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Keynote Lectures

Intelligent Multi-Criteria Decision Support Systems
Nikolaos Matsatsinis, Technical University of Crete, Greece

Operations Research Opportunities in Automated Driving Systems
David Ríos Insúa, ICMAT, Spain

A Tailored Benders Decomposition Approach for Last-mile Delivery with Autonomous Robots
Ivana Ljubic, ESSEC Business School of Paris, France

What Strategic Planners Need to Know in the Age of Uncertainty
Yakov Ben-Haim, Technion - Israel Institute of Technology, Israel

 

Intelligent Multi-Criteria Decision Support Systems

Nikolaos Matsatsinis
Technical University of Crete
Greece
 

Brief Bio
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 Postgraduate Programs of the School of Production Engineering & Management. 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, Business Intelligence, Analytics, e-Business & e-Marketing, Consumer Behaviour Analysis, New Product Development.


Abstract
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 (intelligent agents, recommender systems, expert systems) 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 speech is to present on the one hand the theoretical issues and methodologies of the cooperation of the Multi-Criteria DSS with the Artificial Intelligence and on the other hand to present a series of such IMCDSS and their applications in various fields such as marketing, health, defense, agricultural, etc.



 

 

Operations Research Opportunities in Automated Driving Systems

David Ríos Insúa
ICMAT
Spain
 

Brief Bio
David is AXA-ICMAT Chair in Adversarial Risk Analysis at ICMAT-CSIC and Professor of Statistics and OR at Universidad Complutense. He has had research and/or teaching positions at Purdue, Duke, SAMSI, CNR-IMATI, Paris-Dauphine, Aalto; Leeds and Shanghai ST. He is scientific director of Aisoy Robotics and recently received the ISBA DeGroot award for his book on Adversarial Risk Analysis. He is member of the Spanish Royal Academy of Sciences and an avid surfer.


Abstract
Automated driving systems (ADS) will redefine human transportation. Due to recent breakthroughs in artificial intelligence and computational processing, mass public transportation via autonomous vehicles is no longer in our distant future. However, the transition from current roadways to fully automated roadways will not be instantaneous. This opens up many interesting research opportunities in OR and DS some of which I shall discuss in the talk: ethical decisions in ADS, request to intervene decision support and multiagent decisions in ADS.



 

 

A Tailored Benders Decomposition Approach for Last-mile Delivery with Autonomous Robots

Ivana Ljubic
ESSEC Business School of Paris
France
 

Brief Bio
Ivana Ljubic is Professor of Operations Research at the ESSEC Business School of Paris. Prior to joining ESSEC in 2015, she was appointed at the University of Vienna. She also worked as Visiting Scholar/Professor at the Robert H. Smith School of Business at the University of Maryland, TU Dortmund, TU Berlin, Dauphine University.
Research interests of Ivana Ljubic include combinatorial optimization, optimization under uncertainty, bilevel optimization. She uses tools and methods of mixed integer (non-) linear programming, meta-heuristics and their successful combinations for solving optimization problems with applications in network design, telecommunications, transportation, logistics, routing and bioinformatics. She has published more than 60 articles in leading OR journals.


Abstract
This work addresses an operational problem of a logistics service provider that consists of finding an optimal route for a vehicle carrying customer parcels from a central depot to selected facilities, from where autonomous devices like robots are launched to perform last-mile deliveries. The objective is to minimize a tardiness indicator based on the customer delivery deadlines. We provide a better understanding of how three major tardiness indicators can be used to improve the quality of service by minimizing the maximum tardiness, the total tardiness, or the number of late deliveries. We study the problem complexity, devise a unifying Mixed Integer Programming formulation and propose an efficient branch-and-Benders-cut scheme to deal with instances of realistic size. Numerical results show that this novel Benders approach with a tailored combinatorial algorithm for generating Benders cuts largely outperforms all other alternatives. In our managerial study, we vary the number of available facilities, the coverage radius of autonomous robots and their speed, to assess their impact on the quality of service and environmental costs.
Joint work with: L. Alfandari and M.M. de Silva



 

 

What Strategic Planners Need to Know in the Age of Uncertainty

Yakov Ben-Haim
Technion - Israel Institute of Technology
Israel
 

Brief Bio
Prof. Yakov Ben-Haim initiated and developed info-gap decision theory for modeling and managing deep uncertainty. Info-gap theory is a decision-support tool, providing a methodology for assisting in assessment and selection of policy, strategy, action, or decision in a wide range of disciplines. Info-gap theory has impacted the fundamental understanding of uncertainty in human affairs, and is applied in decision making by scholars and practitioners around the world in engineering, biological conservation, economics, project management, climate change, natural hazard response, national security, medicine, and other areas (see info-gap.com). He has been a visiting scholar in many countries and has lectured at universities, technological and medical research institutions, public utilities and central banks. He has published more than 100 articles and 6 books. He is a professor of mechanical engineering and holds the Yitzhak Moda'i Chair in Technology and Economics at the Technion - Israel Institute of Technology.


Abstract
Strategic planners need two distinct intellectual capabilities. First, extensive topical or disciplinary expertise, supported by a broad understanding of the world, is needed for dealing with complex subtleties of human affairs. Second, methodological expertise in decisions under uncertainty is needed for dealing with unique situations involving innovation, discovery, and surprise by friend or foe.
We employ info-gap decision theory, and the concept of robust-satisficing, in support of strategic planning. Examples from national security, economic forecasting, and project management are presented.
Three arguments support our claim.
First, this dichotomy of intellectual capabilities is based on the uniqueness of historical circumstance, which often induces unprecedented behavior. Each strategic planning situation has many unique attributes of culture, geography, technology, ideology, etc. E.g. Britain’s counterinsurgency (COIN) strategy in Malaya was, in many respects, quite different from its COIN in Northern Ireland, and both were different from British COIN in Kenya, Brunei, Malaysia, Radfan (Yemen) and Dhofar (in Oman). While there are generic aspects of all conflicts, historical distinctiveness and innovation are also characteristic. This makes the identification of useful concrete rules of strategy difficult. Thus, strategists need both profound understanding of human affairs and societies, and expertise in managing surprise and uncertainty.
The second argument for expertise in managing uncertainty is based on Shackle-Popper indeterminism (SPI), which will be discussed. SPI provides a generic epistemic framework for understanding historical idiosyncracy and the prevalence of non-probabilistic Knightian uncertainty.
The third argument is that consensus of analysts' assessments is demanded by decision makers, but pluralism of understanding is prevalent in complex uncertain environments. We propose nurturing plurality of assessment, and embedding those assessments in the analysis of robustness to uncertainty. Specifically, for any proposed policy, the analyst evaluates the robustness (of that policy) to uncertainty (plurality) of assessment. A more robust policy is preferred over a less robust policy. In order to do this, the analyst must have both topical expertise in the relevant disciplines, as well as decision-theoretic expertise in managing uncertainty.



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