Keynote Speakers
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Honorary Speech Prof. Gerhard-Wilhelm Weber Poznań University of Technology EURO Conference Advisor, Chair of IFORS Developing Countries Online Resources, Honorary chair of some EURO and international working groups, Institute of Applied Mathematics, METU, Ankara, Turkey |
Short Biography:
Prof. Gerhard-Wilhelm Weber is a Professor at Poznań University of Technology, Poznan, Poland, at Faculty of Engineering Management. His research is on mathematics, statistics, operational research, data science, machine learning, finance, economics, medicine, neuro-, bio- and earth-sciences, development, cosmology and spirituality. He is involved in the organization of scientific life internationally. He received Diploma and Doctorate in Mathematics, and Economics / Business Administration, at RWTH Aachen, and Habilitation at TU Darmstadt (Germany). He replaced professorships at University of Cologne, and TU Chemnitz, Germany. At Institute of Applied Mathematics, Middle East Technical University, Ankara, Turkey, he was a Professor in Financial Mathematics and Scientific Computing, and Assistant to the Director, and has been a member of five further graduate schools, institutes and departments of METU. G.-W. Weber has affiliations at Universities of Siegen (Germany), Federation University (Ballarat, Australia), University of Aveiro (Portugal), University of North Sumatra (Medan, Indonesia), Malaysia University of Technology, Chinese University of Hong Kong, KTO Karatay University (Konya, Turkey), Vidyasagar University (Midnapore, India), Mazandaran University of Science and Technology (Babol, Iran), Istinye University (Istanbul, Turkey), Georgian International Academy of Sciences, at EURO (Association of European OR Societies) where he is “Advisor to EURO Conferences” and IFORS (International Federation of OR Societies), where he is member in many national OR societies, working groups, IFORS Newsletter and IFORS Developing Countries Online Resources, at Pacific Optimization Research Activity Group, etc. G.-W. Weber has supervised many MSc. and PhD. students, authored and edited numerous books and articles, and given many presentations from a diversity of areas, in theory, methods and practice. He has been a member of many international editorial, special issue and award boards; he participated at numerous research projects; G.-W. Weber received various recognitions.
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Honorary Speech
Dr. Gülnihal Meral Ankara Yıldırım Beyazıt University, Turkey |
Short Biography:
Dr. Gülnihal Meral received her PhD and MSc degrees in Mathematics at Middle East Technical University (METU) and BSc in Ankara University (Türkiye). She worked as a Research Assistant in METU (2002-2009), as an Assistant Prof. (2009-2013) and Assoc. Prof. (2013-2016) in Bülent Ecevit University, Zonguldak. She joined Ankara Yıldırım Beyazıt University in October 2016 as an Assoc. Prof. and is now a full-time professor of Mathematics Department. She is also one of the vice-deans of the Faculty of Engineering and Natural Sciences. She has been to different German Universities (University of Stuttgart, University of Münster, University of Kaiserslautern ) during her Ph.D. studies and for her Post Doctoral Research. Dr. Meral’s research area mainly includes numerical solutions of ordinary and partial differential equations and mathematical modelling, analysis, and numerical simulation of cancer invasion. She has authored and co-authored at various core journals, conference proceedings and has given many presentations on numerical methods and mathematical models with a special focus on cancer models and their analysis.
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Decoding the Data Universe: From Buzzwords to Real-World Implications Prof. DURSUN DELEN Department of Management Science & Information Systems Oklahoma State University, USA |
Short Bio:
Dr. Dursun Delen is the holder of William S. Spears Endowed Chair in Business Administration, Patterson Family Endowed Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. Prior to his academic tenure at OSU, he worked for a privately-owned research and consultancy company as a research scientist for five years, during which he led a number of advanced analytics research projects funded by federal agencies including DoD and NASA. Dr. Delen has authored more than 250 peer reviewed articles and 12 books and textbooks in the broad area of Data Science, Business Analytics, and AI/Machine Learning. He is often invited to companies for consultancy engagements and national and international conferences for keynote addresses. Dr. Delen regularly chairs tracks and minitracks at various business analytics and information systems conferences. Currently, he is the editor-in-chief for the Journal of Business Analytics and AI in Business (in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, Big Data, Applied Soft Computing, and Information Systems Frontiers, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards including the prestigious Eminent Faculty, Fulbright Scholar, Regents’ Distinguished Teacher and Researcher, President’s Outstanding Researcher, and Big Data Mentor awards.
