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Due to the COVID-19 pandemic, we will not organize LION 14 as a physical meeting.

The LNCS volume and the AMAI Special Issue will be published as scheduled.

LION14 Scope

The 14th Learning and Intelligent OptimizatioN Conference

The large variety of heuristic algorithms for hard optimization problems raises numerous interesting and challenging issues. Practitioners using heuristic algorithms for hard optimization problems are confronted with the burden of selecting the most appropriate method, in many cases through expensive algorithm configuration and parameter tuning . Scientists seek theoretical insights and demand a sound experimental methodology for valuating algorithms and assessing strengths and weaknesses. This effort requires a clear separation between the algorithm and the experimenter, who, in too many cases, is "in the loop" as a motivated intelligent learning component. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

This meeting explores the intersections and uncharted territories between machine learning, artificial intelligence, energy, mathematical programming and algorithms for hard optimization problems. The main purpose of the event is to bring together experts from these areas to discuss new ideas and methods, challenges and opportunities in various application areas, general trends and specific developments. We are excited to be bringing the LION conference in Greece for the third time.


  1. Conference proceedings will be published by Springer-Verlag in Lecture Notes in Computer Science

  2. Revised selected papers of LION 14 will be published in a special issue of Annals of Mathematics and Artificial Intelligence

Important dates

December 31, 2019:   January 31, 2020: Paper submission deadline
February 29, 2020: Author Notification
March 14, 2020: Registration opens
April 15, 2020: Camera ready for pre-proceedings, please use the subject line "LION 14 pre-proceedings source files, paper ID"
May 24-28, 2020: Conference
June 28, 2020: Submission of camera-ready papers, formatted according to Springer's LNCS guidelines

Best Paper Award

Thanks to Springer's sponsorship, LION 14 will have a Best Paper Award of EUR 1,000 !

Paper Format

Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template, which is available here . Papers must be submitted in PDF.

Types of Submissions

When submitting a paper to LION14, authors are required to select one of the following three types of papers:
  • Long paper: original novel and unpublished work (max. 15 pages in LNCS format);
  • Short paper: an extended abstract of novel work (max. 6 pages in LNCS format);
  • Work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference.

If you are submitting for a special session, please remember to specify the Special Session number in the title of your submission, like: SUBMITTED TO SPECIAL SESSION NN

Submission System

All papers must be submitted using EasyChair.


The LION 14 conference is open, each accepted paper should be registered with the reduced fee of €300, to cover conference expenses incurred already. Once you have completed your money transfer (HERE), please e‐mail Ilias Kotsireas with a copy of your bank transfer and the following 5 pieces of information:
  1. First Name & Last Name (of the LION 14 Registrant)
  2. Affiliation (University/Institute/Company)
  3. Amount of the money transfer ( € 300 + bank transfer fees)
  4. City, State/Province/Region, Country
  5. working e‐mail address (of the LION 14 Registrant)
Please use "YourFirstName, YourLastName, LION 14 Registration Fee bank money transfer" as the subject line of your e‐mail.
All LION 14 conference registration fees will be acknowledged by e‐mail and an official receipt will be issued for reimbursement purposes from your University/Company/Institution.

LION 14 Registration Fees:
Early participant registration: € 350 March 1, 2020 to May 3, 2020
Late participant registration: € 400 May 3, 2020 to May 24, 2020
Early accompanying person registration: € 150 March 14, 2020 to May 3, 2020
Late accompanying person registration: € 200 May 4, 2020 to May 24, 2020

Invited Talks


UberAir: Optimization Problems in the Sky


Youssef Hamadi, Uber


Uber will soon provide short flights in dense city areas. These flights will use VTOL electric aircrafts carrying up to 4 passengers and a pilot. Demonstration flights are projected to start in 2020 in Dallas and Los Angeles. Commercial operations (UberAir) are projected to begin in 2023 in three cities. To deliver this vision, several challenges have to be tackled. We can class them in several categories: regulation, infrastructure, operations, multimodal integration, pilot augmentation, and autonomy. We can further develop these into several existing and new optimization problems including path-planning, resource consolidation, and distributed negotiation. In this talk, I will present the overall vision for UberAir, along with ongoing developments on the previous problems. I will also cast directions for pilot augmentation and full aircraft autonomy.


