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LION17 Scope

The 17th Learning and Intelligent OptimizatioN Conference

The central theme of this conference is ML to OR pipelines. When integrating multiple data science pipelines, significant challenges arise, especially when considering predictive and prescriptive analytics. Central challenges here include loss functions for training ML models that will be used downstream by optimization approaches and actively taking uncertainty into account in optimization models. This joins with classic LION themes, such as determining appropriate optimization methods through expensive algorithm configuration and parameter tuning , implementing intelligent learning schemes for learning from past algorithm behavior to improve performance in the future, hybridizing different algorithms (evolutionary, etc.) to achieve robust and effective performance, and so on.

This meeting continues the successful LION series (8.Gainesville, 9.Lille, 10.Ischia, 11.Nizhny, 12.Kalamata, 13.Chania, 14.Athens, 15.Athens, 16.Milos), explores the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems.

*IBM is a co-organizer

Important dates

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

  • September 15, 2022, Special Sessions proposals submission deadline
  • September 21, 2022, Special Sessions notification of acceptance
  • January 23, 2023, abstract submission (for full paper submission)
  • February 1, 2023, full paper submission
  • March 1, 2023, presentation only abstracts
  • March 8, 2023, author notification
  • May 1, 2023, conference pre-proceedings
  • June 4-8, 2023, conference at Nice, France


Call for Papers

Topics of Interest

The 17th Learning and Intelligent Optimization conference is an interdisciplinary conference that brings together researchers in operations research, machine learning and related fields. We welcome contributions on all LION themes, i.e., topics containing exact and heuristic optimization involving learning or intelligent techniques, such as determining and applying appropriate optimization methods, expensive algorithm configuration, intelligent learning schemes for learning from past algorithm behavior, hybrid algorithms (evolutionary, etc.) to achieve robust and effective performance. Also, we solicit contributions on this year's special theme "ML and OR pipelines".

We invite submissions presenting new and original research on all topics related and relevant to the conference. All papers will be reviewed in a double-blind process and accepted papers will be presented at the conference.


Papers accepted into the LION17 proceedings will be published in Lecture Notes in Computer Science (LNCS). Submitted papers can have a maximum length of 15 pages (including references) and should use the LNCS template, which can be found here: Papers must be submitted as a PDF in English.

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.


Submit at:

The site will start accepting submissions on November 1, 2022.

Presentation only abstracts: We also offer the opportunity to submit presentation-only abstracts of up to 2000 characters regarding ongoing or recently published work. These can be submitted as PDF but do not need to adhere to the LNCS format.

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: Responsible AI in practice

Organizer : Hendrik Baier1, Laurens Bliek1, Zaharah Bukhsh1, Isel Grau1, Yingqian Zhang1
1Eindhoven University of Technology, Eindhoven, The Netherlands
Abstract: Artificial Intelligence is increasingly used in real-world applications, offering countless opportunities for businesses, health, and education. Besides opportunities, AI systems can also bring many unexpected problems and unintended consequences in practice, which has recently led to a new focus on trustworthy and responsible AI. It is important to develop and deploy AI algorithms that are not only highly performant, but also, for example, fair, unbiased, transparent, explainable/interpretable, robust, accountable, and sustainable, and that protect the privacy of users. However, each of these possible requirements for responsible AI comes with its own set of challenges in implementation and evaluation. Furthermore, once implemented and applied to different domains, their context and interaction can create new trade-offs that need to be taken into account. This workshop aims at gathering researchers who are interested in responsible AI solutions in practice – with a particular focus on human-focused evaluation, practical implementation, or the consideration of new challenges that arise from transferring responsible AI out of the lab and into the real world.

Special Session 2: Data-Driven Optimization with Business Process Mining

Organizer : Om Prakash Vyas1, Jerome Geyer-Klingeberg 2
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 endeavors 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 are 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 tradeoff 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
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 3: Learning and Intelligent Optimization for Physical Systems

Organizer: Michael Vrahatis1, Konstantinos Chatzilygeroudis1
1Department of Mathematics, University 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.

Special Session 4: Combinatorial and Integer Optimization Layers in Deep Learning

Organizer: Elias B. Khalil1, Bistra Dilkina2, Axel Parmentier1
1Industrial Engineering, University of Toronto, Canada
2Computer Science, University of Southern California, USA
3Ecole Nationale des Ponts et Chaussées, France
Abstract: A much closer integration of combinatorial and integer optimization with machine learning (ML) has been advocated in the last few years. For one, combinatorial problems such as matching, subset selection, clustering, etc., are useful inductive biases for machine learning tasks in computer vision and natural language processing. On the other hand, data-driven optimization can benefit from machine learning models to construct “accurate” models for optimal decision-making. The key challenge is that combinatorial problems typically resist the differentiability requirement of end-to-end deep learning, and may also require large solution times. This session will focus on methodological advances towards these challenges, efficient and open-source software frameworks, and applications.

