PROGRAMME

 

INVITED SPEAKERS

Natasha Shakhlevich

School of Computing, University of Leeds

Optimization under Uncertainty: a New Take on an Old Problem

The talk will focus on the efficient methodology for finding solutions to combinatorial optimization problems which are immune to variability in input data, but do not rely on probabilistic characteristics of problem parameters needed for Stochastic Programming, overly pessimistic worst-case scenarios typical for Robust Optimization, or optimality requirements of Stability Analysis. Our goal is to develop fast algorithms for finding most resilient solutions among acceptable ones, which are not necessarily optimal, but keep quality guarantees for the widest range of problem parameters. We present the key steps of the approach and illustrate it on popular examples of 0/1 combinatorial optimization problems and on scheduling problems.

Natasha Shakhlevich is senior lecturer at the School of Computing University of Leeds. She graduated from the Belarusian State University, Minsk, Belarus in 1986. and obtained her PhD on the National Academy of Sciences of Belarus, Minsk, Belarus in 1993.

Up to now, prof. Shaklevitch has worked at several respectable universities and institutes, such as: The Hong Kong Polytechnic University, Department of Management, Department of Business Studies (1997-2000), Institute of Engineering Cybernetics, National Academy of Sciences of Belarus (1986-2001), School of Computing and Mathematical Sciences, University of Greenwich, (2001-2001), and School of Computing University of Leeds (from 2001 till present).

Her area of expertise is Mathematical methods of operational research. Theoretical fields of interest include: algorithms and complexity, robust optimisation, scheduling, models with controllable parameters, linking scheduling and submodular optimisation, multi-objective optimisation, inverse optimisation, batch scheduling, assignment problems with Monge-like cost arrays. Her applied research interests are: healthcare OR, powerline routing, production and transportation models, storage loading, scheduling in Distributed Computing (energy efficiency, resource-usage optimisation, quality-of-service provision, computing at scale, divisible load scheduling).

Prof. Shakhlevich was a principal investigator at several research projects, the most recent ones are Algorithmic Support for Massive Scale Distributed Systems (2020-2023, funded by Engineering and Physical Sciences Research Council - EPSRC), Scheduling with Resource and Job Patterns (2013─2016, EPSRC), Submodular Optimisation Techniques for Scheduling with Controllable Parameters (2012-2013, EPSRC), Quality of Service Provision for Grid Applications via Intelligent Scheduling (2009-2013, EPSRC), Integrated Patient Pathways Management: Mathematical Modelling, Analysis and Optimisation (2007-2011, EPSRC and Mathematical Sciences CASE scheme), etc. She has also participated in five other research projects (from 1998 to 2013). Prof. Shakhlevich had research visits to Tokyo Institute of Technology (Japan), Tohoku University(Japan), University of Sydney (Australia), Université de Nantes (France), Adam Mickiewicz University (Poznan, Poland), Otto-von-Guericke-University Magdeburg (Germany), University of Kaiserslautern (Germany), University of Osnabrück (Germany), Hong Kong Polytechnic University, Technical University Eindhoven (the Netherlands).

Her research results were published in Mathematical Programming, SIAM Journal on Discrete Mathematics, INFORMS Journal on Computing, Algorithmica, Journal of Scheduling, Discrete Applied Mathematics, Journal of the Operational Research Society, European Journal of Operational Research, Computers and OR, Journal of Global Optimization, Journal of Operations Research for Health Care. According to Google Scholar, her h-index is 21 (1166 citations), while according to SCOPUS, her h-index is 17 (663 citations).

Prof. Shakhlevich is also the Associate Editor in two journals: Journal of Scheduling and Asia Pacific Journal of Operational Research.


Nikolaos Matsatsinis

School of Production Engineering and Management of the Technical University of Crete, Greece

Intelligent Multi-Criteria Decision Support Systems and their Applications

Multiple Criteria Decision Analysis/Making (MCDA/MCDM) is constantly increasing its presence in various scientific fields but also in new fields of its application. This expansion became possible mainly due to the integration and implementation of its methodologies in the Decision Support Systems (DSS). This enabled their successful applications in many new fields resulting in their further expansion as Multi-Criteria Decision Support Systems (MCDSS). Their combination with Artificial Intelligence in general and especially in recent years with the field of Machine Learning and Data Mining has led to the development of Intelligent Multi-Criteria Decision Support Systems (IMCDSS) with enormous application in analytics. At the same time, MCDA was used to solve various problems and to develop new methodologies in the field of Machine Learning. The aim of this presentation is to focus on the basic theoretical issues of the Multi-Criteria DSS that utilize Artificial Intelligence or are utilized by it and on the other hand to present a number of their applications in decision making in various fields such as marketing, health, etc.

