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LION18 Scope

The 18th Learning and Intelligent OptimizatioN Conference

This meeting, which continues the successful series of LION events (LION 14 in Athens, LION 16 in Milos Island, LION 17 in Nice), is exploring the intersections and uncharted territories between machine learning, artificial intelligence, 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.

The large variety of heuristic algorithms for hard optimization problems raises numerous interesting and challenging issues. Practitioners are confronted with the burden of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental methodology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the experimenter, who, in too many cases, is "in the loop" as a crucial intelligent learning component. Both issues are related to 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 from different runs or during a single run can improve the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.

Invited talks

Five days five sparks: we believe there is a huge value in listening to and discussing with expert colleagues, something which is very human and not easy on the Internet.

Therefore we decided to give more space to our invited presentations (two hours each, including a Q/A part and a break).

Sun June 9 @16:00Mon June 10 @14:00Tue June 11 @13:30Wed June 12 @13:30Thu June 13 @09:35
Mauricio ResendeFrank HutterRuth MisenerMatthias PoloczekKevin Thierney

Frank Hutter
(Professor for Machine Learning at the University of Freiburg, Germany)

Title: "AI that Builds and Improves AI: Meta-Learning The Next Generation of Learning Methods"

Abstract: Throughout the history of AI, there is a clear pattern that manual elements of AI methods are eventually replaced by better-performing automatically-found ones; for example, deep learning (DL) replaced manual feature engineering with learned representations. The logical next step in representation learning is to also (meta-)learn the best architectures for these representations, as well as the best algorithms and hyperparameters for learning them. In this talk, I will discuss several works along these lines from the field of automated machine learning (AutoML). Specifically, I will discuss the efficiency of AutoML, its relationship to foundation models, its ability to democratize machine learning, and that it can also be extended to optimize various dimensions of trustworthiness (such as algorithmic fairness and robustness). Finally, taking the idea of meta-learning to the extreme, I will deep-dive into a novel approach that learns an entire classification algorithm for small tabular datasets that achieves a new state of the art at the cost of a single forward pass.

Ruth Misener
(Professor in Computational Optimisation, Imperial College London, UK)

Title: "Optimal decision-making problems with trained surrogate models embedded"

Abstract: Several of our recent projects (and complementary projects by other groups worldwide) embed data-driven surrogate models into larger optimal decision-making problems. For example, with the chemicals company BASF, we considered solving inverse problems over trained graph neural networks to design new molecules. This presentation discusses some of the mathematical challenges and practical applications we have explored. We also mention software implementations and close with open challenges in the area.

Matthias Poloczek
(Principal Scientist at Amazon, USA)

Title: "Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces"

Abstract: Impactful applications such as materials discovery, hardware design, AutoML, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. These functions do not have a closed-form and are evaluated for example by running a complex economic simulation, an experiment in the lab or in a market, or a CFD simulation. A series of recent advances have extended the scope of Bayesian optimization to dozens of variables which allows practitioners to optimize their designs more granularly. However, a closer examination reveals that the state-of-the-art methods suffer from degrading performances or failures.

In this talk I will start with a brief introduction to Bayesian optimization and then discuss the problems that BayesOpt methods encounter in high-dimensional spaces and for mixed input variables. To fill the need for a dependable algorithm for combinatorial and mixed space, I will present the Bounce (Bayesian optimization using increasingly high-dimensional combinatorial and continuous embeddings) algorithm (NeurIPS 2023) and show comprehensive experimental results that demonstrate that Bounce reliably achieves excellent results and outperforms the state-of-the-art methods. Time-permitting, I will also discuss the challenges of productionizing ML-based solutions at scale and how to address them.

Kevin Tierney
(Professor of Decision and Operation Technologies at Bielefeld University, Germany)

Title: "Deep Reinforcement Learning for Vehicle Routing Problems"

Abstract: Learning to automatically construct solutions to vehicle routing problems offers a way to find high-quality solutions to routing problems without needing a human to design an algorithm by hand. The primary goals of this work are to democratize Operations Research (OR), allowing people to solve problems without advanced knowledge in OR, and to automatically adjust algorithms to better solve specific instance sets. However, to date these methods have struggled to beat state-of-the-art human-designed heuristics and generally cannot handle complex side constraints. In this talk, I will discuss the state-of-the-art for learning to route with deep reinforcement learning and provide ideas for overcoming current deficits. Furthermore, I will describe our ongoing work on combining deep learned models with high-level search strategies, such as efficient active search (EAS), simulation-guided beam search (SGBS), and using diversification strategies to improve search performance. I provide experimental results on several routing problems, including the traveling salesperson problem and versions of the capacitated vehicle routing problem, emphasizing the steadily narrowing gap between learned methods and “traditional” heuristics.