Special Session 5: Bayesian Optimization and (Machine) Learning: solutions, applications, challenges, and perspectives

Organizer: Antonio Candelieri1, Francesco Archetti1
1University of Milano-Bicocca, Italy
Abstract: Bayesian Optimization (BO) is considered as a powerful learning-based algorithm to globally optimize black-box functions. Although Evolutionary Algorithms are quite popular, they require many function evaluations, easily becoming prohibitive when each evaluation is expensive, in terms of time, money, or resources (e.g., raw materials or computational resources). Recent research activities have been pulled by the two drivers which will be both represented in the session. One is the growing set of application domains, including for instance Safe Bayesian optimization, fair and energy aware algorithmic design, and microcontroller design, but also robot navigation and the ensuing issue of stochastic optimal control. The other is the increasing complexity of new methodological and computational challenges. Several issues will be dealt with in the session among which high dimensional problems, combinatorial optimization, noisy and robust optimization, constrained optimization, multi-objective optimization, multi-task, multi-source, and multi-fidelity optimization, distributed and federated BO, BO on manifolds and BO on probabilistic spaces. Effective solutions of these challenges require a deep reformulation of the search space as well as the probabilistic surrogate model and the acquisition function which are at the core of the BO algorithm.

Location, travel, accommodation

Nice, France

LION17 in Nice offers the opportunity to reconnect with colleagues and exchange late-breaking research in a dynamic and modern city. Located on the French Riviera, the southeastern coast of France on the Mediterranean Sea, at the foot of the French Alps, Nice is the seventh most populous urban area in France. Nice is approximately 13 kilometers from the principality of Monaco and 30 kilometers from the French–Italian border ( Nice Wikipedia ).

Nice offers simple travel connections by air, rail and road from all over Europe, and beyond. Nice's airport serves as a gateway to the region and has many flights from the world with affordable cost and decent travel times. From major European cities, Nice can be conveniently reached by plane within 4 to 6 hours. The airport also offers direct flights to worldwide destinations, including to JFK airport in New York City, USA.

COVID-19 INFORMATION: The 17th Learning and Intelligent Optimization (LION) conference is planned to take place as a physical conference on June 4-8, 2023 in Nice, France . The 2023 LION Organization is closely monitoring the ongoing COVID-19 situation. The safety and well-being of all conference participants is our top priority. After studying and evaluating the announcements, guidance, and news released by relevant national departments, we are prepared to convert LION17 into a hybrid or virtual conference experience. The dates of the conference will remain the same.


Chair and Local Chairs

Organization committee: Kevin Tierney (Bielefeld University, Germany), Meinolf Sellmann (InsideOpt, USA), Paul Shaw (IBM, France)

Head of steering committee:: Roberto Battiti (University of Trento, Italy)

Technical Program Committee (provisional):