Youssef Hamadi’s research is set at the intersection of Optimization and Artificial Intelligence. He completed his PhD at the University of Montpellier, devising new algorithms for Distributed Constraint Solving. In 2013, he defended his Habilitation on the concept of Autonomous Search at the University of Paris Sud. In 2003, he created the Constraint Reasoning Group at Microsoft Research, pushing the limits of Parallel Satisfiability and large-scale Optimization, gaining multiple SAT Competitions, while transferring mathematical modeling and algorithms in several Microsoft products. In 2006, Youssef started to work on the relationships between mathematical programming and Sustainability, creating jointly with CNRS the first European research project on Optimization for Sustainable Development. He joined Uber Elevate in 2019 to work on Autonomous aircrafts.


Combinatorial Methods and Optimization Algorithms for Testing and Explainable AI


Dimitris E. Simos, SBA Research, Austria (joint work with Rick Kuhn (NIST, USA) and Raghu Kacker (NIST, USA))


Combinatorial methods have attracted attention as a means of providing strong software assurance at reduced cost, but when are these methods practical and cost-effective? This talk is comprised of three parts: The first one explains the background, process, and tools available for combinatorial testing, with illustrations from industry experience with the method. The focus is on practical applications, including example of testing to meet FAA-required standards for life-critical software for commercial aviation. Other example applications include modeling and simulation, mobile devices, Internet-of-Things (IoT), testing for a NASA spacecraft, autonomous systems, security applications and large-scale testing for Adobe analytics. The second part offers a survey of the theoretical methods and algorithms available to construct highly optimized software tests using approaches from neural computation, quantum computing and algorithmic engineering where the related computation problem is closely related to NP-hard problems. Last, we present a new approach to producing explanations or justifications of decisions made by artificial intelligence and machine learning (AI/ML) systems, using methods derived from fault location in combinatorial testing.


Dr. Dimitris E. Simos is Key Researcher for the Applied Discrete Mathematics for Information Security research area with SBA Research, located in Vienna at Austria, and leads its Mathematics for Testing, Reliability and Information Security (MATRIS) research group since 2018. He is also an Adjunct Lecturer with Vienna University of Technology and a Distinguished Guest Lecturer with Graz University of Technology. He is a research member of the US NIST Working Group on "Automated Combinatorial Testing for Software" (ACTS) and a research grantee from NIST Information Technology Lab as well as EU's H2020 working programme among others. His research interests include combinatorial designs and their applications to software testing, combinatorial testing in particular, symbolic computation and optimization algorithms, and all aspects of information security. During his career Dimitris has (co)-authored over 100 papers in Discrete Mathematics and their applications to Computer Science and has been awarded the rank of Fellow of the Institute of Combinatorics and its Applications (FTICA) in 2012.

Special Sessions


Automatic Solver Configuration

  • Meinolf Sellmann, GE Global Research, USA
  • Kevin Tierney, Bielefeld University, Germany
  • Carlos Ansòtegui, University of Lleida, Spain
  • Andre Hottung, Bielefeld University, Germany
  • Michael Römer, University of Toronto, Canada
  • Andre Cire, University of Toronto, Canada
  • Patrick De Causmaecker, KU Leuven, Netherlands
  • Tatsushi Nishi, Osaka University, Japan
  • Markus Wagner, Government University of Vienna, Austria
  • Serdar Kadioglu, Brown University, USA
  • Yuri Malitsky, Morgan Stanley, USA
  • Thiago Serra, Bucknell University, USA
  • Josep Pon, University of Lleida, Spain

All optimization solvers have parameters that can be very hard to tune by hand. Automatic solver configuration has emerged as a key tool for improving solver efficiency. In this special session, we invite contributions that include, but are not limited to:

  • methods for finding good solver default parameters
  • methods for tuning solvers instance-specifically
  • methods for building solver portfolios
  • self-configurable search methods, including self-tuning restarts and hyper-reactive search


Massively Parallel Methods for Search and Optimization

  • Philippe Codognet, JFLI / Sorbonne Universitè, Japan / France
  • Salvador Abreu, University of Evora, Portugal
  • Daniel Diaz, University of Paris-1, France

Solving hard optimization problems requires algorithms that can take advantage of the computing power of the underlying hardware, i.e. multi-core architectures and GPGPU-enhanced systems, and in the last decade many experiments have been done for developing parallel methods for solving combinatorial problems on small-scale systems (up to a few hundreds of cores). The next challenge is to design and implement efficient algorithms for massively parallel computers with hundreds of thousands of cores. Several supercomputers, such as the Jean Zay supercomputer in France or the future Fugaku supercomputer in Japan are indeed being developed for targeting large-scale AI applications, most of which include an optimization component. With the performance of supercomputers expected to reach exascale level by 2021 with machines regrouping several millions of cores, the development of massively parallel algorithms appears as a major issue in order to use in an efficient manner the computing power at hand.