Mauricio G. C. Resende
(University of Washington, Seattle)

Title: "Random-Key Optimizers (RKO): Problem independent combinatorial optimization"

Abstract: This tutorial introduces Random-Key Optimizers, a problem-independent approach to solving combinatorial optimization problems. Random keys are randomly generated real numbers in the interval (0,1]. A random key vector is a vector of n random keys and corresponds to a point in n-dimensional unit hypercube. Solutions of combinatorial optimization problems can be encoded as random-key vectors. By using a decoder a solution can be retrieved from a random-key vector. For example, by sorting a random key vector, a permutation will result from the indices of the sorted vector. In a TSP these indices could be cities in a tour. In the random-key optimization problem we seek a point in the n-dimensional unit hypercube that optimizes the decoder function. For a TSP, what is the point in the n-dimensional unit hypercube that decoded with the sorting decoder results in the shortest tour on the n cities. We illustrate this concept with three RKOs: a biased random-key genetic algorithm (BRKGA), a dual annealing RKO, and a Continuous GRASP (C-GRASP) RKO.

Proceedings

Papers accepted into the LION18 proceedings will be published in Lecture Notes in Computer Science (LNCS).

The pre-proceedings are now available here.

Special sessions

In addition to submissions about general LION themes, we also welcome submissions related to one of our special sessions. The special sessions will be part of the regular conference and are subject to the same peer-review as all other submissions.

Special session 1: DATA-DRIVEN OPTIMIZATION WITH BUSINESS PROCESS MINING

Organizer: Om Prakash Vyas1, Jerome Geyer-Klingeberg2
1Indian Institute of Information Technology, Allahabad , India; 2Celonis, Munich, Germany

Abstract: Process Mining, positioned at the interface between Process Science and Data Science, combines event data with process models and intends to gain insights, identify bottlenecks, predict problems, and optimize organizational processes. Process Mining, already being used for high-volume processes in large organizations, will soon become the ‘new normal’ for smaller organizations and processes with few cases as well.

Despite a huge surge in researching endeavours in Process Discovery, Conformance Checking, and Model Enhancement, positioning them as three verticals of Process Mining, there are a number of research challenges that need to be overcome to realize the vision of data driven optimization of business processes. The optimization paradigm in the process mining context is being explored at following levels:

  1. When it comes to creating process models, event logs generated by process-oriented information systems are treated as a critical resource. Conformance checking can be formulated as an optimization problem with the model and log repair. Thus, conformance checking corresponds to solving optimization problems that grow exponentially in the size of the model and the length of traces in the event log.
  2. Optimization metaheuristics have also been widely applied in the context of automated process discovery, with the goal of gradual discovery and advancement of process models to achieve a trade-off between accuracy and simplicity. The most notorious of these approaches are those based on evolutionary (genetic) algorithms. However, several other metaheuristics have been researched, such as Imperialist competition algorithms, swarm particle optimization, and simulated glow in this context. Also the recent advances in generative AI and OCPM (Object Centric Process Mining) is being considered worth exploring in this context.
  3. Data ingestion from diverse source systems is supported by AI, which allows to identify and customize structured and unstructured data from various sources. Thus, various optimization techniques can be used to improve the performance of the data transformation discovery techniques in the context of the synthesis of routine specifications. With rapidly growing applications in this special session invites original unpublished research contributions that demonstrate current findings in the area of application of data science and optimization techniques for process mining, with special reference to algorithms for process discovery, conformance checking, and process model enhancement.

