II Freshmen PhD & Professionals Cruise School 2025 (Marseille – Barcelona – Palma de Mallorca – Palermo – Rome)

AI for Sustainable Blue Economy: Industry, Healthcare and Government

Transforming the Future of Industry, Healthcare, and Government in the Mediterranean

Are you passionate about leveraging AI to drive sustainability? Do you want to be at the forefront of solving some of the most pressing challenges in industry, healthcare, and government? We invite you to join us for the second edition of the AI for Sustainability PhD and Professionals Schools, a unique event that combines cutting-edge research, practical applications, and unparalleled networking opportunities.

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Event Overview

General Chair: Prof. Eng. Roberto Revetria, PhD

Program Chair: Dr. Eng. Anastasiia Rozhok, PhD

Event Dates: January 19th to January 24th
Departure Location: Marseille (France)
Destination: Rome (Civitavecchia) with stops in Barcelona, Palma de Mallorca and Palermo
Participants:  100+ researchers, professionals, and scholars from around the world
Full Board Accommodation: 5 nights and 6 days, meals (5 breakfasts, 6 lunches, 5 dinners)
Extension: Round trip Savona-Savona, including accommodation and meals, route fee (€100)

Guest Lectures from world-famous Professors:

  • Prof. Dr. Habil. Dmitry Ivanov: Human-AI collaboration in supply chain resilience.
  • Prof. Dr. Alexandre Dolgui: Artificial Intelligence in Manufacturing: The organisation and some results of the European project ASSISTANT
  • Prof. Dr. Hamido Fujita: Artificial Intelligence and Machine Learning Challenges and Perspective Solutions
  • Prof. Dr. Andrea Matta: Autonomous Digital Twins for Optimal Control of Discrete Event Systems
  • Prof. Dr. Akram Soliman Elselmy: Engineering Sustainable Port Solutions: The Power of AI and Innovation
  • Prof. Dr. George Angelov: Semiconductor Industry and its AI implications 

    Workshops and Laboratories:
  •  Dr. Matteo Barbieri: AI and its applications to Blue Economy from Supply Chain Management to Services and Government
  • Prof. Angelo Alessandri: Predictive Control for Sustainable Transportation and Logistics
  • Prof. Emanuele Morra: Enhancing Cruise Ship Supply Chain Management with AnyLogistix


Publication: Selected PhD research projects will be published in the Scopus-indexed journal Special Issue, “Transition to Industry 4.0 in Emerging Domains: Methodology and Case Studies.” all Project Abstracts will be published in indexed Proceedings.
CFU Points: PhD students can earn CFU points as part of their academic activities.
Erasmus Funding: Open to European scientists under the Erasmus program.
Certificate of Participation: All participants will receive a certificate.
Social Program: Networking opportunities and social events.
Software Packages: Access to industry-standard tools and software.

Registration information

Fee for students in double room accomodation: €750 (€680 Early Bird until September 27th).

To register to the II Freshmen PhD & Professionals Cruise School 2025 please click here

Registration Form

After the registration, you will receive the Payment instructions.
The payment should be made before October 21st October 31st.

To book an Early Bird Price, register before September 27th.

All prices include Full Board Accommodation: 5 nights and 6 days, meals (5 breakfasts, 6 lunches, 5 dinners)
Extension: Round trip Savona-Savona, including accommodation and meals, route fee (€100)

Download the Program

Why Attend?

Explore the Blue Economy

Dive deep into the Blue Economy, exploring how AI can revolutionize industries connected to the Mediterranean. Learn how modern technologies can bring sustainable solutions to the “Old World,” addressing critical issues in marine and coastal ecosystems, renewable energy, and sustainable tourism.

Network and Collaborate

Sail with over 100 participants, including young PhD students, researchers, seasoned professors, and experienced managers. This is a unique opportunity to forge meaningful connections, engage in stimulating discussions, and build collaborations that could shape your career and the future of AI in sustainability.

Learn from the Best

Gain insights from prominent scholars and industry leaders through lectures, speeches, and interactive sessions. Learn about the latest advancements in AI applications across various domains and discover how these innovations can contribute to a sustainable future.