DC Learning: Theory, Algorithms and Applications

  • Hoai An Le Thi, University of Lorraine, France
  • Tao Pham Dinh, INSA-Rouen, France and Institute for research and applications of Optimization, VinTech, VinGroup, Vietnam
  • Hoai Minh Le, University of Lorraine, France

One of the challenges for the scientists at the present time consists of the optimal exploitation of a huge quantity of data of the information stored in various forms. The knowledge extraction from these data requires the use of sophisticated techniques and high performant algorithms based on solid theoretical foundations and statistics. Based on the powerful arsenal of convex analysis, DC (Difference of Convex functions) programming and DCA (DC Algorithms) are among the few nonconvex optimization approaches that can meet this requirement. Machine Learning represent a mine of optimization problems that are almost all DC programs for which appropriate resolutions should use DC programming and DCA. During the last two decades DC programming and DCA have been successfully applied to modeling and resolution of many problems in Machine Learning.

This symposium welcomes works on advanced DC Programming and DCA for Machine Learning, from both a theoretical and an algorithmic point of view. Especially, those related to modeling and solving practical problems by DCA Learning (DCAL) based algorithms are encouraged.


Intractable Problems of Combinatorial Optimization, Computational Geometry, and Machine Learning: Algorithms and Theoretical Bounds

  • Alexander Kelmanov, Sobolev Institute of Mathematics, Russia
  • Michael Khachay, Krasovsky Institute of Mathematics and Mechanics, Russia

This special session deals with algorithmic analysis for a variety of extremal problems arising in combinatorial optimization, statistical learning, and computational geometry. As a rule, all these problems are strongly NP-hard and hardly approximable in general and remain intractable even in very specific settings, e.g. on the Euclidean plane. In recent decades, the design of polynomial time approximation algorithms and schemes, proving their performance guarantees and establishing the efficient approximability thresholds for the problems in question became one of the main research directions in this field along with the well-known Branch-and-Bound exact methods, heuristics and meta-heuristics.

This session is focused on (but not limited to) the following topics:

  • computational and parametric complexity of novel optimization problems and settings, polynomial time and FPT solvable subclasses of known intractable problems, e.t.c.
  • polynomial time approximation algorithms with theoretical guarantees and approximation schemes
  • asymptotically optimal approximation algorithms
  • randomized algorithms and schemes, concentration of measure, sampling and derandomization techniques
  • theoretic polynomial time approximability thresholds
  • program implementation and empirical evaluation of approximation algorithms


Intelligent Optimization in Health, e-Health, Bioinformatics, Biomedecine and Neurosciences

  • Clarisse Dhaenens, University of Lille , CRIStAL, France
  • Julie Jacques, Lille Catholic University, CRIStAL, France
  • Laetitia Jourdan, University of Lille , CRIStAL, France

This special session aims at putting together works in which optimization approaches and knowledge discovery are jointly concerned to solve problems coming from Health, e-Health, Bioinformatics, biomedecine and neuroscience. The term e-Health or health informatics can be defined as healthcare process supported by electronic processes and communication.

Health, e-Health, Bioinformatics, Biomedecine and Neuroscience represent a great challenge for optimization methods as many problems arizing in these fields can be modelized as large size optimization problems. For example, many bioinformatics problems deal with the manipulation of large sets of variables (SNPs, genes, GWA, proteins ...). Hence, looking for a good combination of these variables require advance search mechanisms. In biomedecine (or medical biology), such optimization problems may also be found by studying molecular interactions. In e-Health a wide range of the services or systems are encountered and are at the edge of medicine and information technology. These services include, but not limited to: electronic health records, telemedicine, the use of mobile devices in collecting health data and providing real-time patient monitoring, healthcare information systems and intelligent medical diagnostic systems. The data provides by such devices are huge and the problems generated required efficient methods. Solving such difficult combinatorial optimization problems require to incorporate knowledge about problems to be solved.