Special session 2: ADVANCES AND PERSPECTIVES IN BAYESIAN OPTIMIZATION

Organizer: Antonio Candelieri1
1University of Milano-Bicocca, Italy

Abstract: Bayesian Optimization (BO) is the most widely adopted learning-and-optimization framework in many real-life applications. The reason underlying its success is that BO is particularly well suited for solving black-box and expensive problems, quite common in crucial sectors such as chemical and material engineering, aerodynamic design, system control, and (Automated) Machine and Deep Learning. The increasing application of BO has required to address new and specific challenges, leading to extensions of the basic framework – aka vanilla BO – from the theoretical and the methodological perspectives. This Special Session will consider both application-driven and theoretical/methodological contributions addressing recent open-challenges and proposing advances and perspectives in BO, such as – but not restricted to: High-Dimensional BO, multi-task and multi-objective BO, multi-fidelity and multiple information sources BO, Safe and Fair BO, cost-aware BO, multiform BO, Transfer Learning for BO, non-Euclidean BO.

Special session 3: Learning and Intelligent Optimization for Physical Systems

Organizer: Konstantinos Chatzilygeroudis1, Michael Vrahatis1
1University of Patras, Greece

Abstract: Several critical challenges arise when operating with physical systems contrary to theoretical models, simulated environments, or static datasets. Firstly, reducing the up-time of experimenting with the systems is essential. Experimenting extensively on a physical system might lead to hardware failures that are expensive to replace. Secondly, the algorithm should never produce behaviors that might harm the humans around it or the system itself (e.g., we do not want to break a robot that costs 2M euros). Therefore, to develop effective Machine Learning or Intelligent Optimization methods on physical systems, one has to consider the above challenges during the process of designing the algorithms. Learning and data-driven methods can learn very complex models/controllers and improve over time which is useful when operating with physical systems. However, such methods require a prohibited amount of samples to work reliably, and providing formal guarantees on the obtained solutions is challenging. On the other hand, traditional mathematical optimization is more often used in physical systems since it can operate with no or little data and provide solid theoretical foundations, but it is not easy to make an algorithm that can improve the performance over time. This special session welcomes submissions on "Learning and Intelligent Optimization for Physical Systems", where the goal is to find novel methods that effectively combine data-driven/ML approaches with mathematical optimization to solve tasks on physical systems. Examples are robot learning for control, sensors, embedded systems/mobile phone algorithms, real-time systems/applications, and human-computer interaction.