Hands-On Experience

Participate in laboratories and workshops designed to provide practical experience. Work on real-world problems, develop innovative solutions, and enhance your skills under the guidance of experts.

Event Highlights

  • Interdisciplinary Approach: Tackle sustainability challenges across industry, healthcare, and government using AI.
  • Unique Venue: Learn aboard a cruise ship, blending travel with education in a dynamic environment.
  • Career Boost: Expand your professional network, explore new ideas, and unlock future opportunities.
  • Global Perspectives: Collaborate with participants from diverse backgrounds and regions.

Who Should Attend?

  • PhD Students & Researchers: Expand knowledge and connect with peers and mentors.
  • Professionals: Explore AI-driven sustainability solutions to enhance your career.
  • Professors & Experts: Mentor future leaders and collaborate with global peers.

Embark on a Voyage of Innovation: AI for Sustainability PhD and Professionals Schools

Sail Towards a Sustainable Future on Our Exclusive Cruise

Imagine a vibrant, self-contained community aboard a cruise ship, sailing from Marseille to Civitavecchia with stops in Barcelona and Mallorca. This unique setting is not just a backdrop but a living metaphor for our journey toward sustainability. The ship represents a microcosm of society, encompassing all the aspects and challenges of a larger social body. By living and learning on board, we can directly observe and address the issues facing industry, healthcare, and government, using AI as our guiding star.

The Blue Economy: A Pillar of Sustainability

The Blue Economy, encompassing all activities related to oceans, seas, and coastal areas, is crucial for sustainable development. The Mediterranean, with its rich cultural heritage and marine biodiversity, serves as an ideal region to explore how AI can transform industry, healthcare, and government for a sustainable future.

AI: The Game Changer

AI can revolutionize the Blue Economy by addressing these challenges with innovative solutions:

  • Predictive Analytics: AI can predict fish stocks, ocean currents, and climate patterns, aiding in sustainable resource management.
  • Pollution Monitoring: Machine learning algorithms can detect and track pollution sources, helping to mitigate their impact.
  • Healthcare Innovations: AI-driven research can accelerate the discovery of marine-based pharmaceuticals and improve public health surveillance.
  • Smart Policy Making: AI tools can analyze vast datasets to inform policy decisions and optimize regulatory frameworks.
  • Operational Efficiency: AI can optimize technical operations, such as HVAC systems, waste management, and food preparation, ensuring sustainable and efficient use of resources.

Key Features of the Laboratories:

  • Business Cases: Analyze and solve real-world problems faced by industries, healthcare, and governments.
  • Practical Applications: Learn by doing, using AI tools to develop solutions for sustainability challenges.
  • Interactive Discussions: Engage with experts and peers to explore the potential of AI in various contexts.
  • Interdisciplinary Approach: Collaborate with participants from diverse academic backgrounds, enriching the learning experience.

Open to All Disciplines

The AI for Sustainability Schools welcome PhD students, researchers, and professionals from all disciplines. Whether you are from STEM fields or Humanities and Social Sciences, your unique perspective is invaluable. Our goal is to foster a holistic understanding of how AI can drive sustainability across different sectors.

Join Us

Set sail on a transformative journey through the Mediterranean as we explore how AI can shape a sustainable future for the Blue Economy. Register now and take advantage of the Early Bird Registration Fee.

Let’s Shape the Future Together!

Guest lecturers

  • Prof. Dr. Habib Dmitry Ivanov – Lecture on “Human-AI Collaboration in Supply Chain Resilience”
  • Prof. Dr. Alexandre Dolgui – Guest lecture on advanced supply chain management techniques
  • Prof. Dr. Hamido Fujita – Guest lecture on cutting-edge AI applications in logistics and supply chain
  • Prof. Andrea Matta: Autonomous Digital Twins for Optimal Control of Discrete Event Systems
  • Prof. Dr. Akram Soliman Elselmy: Engineering Sustainable Port Solutions: The Power of AI and Innovation