Abstract:
In our rapidly evolving digital landscape, terms like “business analytics,” “big data,” “business intelligence,” and “data science” have become part of our everyday lexicon. But what do they really mean? Are they interchangeable, or do they each hold a distinct purpose? Despite their diverse labels, these terms share a common theme: uncovering hidden patterns and extracting insights from data. Whether it’s predicting financial success of Hollywood movies or optimizing treatment regiments of chronic diseases, analytics is the compass guiding our choices. Generally speaking, analytics is the art and science of discovering insight—by way of using sophisticated mathematical/statistical/AI models along with a variety of data and expert knowledge—to support accurate and timely decision making. Why has analytics and data science become so popular? Why now? Is it something new/novel? This presentation will attempt to answer these questions, and by doing so, will demystify the underlying terms/concepts of data science and business analytics with real-world applications.
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Blockchain use cases in sustainable supply chain
Sara Saberi, PhD Associate Professor, Worcetser Polytechnic Institute, USA |
Short Bio:
Sara Saberi, Ph.D., is an Associate Professor of Operations and Industrial Engineering at WPI. She completed her second PhD in Business Administration from the University of Massachusetts Amherst in 2016. Her main research interests lie at the intersection of supply chain management, Blockchain, game theory and network optimization. Her research published in the leading journals including the European Journal of Operational Research, Transportation Research Part E: Logistics and Transportation Review, International Journal of Production Research, and International Journal of Production Economics. She has been awarded multiple grants as PI from APICS’ Research Grant, Alfred P. Slone Foundation/Environmental Law Institute, National Science Foundation, and The U.S. Economic Development Administration. Due to her contributions to the supply chain field, which have benefited manufacturers, she has been honored with the esteemed Norton Assistant Professorship.
Abstract:
This presentation provides theoretical and practical managerial insights on emerging blockchain technology. Specifically, I introduce rudimentary blockchain technology features and their relationship to supply chain management and their use cases in sustainable supply chain networks. I expand my discussion into what managers need to know on the linkage of blockchains to sustainable supply chains and the circular economy. Overall, there is substantial potential for blockchain to support corporate and supply chain sustainability—but care should be taken to understand the pitfalls.
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Partial disassembly line balancing problem in sustainability framework
Prof. Dr. Eren ÖZCEYLAN Gaziantep University, Industrial Engineering Department, Türkiye |
Short Bio:
I am a Professor of the Industrial Engineering Department at the University of Gaziantep, and Branch Chief of the Logistics Association of Türkiye (LODER). Before joining here, I was at Northeastern University to conduct postdoctoral studies about sustainable production and distribution planning.
I have a diverse background with degrees and experience in environmentally conscious logistics and supply chain management, AI implementations, optimization, and management science. With this multidisciplinary background, I have helped practitioners and policymakers develop decision-making skills and tools to create and manage efficient, resilient, and sustainable supply chains.
I was awarded the Science Academy Young Scientist Award in 2016 and the Gaziantep University Science Award in 2017. I have over 100 SCI, and SSCI articles and over 5000 citations. According to the latest research conducted by Stanford University, I am one of the top 2 percent of scientists in the world, I am a TUBITAK and TUSSIDE-approved Model Factory Lean Production and DDX Digital Transformation consultant.
I am also a borad member at Limonist Meta Technology, based in London, offers artificial intelligence-based digital solutions to different sectors such as construction, carpet, garment, cosmetics, and health.