Program
LEGEND
Short papers have 8 minutes for presentation and 2 minutes for questions Long papers have 16 minutes for presentation and 4 minutes for questions
Sunday June 9
15:00 - 19:00 Registration
15:30 - 16:00Opening
16:00 - 18:00Invited talk
Random-Key Optimizers (RKO): Problem Independent Combinatorial Optimization
Mauricio G. C. Resende
Chair:
20:00 Welcome Social Event
Monday June 10
8:30 - 17:00Registration
8:45 - 10:15Special Session: Learning and Intelligent Optimization for Physical Systems
An Approximate-and-Optimize Method for Security-Constrained AC Optimal Power Flow
Jinxin Xiong, Shunbo Lei, Akang Wang and Xiaodong Luo
Deep Learning for the Classification of Ports in Maritime Transport Statistics via AIS Data
Angela Pappagallo, Francesco Ortame, Giulio Massacci, Francesco Sisti and Francesco Pugliese
Heuristic algorithms for the planar intermodal p-hub location: a possibilistic clustering approach
Mario José Basallo Triana, Carlos Julio Vidal Holguín, Juan José Bravo Bastidas and Yesid Fernando Basallo Triana
Evolving Dynamic Locomotion Policies in Minutes
Konstantinos Chatzilygeroudis, Constantinos Tsakonas and Michael Vrahatis
Effective Skill Learning via Autonomous Goal Representation Learning
Constantinos Tsakonas and Konstantinos Chatzilygeroudis
Effective Kinodynamic Planning and Exploration through Quality Diversity and Trajectory Optimization
Konstantinos Asimakopoulos, Aristeidis Androutsopoulos, Michael Vrahatis and Konstantinos Chatzilygeroudis
Session chair:
10:15 - 10:45Coffee break
10:45 - 13:05Exploitation Strategies in Conditional Markov Chain Search: A case study on the three-index assignment problem
Sahil Patel and Daniel Karapetyan
A hybrid deep-learning-metaheuristic framework for bi-level network design problems
Bahman Madadi and Gonçalo Correia
An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation
Christopher Stone, Quentin Renau, Ian Miguel and Emma Hart
Measuring Social Mood on Economy During Covid Times: A BiLSTM Neural Network Approach
Francesco Ortame, Mauro Bruno, Elena Catanese and Francesco Pugliese
auto-sktime: Automated Time Series Forecasting
Marc-Andre Zöller, Marius Lindauer and Marco Huber
Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation
Hannes Waclawek and Stefan Huber
Learning to accelerate a modular QP solver: Challenges and preliminary results
Jeremy Bertoncini, Alberto De Marchi and Simon Gottschalk
A historical review of GRASP with Path Relinking
Anna Martínez-Gavara, Manuel Laguna, Sergio Pérez-Peló, Rafael Marti and Mauricio Resende
Session chair:
13:05 - 14:00Lunch
14:00 - 16:00Invited talk
AI that Builds and Improves AI: Meta-Learning The Next Generation of Learning Methods
Frank Hutter
Chair:
Tuesday June 11
8:00 - 12:30Registration
8:45 - 10:45Special Session: Advances and Perspectives in Bayesian Optimization
Algorithm Switching for Multiobjective Predictions in Renewable Energy Markets
Zijun Li and Aswin Kannan
MLE-free Gaussian Process based Bayesian Optimization
Antonio Candelieri and Elena Signori
An SMC Sampler for Decision Trees with Enhanced Initial Proposal for Stochastic Metaheuristic Optimization
Efthyvoulos Drousiotis, Alessandro Varsi, Paul Spirakis and Simon Maskell
Conditional Importance Resampling for an Enhanced Sequential Monte Carlo Sampler
Soodeh Habibi, Efthyvoulos Drousiotis, Alessandro Varsi, Simon Maskell, Robert Moore and Paul Spirakis
A Bayesian approach for prompt optimization in LLMs
Antonio Sabbatella, Andrea Ponti, Ilaria Giordani and Francesco Archetti
ClassBO: Bayesian Optimization for Heterogeneous Functions
Mohit Malu, Giulia Pedrielli, Gautam Dasarathy and Andreas Spanias
Multi-Agent Collaborative Bayesian Optimization via Consensus
Xubo Yue, Yang Liu, Albert Berahas, Blake Johnson and Raed Al Kontar
Session chair:
10:45 - 11:15Coffee break
11:15 - 12:35Multi-Assignment Scheduler: A New Behavioral Cloning Method for the Job-Shop Scheduling Problem
Imanol Echeverria, Maialen Murua and Roberto Santana
Randomized Greedy Sampling for JSSP
Henrik Abgaryan, Tristan Cazenave and Ararat Harutyunyan
An imitation-based learning approach using DAgger for the Casual Employee Call Timing Problem
Prakash Gawas, Antoine Legrain and Louis Martin Rousseau
Robust Airline Fleet and Crew Scheduling: A Matheuristic Approach
Abtin Nourmohammadzadeh and Stefan Voss
Session chair:
12:35 - 13:30Lunch
13:30 - 15:30Invited talk
Optimal Decision-Making Problems with Trained Surrogate Models Embedded
Ruth Misener
Chair:
15:30 - 16:00Coffee break
16:00 - 17:20Efficient vertex linear orderings to find minimal Feedback Arc Sets (minFAS)
Claudia Cavallaro, Vincenzo Cutello and Mario Pavone
A Real-Time Adaptive Tabu Search for Handling Zoom In/Out in Map Labeling Problem
Vincenzo Cutello, Alessio Mezzina, Mario Pavone and Francesco Zito
Binarized Monte Carlo Search for Selection Problems
Matthieu Ardon, Yann Briheche and Tristan Cazenave
How evolutionary algorithms consume energy depending on the language and its level
Jj Merelo and Mario Garcia Valdez
Session chair:
20:00Conference Banquet
Wednesday June 12
8:45 - 10:15Special Session: Data-driven Optimization with Business Process Mining
Multi-output regression for travel demand estimation in an urban road network
Alexander Krylatov, Raevskaya Anastasiya and Ilya Murzin
WANCE: Learnt Clause Evaluation Method for SAT Solver Using Graph Structure
Yoichiro Iida, Tomohiro Sonobe and Mary Inaba
CLS-Luigi: Analytics Pipeline Synthesis
Anne Meyer, Hadi Kutabi, Jan Bessai and Daniel Scholtyssek
Predictive Optimization for Online Drone Delivery Service Planning
Aditya Paul, S. Travis Waller, Michael W. Levin and David Rey
A Clustering-based uncertainty set for Robust Optimization
Alireza Yazdani, Ahmadreza Marandi, Rob Basten and Lijia Tan
Epidemiological Approaches for Mental Health Research: Exploring Study Designs, Risk Factors, and Causality
Princy Verma, Millie Pant and Mukesh Kumar Barua
Session chair:
10:15 - 10:45Coffee break
10:45 - 12:30Sustainable Development Index - using MILP to assign relative weight to different UNSDG parameters
Keyaan Shah
Approximate dynamic programming for inland empty container inventory management
Sangmin Lee and Trine Boomsma
C2VRPTW: Assigning capacity to vehicles and nodes in a Vehicle Routing Problem for real-world delivery application
Cosimo Birtolo and Francesca Torre
A constrained-JKO scheme for effective and efficient Wasserstein Gradient Flows
Antonio Candelieri, Andrea Ponti and Francesco Archetti
Decoupled Design of Experiments for Expensive Multi-objective Problems
Mickael Binois, Jürgen Branke, Jonathan Fieldsend and Robin Purshouse
Optimizing sustainable and renewable energy portfolios using a robust Chebyshev de novo programming
Noureddine Kouaissah
Session chair:
12:30 - 13:30Lunch
13:30 - 15:30Invited talk
Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Matthias Poloczek
Chair:
15:30 - 16:00Coffee break
16:00 - 17:00Efficient Line Search Method Based on Regression and Uncertainty Quantification
Tomislav Prusina and Sören Laue
Multi-objective Stochastic Optimization with AI Predictions on Management of Battery Energy Storage Systems
Behzad Pirouz and Francesca Guerriero
How to combine interior and exterior path relinking strategies
Ana Dolores López-Sánchez, Isaac Lozano-Osorio, Jesús Sánchez-Oro and Abraham Duarte
Parallelizing High Dimensional Surrogate-Based Discrete Multi-Objective Optimization with Constraints
Rommel Regis
Anytime algorithm configuration
Elias Schede and Kevin Tierney
Session chair:
Thursday June 13
8:45 - 9:50A Stochastic Dynamic Programming Approach for Request Acceptance and Unsplittable Scheduling Decisions under Uncertainty
Marvin Caspar, Konstantin Kloster and Oliver Wendt
A Stochastic Programming Approach for Casualty Response Planning with Operational Details
Pedram Farghadani-Chaharsooghi, Hossein Hashemi Doulabi, Walter Rei and Michel Gendreau
Implicit Manifold Gaussian Process Regression
Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause and Aude Billard
Applying Instance Space Analysis to Optimize the Construction of Matheuristics
Sophie Hildebrandt and Guido Sand
Session chair:
09:35 - 11:35Invited talk
Deep Reinforcement Learning for Vehicle Routing Problems
Kevin Tierney
Chair:
11:35 - 12:00Coffee break
12:00 - 12:40Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection
Moritz Vinzent Seiler, Urban Škvorc, Carola Doerr and Heike Trautmann
A R2 based Multi-objective Reinforcement Learning Algorithm
Sofia Magdalena Borrel Miller and Carlos Ignacio Hernandez Castellanos
Minimizing evolutionary algorithms energy consumption in the low-level language Zig
Jj Merelo
Session chair:
 12:40 - 13:00Closing
13:00Lunch