Human-AI Collaboration in Supply Chain Resilience

In this talk, we discuss how human-based and AI-based decision-making support can be combined when managing supply chain resilience. Our talk focuses on an intelligent digital twin (iDT) framework. New digital technologies and artificial intelligence enable novel approaches and tools, allowing us to move from isolated models to intelligent decision-support systems. Our talk presents a comprehensive decision-making framework for leveraging digital twins in stress-testing and resilience analysis of supply chains. It outlines how digital twins can aid theoretical advancements in SC resilience and viability. An iDT is a system that combines human intelligence with AI to create a digital representation of physical supply chains, use cognitive AI capabilities, and create new knowledge about the system through mutual feedback between human and artificial intelligence. The iDT collects and processes data, employs analytics, mimics human decision-making, and develops new knowledge and decision-making algorithms through human-AI collaboration. 

Prof. Dr. Dr. Habil. Dmitry Ivanov is a Professor of Supply Chain and Operations Management, director of the Digital-AI Supply Chain Lab, and faculty director of M.A. Global Supply Chain and Operations Management at the Berlin School of Economics and Law. His research spans supply chain resilience and digital supply chain twins. Author of the Viable Supply Chain Model and founder of the ripple effect research in supply chains. He gained Dr., Dr. Sc., and Dr. Habil. degrees and won several research excellence awards. His research record counts around 450 publications, with more than 160 papers in prestigious academic journals and the leading books “Global Supply Chain and Operations Management” (three editions), “Introduction to Supply Chain Resilience,” “Introduction to Supply Chain Analytics,” „Structural Dynamics and Resilience in Supply Chain Risk Management, ““Scheduling in Industry 4.0 and Cloud Manufacturing”, “Digital Supply Chain” and „Handbook of Ripple Effects in the Supply Chain“. He delivered invited plenary, keynote, panel, and guest talks at the conferences of INFORMS, IFPR, IFIP, IFAC, DSI, and POM, and over 30 universities worldwide. He has been Chairman, IPC Chair, and Advisory Board member for over 60 international conferences in supply chain and operations management, industrial engineering, control, and information sciences. Recipient of several prestigious academic awards. Principal investigator in several projects about digital supply chain twins and resilience funded by EU Horizon and DFG. Listed in several rankings as one of the most cited researchers in Business and Management. Chair of IFAC CC 5 “Cyber-Physical Manufacturing Systems,” Editor-in-Chief of International Journal of Integrated Supply Management, Editor of Annals of Operations Research, Associate Editor of International Journal of Production Research and OMEGA, guest editor and Editorial Board member in over 20 leading international journals including IISE Transactions and IJPE, to name a few.

Artificial Intelligence in Manufacturing: The organisation and some results of the European project ASSISTANT

The project ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) is a large European project on Artificial Intelligence (AI)  in Manufacturing with participation of 4 Industrial partners and 8 academic or semi-academic (research and innovation) organizations from different countries of EU. 

In this lecture Prof. Alexandre Dolgui outlines the main idea and approach of the project. 

The project provided a set of AI-based digital twins system that helps process engineer and production planner to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool helps decision makers in industry to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allow fast re-planning when necessary. ASSISTANT tool aims to make cost-effective decisions while ensuring product quality, safety and well-being of the workers, and managing the various sources of uncertainties. The resulting digital twin systems are data-driven, agile, autonomous, collaborative and explainable, safe but reactive. 

The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimization, and simulation techniques
to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production
planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

3 case studies (from Siemens, Atlas Copco and Stellantis) were considered to test the tools developed. Examples of applications and some specific models and algorithms will be presented and discussed.

Home page

Artificial Intelligence and Machine Learning Challenges and Perspective Solutions

This is lecture is dedicated to Ph.D students and young researchers to expose research challenges in terms of better quality in themes selection and investigation with innovation and creative thinking. I will outline my research experience and discuss journal scientific publications on hot topics in research and development in terms of quality.

I have selected hot topics in Artificial Intelligence and Machine Learning in supply chain and also operational research in terms of optimization and search techniques.  

