Distinguished Lecturers

To request a Distinguished Lecturer (DL) for your next event, complete the DL Application Form. For more information or to see the full list, go to the Distinguished Lecturers page.

Maria Pia Fanti portrait
Maria Pia Fanti
Automation in Logistics
University Polytechnic of Bari
Bari, Italy
RAS Geographic Region 2

Maria Pia Fanti (IEEE Fellow) received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1983. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983, she has been with the Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy, where she is currently a Full Professor of system and control engineering and Chair of the Laboratory of Automation and Control. Her research interests include discrete-event systems; Petri net; consensus protocols; fault detection; management and modeling of complex systems, such as logistics, production and healthcare systems. She has published +270 papers and two textbooks on these topics. Prof. Fanti was General Chair of the 2011 IEEE Conference on Automation Science and Engineering and of the IFAC Workshop on Dependable Control of Discrete Systems 2009. She is Editor of the IEEE Trans. on Automation Science and Engineering and Associate Editor of the IEEE Trans. on Systems, Man, and Cybernetics: Systems. She is member at large of the Board of Governors of the IEEE SMC Society and of the IEEE Robotics and Automation Society, Co-Chair of the Technical Committee on Discrete Event Systems of the IEEE SMC Society, and of the TC on Automation in Logistics of the IEEE Robotics and Automation Society.

Talk # 1

New Approaches for Managing Logistics Systems: Integrating Information, Communication Technologies and Remote Sensing

Logistics systems of the future are expected to provide resource-efficient, sustainable, safe, equitable and timely handling of goods and management services for the benefit of economy and society, in order to sustain global supply chains and multimodal transportation systems. The increasing availability of artificial intelligence technologies, such as remote sensing, information and communication tools, big data, blockchain, Internet of Things and machine learning, can capture, elaborate and communicate historical and real-time data and provide opportunities for establishing cloud-based and collaborative logistic ecosystems. This talk will present how automation science has potential to enhance the performance of logistics systems by providing novel, integrated hardware and software solutions that affect the economics of different segments of the logistics chain and transportation, by improving throughput and reducing resource requirements and environmental impact. Moreover, the talk will consider novel management techniques and services based on the modern communications, remote sensing, automation and Internet of Things technologies, that are suitable for helping stakeholders and decision makers to manage and optimize logistics systems. Hence, the presentation will focus on the design of cloud-based platforms and Decision Support Systems enabling the integration of supply-chain-related transport processes through logistics artificial intelligence solutions. In this context, some results obtained in European projects frameworks will be discussed.

Talk # 2

Quantized Consensus Algorithm for Distributed Task Assignment: Results and Applications

The distributed coordination problem for networks of dynamic agents is an active research field, which attracts a significant interest due to the need to exploit the capabilities of large-scale networks and systems of the near future. This talk deals with a constrained distributed task assignment problem in which a set of different tasks has to be assigned to a group of agents by minimizing the maximum cost (typically the execution time) under communication, assignment and capacity constraints. To solve this problem, we provide a gossip-based discrete consensus algorithm that, starting from a random assignment of tasks, is able to reach a feasible solution while minimizing the global objective function by only local interactions among agents. The convergence properties and performances of the proposed gossip algorithm are characterized for two distinct network configurations, i.e., peer-to-peer net- works and proximity networks. A simulation based study validates the theoretical analysis by considering more general and complex scenarios. Finally, an application proposing a solution for the distributed dynamic assignment of a set of electric vehicles to a network of charging stations is presented.

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Mengchu Zhou portrait
Mengchu Zhou
Automation in Logistics
New Jersey Institute of Technology (NJIT)
Newark (NJ), United States
RAS Geographic Region 3

MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, USA in 1990. He joined New Jersey Institute of Technology, USA in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 700 publications including 12 books, 400+ journal papers (300+ in IEEE transactions), 12 patents and 28 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of IEEE, IFAC, AAAS and Chinese Association of Automation (CAA).

Talk # 1

What is the Next Industrial Revolution?

Human beings have experienced two major industrial revolutions. The first one took place in the 19th century, which replaced muscle power from humans and animals with mechanical power. The second one started in the middle 20th century, which provided people and societies with Internet. It was built with the technologies from computing, communication, networking and information storage. Both offered unprecedented productivity increases. What will be the next one? This talk intends to answer this question by presenting some recent development of Internet of Things (IoT) and smart systems. IoT was selected by IEEE as a major initiative to develop and advance over the next few years. Several recent studies have predicted the huge growth of IoT and tremendous benefits to the world economy. It was expected that 26 billion IoT units would be installed by year 2020, generating $300 billion in revenue. The IoT will generate an additional $1.9 trillion in economic value. We plan to present a system engineering approach to Internet-of-Things-based smart systems and their applications to smart manufacturing, smart cities, smart gird, smart logistics, and smart healthcare services.

Talk # 2

Scheduling of Multi-robot Cluster Tools in Semiconductor

This talk intends to present Petri nets as a modeling, analysis, optimal scheduling and real-time control tool for single and multi-cluster tools that are widely used in semiconductor manufacturing industry. We illustrate how to use Petri nets to model various wafer production features involved in these highly expensive robotic manufacturing systems. Then we show how to use the resultant Petri net models to establish various schedulability conditions and derive extremely efficient algorithms that can compute optimal schedules for single and multi-cluster tools. When the bounded variation of activity time is caused in a fabrication process, we finally demonstrate how to adjust the scheduled robot wait time to offset such variation in order to achieve desired real-time optimal execution results. Our work focuses on those process-bounded cluster tools in which robots are fast enough such that they have some idle time in realizing an optimal schedule.

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