Nikolaos Matsatsinis is a full Professor of Information and Decision Support Systems in the School of Production Engineering and Management of the Technical University of Crete, Greece. He is President of the Hellenic Operational Research Society (HELORS). He was dean of the Department of Production Engineering and Management from 2007-2011. He is the Director of the Laboratory of Decision Support Systems and the following Postgraduate Programs: Postgraduate Program of the School of Production Engineering & Management; Interdepartmental Postgraduate Program in “Technology & Innovation Management” with the School of Electrical and Computer Engineering, and the Inter-University Postgraduate Program in “Systems Engineering” with the Hellenic Military Academy. He received his B.A. in Physics from Aristotle University of Thessaloniki and his PhD in Intelligent Multicriteria Decision Support Systems for Marketing Decisions from Technical University of Crete. He also has over thirty-five years of experience in Information and Decision Systems development. He has contributed as scientific or project coordinator on over of fifty national and international projects. He has received distinctions from the Hellenic Operational Research Society, and the gold and silver awards of the Greek Healthcare Business Awards 2017 and 2019 respectively. He is Chief Editor of the Operational Research: An International Journal (IF 2019: 1.759) and International Journal of Decision Support Systems and member of the international advisory board of three other scientific journals. He is the author or co-author/editor of twenty-five books and over of one hundred and twenty articles in international scientific journals and books. He has organized and participated in the organization of over of ninety scientific conferences and he has over of two hundred presentations in international and national scientific conferences. Professor Matsatsinis is the Chair of the Organizing Committee of the EURO 2021, of the Association of European Operational Research Societies, which will be held in Athens. His research interests include Decision Support Systems, Artificial Intelligent and Multi-Agent Systems, Recommendation Systems, Operational Research, Multicriteria Decision Analysis, Group Decision Making, Evaluation Methods, e-Business & e-Marketing, Consumer Behaviour Analysis, New Product Development, Business Intelligence, Analytics, Data Analysis and Data Mining, Workflow Management Systems.


Nenad Mladenović

Khalifa University, Abu Dhabi, UAE

Less is more approach in Optimization - possible road to Artificial Intelligence

(joint work with D. Urošević, R. Todosijević, J. Brimberg, D. Aloise,....)

In this talk, we study a recently proposed philosophy for heuristic design known as the Less is more approach (LIMA). To date, more than a dozen papers in the optimization literature have applied this philosophy with surprising success. They show that the popular trend to implement more and more complex algorithms can be counter-productive, and actually produce inferior results. LIMA algorithms with fewer ingredients may not only find better solutions, but tend also to be more user friendly and easier to understand. We will present general LIMA algorithm and show how it can be possibly used as as bridge between Optimization on one side and Artificial Intelligence and Machine Learning, on another.

Nenad Mladenović is full professor at the Department of Industrial and Systems Engineering, at Khalifa University, Abu Dhabi, UAE, and a member of the European academy of sciences (Academia Europea). During his career, he has taught in several countries, including France, the United Kingdom, Canada, Belgium, Serbia, and the UAE, and was also a research professor at the Mathematical Institute of the Serbian Academy of Sciences and Arts. He is a leader or participant in several international research projects in Kazakhstan, Serbia, China, and Russia. Under his supervision, more than dozen doctoral students all over the world completed their PhDs.

Prof. Mladenović received his BSc degree in Mathematics from the Faculty of Mathematics, University of Belgrade, and his MSc and PhD in Mathematics (Operations Research) from the Faculty of Organizational Sciences, University of Belgrade. His research interests include optimization, metaheuristics, and mathematical programming. He was a keynote speaker at numerous international conferences. He is Editor-in-chief of the Yugoslav Journal of Operations Research (YUJOR), a member of Editorial board of several respectable international journals, and Editor of many special issues of international journals and monographs as well. He was a coordinator and member of more than 20 industrial or research projects

Bibliography of Prof Mladenović is very rich and highly cited. Up to now, he has published more than 350 research papers and book chapters in leading OR journals and books published by eminent publishing houses. According to Google scholar, his papers were cited close to 24 000 times. His research paper on VNS (EJOR, 2001), coauthored with P. Hansen, was selected among the 30 most influential published in EJOR in the 30-year history of EJOR and ranked as 8th most cited in 40 years of EJOR. Another paper on VNS (Computers and Operations Research, 1997), also coauthored with P. Hansen, is cited over 4500 and over 2000 times according to Google Scholar and Web of Science, respectively. His current (H, G) – research indices are (28, 67) (Web of Science) and (50, 121) (Google Scholar).

Prof. Mladenović is a member of the European Academy of Sciences (Academia Europea), the Academy of Nonlinear Sciences, and Serbian Scientific Society. He is President of National Society of Industrial and Applied Mathematics in Serbia. During his professional career, Prof Mladenović has received numerous awards and grants, some of them are: “Best Serbian mathematicians in ten years” award from SANU, Belgrade, Serbia (2013), Winner of the International grant competition Research in Brussels Service de Mathematiques de la Gestion, Universite Libre de Bruxelles, Belgium (1998-1999), winner of the award (with professor P. Hansen) for the best paper given by JUPIM (1999), Special Visiting Researcher grant under the Brazilian Scientific Mobility Program, University in Brazil, UFRN, Natal (2013-2016), EPSRC small grant Variable neighborhood search for clustering and data mining (2008), national grant for the scientific project "Mathematical optimization models and methods with applications" granted by the Serbian Ministry of Education, Science, and Technological Development (2002-2005, 2005-2010, 2010-2019), etc.