The hot topics in training in Machine Learning is a crucial aspect that affects the system’s credibility in terms of performance and is employed for robust applications such as healthcare systems. Machines or algorithms, in wide challengeable applications in security or vision or health care early predictions, learn from data. Nevertheless, in most cases, the extensive and unbalanced data and noise make it unreliable in prediction accuracy. Supervised machine learning is and was one of the aspects of providing artificial intelligence-based solutions. However, this was limited due to the difficulty of labeling big data and many crucial problems in weak relations and noise in data. Semi-supervised learning, for example, Multiview learning, could assist in solving these problems. In many published research, there are still problems in providing machine learning models that are unbiased and efficient in terms of robustness and resilience in data-driven systems. Multiclass classification still has problems regarding clear definitions in class classification, bias, imbalance, and weak relations, making machine learning for multiclass classification insecure for classification or regression analytics. This causes limitations in applying such technology in medical applications and diagnosis prediction. In this lecture, I will outline these problems in our one-class classification project. These are related to providing more robust accuracy prediction with some uncertainty that can help us have more accurate classification and prediction. We have applied such findings in health care for heart sickness and seizure early prediction like in https://doi.org/10.1016/j.cmpb.2022.107277 and https://doi.org/10.1016/j.inffus.2023.102023

We also have deep learning models, which have challenges related to evidential deep learning and fairness relative to data. There are essential issues in expanding research in evidential deep learning, in which uncertainty prediction of variational Auto encoders can provide decisions on evidential distribution, which in turn helps to provide a measure of uncertainty in decision.

We currently have a research project titled “Healthcare Risk Prediction on Data Streams Employing Cross Ensemble Deep Learning,” supported by the Japan Science Promotion Society (JSPS). In this project, we have employed a one-class classification deep neural network for health care prediction. In this lecture, I will outline these perspectives and discuss challenging trends.

Professor Dr. Hamido FUJITA (Life Senior Member IEEE)
https://www.webofscience.com/wos/author/record/1136840

Executive Chairman of i-SOMET Incorporated Association, Japan https://i-somet.org/, Distinguished Professor: Iwate Prefectural University, Japan, Professor at Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Editor-in-Chief: Applied Intelligence (Springer), Editor-in-Chief of International Journal of Healthcare Management (Taylor & Francis), Emeritus Editor-in-Chief: Knowledge-Based Systems, Vice President of International Society of Applied Intelligence, Doctor Honoris Causa (Óbuda University, Hungary), Doctor Honoris Causa (Timisoara Technical University, Romania)

He is a Distinguished Professor at Iwate Prefectural University, Japan. He is also a contracted Professor at the Malaysia-Japan International Institute of Technology(MJIIT), Universiti Teknologi Malaysia.   He is also a Research Professor at the University of Granada (Spain), Universiti Teknologi Malaysia, and HUTECH University Vietnam; Expert Excellence Professor at the Shanghai University of Medicine & Health Sciences.  He is currently the Executive Chairman of the i-SOMET Incorporated Association Japan. He is a Highly Cited Researcher in Cross-Field for the years 2019 and 2020, 2021, and 2022 in the Computer Science field, respectively, from Clarivate Analytics. He is Editor-in-Chief of Applied Intelligence (Springer), Editor-in-Chief of Healthcare Management (Tayler&Francis), Editor-in-Chief of Knowledge-Based Systems (2010-2020), and Emeritus Editor of Knowledge-Based Systems
https://www.webofscience.com/wos/author/record/D-6249-2012
‪

Autonomous Digital Twins for Optimal Control of Discrete Event Systems

With the coming of the Industry 4.0 wave, digital representations of production systems have been promoted from marginal to central. Digital twins are not simply conceived as simulation models of their physical counterparts for offline what-if analysis, differently they are developed as self-adaptable and empowered decision-makers timely aligned with the dynamics of the real system. Enriched by these new features, digital twins are widely recognized as the key enablers for the implementation of optimal control of smart manufacturing systems. Despite this new role, there are significant barriers to the adoption of the digital twin concept in industrial applications. The creation and update of digital twin models is still a challenge because of the high skills required to use the simulation applications available in the market, the long development times, and their difficult integration with optimization and artificial intelligence packages. The frequent changes manufacturing systems encounter in their life cycle boost these issues making the system model and the control most rapidly obsolete. This talk describes data-driven approaches for generating and controlling multi-fidelity models for digital twins of manufacturing systems from data acquired from sensors also in non stationary situations. Application examples will also be presented.