Topics of interest include, but are not limited to:

  • Healthcare and healthcare delivery
  • Logistic in Health
  • Health Analytics and Informatics
  • Hospital Information System
  • Other areas related to healthcare
  • Protein structure prediction
  • Protein function analysis
  • Drug design
  • RNAseq and microarray gene expression data analysis
  • Gene regulatory network construction


Scientific Models, Machine Learning and Optimization Methods in Tourism and Hospitality

  • Rodolfo Baggio, Università Bocconi di Milano, Italy
  • Cindy Heo, Ecole hotelière de Lausanne, Switzerland
  • Amir Atiya, Cairo University, Egypt
  • Dimitrios Buhalis, Bournemouth University, United Kingdom
  • Nikolaos Matsatsinis, Technical University of Crete, Greece
  • Stanislav Ivanov, Varna University of Management, Bulgaria
  • Ulrike Gretzel, University of Southern California, USA

Data-based, quantitative and scientific approaches to the management and analysis of the tourism and hospitality sector can provide a competitive edge w.r.t. traditional approaches.

The special session solicits both novel research contributions (regular papers intended for publication in the proceedings/special issue) as well as preliminary ideas by PhD students or by startup companies intended for oral presentation and discussion.

Included topics:

  • Revenue management
  • Persuasive technologies
  • Social networks
  • Personalization
  • Management
  • Marketing
  • Smart Tourism Destination
  • Tourist Path Optimization
  • Forecasting Accuracy and Implications
  • Predictive Analysis using AI
  • Big Data Analytics for Revenue Management
  • Geo-marketing
  • Sentiment analysis and application to the hospitality industry
  • Big Data Analytics
  • Tourism Recommender Systems
  • Logistic optimization in tourism networks
  • Evaluation of Tourists Satisfaction
  • Optimization of tourism impacts
  • Robonomics and automation technologies

    Nature Inspired Algorithms for Combinatorial Optimization Problems

    • Nikolaos Matsatsinis, Technical University of Crete, Greece
    • Yannis Marinakis, Technical University of Crete, Greece
    • Magdalene Marinaki, Technical University of Crete, Greece

    In the last twenty years, after the publication of the Ant Colony Optimization (ACO) and of the Particle Swarm Optimization (PSO) algorithms and after the following success in the application of these two algorithms in various optimization (continuous, combinatorial, multiobjective, . . .) problems, there is a large number of other nature inspired or swarm intelligence algorithms that have been proposed and implemented with remarkable results in different kinds of optimization problems. Some of them, but not an exhaustive enumeration, are the Bat Algorithm, the Cuckoo Search Algorithm, the Firefly Algorithm, the Glowworm Swarm Optimization algorithm, the Krill Herd Algorithm, the Harmony Search algorithm, the Clonal Selection Algorithm, the Grey Wolf Optimizer, the Dragonfly Algorithm, the Antlion Optimizer etc. A number of combinatorial optimization problems from a broad range of fields including logistics, network design, bioinformatics, engineering and business have been tackled successfully with these approaches. This session welcomes submissions of original and unpublished work in all experimental and theoretical aspects of nature inspired algorithms for the solution of combinatorial optimization problems.


    Intractable problems of combinatorial optimization, computational geometry, and machine learning: algorithms and theoretical bounds

    • Alexander Kelmanov, Sobolev Institute of Mathematics, Russia
    • Michael Khachay, Krasovsky Institute of Mathematics and Mechanics, Russia

    This special session deals with algorithmic analysis for a variety of extremal problems arising in combinatorial optimization, statistical learning, and computational geometry. As a rule, all these problems are strongly NP-hard and hardly approximable in general and remain intractable even in very specific settings, e.g. on the Euclidean plane. In recent decades, the design of polynomial time approximation algorithms and schemes, proving their performance guarantees and establishing the efficient approximability thresholds for the problems in question became one of the main research directions in this field along with the well-known Branch-and-Bound exact methods, heuristics and meta-heuristics. This session is focused on (but not limited to) the following topics:

    • computational and parametric complexity of novel optimization problems and settings, polynomial time and FPT solvable subclasses of known intractable problems, e.t.c.
    • polynomial time approximation algorithms with theoretical guarantees and approximation schemes
    • asymptotically optimal approximation algorithms
    • randomized algorithms and schemes, concentration of measure, sampling and derandomization techniques
    • theoretic polynomial time approximability thresholds
    • program implementation and empirical evaluation of approximation algorithms