Abstract:
Remanufacturing, like recycling, is crucial in terms of economic benefits, environmental conservation, and resource preservation. Disassembly operations have a critical role for remanufacturing and recycling processes. The disassembly lines, which are integral to the disassembly system, serve the purpose of separating recycled or remanufactured products into their individual components. Due to the long-term decision-making process and substantial investment required for installing these lines, it becomes crucial for the system to operate at its utmost efficiency. Achieving efficient operation of disassembly lines necessitates appropriate configuration and resolution of the line balancing problem. However, end-of-life products can undergo either complete or partial disassembly procedures. Complete disassembly, involving the complete separation of end-of-life products into their individual components and parts, is often not practical in real-world settings. Given that complete disassembly is often unnecessary and costly, it has become essential to determine the optimal level of disassembly that maximizes profitability. Consequently, businesses commonly opt for partial disassembly, focusing on dismantling predominantly hazardous and high-demand parts while leaving the remaining components intact without disassembling them.
In this speech, we will focus on partial disassembly line balancing problems within the framework of sustainability. The gaps in the literature on partial disassembly line balancing problems will be mentioned and the economic, environmental and social objective functions that can be used will be discussed.
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Algorithmic challenges in Semidefinite Programming: exploring regularity with immobile indices
Prof. Dr. Tatiana Tchemisova University of Aveiro, Portugal |
Short Bio:
Tatiana Tchemisova graduated from the Belarusian State University and received her Ph.D. in Physical and Mathematical Sciences from the National Academy of Sciences of Belarus in 1996. She began her career at the Institute of Mathematics, Minsk, Belarus, and has worked as a researcher, lecturer, and assistant professor at several higher education institutions in Belarus and Portugal. Since 2020, she has been an associate professor at the University of Aveiro, Portugal.
Tatiana's expertise encompasses various aspects of mathematical optimization, including continuous and convex optimization, and the application of optimization methods to operations research, data mining, and decision support. She is the author of over 50 scientific publications and has co-authored research papers in various core journals, book chapters, and conference proceedings. She has educated several graduate students and is currently mentoring others.
Tatiana leads as an editor, guest editor, and editorial board member for several journals, journal special issues, and book projects. Since 2020, Tatiana has been a member of the board of the EURO WISDOM Forum, which aims to promote, support, empower, and encourage the participation of all genders in Operations Research and Management Science
Abstract:
Semidefinite programming (SDP) deals with the problem of minimizing linear functions subject to linear matrix inequalities (LMIs) and belongs to conic optimization. A wide variety of nonlinear convex optimization problems can be formulated as problems involving LMIs, and hence efficiently solved using recently developed interior-point methods. Semidefinite programming has been recognized in combinatorial optimization as a valuable technique for obtaining bounds on the solution of NP-hard problems. It provides important numerical tools for analysis and synthesis in systems and control theory, robust optimization, computational biology, systems and control theory, sensor network location, and data analysis, among others.
Regularity is an important property of optimization problems. Various notions of regularity are known from the literature, being defined for different classes of problems. Usually, optimization methods are based on the optimality conditions, which in turn, often suppose that the problem is regular. The absence of regularity leads to theoretical and numerical difficulties, and solvers may fail to provide a trustworthy result.
In the first part of the talk, we present a novel approach to the algorithmic investigation of the regularity of linear SDP problems. This approach is based on the notions of immobile indices and their immobility orders. For the linear semidefinite problem, we define the subspace of immobile indices and formulate the first-order optimality conditions in terms of a basic matrix of this subspace. These conditions are explicit, do not use constraint qualifications, and have the form of criterion.
In the second part, we present an algorithmic generator constructing nonregular SDP instances with prescribed irregularity degrees and present a database of nonregular test problems created using this generator. Numerical experiments using popular SDP solvers on the problems of this database permit us to conclude that the most efficient solvers are not efficient when applied to nonregular problems.
The presentation is based on joint work with O. Kostyukova (National Academy of Sciences of Belarus) and E. Macedo (University of Aveiro, Portugal)