Important dates

All deadlines are Anywhere on Earth (AoE = UTC-12h).

  • April 1, 2024, registration opens
  • May 3, 2024, early registration deadline
  • May 3, 2024, revision submission closes
  • May 15, 2024, conference pre-proceedings
  • June 9-13, 2024, conference at Ischia, Italy

CONFERENCE FEES AND REGISTRATION

FEES

Conference fees
Early500 €
Late600 €
Accompanying person200 €

Fees include: Participation to all sessions; Conference materials; Publication of accepted papers in LNCS; Coffee breaks, Lunches, Welcome cocktail and Social dinner.
Accompanying person fee includes: Coffee breaks and Lunches.

On site, it will be possible to pay only by cash or instant bank transfer.

REGISTRATION

Please, check if you are registering before (Early registration) or after (Late registration) May 3 and proceed as follows:

      1. Determine the exact amount you are expected to pay for registration (including, if applicable, the fees for accompanying persons and extras)

      2. Pay the correct amount via bank transfer. In doing so, please carefully check (especially when you come for a foreign country) that the amount we will receive is exactly what is due (i.e., be sure to have every commission, transaction fee, or bank expenses paid by you)

        Payment should be done by bank transfer to:

        IBAN IT15 O030 6909 6061 0000 0194 497
        BIC BCITITMM
        Bank Intesa Sanpaolo
        Account ODS Organizing Committee
        Reference Lion18 Registration - Your Last Name - Your First Name
        Fiscal Code The Italian Fiscal Code of "ODS Organizing Committee" is: 92074960649
        Address The postal address of "ODS Organizing Committee" is: Via Ferriera 39, 83100 Avellino (Italy)
      3. When you receive from your bank or from your department the receipt of your payment (a pdf file), then you can proceed to fill the registration form.

      4. The official receipt of your registration fees will be given to you directly at the conference site at check in.

Organization

Chairs

General chair

Paola Festa (University of Napoli “Federico II”, Italy)

Steering Committee

Roberto Battiti (University of Trento, Italy - Head of the Steering Committee)  
Francesco Archetti (Consorzio Milano Ricerche, Italy)  
Christian Blum (Spanish National Research Council (CSIC), Spain)  
Mauro Brunato (University of Trento, Italy)  
Carlos A. Coello-Coello (CINVESTAV-IPN, Mexico)  
Clarisse Dhaenens (University of Lille, France)  
Paola Festa (University of Napoli, Italy)  
Martin Charles Golumbic (University of Haifa, Israel)  
Youssef Hamadi (Tempero Tech, France)  
Laetitia Jourdan (University of Lille, France)  
Nikolaos Matsatsinis (Technical University of Crete, Greece)  
Panos Pardalos (University of Florida, USA)  
Mauricio Resende (University of Washington, USA)  
Meinolf Sellmann (InsideOpt, USA)  
Yaroslav Sergeyev (University of Calabria, Italy)  
Dimitris Simos (SBA Research, Austria)  
Thomas Stuetzle (University of Bruxelles, Belgium)  
Kevin Tierney (Bielefeld University, Germany)

Technical Program Committee:

  • Carlos Ansòtegui (University of Lleida, Spain)
  • Francesco Archetti (Consorzio Milano Ricerche, Italy)
  • Annabella Astorino (ICAR-CNR, Italy)
  • Hendrik Baier (Eindhoven University of Technology, The Netherlands )
  • Roberto Battiti (University of Trento, Italy)
  • Laurens Bliek (Eindhoven University of Technology, The Netherlands )
  • Christian Blum (Spanish National Research Council (CSIC), Spain)
  • Mauro Brunato (University of Trento, Italy)
  • Zaharah Bukhsh (Eindhoven University of Technology, The Netherlands )
  • Sonia Cafieri (Ecole Nationale de l'Aviation Civile, France)
  • Antonio Candelieri (University of Milano Bicocca, Italy)
  • Zhiguang Cao (Singapore Management University, Singapore)
  • Marco Chiarandini (University of Southern Denmark, Denmark)
  • John Chinneck (Carleton University, Canada)
  • Konstantinos Chatzilygeroudis (University of Patras, Greece)
  • Philippe Codognet (JFLI / Sorbonne Universitè, Japan / France)
  • Patrick De Causmaecker (Katholieke Universiteit Leuven, Belgium)
  • Renato De Leone (University of Camerino, Italy)
  • Clarisse Dhaenens (Université Lille 1 (Polytech Lille, CRIStAL, INRIA), France)
  • Luca Di Gaspero (DPIA - University of Udine, Italy)
  • Theresa Elbracht (Bielefeld University, Germany)
  • Adil Erzin (Sobolev Institute of Mathematics)
  • Giovanni Fasano (University Ca'Foscari of Venice, Italy)
  • Daniele Ferone (University of Napoli FEDERICO II, Italy)
  • Paola Festa (University of Napoli FEDERICO II, Italy)
  • Adriana Gabor (Khalifa University, Abu Dhabi)
  • Jerome Geyer-Klingeberg (Celones, Germany)
  • Isel Grau (Eindhoven University of Technology, The Netherlands )
  • Vladimir Grishagin (Nizhni Novgorod State University, Russia)
  • Mario Guarracino (ICAR-CNR, Italy)
  • Francesca Guerriero (University of Calabria, Italy)
  • Ioannis Hatzilygeroudis (University of Patras, Greece)
  • Youssef Hamadi (Tempero, France)
  • Andre Hottung (Bielefeld University, Germany)
  • Laetitia Jourdan (INRIA/LIFL/CNRS, France)
  • Marie-Eleonore Kessaci (Université de Lille, France)
  • Michael Khachay (Krasovsky Institute of Mathematics and Mechanics, Russia)
  • Elias B. Khalil (University of Toronto, Canada)
  • Yury Kochetov (Sobolev Institute of Mathematics, Russia)
  • Ilias Kotsireas (Wilfrid Laurier University, Waterloo, Canada)
  • Dmitri Kvasov (DIMES, University of Calabria, Italy)
  • Dario Landa-Silva (University of Nottingham, United Kingdom)
  • Hoai An Le Thi (Université de Lorraine, France)
  • Daniela Lera (University of Cagliari, Italy)
  • Yuri Malitsky (FactSet, USA)
  • Vittorio Maniezzo (University of Bologna, Italy)
  • Silvano Martello (University of Bologna, Italy)
  • Yannis Marinakis (Technical University of Crete, Greece)
  • Nikolaos Matsatsinis (Technical University of Crete, Greece)
  • Laurent Moalic (University of Haute-Alsace - IRIMAS, France)
  • Hossein Moosaei (Jan Evangelista Purkyně University, Czech Republic)
  • Tatsushi Nishi (Osaka University, Japan)
  • Panos Pardalos (University of Florida, USA)
  • Axel Parmentier (Ecole Nationale des Ponts et Chaussées, France)
  • Konstantinos Parsopoulos (University of Ioannina, Greece)
  • Vincenzo Piuri (Universita' degli Studi of Milano, Italy)
  • Oleg Prokopyev (University of Pittsburgh, USA)
  • Michael Römer (Bielefeld University, Germany)
  • Massimo Roma (SAPIENZA Universita' of Roma, Italy)
  • Valeria Ruggiero (University of Ferrara, Italy)
  • Frédéric Saubion (University of Angers, France)
  • Andrea Schaerf (University of Udine , Italy)
  • Elias Schede (Bielefeld University, Germany)
  • Marc Schoenauer (INRIA Saclay Île-de-France, France)
  • Meinolf Sellmann (InsideOpt, USA)
  • Marc Sevaux (Lab-STICC, Université de Bretagne-Sud, France)
  • Paul Shaw (IBM, France)
  • Dimitris Simos (SBA Research, Austria)
  • Thomas Stützle (Université Libre de Bruxelles (ULB), Belgium)
  • Tatiana Tchemisova (University of Aveiro, Portugal)
  • Kevin Tierney (Bielefeld University, Germany)
  • Gerardo Toraldo (Università della Campania “Luigi Vanvitelli”, Italy)
  • Paolo Turrini (University of Warwick, UK)
  • Michael Vrahatis (University of Patras, Greece)
  • Om Prakash Vyas (Indian Institute of Information Technology , India)
  • Ranjana Vyas (Indian Institute of Information Technology , India)
  • Dimitri Weiß (Bielefeld University, Germany)
  • Daniel Wetzel (Bielefeld University, Germany)
  • David Winkelmann (Bielefeld University, Germany)
  • Dachuan Xu (Beijing University of Technology, Chine)
  • Qingfu Zhang (University of Essex & City U of HK, Hong Kong)
  • Anatoly Zhigljavsky (Cardiff University, United Kingdom)
  • Antanas Zilinskas (Vilnius University, Lithuania)

Location, travel, accommodation

Ischia Island, Naples, Italy

Ischia is one of the wonderful islands in the Gulf of Naples, having volcanic origin and known and appreciated all around the world for its diversified landscape, natural beauty, and thermal water.

Its wonderful thermal hot springs have been used for wellness and therapeutic treatments since the VII century b.C. On Ischia, there are many nice beaches that invite the visitor to take a swim.

Conference Hotel

Hotel Continental Terme has an architecture expressed in the Mediterranean style buildings surrounded by the lush greenery of a park.

To make a reservation fill out this form and send it to Hotel Continental Terme.

The Conference Centre counts 12 comfortable modular meeting rooms hosting from 15 to 300 seats.

It offers to participants the opportunity to combine work with a short holiday of sun, sea and wellness with the added value that only an enchanting place like Ischia can give.