Andrea Matta is Full Professor of Manufacturing and Production Systems at Department of Mechanical Engineering of Politecnico di Milano. He graduated in Industrial Engineering at Politecnico di Milano where he develops his teaching and research activities since 1998. He was Distinguished Professor at the School of Mechanical Engineering of Shanghai Jiao Tong University from 2014 to 2016 and Guest Professor between 2017-2019. He has been visiting scholar at Ecole Centrale Paris (France), University of California at Berkeley (USA), and Tongji University (China). He is member of the technical committee of MADE Competence Center. His research area includes analysis, design and management of manufacturing and health care systems. He has published 190 scientific papers on international and national journals/conference proceedings. He is Editor in Chief of Flexible Services and Manufacturing Journal since 2017, past member of editorial board of OR Spectrum journal and IEEE Robotics and Automation Letters journal. He is member of scientific committee in several international conferences. Member of the Steering Committee of PhD on Mechanical Engineering. Member of the ADA University Advisory Board. He was awarded with the Shanghai One Thousand Talent and Eastern Scholar in 2013.

Engineering Sustainable Port Solutions: The Power of AI and Innovation

Engineering is a vital factor that drives sustainable port development. This development aims to reduce maritime activities’ environmental impact and ensure coastal ecosystems’ long-term viability within the blue economy general frame. By consolidating the power of artificial intelligence (AI) and innovative engineering solutions while adapting to the World Port Sustainability Program (WPSP) Principals, ports can optimize operations, reduce emissions, and enhance sustainability. In this state-of-the-art lecture, Prof. Akram Elselmy will explore some of the key applications of AI in port management, including traffic optimization, energy efficiency, and environmental monitoring. Furthermore, innovative engineering case studies will be presented at the end of the lecture to provide a better perspective of the topic.

By understanding the role of AI and engineering in creating sustainable port solutions, young Engineers, especially those interested in such a topic, will gain valuable insights into the challenges and opportunities within this growing field. This knowledge will equip them with skills and expertise that will help them work on innovative solutions that address the pressing environmental and economic issues facing ports. As the world increasingly focuses on sustainability, there is a growing demand for engineers who can design and implement sustainable port infrastructure and operations.

Prof. Akram Soliman Elselmy, is a Professor of Port Planning and Coastal Engineering, and the dean of College of Engineering and Technology at the Arab Academy for Science, Technology & Maritime Transport. He was the Dean of Port Training Institute, AASTMT from 2008 to 2022. He is a Consultant Engineer of Harbours Planning, Navigation and Shore Protection. He got his PhD 2004 from the School of Civil Engineering, University of Nottingham, UK. Prof. Akram has more than thirty-two years of experience in Port Planning and Coastal Engineering. He also has many distinguished research activities, as he supervised many PhD and MSc theses, in addition to publishing more than fifty research
papers in many international journals and conferences in the fields of Port Planning, Coastal Engineering, Coastal Zone Management and Sustainable Development. He has been the head of the International Maritime Transport and Logistics Conference Organizing Committee, MARLOG, since 2011. Prof. Akram is the Editor in Chief of the International Maritime Transport and Logistics Conference Proceedings, SCOPUS indexed proceedings. He is a Member of the Scientific Program Committee of the 16th International Conference on Marine Navigation and Safety of Sea Transportation TransNav 2025, Member of the PIANC WG for Guidelines for Inland Waterways Infrastructure to Facilitate Tourism, Member of the Peer Reviewer Committee of the Journal of Marine Policy (JMP) and a member of the Peer Reviewer Committee of the International Associations of Maritime Universities Conferences (IAMU).

Prof. Akram is the project Manager of the” YOUTH EMPLOYMENT IN PORTS OF THE MEDITERRANEAN ENI Funded Project by CBC (Cross Border Cooperation) Program (September 2020 to December 2023) (https://www.enicbcmed.eu/projects/yep-med). Furthermore, he acts as a Consultant for the project titled “Studying the Vulnerability Assessment of Egyptian Ports to Climate Change and Appropriate Adaptation Options Based on Cost-Benefit Analysis” – Science, Technology & Innovation Funding Authority (STDF) Funded Project (for the Ministry of Scientific Research).

