Prof. Dmitry Ivanov is Professor of Supply Chain and Operations Management, and director of the Digital-AI Supply Chain Lab at the Berlin School of Economics and Law. His research spans supply chain resilience and digital supply chain twins. He has made influential contributions, particularly exploring structural dynamics of supply chains. Author of the Viable Supply Chain Model and founder of the ripple effect and viability research in supply chains. Recipient of several research excellence awards. His research record counts around 470 publications, with more than 170 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, IEEE, DSI and POM, and over 40 universities worldwide. He has been Chairman, IPC Chair, and Advisory Board member for over 80 international conferences in supply chain and operations management, industrial engineering, control and information sciences. Principal investigator in several projects about digital supply chain twins and resilience funded by EU Horizon and DFG. Several Awards for Best Papers (IJPR, IISE Transactions, Omega), Clarivate Highly Cited Researcher Awards. Ranked #1 worldwide in Supply Chain by ScholarGPS and #1 in Operations Research by Standford/Elsevier ranking. Ranked #1 in German-Austrian-Switzerland Ranking of Top Scientists in Business and Management area. Chair of IFAC CC 5 “Cyber-Physical Manufacturing Systems”, Editor-in-Chief of International Journal of Integrated Supply Management, Editor 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.
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This talk is devoted to supply chain viability and digital ecosystems. We will discuss adaptation-based principles of supply chain viability, design and implementation of digital technology for viable supply chains, and human-AI collaboration in digital twins and viability stress testing. Pilot implementations will be illustrated using anyLogistix supply chain simulation and optimization software, and use cases in industry. Future research directions will be outlined, especially focusing on cross-disciplinary collaboration between management, biology, ecology, and control engineering. |
Prof. Teodor Gabriel Crainic is Adjunct Professor of Operations Research, Transportation, and Logistics, School of Management, Université du Québec à Montréal, and Adjunct Professor of Operations Research, Department of Computer Science and Operations Research, Université de Montréal. He is also a senior scientist at CIRRELT, the Interuniversity Research Center for Enterprise Networks, Logistics, and Transportation. He received the 2006 Merit Award of the Canadian Operational Research Society and is a member of the Royal Society of Canada.
The research interests of Professor Crainic are in network, integer, and combinatorial optimization, meta-heuristics, and parallel computing applied to the planning and management of complex systems. Particularly in transportation and logistics. Major contributions targeted methods for national/regional planning, the design, scheduling, and operation management of consolidation-based carriers, intermodal and logistics networks, routing, scheduling, and city logistics, new business and organizational transportation and logistics models and systems, and planning of urban and inter-urban multimodal multi-stakeholder freight transportation systems.
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Operations Research (OR) is a core component of the digital intelligence Understand-Predict-Decide methodological stream yielding successful decision-support tools for transportation and logistics planning and management (TL). We briefly recall 50 years of the continuously evolving, mutually enriching, and successful OR-TL story, with freight transportation and network design illustrations. Recent developments targeting the integrated service, resource, and revenue planning of urban and long-haul freight transport are discussed next. The problems considered are complex and realistic instances are computationally difficult. Hybridization of combinatorial optimization and advanced learning methods appears promising to address these challenges, while raising its own set of issues and questions. Discussing these will lead naturally to identifying a number of important research perspectives.
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Prof. Gheith Abandah, a PhD and MSE graduate from the University of Michigan and BSc holder from the University of Jordan, boasts a multifaceted career as a Full Professor of Computer Engineering at the University of Jordan, the first President of Aqaba University of Technology, and General Coordinator of the Erasmus+ DeCAIR project for developing AI and Robotics curricula. With 40 years of experience spanning academia and industry, he excels in both teaching and research (AI, Arabic NLP, machine learning) with 77 publications, 1400+ citations, and an h-index of 22. His 15+ years of industry expertise encompass localization, military electronics, and product/project development, where he spearheaded technical teams as Design Engineer, Project Manager, Development Manager, VP Development, and General Manager. An active IEEE Senior Member and member of the IEEE Computer Society's Distinguished Visitor Program, he co-founded the IEEE Jordan Section, chaired it for three terms, led the IEEE Region 8 Committee for Diversity, Equity, and Inclusion, and established the AEECT and JEEIT conferences, showcasing his dedication to service and innovation.
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This keynote surveys how Large Language Models (LLMs) evolved from earlier deep learning architectures (dense and recurrent networks) to transformers, and why transformers became the backbone of modern language systems due to parallel processing and attention-based sequence modeling. It clarifies the three dominant LLM architectural families—encoder-only representation models, decoder-only generation models, and encoder–decoder translation models—then highlights current trends shaping the field: efficiency (including Mixture-of-Experts, distillation, pruning, and quantization), long-context multimodal capabilities, open-source vs. closed-source dynamics, new reasoning and alignment paradigms, small/on-device language models, Retrieval-Augmented Generation (RAG) for grounded outputs, and tool-using agents. The talk concludes with Arabic NLP case studies—diacritization, poetry classification/diacritization, spelling correction, and Jordanian dialect to Modern Standard Arabic translation—showing how adapting foundation models such as ByT5 to Arabic data and error patterns yields strong practical impact. |
Prof. Abdelaziz Berrado is a Professor of Industrial Engineering at Ecole Mohammadia d’Ingénieurs (EMI), Mohammed V University in Rabat. He held leadership and administrative roles at the same institution as Chair of the Industrial Engineering Department, Managing Director of the Doctoral program and Deputy Director of Research and Partnerships.
He holds a Ph.D. in Decision Systems and Industrial Engineering from Arizona State University in 2005.
Pr Berrado has been leading applied research projects focusing on leveraging advanced analytics for knowledge generation and decision support in organizations with varied applications across different industries. In addition to applied research, Pr Berrado is continually investigating avenues to enhance Interpretability and Explainability in Machine Learning. Over the years, he has led several funded research projects with local and international impact and a close interaction with industry.
Pr Berrado is a fellow of IEOM society and a member of INFORMS. Previously, he was a senior engineer and data analytics lead at Intel.
