Multicriteria optimization”, Springer, Berlin-Heidelberg (2005)
M. ¨Ozlen, M. Azizoˇglu, “Multi-objective integer programming: A general approach for generating all non-dominated solutions”, European Journal of Operational Research, 199, 25-35 (2009)
G. Ghiani, G. Laporte, R. Musmanno, "Introduction to logistics systems" , Wiley (2004)
Downey, A., et al. "Thinking python. 2.0". Green Tea Press Supplemental Material:, 2012.

Strategic Programs has a multi-disciplinary approach, addressing project management, strategic management, planning, engineering, negotiation, international legal disputes, claim management, risk assessment, process/resource scheduling, project proposal writing and reporting, etc. The module teaches qualitative and quantitative methodologies and techniques devoted to achieve project efficiency, effectiveness, competitiveness, reliability, risk management. At the end of the module the Student will be able to be proactive within an organization dealing with international programs and to use main methodologies for project management.

Algorithms for strategic management: critical path method (PCM), risk assessment and management, resource allocation and scheduling, quantitative decision support methods [F. Bellotti];
Project Management: Definitions, general concepts and processes. Integrated Project Management, Management of Project Objectives. Time, Cost and Quality Management in Projects. Project Management & Human Resources, Communications, Risks and Supplies. Methodologies and Techniques for Project Management [A. Bruzzone];
Decision Support Methods and Techniques; Multicriteria Analysis and Analytic Hierarchy Process [E.M. Cepolina];
International contracts: competent court and applicable law in default and in the presence of specific negotiated clauses; Public contracts and procurement (general principles) [S. Dominelli].

The examination is oral and only takes place during official calls.
The oral examination comprises the discussion of a project on one or more topics of the course. The examination may cover all course topics and is designed to test the acquisition of knowledge and skills for engineering project management, in particular with regard to strategic and decision-making aspects.
Uniquely for the law part, the lecturer sets an optional, multiple-choice written test during the last lesson, which may exempt the person concerned from the corresponding part of the syllabus in the oral examination.

The oral examination aims to verify the degree of maturity achieved by the learner in identifying, understanding and applying advanced project, problem and risk management techniques. The ability to independently develop solutions to project problems from a multidisciplinary perspective will also be tested.

For classes taught by F. Bellotti
Notes, slides and other materials provided by the lecturer.
For classes taught by A. Bruzzone
Slides and other material provided by the lecturer
For classes taught by E.M. Cepolina
Slides and other materials provided by the lecturer.
For classes taught by S. Dominelli
Slides given by the lecturer, integrated with class notes individually taken by students.

The main aim of the teaching unit is to discuss both the practical and theoretical aspects of strategies for managing energy. In particular, the contribution of the teaching unit to the learning objectives of the Degree Programme consists in providing students with tools to understand and apply strategies for controlling energy flows, optimization and management, with a focus on practical aspects in smart energy microgrids.

The teaching unit aims at outlining the main techniques for managing electricity, also considering the current evolution of energy systems, with specific attention to innovative solutions, such as smart grids, microgrids, and renewable and distributed generation systems

Writen exam on the subjects presented during the teaching unit and on the developed exercises and examples.

Students will be evaluated on theoretical topics presented during the teaching unit, exercises and examples devoted to addressing specific issues within realistic problems proposed during the teaching unit.
The final exam will be written.
The assessment of learning outcomes will be based on:
the ability to describe and explain theoretical concepts (clarity of exposition, use of terminology);
the ability to apply sizing and management techniques to case studies (soundness of solutions, compliance with constraints)
the ability to critically analyze the obtained results.

In addition to the reference texts available at the Department Library, slides and notes useful for study will be available on AulaWeb. Interested students can also integrate with the following texts:
Delfino, F., et al., Microgrid Design and Operation: Toward Smart Energy in Cities, Artech House power engineering series, 2018
Bracco, S., et al., “An Energy Management System for the Savona Campus Smart Polygeneration Microgrid,” IEEE Systems Journal, Vol. 99, 2015.
Bonfiglio, A., et al., “An Optimization Algorithm for the Operation Planning of the University of Genoa Smart Polygeneration Microgrid,” Proceedings of IREP 2013 Symposium-Bulk Power System Dynamics and Control–IX, Rethymnon, Greece, August 25−30, 2013
Bonfiglio, A., et al., “Definition and Experimental Validation of a Simplified Model for a Microgrid Thermal Network and Its Integration into Energy Management Systems,” Energies, Vol. 9, 2016, p. 914.
Bendato, I., et al., “A Real-Time Energy Management System for the Integration of Economical Aspects and System Operator Requirements: Definition and Validation,” Renewable Energy, Vol. 102, 2017, pp. 406–416

The lectures are aimed at providing theoretical and practical knowledge about advanced Information and Communication Technologies which will influence strategic choices in the next future allowing the development of new paradigms and services such as smart cities, manufacturing, factory and agriculture. The lectures will provide a basic know-how about networking technologies such as IP and TCP/UDP architectures and will develop this information to explain concepts such as Cloud Computing and Internet of Things; 5G and Satellite Technology, Automated and Connected Mobility; Big Data Analytics, Artificial Intelligence and Machine Learning, and Cybersecurity.

Enterprise strategic choices are heavily influenced by changes that modify the operating context. One of the most important is digitalization: currently, every business is digital. The knowledge, use, and application of digital technologies, including the Telecommunication ones, will be essential to make decisions in strategic environments.

Written exam consists of three open questions on the topics covered in the course. The exam may include theoretical questions and case studies. There are no differences for Erasmus students. The minimum passing score is a thorough answer to at least two out of three questions.

The exam will assess:
the knowledge and understanding of ICT principles and technologies through theoretical questions;
the ability to apply such knowledge to practical scenarios through case studies;
the clarity of exposition and correct use of technical terminology;
the ability to critically analyze the proposed solutions.

Slides issued by the lecturer
(Optional) Books to deepen the telecommunication networks knowledge, such as:
J. Kurose, K. Ross, “Computer Networking - A Top-Down Approach”, Pearson
L. L. Peterson, B. S. Davie, “Computer Networks: A Systems Approach”, Elsevier
Additional bibliographic material may be indicated during the course.

Internship or Training are devoted to develop a real experience in a Company, Research Lab, Agency or Institution working on innovative aspects related to Strategic Engineering, Modeling, Simulation, Data Analytics and/or Artificial Intelligence. In this period the Candidate should develop knowledge of the specific applicative domain and its characteristics and to finalize a Project Work as well as the related Final Report. These activities last for 6 months and are coordinated by a member of the Course Faculty as well as by a Company Supervisor.

Internship or Training are devoted to develop a real experience in a Company, Research Lab, Agency or Institution working on innovative aspects related to Strategic Engineering, Modeling, Simulation, Data Analytics and/or Artificial Intelligence. In this period the Candidate should develop knowledge of the specific applicative domain and its characteristics and to finalize a Project Work as well as the related Final Report. These activities last for 6 months and are coordinated by a member of the Course Faculty as well as by a Company Supervisor.