    A Tutorial in Robust Machine Learning and AI with Applications


    Theodore B. Trafalis, University of Oklahoma, USA


    Artificial Intelligence (AI) and Machine Learning (ML) are confronted with a number of algorithmic and systems challenges, which are organized in three key research directions; namely robustness, explainability and trust. To date, numerous ML and AI algorithms are designed with the underlying assumption of perfect knowledge about the environment in which they are deployed. However, in many real-life scenarios, this assumption does not hold. For instance, a supervised learning algorithm is, by definition, designed to trust its training data, which leaves the algorithm vulnerable to various forms of noise due to erroneous inputs. Such noise could be the result of unintentional human error, or could even be planned by a strategic adversary. An adversary armed with a deep understanding of the inner workings of a ML algorithm is much more likely to engineer an attack with serious consequences. Thus, if ML algorithms are to be entrusted with ever more critical decisions, it is essential that such algorithms are robust to such errors or attacks.

    The goal of this tutorial is to discuss supervised ML algorithms that are robust to uncertainties. While such techniques can be effective, they often struggle when faced with large datasets. To overcome this limitation, I will formally analyze the corresponding optimization problem in order to arrive at more efficient algorithms that are tailored to the problem at hand. Special emphasis will be given to robust deep learning algorithms, kernel methods and support vector machines. In addition a robust kernel method for imbalanced data will be discussed. Finally, issues related to the bias of ML algorithms and the fairness of the predictions of ML algorithms using robust optimization techniques will be explored. Applications will be also discussed.