Semiconductor Industry and its AI implications 

George Angelov is Deputy Minister of Innovation and Growth and Professor in Microelectronics at the Technical University of Sofia. 

He received MSc degree in Physics from Sofia University “St. Kliment Ohridski”, Bulgaria in 1999 and PhD degree in Microelectronics from Technical University of Sofia, Bulgaria in 2008. From 1999 to 2002 he was with Technology Centre–Institute of Microelectronics, Sofia, Bulgaria. From 2007 to 2012 he was Assistant Professor, from 2013 to 2021 he was Associate Professor and since 2022 he has been Professor at the Department of Microelectronics, Faculty of Electronics Engineering and Technologies (FETT), Technical University of Sofia. 

George Angelov was Chair of Department of Microelectronics (2015-2023), Chairman of the Managing Board of the Cluster of Microelectronics and Industrial Electronics Systems (2016-2023), Head of MINOLab at Sofia Tech Park (2017-2023) and Director of Information and Public Relations at the Technical University of Sofia (2020-2023). 

George Angelov’s business interests are in entrepreneurship, business development, technology transfer, and innovations management. His research interests are in semiconductor device modeling (FinFETs, nanosheet FETs, nanowires, junctionless transistors, CNTs, etc.), design of integrated circuits, failure analysis (FA), microelectronics reliability (EMC & ESD), bioelectronics, energy storage and battery management systems, energy efficiency – monitoring and control systems. 

George Angelov is author of 125 papers, 4 books, and 1 monograph. Member of IEEE (since 2003) – Solid-State Circuits Society and Electron Devices Society; Marquis Who’s Who in the World (since 2008).

Laboratories

  • Dr. Matteo Barbieri (PhD, Sweden) – Laboratory on AI applications in the Blue Economy, focusing on supply chain management, services, and government

Dr. Matteo Barbieri got a PhD in Computer Science from the University of Genoa in 2017, working in the field of Machine Learning applied to the analysis of biomedical data. His interest has since shifted to the productionalization of AI-based solutions in various industries. Matteo moved to Sweden in 2020, and is now a Senior Machine Learning Engineer at Epidemic Sound.

Matteo’s lectures and labs will be focused on the current state of AI in industry, with practical examples of implementations of services based on AI models. Lectures will include success and spectacular failures stories from his experience, suggestions for those who want to embark in a similar journey and an acceptable amount of meme-based slides.

No specific knowledge in ML is strictly required, just bring your laptop, VS code and your curiosity.

  • Prof. Dr. Angelo Alessandri – Predictive Control for Sustainable Transportation and Logistics

The considerable growth of transportation has made the development of decision support systems to deal with problems of sustainable transports a topic of crucial importance. The efficient management of urban/freeway traffic, container terminals, and logistics networks may become the subject of a course where the most important methodologies available from Operations Research and Control Theory are presented to deal with problems concerning the efficient exploitation of limited resources. Typical examples of such management problems will be considered in an increasingly involving teaching path with lessons of both theory and practice. The first type of lessons is devoted to the basic principles of Automatic Control and Optimization. In the second ones, case studies concerning logistics operations at container terminals and distribution in supply chains will be illustrated.

In the course various subjects will be addressed, as it is briefly described in the following. The first subject regards the modelling of complex systems to simulate and analyze their behaviour over time. This is important, for example, when one wants to evaluate the performances of a container terminal. Standard results from queueing theory were employed in the past with the main drawback of suffering from a poor capability of describing the dynamic behaviour of a terminal. As a consequence, to take into account also dynamic aspects, more powerful modelling paradigms were proposed like, for example, discrete-event systems. Discrete-event tools allow one to construct very precise models, but, unfortunately, they are quite demanding from the computational point of view. Such difficulty arises particularly when a model is used to devise management strategies for a container terminal or a distribution chain in real-time due to the large number of variables, even if one has to deal with small container terminals or distribution networks of small companies. 