  • Meinolf Sellmann (Chair) (InsideOpt, USA)
  • Kevin Tierney (Co-Chair) (Bielefeld University, Germany)
  • Carlos Ansòtegui (University of Lleida, Spain)
  • Francesco Archetti (Consorzio Milano Ricerche, Italy)
  • Annabella Astorino (ICAR-CNR, Italy)
  • Amir Atiya (Cairo university, Egypt)
  • Hendrik Baier (Eindhoven University of Technology, The Netherlands )
  • Roberto Battiti (University of Trento, Italy)
  • Laurens Bliek (Eindhoven University of Technology, The Netherlands )
  • Maude Josée Blondin (Université of Sherbrooke, Québec, Canada)
  • Christian Blum (Spanish National Research Council (CSIC), Spain)
  • Juergen Branke (The University of Warwick, United Kingdom)
  • Mauro Brunato (University of Trento, Italy)
  • Dimitrios Buhalis (Bournemouth University, United Kingdom)
  • Zaharah Bukhsh (Eindhoven University of Technology, The Netherlands )
  • Sonia Cafieri (Ecole Nationale de l'Aviation Civile, France)
  • Antonio Candelieri (University of Milano Bicocca, Italy)
  • John Chinneck (Carleton University, Canada)
  • Andre Cire (University of Toronto, Canada)
  • Konstantinos Chatzilygeroudis (University of Patras, Greece)
  • Kostas Chrisagis (City University London, United Kingdom)
  • Andre Augusto Cire (University of Toronto, Canada)
  • Philippe Codognet (JFLI / Sorbonne Universitè, Japan / France)
  • Andre de Carvalho (University of São Paulo, Brasil)
  • Patrick De Causmaecker (Katholieke Universiteit Leuven, Belgium)
  • Renato De Leone (University of Camerino, Italy)
  • Valentina De Simone (University of Campania, Italy)
  • Clarisse Dhaenens (Université Lille 1 (Polytech Lille, CRIStAL, INRIA), France)
  • Luca Di Gaspero (DPIA - University of Udine, Italy)
  • Bistra Dilkina (University of Southern California, USA)
  • Ciprian Dobre (University Politehnica of Bucharest)
  • Adil Erzin (Sobolev Institute of Mathematics)
  • Giovanni Fasano (University Ca'Foscari of Venice, Italy)
  • Paola Festa (University of Napoli FEDERICO II, Italy)
  • Antonio Fuduli (Universita' della Calabria, Italy)
  • Adriana Gabor (Khalifa University, Abu Dhabi)
  • Jerome Geyer-Klingeberg (Celones, Germany)
  • Martin Golumbic (University of Haifa, Israel)
  • Isel Grau (Eindhoven University of Technology, The Netherlands )
  • Vladimir Grishagin (Nizhni Novgorod State University, Russia)
  • Mario Guarracino (ICAR-CNR, Italy)
  • Youssef Hamadi (Tempero, France)
  • Cindy Heo (Ecole hôtelière de Lausanne, Switzerland)
  • Andre Hottung (Bielefeld University, Germany)
  • Laetitia Jourdan (INRIA/LIFL/CNRS, France)
  • Serdar Kadioglu (Brown University, USA)
  • Valeriy Kalyagin (Higher School of Economics, Russia)
  • Alexander Kelmanov (Sobolev Institute of Mathematics, Russia)
  • Marie-Eleonore Kessaci (Université de Lille, France)
  • Michael Khachay (Krasovsky Institute of Mathematics and Mechanics, Russia)
  • Elias B. Khalil (University of Toronto, Canada)
  • Oleg Khamisov (Melentiev Institute of Energy Systems, Russia)
  • Zeynep Kiziltan (University of Bologna, Italy)
  • 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 (Morgan Stanley, USA)
  • Vittorio Maniezzo (University of Bologna, Italy)
  • Silvano Martello (University of Bologna, Italy)
  • Francesco Masulli (University of Genova, Italy)
  • Yannis Marinakis (Technical University of Crete, Greece)
  • Nikolaos Matsatsinis (Technical University of Crete, Greece)
  • Kaisa Miettinen (University of Jyväskylä, Finland)
  • Laurent Moalic (University of Haute-Alsace - IRIMAS, France)
  • Hossein Moosaei (Jan Evangelista Purkyně University, Czech Republic)
  • Serafeim Moustakidis (AIDEAS OU, Greece)
  • Tatsushi Nishi (Osaka University, Japan)
  • Evgeni Nurminski (FEFU, Russia)
  • Panos Pardalos (University of Florida, USA)
  • Axel Parmentier (Ecole Nationale des Ponts et Chaussées, France)
  • Konstantinos Parsopoulos (University of Ioannina, Greece)
  • Jun Pei (Hefei University of Technology, China)
  • Marcello Pelillo (University of Venice, Italy)
  • Ioannis Pitas (Aristotle University of Thessaloniki, Greece)
  • Vincenzo Piuri (Universita' degli Studi of Milano, Italy)
  • Josep Pon, University of Lleida, Spain)
  • Mikhail Posypkin (Dorodnicyn Computing Centre, FRC CSC RAS, Russia)
  • Oleg Prokopyev (University of Pittsburgh, USA)
  • Helena Ramalhinho (Universitat Pompeu Fabra, Spain)
  • Mauricio Resende (, 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)
  • Marc Schoenauer (INRIA Saclay Île-de-France, France)
  • Saptarshi Sengupta (Murray State University, USA)
  • Yaroslav Sergeyev (University of Calabria, Italy)
  • 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)
  • 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)
  • Markus Wagner (Government University of Vienna, Austria)
  • Toby Walsh (The University of New South Wales, Sydney, Australia)
  • Dachuan Xu (Beijing University of Technology, Chine)
  • Luca Zanni (University of Modena and Reggio Emilia, Italy)
  • Qingfu Zhang (University of Essex & City U of HK, Hong Kong)
  • Yingqian Zhang (Eindhoven University of Technology, Netherland)
  • Anatoly Zhigljavsky (Cardiff University, United Kingdom)
  • Antanas Zilinskas (Vilnius University, Lithuania)
  • Julius Žilinskas (Vilnius University, Lithuania)