    Accepted papers

    1. Ojonukpe Sylvester Egwuche, Olumide S. Adewale, Samuel A. Oluwadare and Oladunni A. Daramola Enhancing Network Life-Time of Wireless Sensor Networks through Itinerary Definition and Mobile Agents for Routing among Sensor Nodes
    2. Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen and Vincent F. Yu A Matheuristic Algorithm for solving the Vehicle Routing Problem with Cross-Docking
    3. Antonios Alexos, Serafeim Moustakidis, Christos Kokkotis and Dimitrios Tsaopoulos Physical activity as a risk factor in the progression of Osteoarthritis: A machine learning perspective
    4. Ricardo Silva, Mauricio Resende and Geraldo Mateus GRASP with path-relinking for the generalized quadratic assignment problem
    5. Ali Ekici A Heuristic Algorithm for the Label Printing Problem
    6. Asiye Aydilek and Talat Genc A Novel Modeling of Closed Loop Supply Chain under Uncertainty
    7. Michael Khachay and Yuri Ogorodnikov QPTAS for the CVRP with a moderate number of routes in a metric space of any fixed doubling dimension - submitted to Special Session 4
    8. Annabella Astorino, Rosa Berti, Alessandro Astorino, Vincent Bitonti, Manuel De Marco, Valentina Feraco, Alexei Palumbo, Francesco Porti and Ilario Zannino Early detection of eating disorders through machine learning techniques (short paper)
    9. Okan Ozener and Ali Ekici Inventory Routing Problem: A New Integrated Clustering and Routing Algorithm
    10. Anton Eremeev, Mikhail Y. Kovalyov and Artem Pyatkin On Finding Minimum Cardinality Subset of Vectors with a Constraint on the Sum of Squared Euclidean Pairwise Distances - Submitted to Special Session 4
    11. Konstantin Kobylkin and Irina Dryakhlova Practical Approximation Algorithms for Stabbing Special Families of Line Segments with Equal Disks: submitted to special session 4
    12. Tomáš Dlask and Tomáš Werner A Class of Linear Programs Solvable by Coordinate-wise Minimization
    13. Alexander Krylatov Travel times equilibration procedure for route-flow traffic assignment problem
    14. Anton Eremeev, Nikolai Tiunin and Alexander Yurkov Valuation of Penalty Method for Quadratic Programming Problems in Short Wave Antenna Array Optimization - submitted to Special Session 4
    15. Hossein Moosaei and Milan Hladik (Long paper) Least Squares K-SVCR Multi-class Classification
    16. Matthias Carnein, Heike Trautmann, Albert Bifet and Bernhard Pfahringer confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms
    17. Rafet Sifa and Christian Bauckhage Novelty Discovery with Ensemble Kernel Minimum Enclosing Balls
    18. Moustapha Cheikh and Mounir Zrigui Active Learning Based Framework For Image Captioning Corpus Creation
    19. Sergey Khamidullin, Vladimir Khandeev and Anna Panasenko Randomized Algorithms for Some Sequence Clustering Problems
    20. Meinolf Sellmann and Kevin Tierney Special Session 1: Hyper-parameterized Dialectic Search for Non-Linear Box-constrained Optimization with Heterogenous Variable Types
    21. Flavien Lucas, Romain Billot, Marc Sevaux and Kenneth Sörensen Reducing space search in combinatorial optimization using machine learning tools
    22. Sara Tari, Lucien Mousin, Julie Jacques, Marie-Eléonore Kessaci and Laetitia Jourdan Impact of the Discretization of VOCs for Cancer Prediction using a Multi-Objective Algorithm
    23. Ömer Faruk Yilmaz AUGMECON2 method for a bi-objective U-shaped assembly line balancing problem
    24. Adil Erzin and Roman Plotnikov Two-Channel Conflict-Free Square Grid Aggregation
    25. Martin Olsen Online Stacking using RL with Positional and Tactical Features
    26. Emmanouil Karantoumanis and Nikolaos Ploskas Power consumption estimation in data centers using machine learning techniques
    27. Dario Bezzi, Alberto Ceselli and Giovanni Righini A self-tuning column generation algorithm
    28. Viktor Bengs, Adil El Mesaoudi-Paul, Eyke Hüllermeier, Kevin Tierney and Dimitri Weiß Special Session 1: Realtime Algorithm Configuration: A Preselection Bandit Approach
    29. Eduard Gimadi and Aleksandr Shevyakov An Effective Algorithm for the Three-Stage Facility Location Problem on a Tree-Like Network
    30. Julie Jacques, Hélène Martin-Huyghe, Justine Lemtiri-Florek, Julien Taillard, Laetitia Jourdan, Clarisse Dhaenens, David Delerue, Arnaud Hansske and Valérie Leclercq Application of MOCA-I: a multi-objective classification algorithm for the detection of multi-resistant bacteria
    31. Le Hoai Minh, Hoai An Le Thi and Minh Thi Nguyen DC Programming and DCA for Learning to Rank
    32. Ivan A. Rykov and Edward Kh. Gimadi On asymptotically optimal solvability of Euclidean Max m-k-Cycles Cover Problem
    33. Hoang Phuc Hau Luu, Hoai An Le Thi and Tao Pham Dinh Stochastic Methods based on DCA
    34. Maxime Pinard, Laurent Moalic, Mathieu Brévilliers, Julien Lepagnot and Lhassane Idoumghar A Memetic Approach for the Unicost Set Covering Problem
    35. Nikhil Sathya Kumar, Manoj Ravindra Phirke, Anupriya Jayapal and Vishnu Thangam Dynamic Visual Few-Shot Learning through Classification Parameter Prediction Network: Param-Net
    36. Vinh Thanh Ho and Hoai An Le Thi An Alternative DCA-based Approach for Reduced-Rank Multitask Linear Regression with Covariance Estimation
    37. Malek Sarhani and Stefan Voß PSO-based cooperative learning using chunking
    38. Amelec Silva Optimization of driving efficiency for pre-determined routes
    39. Alexander Lazarev, Darya Lemtyuzhnikova, Alexander Mandel and Nikolay Pravdivets The problem of the hospital surgery department debottlenecking
    40. Antonio Candelieri, Bruno Galuzzi, Ilaria Giordani and Francesco Archetti Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization
    41. Antonio Candelieri, Ilaria Giordani, Riccardo Perego and Francesco Archetti Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
    42. Abtin Nourmohammadzadeh and Stefan Voß A Pareto Simulated Annealing for the Integrated Problem of Berth and Quay Crane Scheduling at Maritime Container Terminals with Multiple Objectives and Stochastic Arrival Times of Vessels
    43. Ricardo Silva and Mauricio Resende experimental methodology for valuating algorithms
    44. Alexander Veremyev, Alexander Semenov, Eduardo Pasiliao and Vladimir Boginski Graph-based Exploration and Clustering Analysis of Semantic Spaces
    45. Rafet Sifa DESICOM as Metaheuristic Search
    46. Manuel Dalcastagnè Heuristic Search Strategies for Noisy Optimization
    47. Valery Kalyagin and Sergey Slashchinin On uncertainty of efficient frontier in portfolio optimization
    48. Gabriele Iommazzo, Claudia D'Ambrosio, Antonio Frangioni and Leo Liberti Learning to configure mathematical programming solvers by mathematical programming
    49. D.N. Gainanov, P.F. Chernavin and V.A. Rasskazova Convex Hulls in Solving Multiclass Pattern Recognition Problem
    50. N. Syed Mujahid Least Correntropic Loss Regression
    51. Andrea Mariello, Manuel Dalcastagnè and Mauro Brunato HotelSimu: Simulation-based Optimization for Hotel Dynamic Pricing


