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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

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 (TO BE CONFIRMED)
(Principal Scientist at Amazon, USA)

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.


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

When submitting a paper to LION18, 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. 4 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. See the conference website or contact the conference organizers for further information.

Please note that concurrent submissions are not allowed, and that a unique author of each accepted paper must register for conference for the paper to be included in the proceedings.

The complete Call for paper can be found 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.


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.


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.

Important dates

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

  • Special Sessions proposals:
    1. submission opens October 12, 2023;
    2. submission closes October 25, 2023;
    3. notification of acceptance October 31, 2023.
  • Abstract only submission:
    1. submission opens October 31, 2023;
    2. submission closes March 4, 2024;
    3. notification of acceptance April 15, 2024.
  • Full Paper submission:
    1. full paper submission opens January 11, 2024;
    2. full paper submission deadline March 4, 2024;
    3. full paper notification of acceptance April 15, 2024.

  • April 1, 2024, registration opens
  • April 30, 2024, early registration deadline
  • May 1, 2024, conference pre-proceedings
  • June 9-13, 2024, conference at Ischia, Italy



Conference fees
Early500 €
Late600 €

Fees include: Participation to all sessions; Conference materials; Publication of accepted papers in LNCS; Coffee breaks and Lunches.
The price of the welcome party and gala dinner will be announced as soon as possible.

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


Please, check if you are registering before or after the Early Registration and proceed as follows:

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

      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
        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.

Sponsors (to be confirmed)



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.

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.


Interested in participating in or sponsoring LION18?

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 will find their email in their websites.