Generally speaking, the availability of a model with a convenient trade-off between precision and computational burden make possible to formulate control problems that aim at exploiting the available resources as much efficiently as possible. For instance, the productivity of a terminal depends on the efficient exploitation of labour and equipment. An increased efficiency can be obtained by a more convenient use of the available transfer machines. Toward this end, a predictive control approach appears particularly appropriate, as it is based on the idea of solving an open-loop finite-horizon optimal control problem at each time step and applying only the first control action. Optimization is performed over a forward horizon from the current time instant by using the available information about the container occupancies in the various areas of the terminal, and the foreseen import and export flows of the various carrier classes. Predictive control is the second main subject of the course.

One of the main advantages of predictive control concerns the ability to consider constraints on both state and input variables of the model. These constraints are very important in the two considered case studies. For example, the space limitations in the yard can be easily taken into account via upper bounds on the state variables that represent the number of containers in the various areas of the terminal. The maximum inventory levels can be treated similarly in the management of a distribution chain, as well as the limitation concerning the capacity of the carriers of the various types of goods. Further constraints can be introduced to account for the available resources that are needed to perform the transfer operations. Moreover, the reduced availability of berths, parking lanes, and rail platforms requires the introduction of binary variables that represent the beginning, progress, and conclusion of such operations.  In the case of distribution chain problem, integer variables are needed to account for the periodic replenishment of goods at fixed intervals of buckets. 

The solution of the predictive control problem becomes more difficult to find when binary or integer variables are introduced. The use of nonlinear dynamic model nonlinear cost functions makes the resulting optimization problem to solve of mixed-integer nonlinear programming type, for which different methodologies can be used, like branch-and-bound and cutting-plane. Such topics will be addressed as third main subject of the course, i.e., the application of optimization.

The proposed course is inspired by the success obtained by predictive control in the control of chemical process and manufacturing. This success indicates that one can expect the rapid diffusion of such paradigm to the management of complex systems, where significant advantages in terms of sustainability can be gained via an increased efficiency in the exploitation of the resources such as power systems and supply chains.

Angelo Alessandri received the “Laurea” degree in Electronics Engineering and the Ph.D. degree in Electronics and Computer Engineering from the University of Genoa, Genoa, Italy, in 1992 and 1996, respectively. From 1996 to 2005, he was a research scientist with the National Research Council of Italy, Genoa. In 2005, he joined the University of Genoa, where he is currently a full professor in the Department of Mechanical, Energetics, Management, and Transportation Engineering. His main research interests include estimation, fault diagnosis, and optimal control. He was an associate editor of the IFAC Journal of Engineering Applications of Artificial Intelligence, the IEEE Transactions on Neural Networks, and the IEEE Transactions on Control Systems Technology. He serves as an editor of the International Journal of Adaptive Control and Signal Processing and as an associate editor of the EUCA European Journal of Control and of the IFAC journal Automatica. 

  • Prof. Dr. Emanuele Morra – Enhancing Cruise Ship Supply Chain Management with AnyLogistix 

As part of this workshop, I will guide participants through the use of AnyLogistix, a powerful tool for supply chain modeling, optimization, and simulation. We will explore how the unique operational challenges of cruise ships, including onboard logistics and port-to-port supply management, can be optimized to increase efficiency. Through live demonstrations and case studies, participants will learn to develop models, run optimization algorithms, and simulate what-if scenarios. The goal is to equip attendees with practical skills in leveraging advanced software to address the complexities of the cruise ship supply chain and improve overall performance.

Additionally, the workshop will feature an interactive digital lab where participants will be divided into teams, each tasked with planning a virtual itinerary for a luxury cruise ship. Teams will face unique challenges posed by extreme destinations such as Antarctica, the Arctic, or the Galapagos Islands, where ensuring adequate supplies and resources becomes critical. The exercise will simulate real-life supply chain scenarios, including the management of fuel, food, and medical supplies, while navigating difficult environmental and logistical conditions.