    Conference General Chairs: Local Organizing Committee Chair: Local Organizing Committee:
    • Stamatis Papangelou
    Technical Program Committee Chair:
    • Roberto Battiti, University of Trento, Director of the LION lab (machine Learning and Intelligent OptimizatioN) for prescriptive analytics (Italy)
    Technical Program Committee:
    1. Francesco Archetti , Consorzio Milano Ricerche, Italy
    2. Annabella Astorino , ICAR-CNR, Italy
    3. Amir Atiya , Cairo university, Egypt
    4. Rodolfo Baggio , Bocconi University, Italy
    5. Roberto Battiti , University of Trento, Italy
    6. Christian Blum , Spanish National Research Council (CSIC), Spain
    7. Juergen Branke , The University of Warwick, United Kingdom
    8. Mauro Brunato , University of Trento, Italy
    9. Dimitrios Buhalis , Bournemouth University, United Kingdom
    10. Sonia Cafieri , Ecole Nationale de l'Aviation Civile, France
    11. Antonio Candelieri , University of Milano Bicocca, Italy
    12. John Chinneck , Carleton University, Canada
    13. Kostas Chrisagis , City University London, United Kingdom
    14. Andre Augusto Cire , University of Toronto, Canada
    15. Patrick De Causmaecker , Katholieke Universiteit Leuven, Belgium
    16. Renato De Leone , University of Camerino, Italy
    17. Luca Di Gaspero , DPIA - University of Udine, Italy
    18. Ciprian Dobre , University Politehnica of Bucharest
    19. Adil Erzin , Sobolev Institute of Mathematics
    20. Giovanni Fasano , University Ca'Foscari of Venice, Italy
    21. Paola Festa , University of Napoli FEDERICO II, Italy
    22. Antonio Fuduli , Universita' della Calabria, Italy
    23. Martin Golumbic , University of Haifa, Israel
    24. Vladimir Grishagin , Nizhni Novgorod State University, Russia
    25. Mario Guarracino , ICAR-CNR, Italy
    26. Youssef Hamadi , Uber AI, France
    27. Cindy Heo , Ecole hôtelière de Lausanne, Switzerland
    28. Laetitia Jourdan , INRIA/LIFL/CNRS, France
    29. Valeriy Kalyagin , Higher School of Economics, Russia
    30. Alexander Kelmanov , Sobolev Institute of Mathematics, Russia
    31. Marie-Eleonore Kessaci , Université de Lille, France
    32. Michael Khachay , Krasovsky Institute of Mathematics and Mechanics, Russia
    33. Oleg Khamisov , Melentiev Institute of Energy Systems, Russia
    34. Zeynep Kiziltan , University of Bologna, Italy
    35. Yury Kochetov , Sobolev Institute of Mathematics, Russia
    36. Ilias Kotsireas , Wilfrid Laurier University, Waterloo, Canada
    37. Dmitri Kvasov , DIMES, University of Calabria, Italy
    38. Dario Landa-Silva , University of Nottingham, United Kingdom
    39. Hoai An Le Thi , Université de Lorraine, France
    40. Daniela Lera , University of Cagliari, Italy
    41. Vittorio Maniezzo , University of Bologna, Italy
    42. Silvano Martello , University of Bologna, Italy
    43. Francesco Masulli , University of Genova, Italy
    44. Nikolaos Matsatsinis , Technical University of Crete, Greece
    45. Kaisa Miettinen , University of Jyväskylä, Finland
    46. Serafeim Moustakidis , AIDEAS OU, Greece
    47. Evgeni Nurminski , FEFU, Russia
    48. Panos Pardalos , University of Florida, USA
    49. Konstantinos Parsopoulos , University of Ioannina, Greece
    50. Marcello Pelillo , University of Venice, Italy
    51. Ioannis Pitas , Aristotle University of Thessaloniki, Greece
    52. Vincenzo Piuri , Universita' degli Studi of Milano, Italy
    53. Mikhail Posypkin , Dorodnicyn Computing Centre, FRC CSC RAS, Russia
    54. Oleg Prokopyev , University of Pittsburgh, USA
    55. Helena Ramalhinho , Universitat Pompeu Fabra, Spain
    56. Mauricio Resende ,, USA
    57. Andrea Roli , University of Bologna, Italy
    58. Massimo Roma , SAPIENZA Universita' of Roma, Italy
    59. Valeria Ruggiero , University of Ferrara, Italy
    60. Frédéric Saubion , University of Angers, France
    61. Andrea Schaerf , University of Udine , Italy
    62. Marc Schoenauer , INRIA Saclay Île-de-France, France
    63. Meinolf Sellmann , GE Research, USA
    64. Yaroslav Sergeyev , University of Calabria, Italy
    65. Marc Sevaux , Lab-STICC, Université de Bretagne-Sud, France
    66. Thomas Stützle , Université Libre de Bruxelles (ULB), Belgium
    67. Tatiana Tchemisova , University of Aveiro, Portugal
    68. Gerardo Toraldo , University of Naples Federico II, Italy
    69. Michael Trick , Carnegie Mellon University, USA
    70. Toby Walsh , The University of New South Wales, Sydney, Australia
    71. David Woodruff , University of California, Davis, USA
    72. Dachuan Xu , Beijing University of Technology, Chine
    73. Luca Zanni , University of Modena and Reggio Emilia, Italy
    74. Qingfu Zhang , University of Essex & City U of HK, Hong Kong
    75. Anatoly Zhigljavsky , Cardiff University, United Kingdom
    76. Antanas Zilinskas , Vilnius University, Lithuania
    77. Andre de Carvalho , University of São Paulo, Brasil
    78. Julius Žilinskas , Vilnius University, Lithuania
    Web Chair:

    Location, travel, accommodation

    Athens, Greece

    Athens is the capital and largest city of Greece. It is one of the world's oldest cities, with its recorded history spanning over 3,400 years. A center for the arts, learning and philosophy, home of Plato's Academy and Aristotle's Lyceum, it is widely referred to as the cradle of Western civilization.

    In modern times, Athens is a large cosmopolitan metropolis and central to economic, financial, industrial, maritime, political and cultural life in Greece. It has a large financial sector, and its port Piraeus is both the largest passenger port in Europe, and the second largest in the world. The heritage of the classical era is still evident in the city, represented by ancient monuments and works of art. The most famous monument is the Parthenon, considered a key landmark of early Western civilization. ( Athens in Wikipedia )

    Athens is served by the Athens International Airport (ATH), located near the town of Spata which is 35 km (22 mi) east of Athens. The airport is served by the Metro, the suburban rail, buses, and also taxis. Athens is the hub of the country's national railway system (OSE), connecting the capital with major cities across Greece and abroad (Istanbul, Sofia and Bucharest).The Port of Piraeus connects Athens to the numerous Greek islands of the Aegean Sea.

    Conference Hotel

    The conference is organized at Cabo Verde Hotel. LION 14 participants are invited to reserve their rooms at the conference hotel located near the Athens International Airport.


    41 Posidonos Avenue,

    GR-190 05, Mati, Attica, Greece

    Telephone: +30 22940 33111

    FAX: +30 22940 33112


    In order to reserve your room, please e-mail Maria, the Reservations Manager at Please make sure to quote the conference name (LION 14) and your arrival and departure dates, as well as any other special requirements.

    To reach Cabo Verde Hotel from Athens International Airport, you may:

  • Take a taxi to Cabo Verde Hotel. This option should take 20 minutes and cost approximately 35 euros.
  • Take the bus to Rafina and then take a taxi to Cabo Verde Hotel. This option should take 40 minutes and cost you approximately 20 euros.
  • Rent a car at the airport and drive to Cabo Verde Hotel. Exit the Airport through the North Gate, where you should turn left. Then turn to the right through the next 2 sets of traffic lights into MARATHON AVE towards Marathon and Rafina. Pass straight at the Rafina junction for 2.5km and turn right towards MATI for 300 meters. At the sea you will see CABO VERDE Hotel (Attica, Greece – 41 POSIDONOS AVE) on your right. This option should take 20 minutes and cost 2,80 euros on tolls.

  • It has come to our attention that scammers are targeting our meeting attendees and exhibitors, attempting to sell attendee lists and solicit bookings at unauthorized hotels. LION14 does not rent or sell email lists to third parties.

    Please be aware of any message coming from sources not affiliated with LION14. Avoid interaction with them. Valid emails come from the organizers only.


Interested in participating in or sponsoring LION14?

If you would like to be alerted about the call for papers, the call for contests and special sessions, and additional organization details please contact the Chairs, you find email in their websites.