Each team will also need to propose solutions for managing an emergency situation onboard, such as a mechanical failure or medical emergency, requiring immediate resource allocation and decision-making under pressure. This hands-on approach will allow participants to apply their knowledge in real-time, using AnyLogistix to test and refine their strategies for maintaining an efficient and resilient cruise ship supply chain.

Prof. Dr. Emanuele Morra is External Professor at the Politecnico of Turin (ITA) in Industrial Plants and Safety. He is Partner of MEVB Consulting Gmbh, a consulting Company with international expertise in simulation and modelling with base in Olten (Switzerland). He is trainer and developer for simulation models in Supply Chain and Industrial Plants, based on anyLogistix and Anylogic softwares. He is reviewer for scientific paper for ASTESJ Journal, Home – Advances in Science, Technology and Engineering Systems Journal (astesj.com).

  • Dr. Sonakshi Ruhela – Behavioral analysis for safety in the Blue economy
  • Prof. Dr. Shawn Mathew – Marketing for Blue economy

Publication Opportunity for PhD Students and Researchers

As part of the II Freshmen PhD & Professionals Cruise School 2025, participants will have the exciting opportunity to present their research projects during the conference. Selected projects will be considered for publication in a Scopus-indexed journal, providing a valuable platform to share your work with the international academic community.

To be considered for publication, participants must submit an abstract of their project via the official submission form. Projects that align with the conference’s focus on AI and sustainability in industry, healthcare, and government will be given priority.

Submission Process:

1. Submit your abstract through the form on our website as per instruction received.

2. Ensure that your research fits within the theme of the Blue Economy and AI-driven sustainability.

Publication Details:

• Selected papers will be published in the journal Special Issue, “Transition to Industry 4.0 in Emerging Domains: Methodology and Case Studies.”, which is indexed in Scopus.

• The journal welcomes both theoretical and applied research related to AI applications in supply chain management, healthcare, transportation, and sustainable development.

Deadline for Abstract Submission: 01.12.2024

Don’t miss this chance to have your research recognized and published in a prestigious journal!

For submitting your Abstract you will receive a link after your Registration and Confirmation.

Who can participate

Our workshop has been primarily designed with first-year PhD students in mind, aiming to provide them with valuable learning opportunities.

However, this experience is not exclusive to them. We warmly invite PhD students from their second and third years, postdoc researchers, assistants, scientific supervisors, and professors who are keen to participate in this workshop.

Erasmus partenrship

Dear Participants, our workshop is not open only to the scientists of Italian Universities, but also to all the scientists of Europe.

In particular, if your university is part of the Socrates programme and cooperating with the partner universities of our workshop, you would be eligible to participate with the Erasmus funds of the university within the “staff and teaching mobility” programme.

If you are interested, please contact us.

Here below you can find some of the universities eligible:

  • AGH University of Krakow
  • Aristotle University of Thessaloniki
  • Catholic University of Ávila (UCAV)
  • Chemnitz University of Technology (TU Chemnitz)
  • Częstochowa University of Technology
  • Gdańsk University of Technology
  • Karlsruher Institut für Technologie
  • Kaunas University of Technology
  • King Juan Carlos University
  • La Rochelle University
  • Management Center Innsbruck
  • National Institute of Applied Sciences (INSA Strasbourg)
  • NOVA University Lisbon
  • Paris-Saclay University
  • Polytechnic University of Catalonia
  • Reykjavik University
  • Riga Technical University
  • Slovak University of Technology in Bratislava
  • Széchenyi István University
  • Tadeusz Kościuszko University of Technology
  • Technical University of Madrid
  • Technical University of Sofia
  • Technische Universität Darmstadt
  • Technische Universität Dresden
  • Transilvania University of Brașov
  • Università di Corsica Pasquale Paoli / Université de Corse Pascal-Paoli
  • Université Savoie Mont Blanc
  • University of A Coruña
  • University of Antwerp
  • University of Aveiro
  • University of Burgos
  • University of Málaga
  • University of Miskolc
  • University of Seville
  • University of Stavanger
  • University of West Bohemia
  • University of Zaragoza
  • Vasile Alecsandri University of Bacău
  • Vilnius Gediminas Technical University
  • Warsaw University of Technology

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